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Multiuser hybrid precoding for mmWave massive MIMO systems with subconnected structure
EURASIP Journal on Wireless Communications and Networking volumeÂ 2021, ArticleÂ number:Â 157 (2021)
Abstract
Hybrid precoding achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multipleinput multipleoutput (MIMO) systems. However, most prior works on multiuser hybrid precoding only consider the fullconnected structure. In this paper, a novel multiuser hybrid precoding algorithm is proposed for the subconnected structure. Based on the improved successive interference cancellation (SIC), the analog precoding matrix optimization problem is decomposed into multiple analog precoding submatrix optimization problems. Further, a nearoptimal analog precoder is designed through factorizing the precoding submatrix for each subarray. Furthermore, digital precoding is designed according to the block diagonalization (BD) technology. Finally, the waterfilling power allocation method is used to further improve the communication quality. The extensive simulation results demonstrate that the sum rate of the proposed algorithm is higher than the existing hybrid precoding methods with the subconnected structure, and has higher energy efficiency compared with existing approaches. Moreover, the proposed algorithm is closer to the stateoftheart optimization approach with the fullconnected structure. In addition, the simulation results also verify the effectiveness of the proposed hybrid precoding design of the uniform planar array (UPA).
1 Introduction
The highspeed and lowlatency characteristics of the fifth generation (5G) are the biggest differences from previous communication systems. Many emerging technologies, such as physical layer technology, network densification technology, etc. [1,2,3,4], have only reached current progress. However, the key problem in the technical development of communication systems today is the shortage of spectrum. Millimeterwave (mmWave) provides new spectrum resources for wireless communication systems and satisfies the bandwidth requirements for 5G services [5, 6]. MmWave largescale multipleinput multipleoutput (MIMO) technology with shorter wavelengths can package largescale antennas into small sizes. Hybrid beamforming technology improves link reliability by compensating for severe mmWave path losses.
Traditional analog beamforming has also been considered in mmWave systems. The idea is to use a lowcost phase shifter (PS) to control the phase of the signal transmitted by each antenna [7,8,9]. The disadvantage is that it cannot transmit parallel data streams to provide multiplexing gain. However, the traditional fulldigital beamforming, although the best multiplexing gain can be obtained [10, 11], each antenna requires a radio frequency (RF) chain, which is expensive and consumes a lot of power. Therefore, a hybrid precoding structure [12,13,14,15] that saves RF consumption and ensures good performance is extremely important.
For hybrid precoding schemes in singleuser MIMO (SUMIMO) systems, existing literatures [16] and [17] give different solutions from different perspectives. Based on the compressed sensing, [16] solves the problem in [18] with an alternative iteration between a locally optimal analog precoder and a baseband digital precoder. In addition, [17] resolves the matrix optimization problem into multiple optimization subproblems by using the iterative algorithm in [19]. In addition, if the number of RF chains is greater than or equal to twice the number of data streams, hybrid beamforming can achieve the same performance as fulldigital beamforming in the paper [20].
Inspired by [16], the work [21] proposes the orthogonal match pursuit algorithm (OMP) for multiuser MIMO (MUMIMO) systems. A hybrid block diagonalization (HyBD) algorithm that analog precoding is designed by exhaustive searching and the equal gain transmission (EGT) is proposed in [22]. In a similar way, two hybrid BD algorithms that maximize the analog beamforming gain by iteratively updating the analog precoder and combiner are proposed in [9, 10]. Moreover, a series of hybrid zeroforcing (HyZF) and hybrid minimummeansquarederror (HyMMSE) schemes are proposed in [24, 24,25,26]. However, those works [21,22,23,24,25,26,27,28] focus on the design of hybrid beamforming techniques based on the fullconnected structure, which requires a lot of power consumption and is not efficient for implementation.
The fullconnected structure means that each antenna element is connected to all RF chains. Because each RF chain is connected to only a subset of transmitting antennas, the subconnected structure can maximize the systemâ€™s energy efficiency. For subconnected structure, different solutions are given in [20, 23, 24, 27, 29,30,31,32,33,34]. However, the hybrid beamforming schemes [23, 29,30,31] are designed for MUMISO systems with single antenna receivers, and only the scheme [9] is designed for MUMIMO systems. Decomposing the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems with the subconnected structure is proposed in [33, 34], but they cannot be directly applicable to mmWave massive MUMIMO systems. Although many scholars have conducted extensive research on hybrid beamforming, there is still much room for improvement in subconnected hybrid precoding design, especially for MUMIMO systems.
In this paper, we focus on the subconnected structure design of hybrid precoding in mmWave massive MUMIMO systems, where the single base station (BS) with multiple subarrays serves several multiantenna users simultaneously. Assuming that the perfect channel state information (CSI) is available at both the BS and users, we propose a nearoptimal hybrid precoding scheme by jointly designing the analog and digital beamformer/combiner. The contributions of this work are summarized as follows:

(1)
The proposed hybrid precoding design scheme is for the subconnected structure in the mmWave massive MIMO system. Compared with the fullconnected structure, it has lower hardware complexity. To reduce the computational complexity, we reformulate the original optimization problem as two mmWave sumrate maximization subproblems according to the idea of hierarchical optimization.

(2)
To solve the sumrate maximization problem, we propose the improved successive interference cancelation (SIC) method which designs the analog precoding scheme by trying to avoid the loss of information at each stage. Then the baseband BD scheme and waterfilling power allocation method are utilized to solve the digital precoding and power allocation matrix, respectively. The proposed algorithm is a closedform solution, and the result of this solution is stable.

