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Diversity gain of millimeterwave massive MIMO systems with distributed antenna arrays
EURASIP Journal on Wireless Communications and Networking volume 2019, Article number: 54 (2019)
Abstract
This paper is concerned with diversity gain analysis for millimeterwave (mmWave) massive MIMO systems employing distributed antenna subarray architecture. First, for a singleuser mmWave system in which the transmitter and receiver consist of K_{t} and K_{r} subarrays, respectively, a diversity gain theorem is established when the numbers of subarray antennas go to infinity. Specifically, assuming that all subchannels have the same number of propagation paths L, the theorem states that by employing such a distributed antenna subarray architecture, a diversity gain of K_{r}K_{t}L−N_{s}+1 can be achieved, where N_{s} represents the number of data streams. This result means that compared to the colocated antenna architecture, using the distributed antenna subarray architecture can scale up the diversity gain proportionally to K_{r}K_{t}. The analysis of diversity gain is then extended to the multiuser scenario as well as the scenario with conventional partially connected radiofrequency structure in the literature. Simulation results obtained with the hybrid analog/digital processing corroborate the analysis results and show that the distributed subarray architecture indeed yields a significantly better diversity performance than the colocated antenna architectures.
1 Introduction
Millimeterwave (mmWave) communication has recently gained considerable attention as a candidate technology for 5G mobile communication systems and beyond [1–5]. The main reason for this is the availability of vast spectrum in the mmWave band that is very attractive for high data rate communications. However, compared to communication systems operating at lower microwave frequencies (such as those currently used for 4G mobile communications), propagation loss in mmWave frequencies is much higher, in orders of magnitude. Fortunately, given the much smaller carrier wavelengths, mmWave communication systems can make use of compact massive antenna arrays to compensate for the increased propagation loss.
Nevertheless, the largescale antenna arrays together with high cost and large power consumption of the mixed analog/digital signal components make it difficult to equip a separate radiofrequency (RF) chain for each antenna element and perform all the signal processing in the baseband. Therefore, research on hybrid analogdigital processing of precoder and combiner for mmWave communication systems has attracted considerably strong interests from both academia and industry [6–18]. In particular, a large body of work has been performed to address challenges in employing a limited number of RF chains for massive antenna arrays. For example, the authors in [6] investigated singleuser precoding in massive MIMO mmWave systems and obtained the optimality of beam steering for both singlestream and multistream transmission scenarios. In [9], the authors showed that the hybrid processing can realize any fully digital processing exactly when the number of RF chains is twice the number of data streams.
Two architectures for connecting the RF chains in the hybrid processing that have been investigated in the literature are fully connected and partially connected. In the former, each RF chain is connected to all the antenna elements, while only a subset of antenna elements is connected to each RF chain in the latter. The partially connected architecture is more energyefficient and implementationfriendly since the number of required phase shifters can be reduced efficiently without a significant performance loss. In the conventional partially connected architecture [10–14], the antenna array is partitioned into a number of smaller disjoint subarrays, each of which is driven by a single transmission chain. More recently, a more general partially connected architecture, referred to as hybridly connected in [15] and overlapped subarraybased in [16], has been proposed. In such a hybridly connected structure, each subarray is connected to multiple RF chains while each RF chain is connected to all the antennas, which corresponds to the subarray in question. In particular, the authors in [15] show that the spectral efficiency of the hybridly connected structure is higher than that of the partially connected structure and that as the number of RF chains increases, its spectral efficiency can close to that of the fully connected structure.
