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SNR maximization and modulo loss reduction for TomlinsonHarashima precoding
EURASIP Journal on Wireless Communications and Networking volume 2018, Article number: 257 (2018)
Abstract
Compared to linear precoding, TomlinsonHarashima precoding (THP) requires less transmit power to eliminate the spatial interference in a multiuser downlink scenario involving a multiantenna transmitter and geographically separated receivers. However, THP gives rise to certain performance losses, referred to as modulo loss and power loss. Based on the observation that part of the users can omit the modulo operation at the receiver during an entire frame, we present an alternative detector, which reduces the modulo loss compared to the conventional detector. In addition, this contribution compares several existing and novel algorithms for selecting the user ordering and the rotation of the constellations at the transmitter, to increase the SNR at the detector and decrease the modulo loss for the alternative detector. Compared to the better of linear precoding and THP with conventional detector, the optimized alternative detector achieves significant gains (up to about 4 dB) for terrestrial wireless communication, whereas smaller gains (up to about 1 dB) are obtained for multibeam satellite communication.
1 Introduction
When using spatial multiplexing in a communication system, consisting of a multiantenna transmitter (TX), a frequencyflat channel, and a number of user terminals, each user receives a linear combination of the signals sent by the different TX antennas. When the TX has full channel information, the interference is known at the TX, in which case dirty paper coding (DPC) is capacityachieving [1]. As the implementation of DPC is rather complex, it is of interest to consider simpler precoding schemes for reducing the interference among the different information streams. Linear precoding (LP) is the most basic scheme, where each antenna transmits a linear combination of the data symbols to be sent to the different users. LP is able to completely eliminate the interference at each user terminal, but has an important drawback: depending on the channel realization, the interference presubtraction term to be generated at the TX can be very large, which causes a substantial TX power penalty. The power penalty associated with LP can be significantly reduced by applying a form of nonlinear precoding, referred to as TomlinsonHarashima precoding (THP), which involves a modulo operation at both the TX and the receiver (RX).
Originally, THP has been introduced to avoid intersymbol interference on frequencyselective singleinput singleoutput channels [2, 3], but THP has also been applied to spatial multiplexing systems operating over flat channels. Depending on the scenario, one distinguishes between singleuser (SU) multipleinput multipleoutput (MIMO) THP, where the both the TX and the RX are equipped with multiple antennas [4–6] multiuser (MU) multipleinput singleoutput (MISO) THP, where a multiantenna TX sends to several singleantenna RXs [7–18]; and MUMIMO THP, involving a multiantenna TX sending to several multiantenna RXs [19–21]. Moreover, THP is envisaged also in communication systems providing physicallayer security [22], and for simultaneous wireless information and power transfer (SWIPT) [23]. To implement THP, the TX requires channel information; whereas in many contributions, perfect channel information is assumed, the design of THP in the presence of imperfect channel information is considered in [4, 6, 11, 16, 20, 23].
THP makes use of a modulo operation at the precoder to reduce its TX power compared to LP. However, as the modulo boundaries are larger than the constellation boundaries, the resulting TX power is larger than in the case without interference presubtraction; the latter difference is referred to as the power loss (PoL) [9]. At the receiver side, the conventional detector again applies a modulo operation, which comes with an additional performance penalty, referred to as the modulo loss (MoL) [9].
Several approaches have been proposed to improve the performance of the THP communication system; however, most of them leave the MoL unaltered, because the modulo operation at the RX is maintained. The PoL is minimized in [14] by applying constellation rotations, and in [15] by rotating and scaling the symbols of the first user. The performance of THP can be improved by an appropriate reordering of the users before precoding. Because of the upstreamdownstream duality, the precoding order for THP can be derived from the decoding order in an upstream system with successive interference cancellation at the RX, referred to as VBLAST [24]. To reduce the complexity of the VBLAST ordering presented in [24], a lowcomplexity ordering algorithm based on a sorted QR decomposition is proposed in [25]. A precoding order derived from the VBLAST ordering is considered in [12]. In [8, 13], algorithms based on a Cholesky decomposition with symmetric permutation are introduced, for optimum and suboptimum ordering in the upstream (interference cancellation at RX) and the downstream (precoding at TX) directions. While these orderings do not depend on the transmitted data symbols, a datadependent precoding ordering algorithm is considered in [10]. The above ordering algorithms for THP pertain to MUMISO; orderings for SUMIMO and MUMIMO are developed in [5] and [19, 21], respectively.
Based on the observation that, depending on the channel realization, part of the users can omit the modulo operation at the RX during an entire frame, an alternative detector is introduced in [17, 18], which applies the modulo operation only when the TX instructs the RX to do so; this way, the MoL is reduced compared to the conventional detector, for which the modulo operation is always active. At the same time, in [17, 18], the constellations are rotated to maximize the number of users which can discard the modulo operation.
In this contribution, we consider MUMISO THP and we assume that perfect channel information is available at the TX and the RX. We investigate how the constellation rotations and the reordering of the users affect the SNR and the MoL of the alternative detector from [17, 18]. Besides applying some existing and some new algorithms which optimize over either the user ordering or the constellation rotations, this contribution also jointly maximizes the SNR at the detector and reduces the MoL, by optimizing over both the ordering and the rotations. Instead of performing an exhaustive search over the rotations and/or the user orderings, we also investigate reducedcomplexity algorithms, among which most are novel as well.
Since a broad range of SNR values is explored, the uncoded error performance is not a suitable performance measure, because it does not provide a proper indication of the performance at low SNR (where coding should be used). Instead, we consider the mutual information (MI) for the specific constellation, averaged over the users and the channel realizations; this average MI represents the informationtheoretical achievable spectral efficiency (information bits per channel use) per user when the communication system makes use of THP on the considered channel. The alternative detector (nonoptimized and optimized) will be compared to the conventional detector (nonoptimized and optimized) for THP and to the LP, in terms of average MI.
Numerical results are presented for a terrestrial Rayleigh fading channel, and for a multibeam satellite channel with full frequency reuse. Whereas THP on Rayleigh fading channels has already been intensively studied, the interest in applying THP in multibeam satellite systems is rather new. Because of the continuously increasing demand for higher throughput, full frequency reuse in multibeam satellite systems recently gained attention. Due to the closer proximity of users operating in the same bandwidth, cochannel interference is higher and becomes a limiting factor when not properly mitigated. Therefore, interference cancellation (in the upstream direction) and precoding (in the downstream direction) techniques suited for terrestrial Rayleigh fading channels are currently envisaged for multibeam satellite systems as well [26–33].
We briefly summarize the main original contributions of this paper:

Whereas other works focus on optimizing over either the constellation rotations or the user ordering, we also investigate the gain obtained from optimizing over both.

We optimize the transmitter to jointly reduce the modulo loss and increase the SNR for the alternative detector introduced in [17].

Whereas in literature the optimizations over the rotations typically use exhaustive searches, we present several novel reducedcomplexity algorithms.

Whereas in literature the optimizations over the user ordering do not aim to reduce the MoL, we present novel user ordering optimizations for reducing the MoL of the alternative detector.