(3)
The theoretical analysis and simulation results of the proposed hybrid precoding scheme are given in detail. We study the influence of various parameters on design performance for our algorithm. Simulation results show that the proposed algorithm has a higher sum rate than the existing hybrid precoding approaches under the subconnected structure, and closes to the stateoftheart optimization approach under the fullconnected structure. Furthermore, the proposed algorithm has higher power efficiency compared with the optimization design algorithm under the fullconnected structure.
Notation: In this paper, bold uppercase and lowercase letters denote matrices and vectors, respectively. \(E[ \cdot ]\) represents the expectation. \({( \cdot )^T}\), \({( \cdot )^{  1}}\), \({( \cdot )^H}\) and \({\left\ \cdot \right\ _F}\) denote the transpose, inversion, conjugate transpose, and Frobenius norm of a matrix, respectively. \({{\mathbf{I}}_N}\) is the \(N \times N\) identity matrix and \({{\mathbf{0}}_{M \times N}}\) is the \(M \times N\) allzero matrix. \({{\mathbb{C}}^{{m} \times n}}\) represents an \(m \times n\) dimensional complex space. Finally, \(\angle {\mathbf{X}}\) denotes a matrix having elements of the form \({e^{j{\varphi_{i,j}}}}\), where \({\varphi_{i,j}}\) is the phase of the (i,Â j) th element of \({\mathbf{X}}\).
2 Methodology
In this paper, we first introduce the existing hybrid precoding methods for mmWave massive MIMO systems. They are almost all based on a fullconnected structure and only consider the case of the uniform linear array (ULA). The research background and related methods are presented in Sect.Â 1. There are many factors that affect communication and rate performance. This paper considers improving system performance from the perspective of algorithm improvement and structure selection.
On the one hand, the application of hybrid precoding can effectively improve the system and sum rate performance. On the other hand, with the rapid development of mmWave communication, it also solves the problem of high energy consumption of traditional precoding. The hybrid precoding can be divided into a fullconnected structure and a subconnected structure. Compared with the fullconnected structure, the subconnected structure uses each RF chain to link an antenna subset, which greatly saves the number of RF layouts, has more application significance, and makes the hybrid precoding design more green and energysaving. Compared with ULA, the uniform planar array (UPA) can use fewer array elements to achieve higher space utilization, which can reduce system cost.
The goal of this paper is to maximize the sum rate of the system by designing a hybrid precoding scheme for multiple users. Under the power limitation of BS, it is solved by two steps: analog precoding and digital precoding. Since the CSI of all users is completely available at the BS, inspired by SIC and based on the subconnected structure we considered, a new analog precoding design scheme is proposed. The optimization sequence is selected according to the difference of each subchannel, and then by considering the continuous optimization of each submatrix, an approximately optimal analog precoding is obtained. In terms of optimizing digital precoding, BD technology is used under equivalent channels to eliminate the interuser interference. Finally, the waterfilling method is used to achieve better power allocation.
In order to verify the effectiveness of the algorithm, we have conducted a variety of experiments to obtain comparison results. Firstly, we introduce several advanced hybrid precoding schemes. Then, the complexity is calculated, and the superiority of the proposed algorithm is proved in simulation. Finally, compare the proposed algorithm with other algorithms in the same environment. The specific analysis can be found in Sect.Â 5.
3 System model and problem formulation
In this paper, we consider a subconnected structure for hybrid precoding in mmWave massive MUMIMO systems, as shown in Fig.Â 1. The BS is equipped with \({N_t}\) antennas and N independent RF chains. Each RF chain is connected to one subarray, and each subarray includes M antennas, then \(NM = {N_t}\). The BS communicates with K users. Each user is equipped with \({N_r}\) antennas to support \({N_s}\)(\({N_s} \ge 1\)) data streams, which means total \(K{N_s}\) data streams are processed by the BS.
At the BS, the signals are processed by a power allocation matrix \({\mathbf{P}} \in {{\mathbb{C}}^{K{N_s} \times K{N_s}}}\) and then, it is processed by an analogue RF precoder \({{\mathbf{F}}_{\mathrm{RF}}} \in {{\mathbb{C}}^{{N_t} \times N}}\) after the baseband digital precoder \({{\mathbf{F}}_{\mathrm{BB}}} \in {{\mathbb{C}}^{N \times K{N_s}}}\). Finally, the preencoded signal is sent to the wireless channel. It should be pointed out that the baseband precoder \({{\mathbf{F}}_{\mathrm{BB}}}\) enables both amplitude and phase modifications, but only phase changes (phaseonly control) can be realized by \({{\mathbf{F}}_{\mathrm{RF}}}\) since it is implemented by using analog phase shifters. Each entry of \({{\mathbf{F}}_{\mathrm{RF}}}\) is normalized to satisfy \({\left {\left. {{\mathbf{F}}_{\mathrm{RF}}^{i,j}} \right } \right. ^2} = \frac{1}{{{N_t}}}\). Moreover, to satisfy the total transmit power constraint, \({{\mathbf{F}}_{\mathrm{BB}}}\) is normalized to satisfy \(\left\ {\left. {{{\mathbf{F}}_{\mathrm{RF}}}{{\mathbf{F}}_{\mathrm{BB}}}} \right\ } \right. _F^2 = K{N_s}\). The structure of \({{\mathbf{F}}_{\mathrm{RF}}} \in {{\mathbb{C}}^{{N_t} \times N}}\) is given as
where \({{{{\bar{\mathbf{a }}}}}_n} \in {{\mathbb{C}}^{{M} \times 1}}, n \in \{ 1,2, \ldots ,N\}\).
Therefore, the received signal vector \({{{{\hat{\mathbf{y }}}}}_k} \in {{\mathbb{C}}^{{N_s} \times 1}}\) at the kth user can be written as
where \({{\mathbf{s}}_k} \in {{\mathbb{C}}^{{N_s} \times 1}},k \in \{ 1,2, \ldots ,K\}\) means the signal vector of the \({N_s}\) data streams. \({\mathbf{F}}_{\mathrm{BB}}^k\) is the \(((k  1){N_s} + 1)\)th to the \(k{N_s}\)th columns of \({{\mathbf{F}}_{\mathrm{BB}}}\), corresponding to the precoding for \({{\mathbf{s}}_k}\). The transmit signal vector \({\mathbf{s}}\) is assumed to satisfy \({\mathbf{{\mathrm{E}} }}\left[ {{\mathbf{s}}{{\mathbf{s}}^H}} \right] = \frac{1}{{K{N_s}}}{{\mathbf{I}}_{K{N_s}}}\). \({\mathbf{s}} = \left[ {\begin{array}{*{20}{l}}{\begin{array}{*{20}{l}}{\begin{array}{*{20}{l}}{{\mathbf{s}}_1^T,}&{{\mathbf{s}}_2^T,}\end{array}}&\ldots \end{array},}&{{\mathbf{s}}_K^T}\end{array}} \right] \in {{\mathbb{C}}^{K{N_s} \times 1}}\) represents the total vector of transmitted signals of K users. \({{\mathbf{H}}_k} \in {{\mathbb{C}}^{{N_r} \times {N_t}}}\) denotes the channel matrix based on the Salehâ€“Valenzuela model between the BS and the kth user. \({{\mathbf{n}}_k} \in {{\mathbb{C}}^{{N_r} \times 1}}\) is an additive Gaussian white noise vector with independent and identically distribution (i.i.d.).
