Wireless Communications and Mobile Computing

Wireless Communications and Mobile Computing / 2021 / Article
Special Issue

Emerging Trends and Challenges in Knowledge Defined Networking for 5G

View this Special Issue

Research Article | Open Access

Volume 2021 |Article ID 5528522 | https://doi.org/10.1155/2021/5528522

Guangyan Liao, Feng Zhao, "Dynamically Subarray-Connected Hybrid Precoding Scheme for Multiuser Millimeter-Wave Massive MIMO Systems", Wireless Communications and Mobile Computing, vol. 2021, Article ID 5528522, 10 pages, 2021. https://doi.org/10.1155/2021/5528522

Dynamically Subarray-Connected Hybrid Precoding Scheme for Multiuser Millimeter-Wave Massive MIMO Systems

Academic Editor: Yan Huang
Received17 Feb 2021
Revised07 Apr 2021
Accepted19 Apr 2021
Published28 Apr 2021

Abstract

Hybrid precoding is widely used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior work on hybrid precoding focused on the fully connected hybrid architectures and the subconnected but fixed architectures in which each radio frequency (RF) chain is connected to a specific subset of the antennas. The limited work shows that dynamic subarray architectures address the tradeoff between achievable spectral efficiency and energy efficiency of mmWave massive MIMO systems. Nevertheless, in the multiuser hybrid precoding systems, the existing dynamic subarray schemes ignore the fairness of users and the problem of user selection. In this paper, we propose a novel multiuser hybrid precoding scheme for dynamic subarray architectures. Firstly, we select a multiuser set among all users according to the analog effective channel information of the base station (BS) and then design the subset of the antennas to each RF by the fairness antenna-partitioning algorithm. Finally, the optimal analog precoding vector is designed according to each subarray, and the digital precoding is designed by the minimum mean-squared error (MMSE) criterion. The simulation results show that the performance advantages of the proposed multiuser hybrid precoding scheme for dynamic subarray architectures.

1. Introduction

Millimeter-wave massive MIMO, as a key technology in the next-generation wireless systems, can achieve multigigabit data rates benefiting from its abundant frequency resource [1] and provide highly directional beamforming gains to compensate for the path loss of mmWave signals [2]. For mmWave massive MIMO systems, hybrid precoding not only solves the problem that the fully digital precoding unsuitable for this systems, but also can achieve performance close to a full digital precoding [3, 4].

The studies of hybrid precoding focused on the fully connected architecture [58] and partially connected architecture [911], and there are two main ways to solve the hybrid precoding problem. One is the joint analog domain precoding and digital domain precoding simultaneously [57, 9]. And another is the analog domain precoding designed by the channel gain at first and then the digital domain precoding designed by the analog equivalent channel [1215], and this way is usually applies to multiuser mmWave systems. However, most previous works did not explore the problem of the tradeoff between spectral efficiency and energy efficiency.

Limited works have been carried out for dynamic subarray-connected structures were proposed in [1620]. This new structure is different from the previous structure, which uses a dynamic subarray connection structure to connect the RF chain and antenna according to the channel state information of the users. In [16], adaptive hybrid precoding (AHP) was developed to achieve the highest system spectral efficiency, but it is only applicable for the number of employed RF chains which is equal to the number of users. In [17], based on knowing the long-term channel characteristics, a technique of dynamically hybrid subarrays was developed, but the user’s fairness was ignored. In [18], consider the number of RF chains which is more than or equal to that of scheduled users and utilized AHP based on the singular value decomposition (SVD). However, that scheme does not study the problem of user selection when the number of users increases. Moreover, in [19], utilize the subchannel difference of the users, and a dynamically connected hybrid precoding was developed. This scheme allocates a different number of antennas for each RF chain, but sometimes some RF chains cannot allocate an antenna, which was unfair to some users. User selection was considered in [20], and a multiuser hybrid precoding framework based on codebooks was proposed, which adopted the antenna-partitioning algorithm based on the maximum increment of the signal-to-interference-noise ratio (SINR), but it has the same problem as [19].