We present the results for both a terrestrial Rayleigh fading channel and a multibeam satellite channel. Whereas the former channel has already been studied extensively in the context of THP, optimization results related to the latter channel are rather scarce in literature.
This contribution is organized as follows. Section 3 outlines the system and channel model of both the terrestrial wireless link and multibeam satellite system. Section 4 describes the MUMISO system with zero forcing (ZF) THP, and Section 5 identifies the modulo and power losses inherent to THP. Section 6 considers an alternative detector which reduces the modulo loss compared to the conventional detector, and in Section 7, various algorithms for optimizing the user ordering and the constellation rotations at the TX are presented. Section 8 provides numerical performance results, pertaining to the conventional and the alternative detector for THP (without and with optimizations) and to LP. Section 9 concludes the paper.
Throughout this paper the following notations are used. Lowercase bold letters refer to a column vector, while uppercase bold letters denote a matrix. I_{N} refers to the N×N identity matrix; (.)^{T} and (·)^{H} stand for the transpose and the hermitian transpose, respectively. We denote by diag(X) a diagonal matrix with the same diagonal elements as the square matrix X. · is the norm of a vector. \({\mathbb {E}}[\cdot ]\) stands for the statistical expectation, and \(I(x;y)={\mathbb {E}_{\text {x,y}}}[\log _{2}(p(yx)/p(y))]\) denotes the mutual information (MI) between the random variables x and y, with joint distribution p(x,y)=p(yx)p(x)=p(xy)p(y). The notation x∼N_{c}(0,R) is used to indicate that x is a vector of complexvalued zeromean circular symmetric Gaussian random variables with autocorrelation matrix E[xx^{H}]=R. The real and imaginary part of x are denoted by \(\mathfrak {R}(x)\) (or x_{R}) and I(x) (or x_{I}), respectively. The cardinality of a set \(\mathcal {S}\) is denoted \(\mathcal {S}\).
2 Methods
This study originates from the need for a more efficient use of the increasingly crowded radio spectrum, which can be achieved by spatial multiplexing. Our scenario consists of a MUMISO system, where a multiantenna basestation sends information to several singleantenna user terminals, using the same carrier frequency. To avoid interference between the different users, the information destined to the users is precoded before transmission. We adopt the common assumption that perfect channel state information (CSI) is present at the transmitter.
To avoid the potentially large power penalty caused by an illconditioned channel in the case of linear precoding, we restrict our attention to a type of nonlinear precoding, referred to as THP. The latter applies a modulo operation at the transmitter and the receiver, thereby reducing the power loss but at the same time introducing a modulo loss.
Several TX optimizations for improving the performance of THP have already been presented in literature. In this contribution, we compare several of those techniques and some combinations thereof, and we propose some novel algorithms as well. These methods and algorithms are validated on two specific channels, i.e, the flat Rayleigh fading channel and the multibeam satellite channel, for which we use statistical models available from literature.
As a performance indicator, we take the MI between the transmitted symbol and the resulting detector input. For given SNR, 10^{4} channel realizations are generated independently. For each realization, the corresponding MI for the different users is evaluated by means of numerical integration; the average MI for the considered SNR is obtained as an arithmetical average over the users and the channel realizations. This computation is performed for SNR ranging from − 15 to 15 dB, with a 1 dB step.
3 MIMO channel model
We consider a TX equipped with N_{T} antennas, sending to N_{U} singleantenna users, all operating at the same carrier frequency. Assuming a frequencyflat channel, the received signals y(k) associated with the kth symbol interval can be written as:
where \(\phantom {\dot {i}\!}\mathbf {x}(k)=(x_{1}(k),\ldots,x_{N_{T}}(k))^{T}\) and \(\phantom {\dot {i}\!}\mathbf {y}(k)=(y_{1}(k),\ldots,y_{N_{U}}(k))^{T}\) represent the signals transmitted by the N_{T} antennas and the corresponding signals received by the N_{U} users, respectively; we assume N_{T}≥N_{U}. The channel is represented by the N_{U}×N_{T} channel matrix H, which is constant over a frame of K symbol intervals. The elements of the additive white Gaussian noise vector \(\phantom {\dot {i}\!}\mathbf {w}(k)\sim \mathrm {N_{c}}(0,N_{0}\boldsymbol {I}_{N_{U}})\) have a variance equal to N_{0}. The quantity \(E_{\text {tr}}=\frac {1}{N_{U}}\mathbb {E}[\mathbf {x}(k)^{2}]\) denotes the average TX energy per symbol interval and per user.
This channel model will be used to describe two types of links, i.e., a terrestrial wireless link and a multibeam satellite link. For notational convenience, the dependence on the symbol index k will be dropped.
3.1 Terrestrial wireless link
In the case of a terrestrial wireless link, we consider the downlink transmission from an N_{T}antenna base station to N_{U} singleantenna terminals over a nondispersive, Rayleigh blockfading channel, where N_{U}≤N_{T}. The elements of the N_{U}×N_{T} channel matrix H are independent identically distributed (i.i.d.) with H_{n,m}∼N_{c}(0,1) for m=1,…,N_{T} and n=1,…,N_{U}.
3.2 Multibeam satellite link
Here, we describe a multibeam geostationary satellite system operating in the Kaband (20 GHz), with N_{T} beams transmitting data to N_{U} singleantenna user terminals. We assume that each user is operating under lineofsight (LOS) conditions and that each user is located in a different beam; the latter implies N_{U}≤N_{T}. Due to the sidelobes in the antenna radiation pattern, the signal transmitted in a beam induces cochannel interference (CCI) to the users located in the other beams [26, 31, 32, 34]. The level of the CCI depends on the users’ relative position to the centers of the other beams and the TX power in those beams. We assume full frequency reuse, so that all users operate in the same frequency band. The satellite antenna is a taperedaperture antenna with normalized power gain B(θ) given by [26, 31]
where θ is the angle between the spot beam center and the RX, seen from the satellite, u=2.07123· sin(θ)/ sin(θ_{3dB}), θ_{3dB} is the onesided halfpower beamwidth, and J_{i}(·) denotes the Bessel function of the first kind and ith order; hence, B(0)=1 and B(θ_{3dB})=1/2. The circle on which the received power is 3 dB below the power at the beam center has a diameter which is referred to as the beam diameter. The beam centers are located on a hexagonal grid; the distance between beam centers equals the beam diameter. From (2), the normalized amplitude gain \(A_{n,m}=\sqrt {B(\theta _{n,m})}\) from the mth antenna to the nth user is obtained, with θ_{n,m} determined by the positions of the nth user and the beam center from the mth antenna.
We also include rain fading [28, 33] in the satellite link model. We use the common assumption that rain attenuation, in dB, can be modeled by a lognormal distribution [31, 33]. The power loss (in dB) experienced by the nth user due to rain attenuation is expressed as \(\phantom {\dot {i}\!}R_{n}=e^{v_{n}}\) [dB], where v_{n} has a Gaussian distribution with mean μ_{n} and variance \(\sigma _{n}^{2}\). The parameters μ_{n} and \(\sigma _{n}^{2}\) depend on the user’s geographical position and on the carrier frequency. We assume that the rain attenuation from a satellite antenna to a specific user is the same for all N_{T} antennas, while the attenuation experienced by different users is uncorrelated. Indeed, in [30], it is explained that the rain correlation decreases steeply with increasing distance and can be neglected for most beam sizes of interest [33]. The channel gain r_{n} (on a linear scale) associated with the rain fading affecting the nth user is then given by \(r_{n}=10^{\frac {R_{n}}{20}}\).
The phase ϕ_{n,m} of the channel gain from the mth antenna feed to the nth user is considered independent of the antenna index m, so that ϕ_{n,m}=ϕ_{n} for all m; this is because the satellite antenna spacing is very small compared to the communication distance [31, 33]. On the other hand, the phases ϕ_{n} and \(\phi _{n^{\prime }}\) corresponding to different users are assumed independent, and uniformly distributed in [0,2π).
The free space loss (FSL) is given by [34]:
with D the distance between the satellite and the considered user, and λ the wavelength associated with the carrier frequency. As D is quasiindependent of the user position in the considered scenario, we take D=D_{0} with D_{0} the distance between the satellite and the earth, which equals 35,786 km for a geostationary satellite; hence, the FSL is assumed to be the same for all users.
We construct the N_{U}×N_{T} channel matrix H as \((\mathbf {H})_{n,m}=\sqrt {G/L}e^{j\phi _{n}}\cdot r_{n}\cdot A_{n,m}\), which combines the FSL (L), the product (G) of the maximum TX and RX antenna power gains, the phases (ϕ_{n}), the rain fading (r_{n}), and the normalized antenna amplitude gains (A_{n,m}). For the numerical results, we use θ_{3dB}=0.2^{∘}, which corresponds to a beam diameter of 250 km. The user’s positions are uniformly distributed within the respective beams, and we consider moderate rain fading, with μ=−2.6 and σ^{2}=1.63 for all users.
4 TomlinsonHarashima precoding