Nevertheless, due to the facts that the antenna arrays in the abovementioned RF architectures are colocated and mmWave signal propagation has an important feature of multipath sparsity in both the spatial and temporal domains [19, 20], it is expected that the potentially available diversity and multiplexing gains are not large for the colocated antenna deployment. In order to enlarge the diversity gain and/or multiplexing gain in mmWave massive MIMO communication systems, this paper considers a more general antenna array architecture, called distributed antenna subarray architecture, which includes colocated array architecture as special cases. It is pointed out that deploying distributed antennas has shown a promising technique to increase spectral efficiency and expand the coverage of wireless communication networks [21–25]. As such, it is of great interest to consider distributed antenna deployment in the context of mmWave massive MIMO systems.
The diversitymultiplexing tradeoff (DMT) is a compact and convenient framework to compare different MIMO systems in terms of the two main and related system indicators: data rate and error performance [26–29]. This tradeoff was originally characterized in [26] for MIMO communication systems operating over independent and identically distributed (i.i.d.) Rayleigh fading channels. Then, the framework has ignited a lot of interests in analyzing various communication systems and under different channel models. For a massive MIMO mmWave system, how to quantify the diversity performance and characterize its DMT is a fundamental and open research problem. In particular, to the best of our knowledge, until now, there is no unified diversity gain analysis for massive MIMO mmWave systems that is applicable to both colocated and distributed antenna array architectures.
To fill this gap, this paper investigates the diversity performance of massive MIMO mmWave systems with the proposed distributed subarray architecture. The focus is on the asymptotical diversity gain analysis in order to find out the potential diversity advantage provided by multiple distributed antenna arrays. The obtained analysis can be used conveniently to compare various massive MIMO mmWave systems with different distributed antenna array structures. Our main contributions are summarized as follows: First, for a singleuser system employing the proposed distributed subarray architecture, a diversity gain expression is obtained when the number of antennas at each subarray increases without bound. This expression clearly indicates that one can obtain a large diversity gain and/or multiplexing gain by employing the proposed distributed subarray architecture. Second, the diversity gain analysis is extended to the multiuser scenario with downlink and uplink transmission, as well as the singleuser system employing the conventional partially connected RF structure based on the distributed subarrays. Simulation results are provided to corroborate the analysis results and show that the distributed subarray architecture yields significantly better diversity performance than the colocated singlearray architecture.
The remainder of this paper is organized as follows: Section 2 describes the massive MIMO mmWave system model and hybrid processing with the distributed subarray architecture in mmWave fading channels. Section 3 provides the asymptotical diversity analysis for the singleuser mmWave system. In Sections 4 and 5, the diversity gain analysis is extended to the multiuser scenario and the scenario with the conventional partially connected RF architecture, respectively. Numerical results are presented in Section 6. Section 7 finally concludes the paper.
Throughout this paper, the following notations are used. The superscripts (·)^{T} and (·)^{H} stand for transpose and conjugate transpose, respectively. Boldface upper and lower case letters denote matrices and column vectors, respectively. diag{a_{1},a_{2},…,a_{N}} stands for a diagonal matrix with diagonal elements {a_{1},a_{2},…,a_{N}}. The expectation operator is denoted by \(\mathbb {E}(\dot)\). [A]_{ij} gives the (i,j)th entry of matrix A. \(\mathbf {A} \bigotimes \mathbf {B}\) is the Kronecker product of A and B. A function a(x) of x is written as o(x) if \({\lim }_{x \to 0}a(x)/x=0\). Finally, \(\mathcal {CN}(0, 1)\) represents a circularly symmetric complex Gaussian random variable with zero mean and unit variance.
2 System model
A singleuser massive MIMO mmWave system is considered as shown in Fig. 1. The transmitter is equipped with a distributed antenna array to send N_{s} data streams to a receiver, which is also equipped with a distributed antenna array. Here, a distributed antenna array means an array consisting of several remote antenna units (RAUs) (i.e., antenna subarrays) that are distributively located, as depicted in Fig. 2. Specifically, the antenna array at the transmitter consists of K_{t} RAUs, each of which has N_{t} antennas and is connected to a baseband processing unit (BPU) via fiber. Likewise, the distributed antenna array at the receiver consists of K_{r} RAUs, each having N_{r} antennas and also being connected to a BPU by fibers. Such a MIMO system shall be referred to as a (K_{t},N_{t},K_{r},N_{r})distributed MIMO (DMIMO) system. When K_{t}=K_{r}=1, the system reduces to a conventional colocated MIMO (CMIMO) system.