In this paper, we proposed a new antenna-partitioning algorithm and hybrid analog/digital precoding schemes for the dynamic subarray characteristic. The main contributions of this paper can be summarized as follows. (1)Proposing a new antenna-partitioning algorithm for multiuser mmWave system with a dynamic subarray characteristic. The algorithm guaranteed fairness because each user can obtain approximately equal channel gain. The new antenna-partitioning algorithm proposed in this paper avoids the problem that the number of antennas in some subarrays which is zero may occur in [20], and it can be used to the number of transmitting antennas and the number of subarrays which is not multiple. However, the AHP scheme in [16] is only applicable when the number of transmitting antennas is a multiple of the number of subarrays(2)The dynamic hybrid structure model is obtained by the new antenna partition algorithm, which is based on the maximum channel gain of each user rather than SINR. Therefore, the spectrum efficiency of the system can be improved by eliminating the interuser interference through digital baseband precoding. Firstly, we design the analog precoding based on phase-only precoding. Then, in order to eliminate the interuser interference and ensure the high performance of the system under low SNR, we design the digital baseband precoding based on the minimum mean square error criterion. The simulation results show the advancement of our scheme

The remainder of the paper is as follows: Section 2 describes the multiuser massive MIMO system model and millimeter-wave channel model. The problem description is proposed in Section 3. Section 4 provides a detailed description of the proposed solution. Simulation results are presented in Section 5. Finally, conclusions are drawn in Section 6.

Notation. In this paper, we use the notation as the following: is a matrix, is a vector, and is a scalar. , , and denote the transpose, transpose-conjugate operation, and pseudo-inverse of , respectively. and denote the Frobenius norm and determinant of . and denote the trace and expectation of . is a complex Gaussian random vector with mean and covariance .

2. System Model and Channel Model

2.1. System Model

In this study, we describe three different multiuser hybrid precoding system models, the fully connected structures, the subconnected structures, and the dynamic subarray structures. Firstly, a fully connected multiuser downlink massive MIMO hybrid precoding system models as shown in Figure 1, where the BS is equipped with RF chains and antennas. We assume that the BS simultaneously communicates with mobile users. Each mobile user is equipped with one RF chain and antennas, where . The BS antennas are tagged as 1,2, …, , and let . The baseband precoding matrix , where is the baseband precoding matrix for th mobile user. The analog precoding weight is a matrix , where is the analog precoding vector for the th RF chain. and are linked by the total power constrains, i.e, . The sample transmitted signal can be represented as [21]. where is the vector of transmitted symbols, which satisfy , and is the average total transmitted power.

Generally, the received downlink signal of the mobile user can be represented as [21]. where is the channel matrix from the BS to the th mobile user. Let . is an additive Gaussian white noise vector such that . denotes the digital vector at the th mobile user.

Then, we describe the two structures shown in Figure 2 and define the form of their analog precoding matrices. The conventional subarray architecture in which each RF chain is connected to a specific subset of the antennas as shown in Figure 2(a), and each RF chain connected to the antenna is fixed in the order. Therefore, we can define the analog precoding matrix as [9]. where denotes the analog weighting vector for the th RF chain, , and .

The dynamic subarray architectures are shown in Figure 2(b), which each RF chain is connected to a dynamic subset of the antennas. The analog precoding matrix is derived with where is the analog precoding vector for the th RF chain, and denotes the element of th antenna connected to the th RF chain. Assume that is a value of . The set contains label of antenna connected to the th RF chain, and let set , .