Let us consider the symbol vector \(\phantom {\dot {i}\!}\mathbf {a}=(a_{1},\ldots,a_{N_{U}})^{T}\) corresponding to a generic symbol interval, with a_{n} denoting the symbol destined to the nth user. In the following, all symbols a_{n} belong to the same constellation, which is either MPAM or M^{2}QAM. In the former case, \(a_{n}\in \mathcal {A}_{M}=\{(M1),(M3),\ldots,(M1)\}\); in the latter case, we have \(a_{n}=a_{R,n}+ja_{I,n}\in \mathcal {A}_{M}+j\mathcal {A}_{M}\). We assume that all constellation points are equiprobable, and define \(\sigma _{a}^{2}=\mathbb {E}[a_{n}^{2}]\); we have \(\sigma _{a}^{2}=\frac {M^{2}1}{3}\) for MPAM and \(\sigma _{a}^{2}=2\frac {M^{2}1}{3}\) for M^{2}QAM.
Interference among the data symbols at each user’s antenna is avoided when the transmitted signal vector x in (1) depends on a in such a way that y_{n} is a function of a_{n}, but not of {a_{i}i≠n}, for n=1,…,N_{U}. In the case of LP, this is simply accomplished by taking x=AH^{+}a, with H^{+}=H^{H}(HH^{H})^{−1} and A denoting a positive scaling factor setting the TX energy for the considered frame, yielding y=Aa+w. However, depending on the channel realization, the entries of H^{+} can become quite large, so that a very high TX energy E_{tr} is needed to achieve a given SNR at the RX. This problem is mitigated by using THP, which is briefly outlined below.
We introduce the LQ decomposition H=LQ of the channel matrix, where L is an N_{U}×N_{U} lower triangular matrix with positive diagonal elements and Q is an N_{U}×N_{T} matrix with orthogonal rows: \(\mathbf {Q}\mathbf {Q}^{H}={\mathbf {I}}_{N_{U}}\). As H is constant over a frame of K symbol intervals, the matrices L and Q must be determined only once per frame. Using the decomposition
where L_{d}=diag(L) and \(\mathbf {B}=\mathbf {L}_{\mathrm {d}}^{1}\mathbf {L}\mathbf {I}_{N_{U}}\) is lower triangular with zero diagonal, the resulting block diagram of the communication system with THP is shown in Fig. 1, assuming an M^{2}QAM constellation. The precoder computes \(\mathbf {u}=\mathbf {L}_{\mathrm {d}}^{1}\mathbf {D}(\mathbf {a}\boldsymbol {\nu })_{\text {mod}}\) where ν=D^{−1}L_{d}Bu. The modulo operator (.)_{mod} acts on each element of the vector a−ν, with the elements of ν denoting the interference presubtraction terms. The N_{U}×N_{U} matrix D is diagonal with elements \(\phantom {\dot {i}\!}(\mathbf {D})_{n,n}=d_{n}=e^{j\theta _{n}}\), which are constant over a frame of K symbol intervals. We introduce the vector \(\phantom {\dot {i}\!}\boldsymbol {\theta }=(\theta _{1},\ldots,\theta _{N_{U}})\); in a conventional THP system, one takes θ=0 (or, equivalently, \(\mathbf {D}=\mathbf {I}_{N_{U}}\)), but in this contribution, we will select θ such that a better performance is achieved compared to the case where θ=0. Equivalently,
where the interference presubtraction terms ν_{n} are given by:
In (5), the operation (c+jd)_{mod}=(c)_{mod2M}+j(d)_{mod2M} represents the complex modulo operation, with (.)_{mod2M} denoting the modulo 2M reduction of a real variable to the interval (−M,M]. The modulo operation can also be represented using the decomposition (a_{n}−ν_{n})_{mod}=a_{n}+2Mk_{n}−ν_{n}, where the real and imaginary part of k_{n} are the unique integers such that both \(\mathfrak {R}(a_{n}+2Mk_{n}\nu _{n})\) and I(a_{n}+2Mk_{n}−ν_{n}) are in the interval (−M,M].
The transmitted signal is x=AQ^{H}u, where the positive factor A is constant over a frame of K symbol intervals; A sets the TX energy E_{tr} for the considered frame. For given H, the quantities A^{2} and E_{tr} are related by:
with \(\sigma _{\text {mod},n}^{2}=\mathbb {E}[(a_{n}\nu _{n})_{\text {mod}}^{2}]\), where the expectation is over all \(\phantom {\dot {i}\!}M^{N_{U}}\) possible symbol vectors a. Since ν_{1}=0, we have \(\sigma _{\text {mod},1}^{2}=\sigma _{a}^{2}\). For n>1, \(\sigma _{\text {mod},n}^{2}\) is a function of (θ_{1},…,θ_{n}) and the channel realization. In the following, we take A^{2} inversely proportional to \(\frac {1}{N_{U}}{\sum }_{n=1}^{N_{U}}\frac {\sigma _{\text {mod},n}^{2}}{L_{n,n}^{2}}\), such that E_{tr} from (7) is a fixed value, irrespective of H and θ. In a practical implementation, A can be computed from (7), with \(\sigma _{\text {mod},n}^{2}\) approximated by an arithmetical average of (a_{n}(k)−ν_{n}(k))_{mod}^{2} over the K symbol intervals of the considered frame.
It can be verified from Fig. 1 that the signal received by the nth user in the case of THP can be written as \(\phantom {\dot {i}\!}y_{n}=Ae^{j\theta _{n}}(a_{n}+2Mk_{n})+w_{n}\), which indicates that a_{n} is scaled by A and rotated by θ_{n}. The nth user performs the operation \(\tilde {y}_{n}=y_{n}e^{j\theta _{n}}/A\), which yields:
where the noise contribution has a variance \(\mathrm {E}[\tilde {w}_{n}^{2}]=N_{0}/A^{2}\) which is independent of the user index n; the presence of k_{n} in (8) is a consequence of the ambiguity introduced by the modulo operation at the TX.
In order to remove the effect of k_{n} from (8), the conventional RX in a THP communication system applies a modulo operation to \(\tilde {y}_{n}\), yielding:
which is fed to the detector. The corresponding SNR of the detector is defined as \(\text {SNR}_{\text {det}}=\sigma _{a}^{2}/\mathrm {E}[\tilde {w}_{n}^{2}]\), which reduces to \(\text {SNR}_{\text {det}}=\sigma _{a}^{2}A^{2}/N_{0}\). As SNR_{det} does not depend on the user index n, all users yield the same performance when the detection is based on \((\tilde {y}_{n})_{\text {mod}}\) from (9). Alternatively, using (7), SNR_{det} can be expressed as:
which indicates that SNR_{det} depends on H and θ through the factor multiplying E_{tr}/N_{0} in (10).
In the case of THP for MPAM, (5) is replaced by:
with ν_{n} given by (6), and the detection is based on \(\mathfrak {R}(\tilde {y}_{n}),\) with:
and k_{R,n} the unique integer for which \(M< a_{n}+2Mk_{R,n}\mathfrak {R}(\nu _{n})\leq M\). Again, the conventional THP detector removes the effect of k_{R,n} from (12) by applying a modulo operation to \(\mathfrak {R}(\tilde {y}_{n})\), i.e., \((\mathfrak {R}(\tilde {y}_{n}))_{\text {mod}2M}=(a_{n}+\mathfrak {R}(\tilde {w}_{n}))_{\text {mod}2M}\). The Eqs. (7) and (10) remain valid, where now \(\sigma _{\text {mod},n}^{2}\) is defined as \(\sigma _{\text {mod},n}^{2}=\mathbb {E}[((a_{n}\mathfrak {R}(\nu _{n}))_{\text {mod}2M})^{2}]\).
As the spectral efficiency of the THP scheme is proportional to the number of users, the maximum efficiency is achieved for N_{U}=N_{T}.
5 Power loss and modulo loss
As ν_{1}=0 (see (6)), we always have \(\sigma _{\text {mod},1}^{2}=\sigma _{a}^{2}\), irrespective of the channel realization; for n>1, the presence of the interference presubtraction terms ν_{n} gives rise to \(\sigma _{a}^{2}\leq \sigma _{\text {mod},n}^{2}\leq \frac {M^{2}+2}{M^{2}1}\sigma _{a}^{2}\) for both MPAM and M^{2}QAM [35]. Hence, when for some n>1 we have \(\sigma _{\text {mod},n}^{2}>\sigma _{a}^{2}\), SNR_{det} from (10) is reduced compared to the case where ν_{n}=0 for all n (implying \(\sigma _{\text {mod},n}^{2}=\sigma _{a}^{2}\) for all n); this reduction of SNR_{det} represents the power loss described in [9].
When using THP, the conventional detector (denoted CD) operates on \(\tilde {y}_{\text {CD},n}=(\tilde {y}_{n})_{\text {mod}}=(a_{n}+\tilde {w}_{n})_{\text {mod}}\). Let us also consider a genieaided detector (denoted GD), which knows k_{n} from (8); the GD operates on \(\tilde {y}_{\text {GD},n}=\tilde {y}_{n}2Mk_{n}=a_{n}+\tilde {w}_{n}\). We introduce the corresponding mutual information (MI) \(I_{\text {GD}}=I(a_{n};\tilde {y}_{\text {GD},n})\) for the GD and \(I_{\text {CD}}=I(a_{n};\tilde {y}_{CD,n})\) for the CD, which do not depend on the user indices. It follows from the data processing theorem [36] that I_{CD}≤I_{GD} for given SNR_{det}, indicating that the CD is affected by a performance penalty compared to the GD; this penalty, which is associated with the modulo operation at the RX, is referred to as the modulo loss.
For 2PAM, N_{U}=N_{T}=7 and θ=0, Fig. 2 shows MI_{avg}, the MI averaged over the channel statistics, as a function of γ_{t}=E_{tr}/N_{0} for the Rayleigh fading terrestrial wireless channel, and as a function of γ_{s}=(E_{tr}/N_{0})·(G/L) for the multibeam satellite channel; the entries “PoL only” and “PoL and MoL” correspond to the GD and CD, respectively. Also shown is MI_{avg} for the cases “no losses” and “MoL only”; these correspond to the GD and CD, respectively, but with SNR_{det} obtained from (10) with \(\sigma _{\text {mod},n}^{2}\) replaced by its lower bound \(\sigma _{a}^{2}\), so that the PoL is removed. The horizontal shift between the curves “no losses” and “PoL only” is about 1 dB for MI_{avg} in the interval (0.1, 0.9); considering that the upper bound on the PoL for 2PAM amounts to \(\frac {M^{2}+2}{M^{2}1}=2\) (3 dB), we conclude that this bound is rather conservative for M=2. The MoL is observed to be larger than the PoL, and increases with decreasing MI_{avg}.
6 Alternative detection strategy
For any channel realization, and irrespective of the symbol vector a, the modulo operation at the TX has no effect for the user with index n=1, because ν_{1}=0 and a_{1} is within the modulo boundary. Hence, we have k_{1}=0 in (8) (for QAM) or k_{R,1}=0 in (12) (for PAM), so that this user can omit the modulo operation at the RX. Similarly, depending on the channel realization and the selection of {θ_{n}} for the considered frame, a user with index n>1 can remove the modulo operation at the RX during the entire frame, irrespective of the symbol vector a, provided that a certain condition C(n) holds for the channel matrix associated with the considered frame. The formulation of this condition depends on the constellation type; the condition C(n) is denoted C_{PAM}(n) and C_{QAM}(n) for MPAM and M^{2}QAM, respectively, with