When the Gaussian symbols are used by the BS, the sum rate achieved will be shown as
where \({P_N}\) is the transmit power, and the noise variance at each user is \({\sigma^2} = 1\). \({{\mathrm{SIN}}}{{{\mathrm{R}}}_{\mathrm{{k}}}}\) is expressed as the signaltointerference noise ratio (\({{\mathrm{SINR}}}\)) of the signal \({{\mathbf{s}}_k}\). It can be calculated by the ratio of the energy of the useful signal in (3) to the interference of the remaining terms plus noise energy.
In this paper, we use the geometric Salehâ€“Valenzuela channel model which is more appropriate for mmWave communication [35, 36]. The normalized mmWave downlink channel for the kth user \({{\mathbf{H}}_k}\) is assumed to be contributed by \({N_{\mathrm{c}}}{N_{\mathrm{p}}}\) propagation paths, where \({N_{\mathrm{c}}}\) is the number of scattering clusters and \({N_{\mathrm{p}}}\) is the number of paths of each cluster. Therefore, the channel of kth user can be expressed as [37]
where \(\alpha _{i,l}^k\) is the complex gain of the ith path in the lth cluster, which follows \({{{\mathcal{C}}}}{{{\mathcal{N}}}}({\mathbf{0 }},{\sigma^2}{\mathbf{I }})\). \(\theta _{i,l}^k\) and \(\varphi_{i,l}^k\) denote the horizontal and elevation angles in (4), respectively. \({\mathbf{a}}_r^k(\theta _{i,l}^k,\varphi_{i,l}^k)\) and \({\mathbf{a}}_t^k(\theta _{i,l}^k,\varphi_{i,l}^k)\) represent the array response vectors of the kth user and the BS, respectively.
For the ULA with U elements, the array response vector can be presented as [34]
where d is the spacing distance between two neighboring antenna elements, and \(\lambda\) is the wavelength of the transmission. But, we do not include \(\varphi\) since the ULA response vector is independent of the elevation angle.
Furthermore, when we consider the UPA with \({W_1}\) and \({W_2}\) elements (\({W_1}{W_2} = U\)) on horizon and vertical, respectively, the array response vector can be given [34]
where \(0 \le x \le ({W_1}  1)\) and \(0 \le y \le ({W_2}  1).\)
4 Proposed nearoptimal hybrid precoding design
We aim to design the analog precoder \({{\mathbf{F}}_{\mathrm{RF}}}\) and digital precoder \({{\mathbf{F}}_{\mathrm{BB}}}\), so as to maximize the total sum rate R, which can be written as
Since the nonzero elements in the analog precoding matrices are usually realized by phase shifters [34], the nonzero elements in \({{\mathbf{F}}_{\mathrm{RF}}}\) satisfy the constantmodulus constraints. Unfortunately, the nonconvex constraints on the constantmodulus constraints lead the optimization to be nonconvex. In other words, it is difficult to find the globally optimal solution of problem (7).
4.1 Analog precoding design
In the case of multiple users, the interuser interference can be effectively eliminated by using the baseband BD technology. After removing the interference between users, R in (7) can be rewritten as
It means we should find the optimal solution \({{\mathbf{F}}_{\mathrm{RF}}}\) in R as far as possible. Based on (1), the limitations of the analog precoding matrix design are constant amplitude and BD. However, these nonconvex constraints make it difficult to maximize the capacity of (8). Based on the special block diagonal structure of the hybrid precoding matrix \({{\mathbf{F}}_{\mathrm{RF}}}\), we observe that the precoding on different subantenna arrays is independent. Inspired by [33, 34], we can resolve the complicated optimization problem (8) into a series of subrate optimization problems, which is much easier solved.
In other words, considering each antenna array connected to each RF chain one by one, we can optimize the sum rate of the first antenna array selected by turning off all their antenna subarrays. After that, we can select the sum rate of the second antenna array that needs to be optimized.
The traditional SIC method is optimized in a recursive order, but the channel state of each antenna subarray is different. We can sort the N antenna subarrays according to the capacity of the channel before optimization. The optimized order of capacity is determined by the pros and cons of the capacity, that is, our optimization order is in the order of screening.
\({C_n}\) is defined as the capacity of the nth antenna subarray in the millimeter wave massive MIMO systems, where \(n = 1,2, \ldots ,N\). After the optimization sequence is determined, we will perform the abovementioned SIC process until the last antenna subarray is optimized. During the calculation, we assume that the digital precoding matrix is fixed. Then the objective in (8) can be expressed as follows
where \({C_{{\mathrm{max}} }} = \sum \nolimits _{n = 1}^N {{{\log }_2}(1 + \frac{{{P_N}}}{{{\sigma^2}K{N_s}}}} {\mathbf{H }}{{\mathbf{F}}_{\mathrm{RF}}}{\mathbf{F}}_{\mathrm{RF}}^H{{\mathbf{H }}^H}) = {C_1} + {C_2} + \cdots {C_N}\). After the analog precoding is obtained, the optimal digital precoding matrix is solved by the baseband BD technology.
We can divide the hybrid precoding matrix \({{\mathbf{F}}_{\mathrm{RF}}}\) into \({{\mathbf{F}}_{\mathrm{RF}}} = ({\mathbf{F}}_{\mathrm{RF}}^{N  1}{\mathbf{F}}_{\mathrm{RF}}^N)\) at the BS. \({\mathbf{F}}_{\mathrm{RF}}^N\) is the Nth column of \({{\mathbf{F}}_{\mathrm{RF}}}\), and \({\mathbf{F}}_{\mathrm{RF}}^{N  1}\) is an \(NM \times (N  1)\) matrix containing the first \(N  1\) columns of \({{\mathbf{F}}_{\mathrm{RF}}}\). Then the sum rate in (9) can be rewritten as
Define auxiliary matrix
Due to the fact that \(\left {{\mathbf{I + XY }}} \right = \left {{\mathbf{I + YX }}} \right\) by defining \({\mathbf{X}} = {\mathbf{S}}_{N  1}^{  1}{\mathbf{HF}}_{\mathrm{RF}}^N\) and \({\mathbf{Y }} = {\mathbf{F}}_{\mathrm{RF}}^{N  {1^H}}{{\mathbf{H }}^H}\). (10) can be simplified as
Obviously, the second term \(1 + \frac{{{P_N}}}{{{\sigma^2}K{N_s}}}{\mathbf{F}}_{\mathrm{RF}}^{{N^H}}{{\mathbf{H }}^H}{\mathbf{S}}_{N  1}^{  1}{\mathbf{HF}}_{\mathrm{RF}}^N\) on the right side of (b) in (12) is the achievable subrate of the Nth antenna subarray and the first term has the same form as (8). Further, we can decompose \({\log _2}(\left {{{\mathbf{S}}_{N  1}}} \right )\) using the similar method in (12) as
Then, after N such decompositions, the total sum rate in (9) can be shown as
where \({{\mathbf{S}}_n} = {{\mathbf{I}}_{K{N_r}}} + \frac{{{P_N}}}{{{\sigma^2}K{N_s}}}{\mathbf{HF}}_{\mathrm{RF}}^n{\mathbf{F}}_{\mathrm{RF}}^{{n^H}}{{\mathbf{H }}^H}\) and \({{\mathbf{S}}_1} = {{\mathbf{I}}_N}\).
According to the analysis above, the capacity of the first and the optimized antenna subarray can be expressed as
where \({C_{n,{\mathrm{max}} }} \in {\mathrm{max}} \left\{ {\left. {\begin{array}{*{20}{l}} {{C_1}}&{{C_2}}&{\begin{array}{*{20}{l}} \cdots&{{C_N}} \end{array}} \end{array}} \right\} } \right.\) represents the first antenna subarray that needs to be optimized. \({{\mathbf{T}}_{n  1}} = {{\mathbf{H }}^H}{\mathbf{S}}_{n  1}^{  1}{\mathbf{H }}\) satisfies the restrictions. Therefore, (15) can be rewritten as