2.2. mmWave Channel Model

Millimeter wave channels with high free-space path loss and large dense antenna arrays are unlikely to follow the rich scattering model at low frequencies. According to the characteristics of millimeter wave propagation, the dense array makes the antenna highly correlated, and the high path loss in high free-space leads to the limited space selectivity. Thus, the millimeter-wave channel is considered to have finite scattering [1]. Traditional MIMO channel modeling methods are not accurate in millimeter wave channel. The geometric channel model can embody the low rank and spatial correlation characteristics of mmWave communications [9]. Thus, we consider a geometric channel model with scatterers for the th user and assume that each scatterer contributes a single propagation path between the BS and the user [18, 21]. Under this model, the th user’s channel can be represented as where is the number of scatterers and is the complex gain of the th path between the BS and the th user. and , respectively, represent the azimuth of arrival (AOA) and departure (AOD) of the th path between the BS and the th user, and the range of values for both and is . and are the antenna array response vectors of the base station and th user, separately. For a uniform linear array (ULA), the array response vector can be written as where is the signal wavelength and denotes the spacing distance between two adjacent antennas. The array response vector takes a similar fashion.

3. Problem Formulation

In this section, we propose the problem of multiuser hybrid precoding scheme based on dynamic subarray architectures. As shown in Figure 2(b), the antennas of each subarray are not adjacent to each other, and the optimal set needs to get at first. Then, design the hybrid precoding matrix and for the dynamic subarray architectures at the BS. In this paper, we assume that the channel state information (CSI) is perfect. The SINR of the th user is written as where , and is obtained by SVD of , which is .

The achievable rate of the th user can be written as We aim to jointly design the , , and to maximize the achievable sum-rate of dynamic subarray architectures, which can be formulated as

The optimization problem of (9) is a nonconvex problem, and it is almost impossible to solve the global optimal solution directly. We decompose problem (9) into three subproblems. Firstly, we design the initial precoding matrix for each user to get the optimal set . Secondly, we design the optimal analog precoding matrix according to , and finally, we design the optimal digital precoding matrix through the equivalent channel matrix . The detail of the scheme will be explained in the next section.

4. Hybrid Precoding Strategy

In this section, we introduce a dynamically subarray-connected hybrid precoding scheme for multiuser mmWave massive MIMO systems, including analog precoding initialization and user selection, dynamic antenna partitioning, and the hybrid precoding optimize for the dynamic subarray structures.

4.1. Initialization Analog Precoding and User Selection

In the multiuser mmWave massive MIMO system, the mmWave channel has a finite number of strong beam directions; thus, the best beams of the users lie on their own scattering paths. When the base station serves all users in the same time slot, the performance of the system will be degraded with the increase of interuser interference. Increasing the number of served users at the same time slot will result in performance degradation [22, 23]. In this paper, we select a group of users with minimum interuser interference and maximum objective channel gains. Generally, maximizing the SINR is the criterion for user selection [15].

Firstly, we define a set of all candidate users and an empty set . The initial analog precoding vector of th user () can be obtained by solving

The SVD of channel can be expressed as . The unconstrained analog precoding vector for user can be written as , where is the first columns of .

The optimization problem (10) can be rewritten as

The solutions of similar problems with (11) are given in [9]. Therefore, the solution of (11) can be obtained directly.

1:Input: , , ,
2:Initialize: , ,
3:for n =1:N do
4: Calculate the SVD of to obtain and , , , where is the first columns of .
5: 
6:end for
7:
8:
9:for :K do
10: 
11: 
12:end for
13:output:

Then, the process of multiuser selection is described as follows. Using the initial analog precoding vectors, the SINR of the user written as

The first user selected by maximum channel gain from set , and then, we update both set and . The remaining users are selected from all candidate users set according to the maximum SINR criterion, which can be described as follows: where is the mmWave channel of the th user in set , and denote the initial analog precoding vector of the th user in set and the initial analog precoding vector of the th user in set , respectively. The user with maximum SINR value is removed from and added to . The update strategy is as follows:

The process of multiuser selection is summarized in Algorithm 1.