C_{PAM}(n) is true ⇔v_{R,n}∈[−1,1) for all possible values of (a_{1},…,a_{n−1})

C_{QAM}(n) is true ⇔v_{R,n}∈[−1,1) and v_{I,n}∈[−1,1) for all possible values of (a_{1},…,a_{n−1})
Indeed, when condition C(n) holds for given n, it is easily verified that \((a_{n}\mathfrak {R}(\nu _{n}))_{\text {mod}}=a_{n}\mathfrak {R}(\nu _{n})\) (for PAM) or (a_{n}−ν_{n})_{mod}=a_{n}−ν_{n} (for QAM); in this case, the modulo operation at the TX has no effect, so that the modulo operation associated with the nth user can be discarded from the RX^{Footnote 1} during the considered frame.
We proposed in [17, 18] an alternative detection strategy to reduce the MoL compared to the CD. Let us denote by \(\mathcal {S}_{C}\subseteq \{1,\ldots,N_{U}\}\) the set of all indices for which the corresponding users can dispose of the modulo operation, i.e., \(n\in \mathcal {S}_{C}\Longleftrightarrow \) C(n) holds. The alternative detector (denoted AD) for the nth user is assumed to know whether \(n\in \mathcal {S}_{C}\) for the channel realization associated with the considered frame, and performs the detection accordingly: detection is based on \(\tilde {y}_{\text {AD},n}=\tilde {y}_{n}=a_{n}+\tilde {w}_{n}\) when \(n\in \mathcal {S}_{C}\), and on \(\tilde {y}_{\text {AD},n}=(\tilde {y}_{n})_{mod}=(a_{n}+\tilde {w}_{n})_{mod}\) when \(n\notin \mathcal {S}_{C}\). Hence, with the AD, only the users with \(n\notin \mathcal {S}_{C}\) are affected by MoL. For given H, the average performance (average over the N_{U} users) of the AD improves with an increasing fraction, \(\rho _{C}=\mathcal {S}_{C}/N_{U}\), of users for which C(n) is met. As the transmitted vector x is not affected by which type of detector is used, the AD, CD, and GD exhibit the same PoL, for given H and θ.
The practical operation of the AD implies that, first, the TX verifies which user indices belong to \(\mathcal {S}_{C}\). The verification for user n can be accomplished by checking whether \(a_{n}(k)\mathfrak {R}(\nu _{n}(k))\) (for PAM) or a_{n}(k)−ν_{n}(k) (for QAM) is within the modulo boundaries for all symbol indices k belonging to the frame; for growing frame size K, this condition becomes equivalent to C(n). Next, the TX informs the users accordingly; this requires the transmission of only one (properly encoded) bit of channel information per user and per frame, which typically represents a very small overhead [18].
For 2PAM, N_{U}=N_{T}=7 and θ=0, the MI averaged over the channel statistics and over the users (denoted MI_{avg}) for the CD, GD, and AD is shown in Fig. 3, for both the terrestrial Rayleigh fading channel and the multibeam satellite channel. We observe that the AD recovers a major part of the MoL; when MI_{avg} is in the interval (0.1, 0.8), the AD provides a gain (compared to the CD) between 1 and 5 dB for the terrestrial channel and between 1.5 and 8 dB for the satellite channel. The MoL of the AD is smaller for the satellite channel than for the terrestrial channel. This is explained by noticing that \(\mathbb {E}[H_{n,m}^{2}]\) is the same for all (n,m) for the terrestrial channel, whereas for the satellite channel \(\mathbb {E}[H_{n,m}^{2}]\) is typically larger than \(\mathbb {E}[H_{i,m}^{2}]\) with i≠n when the nth user is located in the mth beam; hence, on average, the interference on the terrestrial channel is larger than on the satellite channel, yielding a larger fraction ρ_{C} for the multibeam satellite channel and, hence, a smaller MoL for the AD on the latter channel.
7 Transmitter optimization
In this section, we optimize the TX for given H, by a proper selection of (i) the angles θ_{n} in \(\phantom {\dot {i}\!}d_{n}=e^{j\theta _{n}}\), corresponding to a rotation of the symbols a_{n}, and (ii) the permutation of the rows of H, which corresponds to a reordering of the users. As H is constant over an entire frame, the optimization must be carried out only once per frame. The selection of the rotations and the permutation both affect SNR_{det} and ρ_{C}, and, therefore, the performance of the CD (which depends only on SNR_{det}) and the AD (which depends on both SNR_{det} and ρ_{C}). In the following, when the row permutation is not optimized, we take for each frame a same fixed permutation irrespective of H; when the rotations are not optimized, we take θ=0.
As the M^{2}QAM constellation has a symmetry angle of π/2, we limit θ_{n} to the interval [0,π/2) for n=1,…,N_{U}, without loss of generality; we will restrict θ_{n} to the finite set \(\Theta =\left \{0,\frac {\pi }{2Q},\frac {2\pi }{2Q},\ldots,\frac {(Q1)\pi }{2Q}\right \}\) with size Q. Similarly, in the case of MPAM, θ_{n} will be restricted to the set \(\Theta =\{0,\frac {\pi }{Q},\frac {2\pi }{Q},\ldots,\frac {(Q1)\pi }{Q}\}\), because the constellation symmetry angle equals π.
Finding the best rotations and permutation requires a search over \(\phantom {\dot {i}\!}Q^{N_{U}}\) angle vectors θ and over N_{U}! row permutations of H, which becomes computationally prohibitive for a large number of users. Therefore, some reducedcomplexity searches will be explored, at the expense of a performance penalty.
The algorithms will be denoted by a label of the type (X, Y), where X refers to the performance indicator to be optimized (X = MoL, X = SNR, and X = MoL+SNR, for minimizing the MoL, maximizing the SNR, or jointly maximizing SNR and minimizing MoL, respectively), and Y indicates over which parameters the optimization is conducted (Y = R and Y = P for optimization over the rotation angles and the row permutations respectively). The reducedcomplexity version of the algorithm (X,Y) is denoted (X,Y)_{RC}. In Sections 7.1 and 7.2, we restrict our attention to algorithms which optimize over the rotation angles and over the row permutations, respectively; in Section 7.3, these algorithms are combined to jointly optimize over the rotation angles and the row permutations.
For THP using the CD, the MoL cannot be reduced. However, algorithms that reduce the MoL of the AD might also increase SNR_{det} as a secondary effect, in which case THP with CD would also benefit from these algorithms. However, THP with CD will gain more from algorithms directly aiming at maximizing SNR_{det}. Therefore, the former algorithms will be considered only for THP with AD, whereas the latter will be applied to THP with CD and to THP with AD.
7.1 Optimization over rotations
It can be verified from (5), (6), and (11) that when replacing all θ_{n} by θ_{n}+ϕ for n=1,…,N_{U}, neither the interference presubtraction terms ν_{n} nor the magnitudes u_{n} are affected by the choice of ϕ. Hence, without loss of optimality, we choose θ_{1}=0 when selecting the rotations.
Below, we present the following rotation optimization algorithms: the exhaustivesearch algorithm (SNR, R,) which is similar^{Footnote 2} to the algorithm from [14]; the treesearch algorithm (MoL, R) from [17]; and (MoL+SNR, R), which is a novel extension of (MoL, R). In addition, the corresponding reducedcomplexity algorithms are derived, by limiting the search space in the tree; this requires turning (SNR, R) into a treesearch algorithm.
7.1.1 Maximizing the SNR
The selection of θ affects SNR_{det} only through \(\sigma _{\mathrm {mod,n}}^{2}\) with n>1; as a consequence, the maximization of SNR_{det} is equivalent to the minimization of the PoL. The (SNR, R) algorithm selects \(\phantom {\dot {i}\!}\boldsymbol {\theta }=(0,\theta _{2},\ldots,\theta _{N_{U}})\) that maximizes SNR_{det}. For given H, this optimization involves the computation of SNR_{det} from (10) for all \(\phantom {\dot {i}\!}Q^{N_{U}1}\) possible θ, followed by the selection of the vector θ which yields the largest SNR_{det} for a given E_{tr}/N_{0}. Taking into account that \(\sigma _{\text {mod},n}^{2}=\mathbb {E}\left [(a_{n}\nu _{n})_{\text {mod}}^{2}\right ]\) for n>1 depends on (θ_{2,}…,θ_{n}), and represents an expectation over (a_{1},…,a_{n}), the computational complexity associated with the maximization of SNR_{det} becomes prohibitively large for large N_{U}.
7.1.2 Minimizing the modulo loss
The MoL of the AD from Section 6 decreases when the condition C(n) is met for a larger number of users. We can increase \(\mathcal {S}_{C}\), compared to the case where θ=0, by choosing the appropriate θ for a given channel realization. Instead of exhaustively going through all \(\phantom {\dot {i}\!}Q^{N_{U}1}\) possible vectors θ=(0,θ_{2},…,θ_{N}) for maximizing \(\mathcal {S}_{C}\), a more efficient algorithm, referred to as (MoL, R), has been devised in [17]. The (MoL, R) algorithm finds the angles \(\phantom {\dot {i}\!}(\theta _{2},\ldots,\theta _{N_{c}})\), such that C(n) holds for the largest number (N_{C}) of consecutive user indices n=1,2,…,N_{C}, with N_{C}≤N_{U}. Obviously, \(\max _{\boldsymbol {\theta }}N_{C}\leq \max _{\boldsymbol {\theta }}\mathcal {S}_{C}\), but the (MoL, R) algorithm has a smaller complexity than the exhaustive algorithm. The (MoL, R) algorithm is explained below.
Let us introduce the notion of a suitable ntuple: (0,α_{2},…,α_{n}) is a suitable ntuple if and only if the selection (θ_{1},θ_{2},…,θ_{n})=(0,α_{2},…,α_{n}) yields condition C(i) for i=1,…,n (in which case the modulo operation for the users with consecutive indices 1,..., n can be omitted). It is easily verified that the first n−1 elements of a suitable ntuple form a suitable (n−1)tuple; hence, the set of all suitable ntuples for n=1,…,N_{C} can be represented by a tree. The tree consists of N_{C} levels, with each node at level n denoting a suitable ntuple. The children of a parent node (0,α_{2},…,α_{n−1}) are the suitable ntuples of which the first n−1 elements equal the elements of the parent node. Level 1 of the tree contains the root node representing the 1tuple (0), which is the parent of all suitable 2tuples. As an example, Fig. 4 shows a tree with three levels, i.e., N_{C}=3, along with the corresponding suitable ntuples.
In order to determine the children of a parent node (0,α_{2},…,α_{n−1}), we have to determine for which θ_{n}∈Θ the vector (0,α_{2},…,α_{n−1},θ_{n}) is a suitable ntuple. As (0,α_{2},…,α_{n−1}) is a suitable (n−1)tuple, the modulo operation at the TX has no effect for the users with indices 1,…,n−1, so that ν_{n} from (6) can be decomposed as a linear combination of the symbols (a_{1},…,a_{n−1}), i.e., \(\nu _{n}={\sum }_{i=1}^{n1}\beta _{n,i}a_{i}\), where the coefficients β_{n,i} depend on (θ_{1},…,θ_{i}), on the channel realization, and on the type (PAM or QAM) of constellation. More specifically, for QAM, these coefficients can be computed recursively as:
The recursion for PAM is obtained by replacing in the first line of (13) β_{l,i} by \({\mathfrak {R}}(\beta _{l,i})\). The linear decomposition of ν_{n} allows an efficient verification of whether C(n) is met: denoting by ν_{R,n}_{max} and ν_{I,n}_{max} the maximum values of ν_{R,n} and ν_{I,n} over all possible (a_{1},…,a_{n−1}), the condition C(n) holds when ν_{R,n}_{max}<1 (for MPAM), or when ν_{I,n}_{max}<1 and ν_{R,n}_{max}<1 (for M^{2}QAM); it can be verified that \(\nu _{\mathrm {R},n}_{\text {max}}=(M1){\sum }_{i=1}^{n1}\beta _{\mathrm {R},n,i}\) for MPAM and \(\nu _{\mathrm {R},n}_{\text {max}}=\nu _{\mathrm {I},n}_{\text {max}}=(M1){\sum }_{i=1}^{n1}\left (\beta _{\mathrm {R},n,i}+\beta _{\mathrm {I},n,i}\right)\) for M^{2}QAM, where \(\beta _{\mathrm {R},n,i}={\mathfrak {R}}(\beta _{n,i})\) and β_{I,n,i}=I(β_{n,i}).
The (MoL, R) algorithm from [17] reduces the MoL of the AD, but disregards the effect of the symbol rotations on the PoL. The algorithm performs a depthfirst search in the tree, until a suitable N_{C}tuple is found. Two cases must be distinguished:

When N_{C}=N_{U}, the search is ended when finding the first suitable N_{U}tuple, which is used as vector θ. In this case, none of the users requires the modulo operation at the RX.

When N_{C}<N_{U}, the entire tree must be searched in order to find out that there exist no suitable ntuples with n=N_{C}+1, after which one of the suitable N_{C}tuples is selected. The corresponding θ is obtained by appending N_{U}−N_{C} zeroes to the selected suitable N_{U}tuple. In this case, the modulo operation at the RX is needed only for the users with n=N_{C}+1,…,N_{U}.
7.1.3 Minimizing modulo loss and power loss
The above (MoL, R) algorithm can be modified into the (MoL+SNR, R) algorithm, which takes, besides the MoL, also the PoL into account. The (MoL+SNR, R) algorithm minimizes the MoL of the AD, but in case several suitable N_{C}tuples exist, the one yielding the higher SNR_{det} (or, equivalently, the smaller PoL) is selected.
This algorithm finds all suitable N_{C}tuples and constructs the corresponding vectors θ by appending N_{U}−N_{C} zeroes if N_{C}<N_{U}; for each of the resulting θ, SNR_{det} is computed according to (10), and the vector θ yielding the largest SNR_{det} is selected. As a modulo operation is required for n=N_{C}+1,…,N_{U}, the evaluation of the corresponding \(\sigma _{n,\text {mod}}^{2}\) requires a numerical averaging over all possible (a_{1},…,a_{n}), involving a summation of M^{n} (for MPAM) or M^{2n} (for M^{2}QAM) terms. The resulting computational complexity is, however, less than with the (SNR, R) algorithm, because SNR_{det} must be computed only for a number of vectors θ equal to the number of the suitable N_{C}tuples, rather than for all \(\phantom {\dot {i}\!}Q^{N_{U}1}\) possible vectors θ.
7.1.4 Complexity reduction
The above (MoL, R) and (MoL+SNR, R) algorithms involve an exhaustive search in the tree of suitable ntuples. We obtain novel algorithms with significantly reduced computational complexity, by restricting the number of nodes at each level of the tree to L, with L representing a design parameter. Let us denote by #(n) the number of suitable ntuples, or, equivalently, the number of nodes at level n in the original tree, with n=1,…,N_{C}; note that #(1)=1. Suppose that #(n)≤L for n=1,…,i−1, but #(i)>L. The reducedcomplexity algorithms keep at level i only the best L nodes, i.e., those yielding the smallest L values of ν_{R,i}_{max} (for the (MoL,R) _{RC} algorithm) or the largest L values of SNR_{det} (for the (MoL+SNR,R)_{RC} algorithm). All children, issuing from the L remaining parent nodes at level i, are determined, and when the total number of children at level i+1 exceeds L, again only the best L are kept. This procedure is continued until at most L nodes at level N_{C} are obtained.
A similar complexity reduction can be applied to the (SNR, R) algorithm, yielding the (SNR,R)_{RC} algorithm. The set of all \(\phantom {\dot {i}\!}Q^{N_{U}1}\) vectors \(\phantom {\dot {i}\!}\boldsymbol {\theta }=(0,\theta _{2},\ldots,\theta _{N_{U}})\) can also be represented by a tree, where the level n has Q^{n−1} nodes (n=1,…,N_{U}) and each node at levels 1,…,N_{U}−1 has exactly Q children. When the number of nodes at level i exceeds L, the (SNR,R)_{RC} algorithm keeps only the best L nodes, i.e., those yielding the smallest L values of \({\sum }_{n=1}^{i}\frac {\sigma _{\text {mod},n}^{2}}{L_{n,n}^{2}}\), for i=2,…,N_{U}.
The upper bound on the complexity of these algorithms is proportional to (N_{U}−1)LQ, instead of exponential in N_{U}; in Section 8, we point out that reducedcomplexity algorithms yield only a small performance loss, caused by not searching the entire tree.
7.2 Optimization over row permutations
Denoting by P an N_{U}×N_{U} permutation matrix, the matrix L, which results from the LQ decomposition of PH, depends on P. Hence, applying a row permutation to H (which corresponds to selecting a precoding order for the users) affects both the value of SNR_{det} for given E_{tr}/N_{0} and the fraction ρ_{C} of users for which the condition C(n) holds. In principle, both the maximization of SNR_{det} and of ρ_{C} for given H can be achieved by means of an exhaustive search over all possible P. However, the associated computational complexity becomes prohibitively large for large N_{U}, because N_{U}! possible permutation matrices exist.
Below, we present the (SNR, P) and (MoL, P) algorithms, which straightforwardly perform a full search over all row permutations to achieve the maximum value of SNR_{det} and N_{C}, respectively. As these algorithms have a high computational cost, we also consider reducedcomplexity algorithms. The (SNR, P) _{RC} algorithm performs a sorted LQ decomposition; this algorithm is a straightforward adaptation from [25], where a sorted QR decomposition is presented as a lowcomplexity alternative to the user ordering in VBLAST [24], at the expense of only as small performance loss. As the user ordering algorithms from literature do not aim at reducing the MoL, the (MoL,P) _{RC} algorithm and its extension (MoL+SNR,P) _{RC} are entirely novel.
7.2.1 Maximizing the SNR
When aiming at maximizing SNR_{det} from (10) for given H and θ, one has to compute for each row permutation the quantities \(\sigma _{\text {mod},n}^{2}\) and L_{n,n} for all N_{U} users; note that \(\sigma _{\text {mod},n}^{2}\) depends on θ and represents an expectation over n data symbols (a_{1},…,a_{n}), involving a summation of M^{n} (MPAM) or M^{2n} (M^{2}QAM) terms. To avoid the numerical complexity associated with the evaluation of \(\sigma _{\text {mod},n}^{2}\) for all N_{U} users, the (SNR,P) algorithm maximizes \({\sum }_{n=1}^{N_{U}}L_{n,n}^{2}\) instead of SNR_{det} without a significant performance loss; this optimization does not depend on θ.
In order to avoid the high complexity of the exhaustive search associated with the (SNR, P) algorithm, we consider instead a suboptimum lowcomplexity algorithm which performs a sorted LQ decomposition. This algorithm, referred to as (SNR, P) _{RC}, is outlined in Algorithm 1 and aims at maximizing minn (L_{n,n}). When the first i rows of PH have been determined, the (SNR, P) _{RC} algorithm selects among the N_{U}−i remaining rows from H the row for which the projection, on the subspace orthogonal to the first i rows of PH, is the smallest, and makes the selected row the (i+1)th row of PH; the magnitude of this projection equals L_{i+1,i+1}. The algorithm is initialized by selecting as the first row of PH the row from H with the smallest magnitude. The algorithm stops when i=N_{U}−1, at which point the one remaining row from H becomes the last row from PH. The row permutation of H resulting from (SNR, P) _{RC} is such that minn (L_{n,n}) cannot be further increased by swapping any two consecutive rows from PH. The complexity of (SNR, P) _{RC} is proportional to (N_{U}−1)N_{U} rather than N_{U}!.
7.2.2 Minimizing the modulo loss
For given H and fixed θ, the MoL resulting from the AD can be minimized by selecting the permutation matrix P yielding the largest fraction (ρ_{C}) of users for which the condition C(n) is met. For each of the N_{U}! row permutations of H, the corresponding ρ_{C} is obtained by verifying for which n∈{1,…,N_{U}} the condition C(n) holds. Checking whether C(n) is met for a given n requires the evaluation of the interference presubtraction term ν_{n} for all M^{n−1} (for MPAM) or M^{2(n−1)} (for M^{2}QAM) possible (a_{1},....,a_{n−1}), which involves a high computational complexity when N_{U} is large. Instead, the (MoL, P) algorithm determines among all N_{U}! permutations the permutation which maximizes N_{C}, the number of consecutive user indices 1,2,…,N_{C} for which C(n) holds. As explained in Section 7.1.2, when C(i) is met for i=1,…,n−1, then C(n) holds if and only if \((M1){\sum }_{i=1}^{n1}\beta _{\mathrm {R},n,i}<1\) (for MPAM) or \((M1){\sum }_{i=1}^{n1}\left (\beta _{\mathrm {R},n,i}+\beta _{\mathrm {I},n,i}\right)\) (for M^{2}QAM). Hence, checking whether C(n) holds is far less complex when maximizing the amount of consecutive users N_{C} instead of the total amount \(\mathcal {S}_{C}\).
The complexity of (MoL, P), which performs an exhaustive search over all N_{U}! row permutations of H, can be avoided by using the algorithm described in Algorithm 2, and referred to as (MoL, P) _{RC}. The (MoL, P) _{RC} algorithm consists of N_{U}−1 steps. During the ith step, we select P_{i}H yielding the largest N_{C}, from a set of N_{U}−i+1 row permutations of H; this set contains the matrix P_{i−1}H, resulting from the previous step, and the N_{U}−i permutations of P_{i−1}H obtained by swapping the ith row from P_{i−1}H with a row having a row index larger than i; as these N_{U}−i+1 matrices have the first i−1 rows in common, they give rise to the same elements L_{m,n} with m≤n and n=1,…,i−1 and, therefore, to the same interference presubtraction terms (ν_{1},…,ν_{i−1}); as a consequence, N_{C} can never be lower than in a previous step. The algorithm starts with i=1, taking \(\phantom {\dot {i}\!}\mathbf {P}_{0}=\mathbf {I}_{N_{U}}\). When during the ith step of Algorithm 2, the largest N_{C} is achieved for more than one of the possible row permutations, we select the permutation which maximizes L_{i,i}, so that β_{R,n,i} and β_{I,n,i} are minimized; this selection contributes to minimizing ν_{R,n}_{max} and ν_{I,n}_{max}, with n>i. In case N_{C}<i in the ith step, we stop our search because we cannot further increase the number of consecutive users for which condition C(n) holds.