The transmitter accepts as its input N_{s} data streams and is equipped with \(N_{t}^{(\text {rf})}\) RF chains, where \(N_{s} \leq N_{t}^{\left (\text {rf}\right)} \leq N_{t} K_{t} \). Given \(N_{t}^{(\text {rf})}\) transmit RF chains, the transmitter can apply a lowdimension \(N_{t}^{(\text {rf})} \times N_{s}\) baseband precoder, W_{t}, followed by a highdimension \(K_{t}N_{t} \times N_{t}^{(\text {rf})}\) RF precoder, F_{t}. Note that for the baseband precoder W_{t}, amplitude and phase modifications are feasible while only phase changes can be made by the RF precoder F_{t} through the use of variable phase shifters and combiners. The transmitted signal vector can be written as:
where s is the N_{s}×1 symbol vector such that \(\mathbb {E}\left [\mathbf {s}\mathbf {s}^{H}\right ] = \frac {P}{N_{s}}\mathbf {I}_{N_{s}}\). Thus, P represents the average total input power. Considering a narrowband block fading channel, the K_{r}N_{r}×1 received signal vector is:
where H is K_{r}N_{r}×K_{t}N_{t} channel matrix, and n is a K_{r}N_{r}×1 vector consisting of i.i.d. \(\mathcal {CN}(0, 1)\) noise samples. Throughout this paper, H is assumed known to both the transmitter and receiver. Given that \(N_{r}^{(\text {rf})}\) RF chains \(\left (\text {where} N_{s} \leq N_{r}^{(\text {rf})} \leq N_{r}K_{r}\right)\) are used at the receiver to detect the N_{s} data streams, the processed signal vector can be given by:
where W_{r} is the \(N_{r}^{(\text {rf})} \times N_{s} \) baseband combining matrix, and F_{r} is the \( K_{r} N_{r} \times N_{r}^{(\text {rf})} \) RF combining matrix.
Furthermore, according to the architecture of RAUs at the transmitting and receiving ends, H can be written as:
In the above expression, g_{ij} represents the large scale fading parameter between the ith RAU at the receiver and the jth RAU at the transmitter. We assume that g_{ij} is constant over many coherence time intervals. The normalized subchannel matrix H_{ij} represents the MIMO channel between the jth RAU at the transmitter and the ith RAU at the receiver.
Based on the extended SalehValenzuela model, a clustered channel model is used often in mmWave channel modeling and standardization [6, 14, 15], and it is also adopted in this paper. As in [6], each scattering cluster is assumed to contribute a single propagation path for simplicity of exposition. Using this model, the subchannel matrix H_{ij} is given by:
where L_{ij} denotes the number of propagation paths, \(\alpha _{ij}^{l}\) denotes the complex gain of the lth ray, and \(\phi ^{rl}_{ij} \left (\theta ^{rl}_{ij}\right)\) and \(\phi ^{tl}_{ij} \left (\theta ^{tl}_{ij}\right)\) denote its random azimuth (elevation) angles of arrival and departure, respectively. Without loss of generality, the complex gains \(\alpha _{ij}^{l}\) are assumed to be \(\mathcal {CN}(0, 1)\)^{Footnote 1}. The vectors \(\mathbf {a}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij}\right)\) and \(\mathbf {a}_{r} \left (\phi ^{rl}_{ij},\theta ^{rl}_{ij} \right)\) stand for the normalized transmit/receive array response vectors at the corresponding angles of departure/arrival. The array response vector of an Nelement uniform linear array (ULA) is:
where λ is the wavelength of the carrier, and d is the interelement spacing. It is pointed out that the angle θ is not included in the argument of a^{ULA} since the response for an ULA is independent of the elevation angle. In contrast, for a uniform planar array (UPA), which is composed of N_{v} and N_{h} antenna elements in the vertical and horizontal directions, respectively, the array response vector is represented by:
where
and
3 Diversity gain analysis
The most common performance metric of a digital communication system is the error probability, which can be defined either as the probability of symbol error or the probability of bit error (i.e., the bit error rate (BER)). When communicating over a fading channel, errors obviously depend on specific channel realizations. As such, the random behavior of a fading channel needs to be taken into account, which leads to the concept of average error probabilities [30]. Determining the exact expressions for the average error probabilities for a digital communication system operating over a certain fading channel is usually tedious and might not give a clear insight about the system behavior. As such, there is a need to characterize the performance of a communication system in an insightful and simple way. A popular approach is to shift the focus from exact performance analysis to asymptotic performance analysis, i.e., analyzing the performance at the high signaltonoise (SNR) region, as done in [31]. This is a reasonable approach since the performance of practical interest is in the high SNR region, and in such a region, good approximation can be made on the exact analysis.