4.2. Dynamic Subarray for Multiuser mmWave System

The dynamic subarray hybrid architecture is a more efficient structure, in which the antenna elements are dynamically partitioned to each RF chain based on the long-term channel information [16, 18], and it addresses the tradeoff between the achievable spectral efficiency and energy efficiency. The existing antenna partition algorithm for dynamic subarray hybrid architecture is proposed in [20], but it has poor user fairness because of insufficient conditions. It uses the maximum increment of the SINR for each selected user, the conditions to ensure that each RF chain can be allocated one antenna at least have not been set, resulting in some RF chains not being partition of any antenna under special circumstances, such as some users can obtain the maximum SINR increment in all loop. In this subsection, based on the dynamic subarray hybrid structure proposed in Section 2, we design the new antenna partition algorithm to obtain higher spectral efficiency and guarantee user fairness.

The objective of this subsection is to address the optimal of the user . We assume that the number of chain is equal to the number of user , and the BS transmits data to every user via only one stream. As shown in Figure 2(b), there are two constraints as where , and the constrain of (16) ensures that each antenna is only connected to one RF chain, and the constrain of (17) ensures that each RF chain is connected to antennas at least.

Then, in order to ensure that each user gets maximize channel gain and ensure fairness among users, we neglect the resulting interference among users in this stage, and the user interference elimination problem is solved in the next subsection. Directly solving three unknown matrix variables , , and will lead to high computational complexity. Therefore, we use the initial analog precoding vectors as significant information for partitioning antenna elements and let the dynamic subarray of each user start out as an empty set. The antenna-partitioning problem is described as where is a vector, which is obtained by according to (4), and represents the antenna being added to the subarray of th user. Then, set and subarray are updated as follows:

1:Input: , , , ,
2:Initialize: , and are obtain by Algorithm 1, ,
,
3:for :N do
4:   for :K do
5:     
6:     
7:     
8:   end for
9:end for
10:while do
11:   
12:   
13:   
14:   
15:end while
16:output:

Within each inner loop of Algorithm 2, due to the constrain in (17), (18), and (19), will be carried out for all users once, and this inner loop will be carried out for times. After the loop, if , it means that there are still remaining antennas unallocated, and we allocate the remaining antennas to the user with the smallest channel gain. The details of the process are described in Algorithm 2.

For the simplicity of the process analysis, we assume that the number of chain is equal to the number of user , and the BS transmits data to every user via only one stream. In fact, by changing the constraints (16) and (17), this new antenna partition algorithm can easily be extended to user multistream transmission.

4.3. Hybrid Precoding Design

In this subsection, we will design the hybrid precoding for dynamic subarray hybrid architecture that obtained in the previous subsection. For the dynamic subarray hybrid architecture, the beam shape and width are changed after the antenna partitioning of this architecture, and the initial analog precoding vector is invalid. Therefore, the analog precoding needs to be redesigned.

Utilize the subarray , the channel matrix for each user can be updated as where is a channel matrix for th user, and define the effective channel for th user, where can obtain by SVD of .

In order to obtain the large array gain, we consider the phase-only control at the RF domain [24] and perform the analog precoding for each user according to where is the phase of the th element in the vector and is the conjugate transpose of .

In the stage of digital baseband precoding, we focused on the elimination of interuser interference. Some classical digital precoding algorithms are used to eliminate the interuser interference, such as ZF and BD. However, in multiuser systems with a low signal-to-noise ratio (SNR), ZF or BD precoding performs poorly [12]. Thus, we use the minimum mean-squared error criteria to design digital baseband precoding. According to the received signal model in (2), the estimated symbol of th user is given by

The estimated symbols for all users given (22) can be stacked into one vector. where , , , , and . The aim is to get estimated symbols close to . The mean-squared error cost function can be expanded as where is the scaling factor.

Then, the problem of minimum mean-squared error can be expressed as

The analog precoding matrix can be obtained by (21); thus, the problem (25) will be transformed into a convex optimization problem. The closed-form optimal solution of the similar problem is given in [25]. Its optimal solution is given by where , the optimal scaling factor , normalizes .