where \({{\mathbf{G}}_{n  1}} \in {{\mathbb{C}}^{M \times M}}\) is the corresponding subarray of \({{\mathbf{T}}_{n  1}}\) by only keeping the rows and columns of \({{\mathbf{T}}_{n  1}}\) from the \((M(n  1) + 1)\)th one to the (Mn)th one, respectively. It can be presented as
where \({\mathbf{R }} = {\left[ {\begin{array}{*{20}{l}} {{{\mathbf{0}}_{M \times M(n  1)}}}\\ {{{\mathbf{I}}_M}}\\ {{{\mathbf{0}}_{M \times M(N  n)}}} \end{array}} \right] ^T}\) is the corresponding selection matrix. Defining the singular value decomposition (SVD) of \({{\mathbf{G}}_{n  1}}\) as \({{\mathbf{G}}_{n  1}} = {\mathbf{V }{\varvec{\Sigma }} }{{\mathbf{V }}^H}\), where \({\varvec{\Sigma }} \in {{\mathbb{C}}^{M \times M}}\) is the singular value of \({{\mathbf{G}}_{n  1}}\), and \({\mathbf{V }} \in {{\mathbb{C}}^{M \times M}}\) is the right singular value vector of \({{\mathbf{G}}_{n  1}}\).
The optimal solution of (17) can be obtained as
where \({{{{\bar{\mathbf{a }}}}}_{N,{\mathrm{opt}}}} \in {{\mathbb{C}}^{M \times 1}}\) represents the first column of \({\mathbf{V }}\). Since the elements of \({{{{\bar{\mathbf{a }}}}}_{N,{\mathrm{opt}}}}\) do not obey the constraint in Sect.Â 3, the analog precoding vector \({\mathbf{F}}_{\mathrm{RF}}^{{N_{{\mathrm{opt}}}}}\) cannot be directly chosen as \({{{{\bar{\mathbf{a }}}}}_{N,{\mathrm{opt}}}}\). Then, by calculating the MMSE between \({\mathbf{F}}_{\mathrm{RF}}^{{N_{{\mathrm{opt}}}}}\) and the solution \({\mathbf{F}}_{\mathrm{RF}}^N\) in the constrained case, the conclusion that the \({\mathbf{F}}_{\mathrm{RF}}^N\) shares the phase of the corresponding element of \({\mathbf{F}}_{\mathrm{RF}}^{{N_{{\mathrm{opt}}}}}\) can be obtained.
Matrices \({\varvec{\Sigma }}\) and \({\mathbf{V }}\) are, respectively, separated into following two parts:
Further, the \({C_{n,{\mathrm{max}} }}\) given by (16) can also be rewritten as
In order to find the \({\mathbf{F}}_{\mathrm{RF}}^n\) closest to \({\mathbf{F}}_{\mathrm{RF}}^{{n_{{\mathrm{opt}}}}}\), we reasonably assume that \({\mathbf{F}}_{\mathrm{RF}}^n\) is orthogonal to \({{\mathbf{v}}_2}\) which is \({\mathbf{F}}_{\mathrm{RF}}^{{n^H}}{{\mathbf{v}}_2} \approx 0\). Due to \(\left {{\mathbf{I + XY }}} \right = \left {{\mathbf{I + YX }}} \right\) and effective theory of high signaltonoiseratio (\({{\mathrm{SNR}}}\)) approximation, i.e.,
Thus, (20) can be expressed as
From (22), we observe that maximizing \({C_{n,{\mathrm{max}} }}\) is equivalent to maximize the square of the inner product between two vectors \({\mathbf{F}}_{\mathrm{RF}}^{{n_{{\mathrm{opt}}}}}\) and \({\mathbf{F}}_{\mathrm{RF}}^n\). Based on this fact, the optimization problem (15) is equivalent to the following
The function of MMSE in all antenna subarrays can be expressed as
where \(\varphi (m,n) = \angle {{\mathbf{F}}_{\mathrm{RF}}}(m,n)  \angle {\mathbf{F}}_{\mathrm{RF}}^{{\mathrm{opt}}}(m,n)\). It is clear that when \(\varphi (m,n) = 0\), the objective function is minimized.
Therefore, the analog precoding matrix can be chosen as
where \(\angle {{{{\bar{\mathbf{a }}}}}_{n,opt}}\) represents the phase vector of \({{{{\bar{\mathbf{a }}}}}_{n,opt}}\).
Therefore, the sum rate optimization problem can be transformed into a series of subrate optimization problems which can be optimized one by one. After that, according to the idea of SIC after sorting, we only need to continuously update \({{\mathbf{S}}_N}\),and the process is shown in Fig.Â 2.
According to the capacity \({C_{n,{\mathrm{max}} }} \in {\mathrm{max}} \left\{ {\left. {\begin{array}{*{20}{l}} {{C_1}}&{{C_2}}&{\begin{array}{*{20}{l}} \cdots&{{C_N}} \end{array}} \end{array}} \right\} } \right.\), \({\mathbf{F}}_{\mathrm{RF}}^{1,{\mathrm{max}} }\) indicates the analog precoding corresponding to the first optimized antenna array. \({\mathbf{F}}_{\mathrm{RF}}^{2,{\mathrm{max}} }\) is the second analog precoding that needs to be optimized. This process is repeated until the last antenna subarray is optimized.
4.2 Digital precoding design
Based on the above solution process, the analog precoding matrix \({{\mathbf{F}}_{\mathrm{RF}}}\) can be obtained. In order to obtain the best digital precoding, BD technology is adopted. The MUMIMO channel is divided into multiple SUMIMO channels, which is the main idea of applying BD technology. If it can be guaranteed that the signal received by the kth user is in the null space of channels of other users, then the interuser interference will be eliminated. First of all, the transit matrix \({{\mathbf{H}}_{{\mathop {\mathrm{int}}} ,k}}\) can be expressed as
In order to eliminate interference, the constraint can be expressed as
To get the digital precoder, \({{{{\tilde{\mathbf{H }}}}}_k}\) can be defined as
Then, the digital precoding \({\mathbf{F}}_{\mathrm{BB}}^k\) should fall in the null space of \({{{{\tilde{\mathbf{H }}}}}_k}\). Therefore, SVD of \({{{{\tilde{\mathbf{H }}}}}_k}\) can get
where \({{{{\tilde{\mathbf{U }}}}}_k}\) and \({{{{\tilde{\varvec{\Sigma }}}}}_k}\) represent the left singular value vector of \({{{{\tilde{\mathbf{H }}}}}_k}\) and the diagonal matrix of \({{{{\tilde{\mathbf{H }}}}}_k}\), respectively. \({{{\tilde{\mathbf{V }}}}}_k^{(1)} = {{{{\tilde{\mathbf{V }}}}}_k}(:,1:(K  1){N_s})\) and \({{{\tilde{\mathbf{V }}}}}_k^{(0)} = {{{{\tilde{\mathbf{V }}}}}_k}(:,(K  1){N_s} + 1:end)\) represent the subspace orthogonal basis of \({{{{\tilde{\mathbf{H }}}}}_k}\) and the null space orthogonal basis of \({{{{\tilde{\mathbf{H }}}}}_k}\), respectively. Then we can know
The channel becomes \({{\mathbf{H}}_{{\mathop {\mathrm{int}}} ,k}}{{{\tilde{\mathbf{V }}}}}_k^{(0)}\) called an equivalent channel. SVD of the equivalent channel shows
where \({{\mathbf{S}}_k}\) represents the diagonal matrix of equivalent channel (\({{\mathbf{H}}_{{\mathop {\mathrm{int}}} ,k}}{{{\tilde{\mathbf{V }}}}}_k^{(0)}\)). To eliminate interuser interference, taking the \({\mathbf{V}}_k^{(1)}\) corresponding to the nonzero singular value matrix as the precoding matrix, and the final digital precoding matrix is given by
There are two types of BD algorithms: average power allocation and waterfilling power allocation. Since the transmission capacity of each channel is usually different, the application of average power distribution results in the waste of communication resources and even the loss of communication capacity. The principle of the waterfilling method is that after each userâ€™s channel is divided into N independent subchannels, the channel of each user of the multichannel system may be equal to the channel of each bandwidth B. According to the Shannon formula, the subchannel capacity of the kth user is:
where \({{p_k}}\), \(\left {{f_k}} \right\), and \({{n_0}}\) are the transmission power, frequency response, and noise component of the kth subchannel, respectively. Because when N is large enough, the SNR of each channel can be regarded as a constant. In the case of known channel SNR, we can assign different power signals to each different channel to achieve the maximum sum rate. Therefore, the maximum sum capacity can be expressed as:
where \({{P_N}}\) is the total power. According to the Lagrangian multiplier algorithm, the power \({{p_k}}\) is:
where \(\lambda\) is the Lagrangian multiplier factor, \(\frac{B}{\lambda }\) is called the waterfilling line of the waterfilling principle.
The principle of waterfilling can reach the theoretical maximum of sum rate, and get better communication quality, thus it is widely used. The whole process of the algorithm in this paper is shown in TableÂ 1.
5 Results and discussion
In this section, we evaluate the performance of the proposed hybrid beamforming schemes with the subconnected structure in MUMIMO systems, the corresponding simulation results are described below [38]. All simulation results are averaged over 1000 channel realizations based on MATLAB platform, the Win10 system, the processor: Inter (R) Core (TM) i58250 U CPU @ 1.60 GHz, the RAM:8.00 GB, and the system type: 64bit operating systems. For simplicity, the propagation environment is modeled as a \({N_{\mathrm{c}}} = 8\) cluster with \({N_{\mathrm{p}}} = 10\) rays per cluster, and the interelement spacing d is assumed to be half wavelength. The AoA and AoD of each element are uniformly distributed in \(\left[ {0,2\pi } \right]\), respectively. Typical mmWave massive MIMO configurations with \({N_t} = 128\), \(N = 16\) and \({N_r} = 16\) are considered. The number of users is provided as \(K{{\mathrm{= 4}}}\). The noise variance at each user is \({\sigma^2} = 1\). The \({{\mathrm{SNR}}} = \frac{{{P_N}}}{{{\sigma^2}}}\). (Note: Unless otherwise specified, the above parameters are default parameters.) It is worth noting that we focus on the hybrid beamforming design of massive MIMO systems with subconnected architecture in the paper. But we contrast the performance of the proposed method and the stateoftheart hybrid beamforming design methods with fullconnected architecture, which includes the least number of RF chains (the least number of RF chains is equal to the number of the transmitted streams) based HyEB scheme [28], the fulldigital dirty paper coding (DPC) method [39]. Since the DPC realized with the iterative waterfilling algorithm has been certified to be capacityreaching in the broadcast channel, it is used as the performance upper bound of the hybrid ones. For the comparison of subconnected structure methods, we will find the analog precoder by the SIC method [33]. The digital precoding is obtained by the BD technology. The above method is named SICBD algorithm in the system. In addition, we choose the FullAnalog precoding algorithm to compare with other algorithms. In this scheme, we consider the same parameter conditions as other algorithms, but do not consider interuser interference. That is, the FullAnalog scheme in this case is the upper limit of the multiuser. For more convenient comparison and analysis, we define the fullconnected as FC and subconnected as SC in the following.
5.1 A. Performance for the sum rate
We first evaluate the sum rate performance for different methods versus SNR in ULA, and the corresponding simulation results are shown in Fig.Â 3. Here, Fig.Â 3 illustrates that the proposed precoding algorithm is proved valid when SNR increases from âˆ’Â 20 to 20 dB. The result under a massive MIMO system with \({N_t} = 128\) is represented by (a), and the result under a MIMO system with \({N_t} = 32\) is represented by (b). The simulation results also demonstrate that with an increasing SNR, the proposed hybrid precoding based on SC structure has a more near performance to those of the HyEB [28] on FC structure. And it is much higher than FullAnalog. To further investigate the performance of the proposed design scheme with small antenna arrays, Fig.Â 3b demonstrates the sum rate comparison for different beamforming schemes versus SNR when the number of BS antennas is small (\({N_t} = 32\)). In addition, the proposed algorithm has the objective capacity, it is still slightly higher than SICBD and FullAnalog.
The performance of the sum rate versus SNR for different precoding algorithms in UPA is displayed in Fig.Â 4, where (a) represents \({N_t} = 128\) and (b) represents \({N_t} = 32\). It can be seen from Fig.Â 4 that the sum rate of each algorithm under UPA decreases slightly compared with that under ULA. The performance of the proposed algorithm in Fig.Â 4a is significantly better than that of SICBD. In Fig.Â 4b, the proposed algorithm is closer to the HyEB [28]. The FullAnalog algorithm is much lower than other algorithms. Although the use of UPA in the MUMIMO channel will cause the overall performance of the proposed algorithm to slightly decrease, the trend of change is still consistent with the use of ULA. Furthermore, when the antenna deployment mode is changed from a linear array to an area array, the area of the antenna array deployed by the base station is greatly saved, and the space utilization rate of the base station and users on the device is effectively improved.
5.2 B. Performance for the number of BS antennas
The performance of the sum rate versus the BS antennas for different precoding algorithms is displayed in Fig.Â 5, where SNR = 0 dB. We note that the performance of all algorithms can be improved by increasing the number of BS antennas. When the number of BS antennas is large, the performance gap between the SC beamforming scheme and FC hybrid beamforming scheme becomes larger. But the proposed design scheme is better than the SICBD. Moreover, compared with the small number of BS antennas, the performance gap between the proposed beamforming scheme and the HyEB [28] scheme is small. The FullAnalog method is far lower than the proposed algorithm.
5.3 C. Performance for the number of users
FigureÂ 6 compares the sum rate performance of different precoding schemes versus the number of users with SNR = 5 dB, where the number of users changes from 2 to 12. We can see that the proposed method is very close to the SICBD, but the overall performance is still better than the SICBD. As the number of users increases, the sum rate performance of different design methods becomes large. Furthermore, it can also be explained that with the increase in the scale of the system, the proposed design scheme effectively eliminates interuser interference, so as to improve the performance of the system. The sum rate of the FullAnalog algorithm does not change significantly with the number of users, and its growth rate is the smallest compared to other algorithms.