For most cases, the average BER function can be approximated in the high SNR region as [31]:
where \(\bar {\gamma }\) stands for the average received SNR, and G_{d} and G_{c} are just the diversity and coding gains, respectively. At high SNR, the diversity gain determines the slope of the BER curve versus \(\bar {\gamma }\) in a loglog scale, whereas the coding gain determines how the curve is shifted along the horizontal axis with respect to a benchmark BER curve \(\bar {\gamma }^{G_{d}}\). Therefore, this yields a simple parameterized average BER characterization for high SNR, which can provide meaningful insights on the system performance behavior.
In this section, the diversity gain is first examined for a generalized selection combining. The main result is then invoked in the diversity analysis of the distributed mmWave massive MIMO system studied in this paper.
3.1 Diversity gain of generalized selection combining
Selection combining (SC) is the most popular lowcomplexity combining scheme. In selection combining, the receiver estimates the SNRs of all available diversity branches and then select the one with the highest SNR for detection. For generalized selection combining (GSC) considered here, the receiver also estimates the SNRs of all available diversity branches. However, instead of selecting the branch with the highest SNR, it selects a branch with the lth highest SNR for detection. It is pointed out that, while such a GSC scheme has no practical interest in its own right, its diversity analysis can be used in the performance analysis of the mmWave massive MIMO system considered in this paper.
Lemma 1
Consider a GSC system with L receive antennas operating over i.i.d. Rayleigh fading channels. If the receiver selects the branch with the lth highest SNR for detection then the system achieves diversity gain:
Proof
Let F(γ) and f(γ) be the cumulative distribution function (CDF) and probability density function (PDF) of the instantaneous SNRs in all branches, respectively. Let \(\bar {\gamma }\) denote the average receive SNR of each branch. With Rayleigh fading, it follows from [31] that F(γ) and f(γ) can be expressed as:
and
If the receiver selects the branch with the lth highest SNR for detection, then based on the theory of order statistics [32], the PDF of the instantaneous receive SNR at the receiver, denoted γ_{l}, is given by:
Applying the above PDF in Proposition 1 in [31] leads to the desired result. □
Lemma 1 can be extended to the case of independent but not identically distributed (i.n.i.d.) Rayleigh fading channels and the result is stated in the next lemma.
Lemma 2
Suppose that the GSC system with L receive antennas operates over the i.n.i.d. Rayleigh fading channels. If it selects the path with the lth highest SNR for detection, then it can achieve diversity gain:
Proof
Let \(\bar {\gamma }_{\text {max}}\) and \(\bar {\gamma }_{\text {min}}\) denote the maximum and minimum values of the average receive SNRs of all these L diversity paths, respectively. Furthermore, let \(\mathcal {A}\) and \(\mathcal {B}\) denote two GSC systems, each equipped with L receive antennas and operating over i.i.d. Rayleigh fading channels such that the average receive SNRs equal to \(\bar {\gamma }_{\text {max}}\) and \(\bar {\gamma }_{\text {min}}\), respectively. It is known from Lemma 1 that the diversity gains of these two systems are the same and equal to L−l+1 if both systems select the branch with the lth highest instantaneous SNR for detection. Furthermore, since the GSC system under consideration cannot have better diversity performance than system \(\mathcal {A}\) and cannot have worse diversity performance than system \(\mathcal {B}\), it can then be concluded that the i.n.i.d. system must also achieve the diversity gain of L−l+1. □
3.2 Diversity gain analysis of the distributed mmWave massive MIMO system
From the structure and definition of the channel matrix H in Section 2, there is a total of \(L_{s}={\sum \nolimits }_{i=1}^{K_{r}}{\sum \nolimits }_{j=1}^{K_{t}}L_{ij}\) propagation paths. Naturally, H can be decomposed into a sum of L_{s} rankone matrices, each corresponding to one propagation path. Specifically, H can be rewritten as:
where
\(\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{ij},\theta ^{rl}_{ij} \right)\) is a K_{r}N_{r}×1 vector whose bth entry is defined as:
where \(Q_{i}^{r}=((i1)N_{r}, iN_{r}]\). And \(\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij},\theta ^{tl}_{ij}\right)\) is a K_{t}N_{t}×1 vector whose bth entry is defined as:
where \(Q_{j}^{t}=((j1)N_{t}, jN_{t}]\).
Lemma 3
Suppose that the antenna configurations at all RAUs are either ULA or UPA. Then, all L_{s} vectors \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{ij}, \theta ^{rl}_{ij} \right)\right \}\) are orthogonal to each other when N_{r}→∞. Likewise, all L_{s} vectors \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij} \right)\right \}\) are orthogonal to each other when N_{t}→∞.
Proof
It follows immediately from (18) and (19) that if u≠v, then vectors \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{up}, \theta ^{rl}_{up} \right)\right \}\) and \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{vq}, \theta ^{rl}_{vq} \right)\right \}\) are orthogonal. On the other hand, when u=v and p≠q, it is known from Lemma 1 and Corollary 2 in [6] (also see [33]) that vectors \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{up}, \theta ^{rl}_{up} \right) \right \}\) and \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{vq}, \theta ^{rl}_{vq} \right)\right \}\) are orthogonal. The proof that \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij} \right) \right \}\) is a set of orthogonal vectors can be shown similarly. □
Theorem 1
Suppose that both sets \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{ij},\theta ^{rl}_{ij} \right)\right \}\) and \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij} \right)\right \}\) are orthogonal vector sets when N_{r}→∞ and N_{t}→∞. Let N_{s}≤L_{s}. Then, the distributed massive MIMO system with large N_{r} and N_{t} can achieve a diversity gain of:
Remarks 1
When N_{t} and N_{r} are large enough, (33) indicates that the system multiplexing gain is at most equal to L_{s}. This is reasonable since there exist only L_{s} effective singular values in the channel matrix H. Theorem 1 provides a simple diversitymultiplexing tradeoff of a mmWave massive MIMO system: adding one data stream to the system decreases the diversity gain by one, whereas removing one data stream increases the diversity gain by one. Such a tradeoff is useful in designing a system to meet requirements on both data rate and error performance.
Remarks 2
Under the case where N_{t} and N_{r} are large enough, it can be found from the proof of Theorem 1 that the diversity performance of the mmWave massive MIMO system only depends on the singular value set \(\left \{\tilde {\alpha }^{l} \right \}\) and is not influenced by how submatrices \(\left \{\sqrt {g_{ij}}\mathbf {H}_{ij}\right \}\) are placed in the channel matrix H (see further discussion of Fig. 9 on this point).
Corollary 1
Consider the scenario that the antenna configuration at each RAU is ULA. Also assume that L_{ij}=L for any i and j. Let N_{s}≤K_{r}K_{t}L. When both N_{t} and N_{r} are very large, the distributed massive MIMO system can achieve a diversity gain:
In particular, when K_{r}=K_{t}=1, the massive MIMO system with colocated antennas arrays can achieve a diversity gain:
Remarks 3
Corollary 1 implies that for a mmWave colocated massive MIMO system, its diversity gain and multiplexing gain are limited and at most equal to the number of paths L. However, these gains can be increased by employing the distributed antenna architecture and can be scaled up proportionally to K_{r}K_{t}.
4 Diversity gain analysis with the conventional partially connected structure
The previous section has analyzed the diversity gain for the massive MIMO system with the general fully connected RF architecture. This section focuses on a massive MIMO system employing the conventional partially connected RF architecture as illustrated in Fig. 3. Here, the transmitter equipped with K_{t} RF chains sends N_{s} data streams to the receiver equipped with K_{r} RF chains. Each of RF chains at the transmitter or receiver is connected to only one RAU. It is assumed that N_{s}≤ min{K_{t},K_{r}}. The numbers of antennas per each RAU at the transmitter and receiver are fixed as N_{t} and N_{r}, respectively. Note that N_{t}≫N_{s} and N_{r}≫N_{s}. Both the transmitter and receiver employ very small digital processors and very large analog processors, represented respectively by W_{t} and F_{t} for the transmitter and W_{r} and F_{r} for the receiver.
As before, denote by s the transmitted symbol vector, by H the fading channel matrix, and by n the noise vector. Then, at the receiver, the processed signal vector z is given by (3), whereas H is described as in (4). Due to the partially connected RF architecture, the analog processors F_{t} and F_{r} are block diagonal matrices, expressed as:
and
where f_{ti} stands for the N_{t}×1 steering vector of phases for the ith RAU at the transmitter, and f_{rj} stands for the N_{r}×1 steering vector of phases for the jth RAU at the transmitter.