7.2.3 Tradeoff between minimizing the modulo loss and maximizing SNR_{det}
When more than one permutation achieves the maximal N_{C} during the ith step of the (MoL, P) _{RC} algorithm from the previous section, we selected the permutation which maximizes L_{i,i}, in order to increase the chance that N_{C} will be larger in the next step. However, this choice tends to decrease SNR_{det} (as follows from the numerical results in Section 8.2.4).
Instead, we consider the (MoL+SNR,P)_{RC} algorithm, which makes a tradeoff between minimizing the MoL and maximizing \({\sum }_{n=1}^{N_{U}}L_{n,n}^{2}\). More specifically, when more than one permutation achieves the maximal N_{C} during the ith step, we now select the permutation which minimizes L_{i,i}. This algorithm thus selects in the ith step, among all permutations achieving the largest N_{C} in that step, the one that maximizes \({\sum }_{n=1}^{N_{U}}L_{n,n}^{2}\).
7.3 Joint optimization over rotations and permutations
For a further improvement of the performance of the AD, a rotation optimization algorithm from Section 7.1 can be combined with a user ordering optimization algorithm from Section 7.2.
The rotation angle vectors θ resulting from the (SNR, R) and (MoL, R) algorithms depend on the considered row permutation matrix P. The (MoL, P) algorithm yields a permutation matrix P which depends on the considered θ, whereas the permutation matrix P resulting from the (SNR, P) algorithm is independent of θ. Hence, when envisioning the joint optimization over P and θ involving (SNR, P), one can perform a consecutive optimization, where first (SNR, P) is applied, followed by algorithm U, with U∈ {(SNR, R), (MoL, R), (MoL+SNR, R)}. In the case of a joint optimization involving algorithm V, with V∈ {(MoL, P), (MoL+SNR, P}), we consider a nested optimization, where V is the outer algorithm, and W, with W∈ {(SNR, R), (MoL, R), (MoL+SNR, R)} is used as the inner algorithm. The same considerations are valid in case reducedcomplexity algorithms are applied. To the best of our knowledge, the algorithms performing joint optimization over P and θ have not been presented in literature.
8 Numerical results
We assess the performance of the various optimization algorithms and detectors considered, in terms of their SNR gain compared to the CD without TX optimization. When, for a given constellation, a particular configuration X (consisting of optimization algorithm and detector) and the CD without TX optimization give rise to MI_{avg}=MI_{ref} at E_{tr}/N_{0}=(E_{tr}/N_{0})_{X} and E_{tr}/N_{0}=(E_{tr}/N_{0})_{CD}, respectively, the SNR gain (in dB) of configuration X at MI_{avg}=MI_{ref} equals \(G_{\text {dB}}=10\log _{10}\left (\frac {(E_{\text {tr}}/N_{0})_{\text {CD}}}{(E_{\text {tr}}/N_{0})_{X}}\right)\). This means that to obtain MI_{avg}=MI_{ref}, the TX power for configuration X is G_{dB} dB less than for the CD without optimization.
The SNR gains resulting from the configurations for THP considered above are displayed in tabular form in Sections 8.2 to 8.4, allowing a direct comparison of the various existing and novel configurations. In Section 8.5, the CD and the AD, both without optimization and with the best performing optimization, are compared in terms of the average MI versus γ_{t} (terrestrial channel) or γ_{s} (satellite channel), while in Section 8.6, these configurations for THP are compared to LP.
8.1 Fullsearch versus reducedcomplexity optimization
The complexity of the algorithms for optimizing θ is reduced by limiting the search tree to at most L nodes at each level; the resulting complexity is proportional to LQ. We consider different (L,Q) yielding a fixed LQ and select the combination obtaining the highest average (over the range of MI) SNR gain of the AD for (MoL+SNR,R) _{RC} in the case of 2PAM; the selected (L,Q) are shown in Table 1 for LQ=2, 4, 8, 16, 32. We have verified (results not shown for conciseness) that, with increasing LQ, the SNR gain of the AD resulting from the (MoL+SNR,R) algorithm increases, but the SNR gain increments get smaller. For terrestrial communication, we select (L,Q)=(4,4), because the gain increment is smaller than 0.1 dB when moving from (L,Q)=(4,4) to (L,Q)=(8,4). For satellite communication, we select (L,Q)=(1,8), because the SNR gains for (L,Q)=(1,8), (1,16), (1,32) are nearly the same. These selections are applied to all reducedcomplexity algorithms involving an optimization over θ, and for all constellations. We have verified that the reduction of the SNR gain, caused by applying the reducedcomplexity optimization (with (L,Q)=(4,4) for terrestrial communication and (L,Q)=(1,8) for satellite communication) rather than the fullsearch optimization (with Q=4), is limited to only about 0.6 dB and 0.2 dB for the terrestrial channel and the satellite channel, respectively, for the algorithms involving an optimization over θ only, in the case of 2PAM.
When optimizing over the row permutation, an exhaustive search is avoided by using the reducedcomplexity optimizations. We have verified in the case of 2PAM that, for both satellite and terrestrial channels, the loss is limited to less than 0.25 dB, when replacing the (SNR, P) and (MoL, P) algorithms by (SNR, P) _{RC} and (MoL, P) _{RC}, respectively.
8.2 CD and AD performance for 2PAM
Tables 2 and 3 show the SNR gains pertaining to the CD and the AD, respectively, for 2PAM. Results are given for the case without TX optimization (θ=0, \(\mathbf {P}=\mathbf {I}_{N_{u}}\)), and for the reducedcomplexity optimization algorithms from Sections 7.1.4 and 7.2. For each category (i.e., optimization over rotations only, optimization over permutation only, joint optimization over rotations and permutation), the SNR gain of the best algorithm for the considered MI value is displayed in italics; the entries in bold refer to the overall best algorithm for the considered MI. The SNR gains are discussed below.
8.2.1 CD performance
Table 2 displays the SNR gains for the CD. As the CD always performs a modulo operation, its MoL cannot be reduced by any of the optimization algorithms. Consequently, as far as the optimization over θ only, over P only and over (P,θ) is concerned, the largest SNR gains are obtained for (SNR,R)_{RC}, (SNR,P)_{RC} and ((SNR,P),(SNR,R))_{RC}, respectively; for conciseness, the results for the other algorithms are not shown. Comparing (SNR,R)_{RC} and (SNR,P)_{RC}, the former outperforms the latter for terrestrial communication, whereas the opposite holds for satellite communication. The SNR gains resulting from ((SNR,P),(SNR,R))_{RC} are approximately the sum of the gains provided by (SNR,R)_{RC} and (SNR,P)_{RC} individually and are in the range (1.4 dB, 3.2 dB) for terrestrial communication and (0.6 dB, 1.0 dB) for satellite communication. Compared to the better of (SNR,R)_{RC} and (SNR,P)_{RC}, the ((SNR,P),(SNR,R))_{RC} algorithm provides an additional gain ranging from about 0.2 to 0.5 dB.
8.2.2 AD performance without optimization
The nonoptimized AD already provides a substantial SNR gain over the nonoptimized CD. This gain is in the range (1.0 dB, 5.1 dB) for terrestrial communication and (1.0 dB, 6.7 dB) for satellite communication. The largest gains occur at low MI, where the MoL of the nonoptimized CD is the largest.
8.2.3 AD performance when optimizing θ only
When only the rotations are optimized, we observe from Table 3 that for most values of the MI, (MoL,R)_{RC} performs slightly better than (SNR,R)_{RC}, and that both of these algorithms are outperformed by (MoL+SNR,R)_{RC}. Compared to the nonoptimized AD, (MoL+SNR,R)_{RC} provides an additional gain in the range (0.8 dB, 1.7 dB) for the terrestrial link and (0.8 dB, 1.2 dB) for the satellite link.
The value of θ affects both ρ_{C} and SNR_{det}. Table 4 shows \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\) (in dB) and \(\mathbb {E}[\rho _{C}]\) (averages are over the channel realizations), for the nonoptimized AD and for the AD with optimization over θ. We observe that (SNR,R)_{RC} (which maximizes SNR_{det}) not only provides that largest SNR_{det}, but also yields a value of \(\mathbb {E}[\rho _{C}]\) which is larger than for the nonoptimized case. Similarly, (MoL,R)_{RC} (which maximizes N_{C}) gives rise to the largest \(\mathbb {E}[\rho _{C}]\) and to a value of \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\) which is larger than for the case without optimization. Hence, the maximization of one parameter (SNR_{det} or N_{C}) also increases the other parameter, so that both parameters benefit from either maximization; therefore, all three algorithms provide a SNR gain which is larger than when no optimization is carried out. The (MoL+SNR,R)_{RC} algorithms achieves the same \(\mathbb {E}[\rho _{C}]\) as (MoL,R)_{RC}, and in addition provides a higher \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\). This explains the (slight) superiority of (MoL+SNR,R)_{RC} over both (MoL,R)_{RC} and (SNR,R)_{RC}, observed in Table 3.