In the process of MMSE-based digital precoding computation, the main computational complexity comes from formula (26). In (26), the complexity of matrix multiplication is , and the complexity of pseudo-inverse is . Therefore, the total complexity is . Since the baseband digital precoding design in hybrid precoding is carried out in low dimensions, the increase in the number of base station transmitting antennas will not bring huge computational complexity. In practical application, this method may bring some challenges when the number of users increases rapidly. In general, the MMSE-based baseband digital precoding is an effective method.

5. Simulation Results

In this section, simulation results are given to illustrate the performance advantages of the proposed new antenna-partitioning algorithm and hybrid analog/digital precoding scheme in spectrum efficiency, energy efficiency, and user fairness. We compare the performance of three hybrid precoding schemes with different structures, namely, conventional subconnected structure, existing dynamic subarray-connected structure [20], and fully connected structure. Firstly, we consider a mmWave channel and dynamic subarray architecture described in Section 2. The carrier frequency is set as 28 GHz [1]. The number of scatterers for each user is . The transmit antenna arrays are ULAs with antenna spacing . We assume that both the AoAs and AoDs are following the uniform distribution within . Finally, SNR is defined .

5.1. Performance Comparisons of Spectrum Efficiency

Figure 3 shows the spectral efficiency comparison in different architectures, where the BS is equipped with 64 antennas and 2 RF chains serving two users, and each user is equipped with two antennas. We observe from Figure 3 that the performance of the proposed new antenna-partitioning-based hybrid precoding scheme outperforms the existing dynamic subarray precoding scheme [20] in the whole simulated SNR range. For instance, when  dB, our proposed scheme can achieve 86.1% of the spectral efficiency obtained by fully connected architecture, and compared with the conventional subarray architecture, the spectral efficiency of the proposed scheme is improved by 11.1%. Moreover, compared with the existing dynamic subarray architecture scheme, the spectral efficiency of the proposed scheme is also improved; especially at low SNR, the spectral efficiency is improved by 4.7% when  dB.

In Figure 4, the BS is equipped with 128 antennas and 2 RF chains serving two users. Similar to the trend in Figure 3, the spectrum efficiency of all architectures is improved, and our proposed scheme is still superior to the existing dynamic subarray architecture scheme. As can be seen from Figures 3 and 4, our proposed scheme is closer to the performance achieved by fully connected architecture, and the advantages are obvious at low SNR.

Figure 5 provides a spectrum efficiency comparison against the number of BS transmitting antennas. The BS is equipped with different antennas and two RF chains serving two users. As observed form Figure 5, the spectral efficiency increases with the increase of the number of antennas, but the rate of increase slows down when the  dB and the  dB. More importantly, the existing dynamic subarray schemes [20] do not show significant advantages when the BS equipped small transmitting antennas, such as 16 and 32. Moreover, our proposed scheme in this paper has obvious advantages in the whole simulation range.

5.2. Performance Comparisons of Energy Efficiency

The energy consumption model was proposed in [26], and based on this model, the energy efficiency can be defined as where is the total energy consumption, is the transmission power, is the energy consumed by RF chain, and is the energy consumed by phase shifter. and are the number of required RF chains and phase shifters, respectively.

In this paper, we use [26] and [27] in simulation, is constrained to . The energy efficiency comparison against the number of RF chains is shown in Figure 6, where , , and dB. We can see that with

the increase of the number of RF chains, the energy efficiency gradually decreases, which is consistent with the theoretical analysis. Moreover, it is clear that the dynamic subarray-connected architectures have higher energy efficiency than the fully connected architectures. In addition, compared with the existing dynamic subarray schemes, the energy efficiency of the proposed scheme is improved by 3.5% on average.