Theorem 2
Consider the case that the antenna array configuration at each RAU is ULA and L_{ij}=L for any i and j. In the limit of large N_{t} and N_{r}, the distributed massive MIMO system with partially connected RF architecture can achieve a diversity gain:
Remarks 4
Comparing the diversity gain given in Corollary 1 with that given in Theorem 2 reveals that when N_{s}=1, the diversity gains with the two systems under consideration are the same. However, when N_{s}>1, the proposed distributed antenna system with fully connected RF architecture achieves a higher diversity gain than the system with the partially connected architecture, and the gap between the two diversity gains is (N_{s}−1)[ (K_{r}+K_{t}−N_{s}+1)L−1].
5 Diversity gain analysis for the multiuser scenario
This section considers the downlink transmission in a multiuser massive MIMO system as illustrated in Fig. 4. Here, the base station (BS) employs K_{b} RAUs with each having N_{b} antennas and \(N_{b}^{(\text {rf})}\) RF chains to transmit data streams to K_{u} mobile stations. Each mobile station (MS) is equipped with N_{u} antennas and \(N_{u}^{(\text {rf})}\) RF chains to support the reception of its own N_{s} data streams. This means that there is a total of K_{u}N_{s} data streams transmitted by the BS. The numbers of data streams are constrained as \(K_{u}N_{s} \leq N_{b}^{(\text {rf})}\leq K_{b}N_{b}\) for the BS and \(N_{s} \leq N_{u}^{(\text {rf})}\leq N_{u}\) for each MS.
At the BS, denote by F_{b} the \(K_{b}N_{b} \times N_{b}^{(\text {rf})}\) RF precoder and by W_{b} the \(N_{b}^{(\text {rf})} \times N_{s}K_{u}\) baseband precoder. With the narrowband flat fading channel model, then the received signal vector at the ith MS is given by:
where s is the signal vector for all K_{u} mobile stations, which satisfies \(\mathbb {E} \left [ \mathbf {s} \mathbf {s}^{H} \right ] = \frac {P}{K_{u}N_{s}}\mathbf {I}_{K_{u}N_{s}}\), and P is the average transmit power. The N_{u}×1 vector n_{i} represents additive white Gaussian noise, whereas the N_{u}×K_{b}N_{b} matrix H_{i} is the channel matrix corresponding to the ith MS, whose entries H_{ij} are described as in Section 2. Furthermore, the signal vector after combining can be expressed as:
where F_{ui} is the \(N_{u} \times N_{u}^{(\text {rf})}\) RF combining matrix and W_{ui} is the \(N_{u}^{(\text {rf})} \times N_{s}\) baseband combining matrix for the ith MS.
Theorem 3
Consider the case that all antenna array configurations for the downlink transmission are ULA and L_{ij}=L for any i and j (i.e., all subchannels H_{ij} have the same number of propagation paths). In the limit of large N_{b} and N_{u}, the downlink transmission in a massive MIMO multiuser system can achieve a diversity gain:
Remarks 5
Theorem 3 implies that when N_{b} and N_{u} are large enough, the available diversity gain G_{d} does not depend on the number of mobile users K_{u}.
Remarks 6
In a similar fashion, it is easy to prove that the uplink transmission in a massive MIMO multiuser system can also achieve a diversity gain G_{d}=K_{b}L−N_{s}+1. Moreover, it can also be proved that when L=1, the system diversity gain is equal to G_{d}=K_{b} for the case N_{u}=1, i.e., each MS has only one antenna.
6 Simulation results and discussion
For all simulation results presented in this section, it is assumed that each subchannel matrix H_{ij} consists of L_{ij}=L=3 paths, each of the large scale fading coefficients g_{ij} equals to g=−20 dB (except for Fig. 9), and the numbers of transmit and receive RF chains are twice the number of data streams [9] (i.e., \(N_{t}^{(\text {rf})}=N_{r}^{(\text {rf})}=2N_{s}\)). It is further assumed that the variance of AWGN samples is unity, and hence, the input SNR is the same as the average input power P/N_{s}. For simplicity, only ULA array configuration with d=0.5 is considered at RAUs and BPSK modulation is employed for each data stream. With such system configurations, the instantaneous BER is given by \(Q(\sqrt {2 \gamma })\) [34], where γ denotes the instantaneous receive SNR and the Q function is defined as \(Q(x)=\int _{x}^{\infty } \exp { \left (\frac {y^{2}}{2} \right)} \mathrm {d}y\). For ease of comparison and discussion, introduce the concept of designed SNR as \(\overline {\text {SNR}}_{\text {dg}} = PN_{r}N_{t}/(N_{s}L)\). This means that \(P= \overline {\text {SNR}}_{\text {dg}} N_{s}L/(N_{r}N_{t})\) for a given designed SNR \(\overline {\text {SNR}}_{\text {dg}}\). In fact, there exists a power scaling law for mmWave communications which states that the data transmit power P can be scaled down proportionally to 1/(N_{r}N_{t}) in order to maintain a desirable BER performance [35].
In all simulations, unless stated otherwise, there are three main steps for hybrid digitalanalog processing as follows:

Perform the SVD for channel matrix H and find the optimal overall digital precoder and combiner for N_{s} data streams.

Form an analog precoder and an analog combiner based on the optimal overall digital precoder and combiner, respectively.

Perform zeroforcing (ZF) digital detection based on the analog precoder and analog combiner and complete the data detection operation.
First studied is the diversity performance of a massive MIMO mmWave system with distributed antenna arrays. With N_{r}=N_{t}=N=50 and K_{r}=K_{t}=K=2, Fig. 5 plots BER curves versus the designed SNR for different numbers of data streams, N_{s}=2,4,6. For comparison, the BER curve obtained in the case of colocated antenna arrays are also plotted for N_{s}=1,2,3. It can be seen that even for the larger number of data streams, the BER performance with distributed antenna arrays is clearly better than that with colocated antenna arrays. Furthermore, as N_{s} decreases, the BER performance with either distributed or colocated antenna arrays is improved. These observations are expected and agree with Corollary 1, which states that using distributed antenna arrays yields higher diversity gains than using colocated antenna arrays. To verify exactly the diversity gain result given in Corollary 1, Fig. 6 plots the diversity gain verifying (GDV) curves produced by simulating the GSC systems. It can be seen that a BER curve with either distributed or colocated antenna arrays has the same slope in the high SNR region as the corresponding GDV curve.
Illustrated in Fig. 7 is the performance with the conventional partially connected (PC) RF architecture analyzed in Section 4. With this structure, one first carries out the SVDs for subchannel matrices {H_{ij}} rather than for the whole channel matrix H and then forms the analog precoder and analog combiner. Let K_{r}=K_{t}=K. With N_{r}=N_{t}=N=50, Fig. 7 plots the BER curves for the following four cases: (K=1,N_{s}=1), (K=2,N_{s}=2), (K=3,N_{s}=3), and (K=4,N_{s}=4). It is known from Theorem 2 that the diversity gains for the four cases are identical and equal to G_{d}=L=3. To illustrate this, a DGV curve with diversity gain G_{d}=3 is also plotted in this figure. It can be seen that the system with the conventional PC structure for the four cases can achieve the full diversity gain 3, while the coding gain increases when both K and N_{s} increase. For comparison, the BER curve obtained with the general fully connected (FC) RF structure when N_{s}=4 and K=2 is also plotted. The theoretical limit on the diversity gain in this case is 9, which agrees well with the DGV curve having G_{d}=9. Observe that in the high SNR region, the general FC structure yields significantly better diversity performance than the conventional PC structure.
Next, when N_{s}=1, we consider the diversity performance with the multiuser downlink scenario where there are 5 or 10 mobile users, each having 10 antennas and each RAU at the BS is equipped with 50 antennas. Due to the fact that there is no cooperation among the users, one first carries out the SVDs for subchannel matrices {H_{i}} rather than for the whole channel matrix H and then forms the analog precoder for the BS and analog combiners for the users. Note that the BS needs to carry out ZF digital preprocessing before transmitting data. Figure 8 plots the BER curves versus the designed SNR for different numbers of subarrays at the BS, namely K_{b}=1,3,5. It can be observed from this figure that as K_{b} increases, the diversity performance of the multiuser system improves remarkably. This is because, as established in Theorem 3, the diversity gain becomes larger with increasing K_{b}. Furthermore, it can be seen from Fig. 8 that the system has the same diversity gain for different numbers of users while the coding gain increases as K_{u} decreases. This observation agrees with Remark 5.
Finally, the diversity performance of the singleuser massive MIMO mmWave system is examined under the scenario that the distributions of large scale fading coefficients, {g_{ij}}, are inhomogeneous. To this end, let G=[g_{ij} (dB)] denote the large scale fading coefficient matrix. When N_{r}=N_{t}=N=50 and K_{r}=K_{t}=K=2, simulation is performed for the following six inhomogeneous G:
It can be found that the diversity performance for the six inhomogeneous cases are almost the same (see Remark 2). In order to illustrate this interesting phenomenon, Fig. 9 plots the BER curves versus the designed SNR with G_{1} and G_{2}, respectively. For comparison, the two BER curves for the homogeneous distributions with g=−20 dB and g=−25 dB are also plotted. As expected, the BER curves with the inhomogeneous coefficient distributions are between the two BER curves with homogeneous coefficient distributions. It can be concluded from this figure that the case of inhomogeneous coefficient distributions has the same diversity gain as in the case of homogeneous coefficient distributions.
7 Conclusions
This paper has provided asymptotical diversity analysis for massive MIMO mmWave systems with colocated and distributed antenna architectures when the number of antennas at each subarray goes to infinity. Theoretical analysis shows that with a colocated massive antenna array, scaling up the number of antennas of the array can increase the coding gain but not the diversity gain. However, if the array is built from distributed subarrays (RAUs), each having a very large number of antennas, then increasing the number of RAUs does increase the diversity gain and/or multiplexing gain [36, 37]. As such, the analysis leads to a novel approach to improve the diversity and multiplexing gains of massive MIMO mmWave systems. It is acknowledged that the asymptotical diversity analysis obtained in this paper is under the idealistic assumption of having perfect CSI. Performing the diversity analysis for massive MIMO mmWave systems under imperfect CSI is important and deserves further research.