8.2.4 AD performance when optimizing P only
When only the row permutation is optimized, Table 3 indicates that (i) for terrestrial communication (SNR,P)_{RC} is the best algorithm for most values of the MI, whereas (MoL+SNR,P)_{RC} is the best for small MI, and (ii) for satellite communication (MoL+SNR,P)_{RC} is the best algorithm^{Footnote 3} for most values of the MI, whereas (SNR,P)_{RC} is the best only for large MI. Compared to the nonoptimized AD, the better of (SNR,P)_{RC} and (MoL+SNR,P)_{RC} provides an additional gain in the range (0.8 dB, 2.4 dB) for the terrestrial link and (0.3 dB, 0.6 dB) for the satellite link.
Table 3 indicates that for some values of the MI, (SNR,P)_{RC} or (MoL,P)_{RC} perform worse, compared to the case of no optimization. The explanation follows from Table 5, which shows \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\) and \(\mathbb {E}[\rho _{C}]\) for the AD in the absence of optimization, and for (SNR,P)_{RC}, (MoL,P)_{RC} and (MoL+SNR,P)_{RC}. We observe that (SNR,P)_{RC} (which maximizes SNR_{det,max}) yields the largest value of \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\), but the corresponding \(\mathbb {E}[\rho _{C}]\) is smaller than for no optimization. Similarly, \(\mathbb {E}[\rho _{C}]\) is largest for (MoL,P)_{RC} (which maximizes ρ_{C}), but the corresponding \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\) is smaller than for no optimization. Hence, maximizing SNR_{det,max} or ρ_{C} automatically reduces ρ_{C} or SNR_{det}, respectively; the resulting performance is a combination of both effects, which in some cases can be worse than when no optimization is carried out. The algorithm (MoL+SNR,P)_{RC} yields a value of \(\mathbb {E}[\rho _{C}]\) which is only slightly smaller than for (MoL,P)_{RC}; the corresponding \(\frac {\mathbb {E}[\text {SNR}_{det}]}{E_{\text {tr}}/N_{0}}\) is significantly larger than for (MoL,P)_{RC}, and, for terrestrial communication, even larger than without optimization.
8.2.5 AD performance when optimizing (P,θ)
Let us consider the SNR gains from Table 3, related to the joint optimization of the row permutation and the rotation angles.
For terrestrial communication, we observe that the combined algorithms involving (SNR,P)_{RC} outperform those involving (MoL,P)_{RC}. For a large range of MI, the best performance is obtained for ((SNR,P),(MoL,R))_{RC}, whereas ((SNR,P),(SNR,R))_{RC} slightly outperforms ((SNR,P),(MoL,R))_{RC} at high MI. Compared to the AD without TX optimization, additional gains in the range (2.2 dB, 3.0 dB) are achieved; these gains are about 0.5 dB to 1.1 dB larger than the maximum gains resulting from the optimization over only P or only θ.
In the case of satellite communication, ((MoL+SNR,P), (SNR,R))_{RC} slightly outperforms all other optimization strategies for a large range of MI, whereas ((SNR,P), (SNR,R))_{RC} performs better for the largest MIs. Compared to the AD without TX optimization, additional gains in the range (1.0 dB, 1.4 dB) are achieved; these gains are about 0.2 dB to 0.3 dB larger than the maximum gains resulting from the optimization over only P or only θ. However, in case one wants to avoid the nested algorithms because of their high computational complexity, ((MoL+SNR,P),(SNR,R))_{RC} can be replaced by ((SNR,P),(MoL+SNR,R))_{RC}, giving rise to only a small reduction in gain, not exceeding 0.2 dB.
8.3 CD and AD performance for 4PAM
8.3.1 CD performance
Similarly to 2PAM, for 4PAM, we need to consider for the CD only (SNR,R)_{RC}, (SNR,P)_{RC}, and ((SNR,P),(SNR,R))_{RC}. The resulting SNR gains are shown in Table 6. We observe that the gains resulting from (SNR,R)_{RC} are much smaller than for 2PAM; this is because without optimization, the power loss for 4PAM is smaller than for 2PAM (as indicated by the upper bound [35] on \(\sigma _{\text {mod},n}^{2}/\sigma _{a}^{2}\)). The best algorithm is ((SNR,P),(SNR,R))_{RC}, which only slightly (by less than 0.07 dB) outperforms the (less complex) (SNR,P)_{RC} algorithm. The resulting SNR gains are in the range (1.3 dB, 2.6 dB) for the terrestrial channel and (0.3 dB, 0.5 dB) for the satellite channel.
8.3.2 AD performance without optimization
Table 7 shows the results for the AD. Without optimization, the AD provides a gain compared to the nonoptimized CD, in the range (0.3 dB, 1.8 dB) for terrestrial communication and (0.2 dB, 2.2 dB) for satellite communication. Note that these gains are smaller compared to the case of 2PAM transmission: with 4PAM, the interference presubtraction terms have larger peak values, causing the condition C_{PAM}(n) to hold less frequently.
8.3.3 AD performance when optimizing θ only
In the case of terrestrial communication, (MoL,R)_{RC} yields the largest SNR gain for a large range of MI, whereas (SNR,R)_{RC} performs only slightly better for high MI; the resulting gains compared to the nonoptimized AD are in the range (0.2 dB, 1.0 dB).
For satellite communication, (SNR,R)_{RC} performs best, yielding gains compared to the nonoptimized AD in the range (0.2 dB, 1.6 dB).
8.3.4 AD performance when optimizing P only
For the terrestrial link, the largest SNR gain results from (SNR,P)_{RC} for a large range of MI, whereas (MoL+SNR,P)_{RC} performs better at small MI; compared to the nonoptimized AD, gains in the range (1.2 dB, 2.5 dB) are achieved.
For the satellite link, the largest SNR gains at small, medium and large MI are obtained for (MoL,P)_{RC}, (MoL+SNR,P)_{RC} and (SNR,P)_{RC}, respectively; note that for small MI, the SNR gains resulting from (MoL,P)_{RC} and (MoL+SNR,P)_{RC} are nearly the same. Compared to the nonoptimized AD, the gains are in the range (0.3 dB, 1.4 dB).
8.3.5 AD performance when optimizing (P,θ)
On the terrestrial channel, ((SNR,P),(MoL,R))_{RC} yields the largest SNR gain for a large range of MI, but this algorithm is outperformed by ((MoL+SNR,P), (MoL+SNR,R))_{RC} for small MI. The gain compared to the nonoptimized AD is in the range (1.8 dB, 2.5 dB); these gains are about 0.2 dB to 1.2 dB larger than the maximum gains resulting from the optimization over only P or only θ. When one wants to avoid nested algorithms because of their high computational complexity, ((SNR,P),(MoL,R))_{RC} can be used also at small MI, at the expense of a loss of about 0.6 dB.
When using the satellite channel, ((MoL+SNR,P), (SNR,R))_{RC} gives rise to the largest SNR gain for a large range of MI, whereas ((SNR,P),(SNR,R))_{RC} performs best for large MI. The gain compared to the nonoptimized AD is in the range (0.6 dB, 3.0 dB); these gains are about 0.2 dB to 1.5 dB larger than the maximum gains resulting from the optimization over only P or only θ. When nested algorithms must be avoided for complexity reasons, ((SNR,P),(SNR,R))_{RC} should be used for the entire range of MI, at the expense of a loss ranging from 0.2 to 1.5 dB.
8.4 CD and AD performance for 4QAM
Because of the π/4 angular symmetry of the QAM constellation, optimizing over θ provides only a negligible gain; therefore, for both the AD and the CD, only the optimization of the row permutation is considered.
As the CD always performs a modulo operation, (SNR,P)_{RC} automatically yields the best performance among all row permutation optimizations considered. Table 8 shows a resulting SNR gain in the range (1.1 dB, 2.8 dB) for the terrestrial link and (0.2 dB, 0.5 dB) for the satellite link.
The SNR gains for the AD are displayed in Table 9. Without optimization, a gain compared to the nonoptimized CD is achieved, in the range (0.6 dB, 2.9 dB) for terrestrial communication and (0.5 dB, 3.9 dB) for satellite communication. When optimizing P for the terrestrial channel, (SNR,P)_{RC} yields the largest SNR gain for a wide range of MI, whereas (MoL+SNR,P)_{RC} has the best performance for small MI. For the satellite channel, (MoL,P)_{RC} performs best for the lower range of MI, whereas for higher MI either (SNR,P)_{RC} or (MoL+SNR,P)_{RC} provides the large gain. The best performing algorithms yield additional gains compared to the nonoptimized AD, in the range (1.0 dB, 2.5 dB) for terrestrial communication and (0.2 dB, 1.5 dB) for satellite communication.
8.5 Performance comparison summary for THP
Here, we summarize the performances of the algorithms considered above. We present the average MI achieved by the CD and the AD, with and without TX optimization, as a function of γ_{t} (terrestrial channel) or γ_{s} (satellite channel). In the case of TX optimization, we always select the RC algorithm providing the largest MI for the considered value of γ_{s} or γ_{t}. As indicated in Tables 3 and 9, the AD in combination with an algorithm involving the novel (MoL,P)_{RC} or (MoL+SNR,P)_{RC} is often optimum at low MI (where the MoL of the CD without TX optimization is large).
Figures 5, 6, and 7 pertain to 2PAM, 4PAM, and 4QAM, respectively. The following observations are made.