In order to compare the computational complexity of proposed schemes, we list the running time of five schemes in TableÂ 2 with the average time over 100 random channel realizations. Regardless of the computer hardware, we can find that the running time of fullconnected structure schemes is tremendously large. Although the fullconnected structure has better performance, it has the disadvantages of complicated layout, high cost, and excessive power consumption. For the subconnected structure, the hardware complexity and energy consumption are reduced, and the performance is not significantly different from the fullconnected structure. The proposed algorithm is slightly slower than SICBD in running time, but its performance is ahead of SICBD due to its screening optimization. The fullanalog algorithm takes faster time but the performance difference is obvious.
5.4 D. Performance for data streams per user
FigureÂ 7 shows the sum rates achieved by different hybrid precoding schemes when the number of data streams per user is different, where \({N_s} = 2\), 4. Considering the costs and power consumption, we find that the performance of different hybrid precoding schemes with SC is similar but the proposed method is more closer to that of the HyEB [28] scheme as the number of data streams per user is small, i.e., \({N_s}= 2\). When the number of data streams provided by the system increases, the gaps between the sum rates of different schemes become larger correspondingly. However, the proposed hybrid precoding scheme still performs better than SICBD when the number of data streams is different.
5.5 E. Performance for the power efficient
As mentioned in Sect.Â 1, the power consumption is an important issue which should be considered for both the SC and FC hybrid precoding. In this subsection, we aim to compare the power efficiency performance of different hybrid precoding design schemes.
To better compare the performance of the two hybrid precoding structures, the power efficiency \(\eta\) is defined as the ratio between the achievable rate R and the total power consumption \({P_{{{\mathrm{total }}}}}\), which is expressed as follows:
where the unit of \(\eta\) is bps/Hz/J and \({P_{{{\mathrm{total }}}}}\) is the total power consumption of the system.
Considering the hybrid precoding architecture, we can note that in the hybrid precoding architecture, the power is depleted by five blocks [40]: (a) the phase shifter (PS) on the transmitter side; (b) the RF chains on the transmitter side; (c) digitaltoanalog converters (DAC) on the transmitter side; (d) the baseband (BB) processor; (e) the power amplifiers (PA) on the transmitter side.
Considering the fulldigital precoding for MIMO, the amounts of power consumed by BS and users in fulldigital MIMO architecture are written as
where \({P_{\mathrm{BB}}}\), \({P_{{{\mathrm{RF}}}}}\), \({P_{{\mathrm{PA}}}}\), \({P_{{\mathrm{PS}}}}\), and \({P_{{\mathrm{DAC}}}}\) are the power of BB, the power of each RF chain, the power of each PA, the power of each PS, and the power of each DAC, respectively.
Different from the fulldigital precoding for MIMO, the total power consumption \({P_{{{\mathrm{total }}}}}\) in the hybrid precoding architecture can be written as
The simulation parameters according to [41,42,43] are set as follows: \({P_{\mathrm{BB}}}=243\,\mathrm{mW}\), \({P_{{{\mathrm{RF}}}}}=40\,\mathrm{mW}\), \({P_{{\mathrm{PA}}}}=16\,\mathrm{mW}\), \({P_{{\mathrm{DAC}}}}=110\,\mathrm{mW}\) and \({P_{{\mathrm{PS}}}}=10\,\mathrm{mW}\).
Here we note that for the FC and the SC structures, the number of phase shifters \({N_{{\mathrm{PS}}}}\) can be written as
FigureÂ 8 compares the power efficiency for different hybrid precoding schemes versus SNR. It is observed from Fig.Â 8 that we discover that the performance of different hybrid precoding methods with SC is similar, but it is higher than hybrid precoding schemes with FC structures. It is obvious that the proposed algorithm has always been superior to the SICBD in the whole range. It can be noticed that the proposed method can issue the signal more efficiently than SICBD with the same SNR and power consumption, which means it has higher power efficiency. What is more, the fulldigital MIMO architecture requires more hardware and produces higher power consumption, its power efficiency performance is relatively low compared with the hybrid architecture. Therefore, the fulldigital MIMO architecture is rarely used for signal propagation in practical applications.
5.6 F. Performance for sensitivity of channel estimation errors
Finally, we evaluate the impact of imperfect CSI on the proposed hybrid precoding. Let \({{{\tilde{\mathbf{H }}}}}\) represents the estimated channel, then it can be modeled as [44]
where \(\xi \in [0,1]\) expresses the accuracy of estimated CSI, and \({\mathbf{E }}\) is the error matrix with entries following the distribution i.i.d. \({{{\mathcal{C}}}}{{{\mathcal{N}}}}(0,1)\).
It can be noticed from Fig.Â 9 that the proposed hybrid precoding method seems to be insensitive to the CSI accuracy in SNR conditions. Even when the channel estimation accuracy is not high, the proposed method can obtain a considerable sum rate. It is particularly noticeable at low SNR. When SNRÂ =Â 15 and \(\xi = 0.9\), the performance of the proposed method is quite close to that in the perfect CSI condition. It can still achieve about 96.9% of the perfect CSI conditionâ€™s sum rate. Even when \(\xi = 0.6\), the performance of the proposed method can still achieve about 84.1% of the rate in the perfect CSI condition. In this case, only 19.16 bps/Hz is lost compared to the case where the CSI is completely known in the transmission end. Therefore, the proposed method has strong robustness and certain practical value.
6 Conclusions
This paper has proposed a hybrid precoding scheme for MUMIMO systems. According to the structure of the optimal hybrid precoding matrix, we decompose the maximum achievable rate optimization problem into a series of subrate optimization problems. Firstly, we focus on the design of the analog hybrid precoder and optimize it to maximize the overall analog beamforming gain, then perform BD technology on the equivalent baseband channel. Finally, the sum rate performance is improved again by waterfilling power allocation. The simulation results agree with the theoretical analysis. It proves that the proposed multiuser scheme can achieve an appropriate compromise between hardware complexity and system performance. Both the sum rate and energy efficiency are improved, and the algorithm has strong robustness. The perspective of this work contains an extension to mmWave MIMO systems relying on lens antenna arrays [45], which have a small number of radiofrequency chains. In that work, the impact of pilotdata transmission [46, 47] for the overall system performance is also considered for practical applications. In future work, it is possible to add consideration to the performance change of the receiver as an imperfect receiver, which will be more practical in future applications.
Data Availability
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Abbreviations
 5G:

the fifth generation
 mmWave:

millimeterwave
 MIMO:

multipleinput multipleoutput
 PS:

phase shifter
 RF:

radio frequency
 SUMIMO:

singleuser MIMO
 MUMIMO:

multiuser MIMO
 OMP:

orthogonal match pursuit
 HyBD:

hybrid block diagonalization
 EGT:

equal gain transmission
 HyZF:

hybrid zeroforcing
 HyMMSE:

hybrid minimummeansquarederror
 BS:

base station
 CSI:

channel state information
 SIC:

successive interference cancelation
 i.i.d.:

independent and identically distribution
 SINR:

signaltointerference noise ratio
 ULA:

uniform linear array
 UPA:

uniform planar array
 SVD:

singular value decomposition
 DPC:

dirty paper coding
References
J. Hoydis, S. Ten Brink, M. Debbah, Massive MIMO in the UL/DL of cellular networks: how many antennas do we need? IEEE J. Sel. Areas Commun. 31(2), 160â€“171 (2013)
E.G. Larsson, O. Edfors, F. Tufvesson, T.L. Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186â€“195 (2014)
E. BjÃ¶rnson, L. Sanguinetti, M. Kountouris, Deploying dense networks for maximal energy efficiency: small cells meet massive MIMO. IEEE J. Sel. Areas Commun. 34(4), 832â€“847 (2016)
C. Li, J. Zhang, K.B. Letaief, Throughput and energy efficiency analysis of small cell networks with multiantenna base stations. IEEE Trans. Wirel. Commun. 13(5), 2505â€“2517 (2014)
A.L. Swindlehurst, E. Ayanoglu, P. Heydari, F. Capolino, Millimeterwave massive MIMO: the next wireless revolution? IEEE Commun. Mag. 52(9), 56â€“62 (2014)
W. Roh, J.Y. Seol, J. Park, B. Lee, J. Lee, Y. Kim, J. Cho, K. Cheun, F. Aryanfar, Millimeterwave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results. IEEE Commun. Mag. 52(2), 106â€“113 (2014)
V. Venkateswaran, A.J. van der Veen, Analog beamforming in MIMO communications with phase shift networks and online channel estimation. IEEE Trans. Signal Process. 58(8), 4131â€“4143 (2010)
S. Kutty, D. Sen, Beamforming for millimeter wave communications: an inclusive survey. IEEE Commun. Surv. Tutor. 18(2), 949â€“973 (2015)
S. Hur, T. Kim, D.J. Love, J.V. Krogmeier, T.A. Thomas, A. Ghosh, Millimeter wave beamforming for wireless backhaul and access in small cell networks. IEEE Trans. Commun. 61(10), 4391â€“4403 (2013)
J. Joung, A.H. Sayed, Multiuser twoway amplifyandforward relay processing and power control methods for beamforming systems. IEEE Trans. Signal Process. 58(3), 1833â€“1846 (2009)
A. Azizzadeh, R. Mohammadkhani, S.V.A.D. Makki, E. BjÃ¶rnson, BER performance analysis of coarsely quantized uplink massive MIMO. Signal Process. 161, 259â€“267 (2019)
J. Zhang, Y. Huang, T. Yu, J. Wang, M. Xiao, Hybrid precoding for multisubarray millimeterwave communication systems. IEEE Wirel. Commun. Lett. 7(3), 440â€“443 (2017)
L. Dai, B. Wang, M. Peng, S. Chen, Hybrid precodingbased millimeterwave massive MIMONOMA with simultaneous wireless information and power transfer. IEEE J. Sel. Areas Commun. 37(1), 131â€“141 (2018)
K. Song, B. Ji, Y. Huang, M. Xiao, L. Yang, Performance analysis of heterogeneous networks with interference cancellation. IEEE Trans. Veh. Technol. 66(8), 6969â€“6981 (2017)
C. Zhang, Y. Huang, Y. Jing, S. Jin, L. Yang, Sumrate analysis for massive MIMO downlink with joint statistical beamforming and user scheduling. IEEE Trans. Wirel. Commun. 16(4), 2181â€“2194 (2017)
O. El Ayach, S. Rajagopal, S. AbuSurra, Z. Pi, R.W. Heath, Spatially sparse precoding in millimeter wave MIMO systems. IEEE Trans. Wirel. Commun. 13(3), 1499â€“1513 (2014)
M. Majidzadeh, A. Moilanen, N. Tervo, H. Pennanen, A. TÃ¶lli, M., Latvaaho, Hybrid beamforming for singleuser MIMO with partially connected RF architecture, in 2017 European Conference on Networks and Communications (EuCNC) (IEEE, 2017), pp. 1â€“6
F. Sohrabi, W. Yu, Hybrid digital and analog beamforming design for largescale antenna arrays. IEEE J. Sel. Top. Signal Process. 10(3), 501â€“513 (2016)
Z. Pi, Optimal transmitter beamforming with perantenna power constraints, in 2012 IEEE International Conference on Communications (ICC) (IEEE, 2012), pp. 3779â€“3784
F. Sohrabi, W. Yu, Hybrid beamforming with finiteresolution phase shifters for largescale MIMO systems, in 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (IEEE, 2015), pp. 136â€“140
T.E. Bogale, L.B. Le, Beamforming for multiuser massive MIMO systems: digital versus hybrid analogâ€“digital, in 2014 IEEE Global Communications Conference (IEEE, 2014), pp. 4066â€“4071
W. Ni, X. Dong, Hybrid block diagonalization for massive multiuser MIMO systems. IEEE Trans. Commun. 64(1), 201â€“211 (2015)
A. Li, C. Masouros, Hybrid analogâ€“digital millimeterwave MUMIMO transmission with virtual path selection. IEEE Commun. Lett. 21(2), 438â€“441 (2016)
A. Li, C. Masouros, Hybrid precoding and combining design for millimeterwave multiuser MIMO based on SVD, in 2017 IEEE International Conference on Communications (ICC) (IEEE, 2017), pp. 1â€“6
J. Jiang, Y. Yuan, L. Zhen, Multiuser hybrid precoding for dynamic subarrays in mmWave massive MIMO systems. IEEE Access 7, 101718â€“101728 (2019)