8 Appendix 1: Proof of Theorem 1
Proof
The distributed massive MIMO system can be considered as such a colocated system with L_{s} paths that have complex gains \(\left \{\tilde {\alpha }_{ij}^{l} \right \}\), receive array response vectors \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{ij}, \theta ^{rl}_{ij}\right)\right \}\), and transmit response vectors \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij} \right)\right \}\). Furthermore, order all paths in a decreasing order of the absolute values of the complex gains \(\left \{\tilde {\alpha }_{ij}^{l} \right \}\). Then, the channel matrix can be written as:
where \(\tilde {\alpha }^{1}\geq \tilde {\alpha }^{2}\geq \cdots \geq \tilde {\alpha }^{L_{s}}\). One can rewrite H in a matrix form as:
where D denotes a L_{s}×L_{s} diagonal matrix with \([\mathbf {D}]_{ll}=\tilde {\alpha }^{l}\), and A_{r} and A_{t} are defined as follows:
and
Since both \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}, \theta ^{rl} \right)\right \}\) and \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl},\theta ^{tl}\right)\right \}\) are orthogonal vector sets when N_{r}→∞ and N_{t}→∞, A_{r} and A_{t} are asymptotically unitary matrices. Then, one can form a singular value decomposition (SVD) of matrix H as:
where Σ is a diagonal matrix containing all singular values on its diagonal, i.e.:
and the submatrix \(\tilde {\mathbf {A}}_{t}\) is defined as:
where ψ_{l} is the phase of complex gain \(\tilde {\alpha }^{l}\) corresponding to the lth path. □
Based on (33), the optimal precoder and combiner are chosen respectively as:
and
To summarize, when N_{t} and N_{r} are large enough, the massive MIMO system can employ the optimal precoder and combiner given in (36) and (37), respectively. Then, it follows from the above SVD analysis that the instantaneous SNR of the lth data stream is given by:
Now the detection of the lth data stream is equivalent to the detection in a generalized selection combining system, which selects the path with the lth highest SNR for detection. Therefore, it follows from Lemma 2 that the detection performance of the lth data stream has a diversity gain L_{s}−l+1. Since the overall BER is the arithmetic mean of individual BERs, i.e., \(\overline {\text {BER}}=\frac {1}{N_{s}}\sum _{l=1}^{N_{s}} \overline {\text {BER}}(l)\), the system’s diversity gain equals to the diversity gain in detecting the N_{s}th data stream, which is the worst among all data streams. Therefore, the result in (20) is obtained. \(\square \)
9 Appendix 2: Proof of Theorem 2
Proof
When N_{t} and N_{r} are very large, the diversity gain analysis is similar to that in Theorem 1. For the first data stream that enjoys the best path, it is simple to see that its diversity gain is the largest and equal to K_{r}K_{t}L. This is because the detection of the first data stream is equivalent to a selection combining system operating with K_{r}K_{t}L paths. However, for the second data stream, due to the structure of F_{t} and F_{r}, its detection is equivalent to a selection combining system operating with (K_{r}−1)(K_{t}−1)L paths. Therefore, it can be inferred that its diversity gain is equal to (K_{r}−1)(K_{t}−1)L. Similarly, for the last data stream among the N_{s} data streams, its diversity gain is (K_{r}−N_{s}+1)(K_{t}−N_{s}+1)L. It then follows that the diversity gain of the whole system is just (K_{r}−N_{s}+1)(K_{t}−N_{s}+1)L. □
10 Appendix 3: Proof of Theorem 3
Proof
For the downlink transmission in a massive MIMO multiuser system, the overall equivalent multiuser basedband channel can be written as:
On the other hand, when both N_{b} and N_{u} are very large, both receive and transmit array response vector sets, \(\left \{\tilde {\mathbf {a}}_{r} \left (\phi ^{rl}_{ij}, \theta ^{rl}_{ij} \right)\right \}\) and \(\left \{\tilde {\mathbf {a}}_{t} \left (\phi ^{tl}_{ij}, \theta ^{tl}_{ij} \right)\right \}\), are asymptotically orthogonal. Therefore, the diversity performance for the ith user depends only on the subchannel matrix H_{i} and the choices of F_{ui} and F_{b}. The subchannel matrix H_{i} has a total of K_{b}L propagation paths. Similar to the proof of Theorem 1, by employing the optimal RF precoder and combiner for the ith user, the user can achieve a maximum diversity gain K_{b}L−N_{s}+1. It is then concluded that the downlink transmission can achieve a diversity gain G_{d}=K_{b}L−N_{s}+1. □
Notes
The different variances of \(\alpha _{ij}^{l}\) can easily accounted for by absorbing into the large scale fading coefficients g_{ij}.
Abbreviations
 BS:

Base station
 BPU:

Baseband processing unit
 BER:

Bit error rate
 CMIMO:

Colocated MIMO
 CDF:

Cumulative distribution function
 DMT:

Diversitymultiplexing tradeof
 f GDV:

Diversity gain verifying
 DMIMO:

Distributed MIMO
 FC:

Fully connected
 GSC:

Generalized selection combining
 i.i.d.:

Independent and identically distributed
 mmWave:

Millimeterwave
 MS:

Mobile station
 PDF:

Probability density function
 RF:

Radiofrequency
 RAU:

Remote antenna unit
 SNR:

Signaltonoise
 SC:

Selection combining
 SVD:

Singular value decomposition
 ULA:

Uniform linear array
 UPA:

Uniform planar array
 ZF:

Zeroforcing
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Yue, DW., Xu, S. & Nguyen, H.H. Diversity gain of millimeterwave massive MIMO systems with distributed antenna arrays. J Wireless Com Network 2019, 54 (2019). https://doi.org/10.1186/s1363801913668
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DOI: https://doi.org/10.1186/s1363801913668