Figure 7 shows the energy efficiency comparison against the number of transmitting antennas of BS, where , 32, 64, 128, and 256,  dB, , and . As the number of base station antennas increases, the energy efficiency first increases and then decreases, especially for fully connected architectures, which are faced with rapid performance degradation. In addition, the proposed schemes achieve better energy efficiency than existing dynamic subarray schemes and can maintain a high and stable energy efficiency between 64 and 256 antennas.

5.3. User Fairness Analysis

As an indicator of the allocation of shared resources, fairness is widely used in various fields, for example, data collection in cognitive radio networks [28, 29] and data transfer in wireless communication systems. In a multiuser communication system, it is important to ensure that each user has access to roughly the same data transfer rate. In Section 4, we introduce user selection to provide better service for a small number of users, where it is important to ensure fairness among users. In order to investigate the fairness of each user, we introduce the Jain fairness index [30]. The Jain fairness index is a very effective measure of distributive fairness. Jain fairness index defines fairness among multiple users as where is the coefficient of fairness between users, . According to the definition of the Jain fairness index, if the value of tends to , the fairness is worse; on the other hand, if the value of tends to 1, the fairness is better. In Figure 8, we compared the fairness of different schemes with different numbers of users , where ,  dB, and . We observe that our proposed scheme can make the fairness index close to 1 under the different number of users. However, the fairness index of the existing dynamic subarray schemes [20] decreases with the increase of the number of users.

6. Conclusion

In this paper, we propose a new antenna-partitioning algorithm and a multiuser hybrid precoding scheme for the dynamic subarray of multiuser mmWave massive MIMO systems. The idea is to ensure that each user has the same opportunity to obtain the optimal antenna according to the maximum channel gain of the selected user. Then, we design the hybrid precoding with high performance for dynamic subarray architecture. Simulation results show that the proposed scheme in this paper can make the spectrum efficiency closer to the fully connected architectures and the energy efficiency closer to the traditional subconnected architectures. Moreover, the scheme proposed in this paper, especially at low SNR, is more advanced than the existing dynamic subarray schemes. In terms of user fairness, our scheme can almost make every user reach the same achievable rate. For future work, we are interested to evaluate performance in the dynamic subarray architecture with uniform planar arrays (UPAs) and under imperfect channel state information.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts interest regarding the publication of paper.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61871466 and in part by the Key Science and Technology Project of Guangxi under Grant AB19110044.