Z. Wang, M. Li, Q. Liu, A.L. Swindlehurst, Hybrid precoder and combiner design with lowresolution phase shifters in mmWave MIMO systems. IEEE J. Sel. Top. Signal Process. 12(2), 256â€“269 (2018)
N. Song, T. Yang, H. Sun, Overlapped subarray based hybrid beamforming for millimeter wave multiuser massive MIMO. IEEE Signal Process. Lett. 24(5), 550â€“554 (2017)
C. Hu, J. Liu, X. Liao, Y. Liu, J. Wang, A novel equivalent baseband channel of hybrid beamforming in massive multiuser MIMO systems. IEEE Commun. Lett. 22(4), 764â€“767 (2017)
S. Payami, M. Ghoraishi, M. Dianati, M. Sellathurai, Hybrid beamforming with a reduced number of phase shifters for massive MIMO systems. IEEE Trans. Veh. Technol. 67(6), 4843â€“4851 (2018)
A. Li, C. Masouros, Energyefficient SWIPT: from fully digital to hybrid analogâ€“digital beamforming. IEEE Trans. Veh. Technol. 67(4), 3390â€“3405 (2017)
F. Sohrabi, W. Yu, Hybrid analog and digital beamforming for mmWave OFDM largescale antenna arrays. IEEE J. Sel. Areas Commun. 35(7), 1432â€“1443 (2017)
J.C. Chen, Hybrid beamforming with discrete phase shifters for millimeterwave massive MIMO systems. IEEE Trans. Veh. Technol. 66(8), 7604â€“7608 (2017)
L. Dai, X. Gao, J. Quan, S. Han, I. ChihLin, Nearoptimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems, in 2015 IEEE International Conference on Communications (ICC) (IEEE, 2015), pp. 1334â€“1339
X. Gao, L. Dai, S. Han, I. ChihLin, R.W. Heath, Energyefficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE J. Sel. Areas Commun. 34(4), 998â€“1009 (2016)
Z. Pi, F. Khan, An introduction to millimeterwave mobile broadband systems. IEEE Commun. Mag. 49(6), 101â€“107 (2011)
R.W. Heath, N. GonzalezPrelcic, S. Rangan, W. Roh, A.M. Sayeed, An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J. Sel. Top. Signal Process. 10(3), 436â€“453 (2016)
M.R. Akdeniz, Y. Liu, M.K. Samimi, S. Sun, S. Rangan, T.S. Rappaport, E. Erkip, Millimeter wave channel modeling and cellular capacity evaluation. IEEE J. Sel. Areas Commun. 32(6), 1164â€“1179 (2014)
Y. Zhang, J. Du, Y. Chen, M. Han, X. Li, Optimal hybrid beamforming design for millimeterwave massive multiuser MIMO relay systems. IEEE Access 7, 157212â€“157225 (2019)
N. Jindal, W. Rhee, S. Vishwanath, S.A. Jafar, A. Goldsmith, Sum power iterative waterfilling for multiantenna gaussian broadcast channels. IEEE Trans. Inf. Theory 51(4), 1570â€“1580 (2005)
R. MÃ©ndezRial, C. Rusu, N. GonzÃ¡lezPrelcic, A. Alkhateeb, R.W. Heath, Hybrid MIMO architectures for millimeter wave communications: phase shifters or switches? IEEE Access 4, 247â€“267 (2016)
C.E. Chen, An iterative hybrid transceiver design algorithm for millimeter wave MIMO systems. IEEE Wirel. Commun. Lett. 4(3), 285â€“288 (2015)
T.S. Rappaport, J.N. Murdock, F. Gutierrez, State of the art in 60GHz integrated circuits and systems for wireless communications. Proc. IEEE 99(8), 1390â€“1436 (2011)
C.A. Balanis, Antenna Theory: Analysis and Design (Wiley, Hoboken, 2016)
R.A. Horn, C.R. Johnson, Topics in Matrix Analysis (Cambridge University Press, Cambridge, 1991)
X. Gao, L. Dai, S. Zhou, A.M. Sayeed, L. Hanzo, Wideband beamspace channel estimation for millimeterwave MIMO systems relying on lens antenna arrays. IEEE Trans. Signal Process. 67(18), 4809â€“4824 (2019)
J. Du, M. Han, L. Jin, Y. Hua, X. Li, Semiblind receivers for multiuser massive MIMO relay systems based on block Tucker2PARAFAC tensor model. IEEE Access 8, 32170â€“32186 (2020)
Z. Zhou, L. Liu, J. Zhang, FDMIMO via pilotdata superposition: tensorbased DOA estimation and system performance. IEEE J. Sel. Top. Signal Process. 13(5), 931â€“946 (2019)
Acknowledgements
The authors acknowledged the anonymous reviewers and editors for their efforts in constructive and generous feedback.
Funding
This research was supported by the grant from the National Natural Science Foundation of China (Nos. 61601414, 61702466), the National Key Research and Development Program of China (No. 2016YFB0502001), and the Fundamental Research Funds for the Central Universities (No. 2018CUCTJ082).
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Z.W and L.J performed software. Z.W and Y.Z performed validation. J.D and Z.W were responsible for writingâ€”original draft preparation. Y.Z., Y.G and L.J were involved in writingâ€”review, proofreading and editing. J.D and Y.G were responsible for supervision. Conceptualization and methodology were performed by J.D. All authors have read and agreed to the published version of the manuscript.
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Du, J., Wang, Z., Zhang, Y. et al. Multiuser hybrid precoding for mmWave massive MIMO systems with subconnected structure. J Wireless Com Network 2021, 157 (2021). https://doi.org/10.1186/s13638021020310
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DOI: https://doi.org/10.1186/s13638021020310
Keywords
 Beamforming
 Massive MIMO
 Nearoptimal
 Hybrid precoding
 Subconnected structure