For the satellite channel using the CD, TX optimization provides only a modest gain. The optimized CD performs worse than the nonoptimized AD, except for large MI where the former is only marginally better than the latter.

For the terrestrial channel using the CD, TX optimization provides a larger gain than on the satellite channel. The curves for the optimized CD and the nonoptimized AD intersect, with the latter outperforming the former in the lower range of MI, where the MoL of the nonoptimized CD is large.

For both types of channel, the best performance is achieved by the optimized AD; the resulting gain compared to the nonoptimized CD is largest for 2PAM, because the fraction of the users that can remove the modulo operation is larger for 2PAM than for 4PAM and 4QAM.
8.6 Comparison with LP
The LP is not affected by MoL and gives rise to a SNR at the detector given by:
Note that the performance resulting from LP is not affected by the user ordering nor the constellation rotations. If THP were without PoL, the corresponding SNR at the detector (10) would become
We have SNR_{det,LP}≤SNR_{det,THP,noPoL}, with equality if and only if the rows of H are orthogonal. In spite of this inequality, LP can outperform THP, when the latter is affected by a large amount of PoL and MoL.
We have included in Figs. 5, 6, and 7 also the average MI resulting from LP. Comparing the LP with the nonoptimized CD for a given constellation, we observe that at large MI the nonoptimized CD performs better, whereas the opposite occurs at low MI; this is because the MoL increases with decreasing MI, favoring LP at low MI. The crossing point, of the curves for LP and the nonoptimized CD, occurs at larger MI for the satellite channel, compared to the terrestrial channel: the rows of H for the former channel tend to be more orthogonal (resulting in smaller interference), yielding a ratio SNR_{det,LP}/SNR_{det,THP,noPoL} closer to 1.
The optimized AD reduces the MoL and/or increases SNR_{det,THP,noPoL} of the THP scheme. On the terrestrial channel, the optimized AD outperforms LP over the entire range of MI and for all constellations considered. On the satellite channel, only for 2PAM the optimized AD is the best over the entire range of MI; for 4PAM and 4QAM, LP outperforms the optimized AD for MI < 0.8 and MI < 1.5, respectively.
Figure 8 compares the different constellations considered, in terms of the maximum average MI over the LP and the optimized AD. For a given operating point, it can be seen from Figs. 5, 6, and 7 whether this maximum MI is achieved by the LP or by the optimized AD. The following observations can be made.

For the terrestrial channel, the best selection of constellations is (i) 2PAM (with optimized AD) for MI <0.7; (ii) 4PAM (with optimized AD) for 0.7 < MI <0.9; and (iii) 4QAM (with optimized AD) for MI >0.9. In the interval 0.7 < MI <0.9, 4PAM is only marginally better than either of 2PAM and 4QAM, so that only a small loss (less than 0.5 dB) is incurred when selecting 2PAM for MI <0.8 and 4QAM for MI >0.8 instead. According to Figs. 5, 6, and 7, the latter selection of constellations achieves a significant gain of up to about 4 dB, compared to the better of nonoptimized CD and LP.

For the satellite channel, the best selection of constellations is (i) 2PAM (with optimized AD) for MI <0.6; (ii) 4QAM (with LP) for 0.6 < MI <1.5; and (iii) 4QAM (with optimized AD) for MI >1.5. According to Figs. 5, 6, and 7, this optimum selection achieves only a moderate gain (up to about 1 dB) over the better of nonoptimized CD and LP. Considering the higher complexity of the optimized AD, one might prefer to always use LP (with 2PAM for MI <0.15 and 4QAM for MI >0.15) instead.
From the above comparison, it follows that the channel statistics have a major impact on which algorithm and which constellation are optimum at a given MI: for the terrestrial channel, the optimized AD (combined with the proper constellation) provides the best performance over the entire range of MI, whereas the LP with 4QAM performs best for the satellite channel over the medium range (0.6 < MI <1.5). This different behavior is attributed to the smaller interference on the satellite channel.
9 Conclusions
In this contribution, we consider a MUMISO communication system with THP. The receiver uses a CD (always performing a modulo operation) or an AD (performing a modulo operation only when needed). We investigate the effect of reducedcomplexity algorithms that select the symbol rotations at the TX and the ordering of the users, to reduce the MoL and to increase the SNR at the detector; several of these algorithms are novel.
Taking the average MI at the detector as a performance measure, results are presented for a terrestrial wireless channel and for a multibeam satellite channel; we consider 2PAM, 4PAM, and 4QAM constellations, since the PoL and MoL are the largest for small constellation sizes.
For the CD, the largest MI is obtained when the SNR at the detector is maximized, by selecting first the user ordering and then the rotations of the constellations; the latter step brings a substantial additional gain only for 2PAM.
For the AD, no single algorithm is optimum for the entire range of MI; as the MoL is larger for small MI, the better algorithms are those which reduce the MoL for small MI and increase the SNR at the detector for large MI. When optimizing the TX, the gains resulting from the AD are considerably larger than those from the CD, especially at small MI (where the MoL of the CD is large); hence, the AD with TX optimization outperforms the CD with TX optimization. These gains are larger for 2PAM than for 4PAM and 4QAM, mainly because the optimization of the constellation rotations has a larger effect for 2PAM.
When selecting the best constellation for the optimized AD and for the LP, it is found that, on the terrestrial channel, the optimized AD outperforms the LP; compared to the better of LP and nonoptimized CD, the optimized AD achieves a significant gain (up to about 4 dB). In contrast, on the satellite channel, the optimized AD actually performs worse than the LP for the medium range of MI and achieves only a modest gain (up to about 1 dB) for small and large MI. Hence, the application of the optimized AD is quite promising for wireless terrestrial communication, whereas its usefulness for multibeam satellite communication is rather limited.
Although the focus of this contribution is on MUMISO THP, the concepts are easily extended to SUMIMO and MUMIMO scenarios.
Notes
When condition C(n) holds, it does not matter whether or not the modulo operation at the TX is active for the nth user. Hence, from a practical point of view, having the modulo operation at the TX active for all N_{U} users is the simplest choice.
The algorithm from [14] maximizes SNR_{det} for the CD, with \(\sigma _{\mathrm {mod,n}}^{2}\) computed as an arithmetical average over a block of transmitted data, instead of an expectation over the data. Both approaches yield similar results for long frames.
We make abstraction of the 0.02 dB higher SNR gain at MI = 0.3 for (MoL,P)_{RC} compared to (MoL+SNR,P)_{RC}, which is negligible and might be caused by limited numerical accuracy.
Abbreviations
 AD:

Alternative detector
 CCI:

Cochannel interference
 CD:

Conventional detector
 CSI:

Channel state information
 DPC:

Dirty paper coding
 FSL:

Free space loss
 LOS:

Line of sight
 LP:

Linear precoding
 MI:

Mutual information
 MIMO:

Multipleinput multipleoutput
 MISO:

Multipleinput singleoutput
 MoL:

Modulo loss
 MU:

Multiuser
 PAM:

Phase amplitude modulation
 PoL:

Power loss
 QAM:

Quadrature amplitude modulation
 RC:

Reducedcomplexity
 RX:

Receiver
 SNR:

Signaltonoise ratio
 SU:

Singleuser
 THP:

TomlinsonHarashima precoding
 TX:

Transmitter
 ZF:

Zeroforcing
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Funding
This work was supported by the Research Foundation Flanders (FWO) under grant no. EOS30452698.
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ED conceived the study, designed the novel algorithms, performed the simulation experiments, and drafted the manuscript. MM reviewed and edited the manuscript. Both authors read and approved the final manuscript.
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E. Debels is currently pursuing the PhD degree at Ghent University. M. Moeneclaey is Full Professor at Ghent University.
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Debels, E., Moeneclaey, M. SNR maximization and modulo loss reduction for TomlinsonHarashima precoding. J Wireless Com Network 2018, 257 (2018). https://doi.org/10.1186/s1363801812627
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DOI: https://doi.org/10.1186/s1363801812627