References

  1. T. S. Rappaport, S. Sun, R. Mayzus et al., “Millimeter wave mobile communications for 5G cellular: it will work!,” IEEE Access, vol. 1, pp. 335–349, 2013. View at: Publisher Site | Google Scholar
  2. S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter-wave cellular wireless networks: potentials and challenges,” Proceedings of the IEEE, vol. 102, no. 3, pp. 366–385, 2014. View at: Publisher Site | Google Scholar
  3. O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499–1513, 2014. View at: Publisher Site | Google Scholar
  4. J. Zhao, F. Gao, W. Jia, S. Zhang, S. Jin, and H. Lin, “Angle domain hybrid precoding and channel tracking for millimeter wave massive MIMO systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 10, pp. 6868–6880, 2017. View at: Publisher Site | Google Scholar
  5. R. Méndez-Rial, C. Rusu, N. González-Prelcic, and R. W. Heath, “Dictionary-free hybrid precoders and combiners for mmWave MIMO systems,” in 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 151–155, Stockholm, Sweden, 2015. View at: Publisher Site | Google Scholar
  6. X. Yu, J. Shen, J. Zhang, and K. B. Letaief, “Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 485–500, 2016. View at: Publisher Site | Google Scholar
  7. W. Ni, X. Dong, and W. Lu, “Near-optimal hybrid processing for massive MIMO systems via matrix decomposition,” IEEE Transactions on Signal Processing, vol. 65, no. 15, pp. 3922–3933, 2017. View at: Publisher Site | Google Scholar
  8. Y. Wang and W. Zou, “Low complexity hybrid precoder design for millimeter wave MIMO systems,” IEEE Communications Letters, vol. 23, no. 7, pp. 1259–1262, 2019. View at: Publisher Site | Google Scholar
  9. X. Gao, L. Dai, S. Han, I. Chih-Lin, and R. W. Heath, “Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 4, pp. 998–1009, 2016. View at: Publisher Site | Google Scholar
  10. Y. Liu, Q. Feng, Q. Wu, Y. Zhang, M. Jin, and T. Qiu, “Energy-efficient hybrid precoding with low complexity for mmWave massive MIMO systems,” IEEE Access, vol. 7, pp. 95021–95032, 2019. View at: Publisher Site | Google Scholar
  11. Y. Chen, D. Chen, T. Jiang, and L. Hanzo, “Millimeter-wave massive MIMO systems relying on generalized sub-array-connected hybrid precoding,” IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 8940–8950, 2019. View at: Publisher Site | Google Scholar
  12. D. H. N. Nguyen, L. B. Le, T. Le-Ngoc, and R. W. Heath, “Hybrid MMSE precoding and combining designs for mmWave multiuser systems,” IEEE Access, vol. 5, pp. 19167–19181, 2017. View at: Publisher Site | Google Scholar
  13. X. Gao, L. Dai, Y. Sun, S. Han, and I. Chih-Lin, “Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems,” in 2017 IEEE International Conference on Communications (ICC), pp. 1–6, Paris, 2017. View at: Publisher Site | Google Scholar
  14. J. Chen, “Energy-efficient hybrid precoding design for millimeter-wave massive MIMO systems via coordinate update algorithms,” IEEE Access, vol. 6, pp. 17361–17367, 2018. View at: Publisher Site | Google Scholar
  15. D. Zhang, Y. Wang, X. Li, and W. Xiang, “Hybrid beamforming for downlink multiuser millimetre wave MIMO-OFDM systems,” IET Communications, vol. 13, no. 11, pp. 1557–1564, 2019. View at: Publisher Site | Google Scholar
  16. X. Zhu, Z. Wang, L. Dai, and Q. Wang, “Adaptive hybrid precoding for multiuser massive MIMO,” IEEE Communications Letters, vol. 20, no. 4, pp. 776–779, 2016. View at: Publisher Site | Google Scholar
  17. S. Park, A. Alkhateeb, and R. W. Heath, “Dynamic subarrays for hybrid precoding in wideband mmWave MIMO systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 5, pp. 2907–2920, 2017. View at: Publisher Site | Google Scholar
  18. L. Zhang, L. Gui, Q. Qin, and Y. Tu, “Adaptively-connected structure for hybrid precoding in multi-user massive MIMO systems,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–7, Bologna, Italy, 2018. View at: Publisher Site | Google Scholar
  19. X. Jing, L. Li, H. Liu, and S. Li, “Dynamically connected hybrid precoding scheme for millimeter-wave massive MIMO systems,” IEEE Communications Letters, vol. 22, no. 12, pp. 2583–2586, 2018. View at: Publisher Site | Google Scholar
  20. J. Jiang, Y. Yuan, and L. Zhen, “Multi-user hybrid precoding for dynamic subarrays in mmWave massive MIMO systems,” IEEE Access, vol. 7, pp. 101718–101728, 2019. View at: Publisher Site | Google Scholar
  21. A. Alkhateeb, G. Leus, and R. W. Heath, “Limited feedback hybrid precoding for multi-user millimeter wave systems,” IEEE Transactions on Wireless Communications, vol. 14, no. 11, pp. 6481–6494, 2015. View at: Publisher Site | Google Scholar
  22. Z. Cai, Y. Duan, and A. G. Bourgeois, “Delay efficient opportunistic routing in asynchronous multi-channel cognitive radio networks,” Journal of Combinatorial Optimization, vol. 29, no. 4, pp. 815–835, 2015. View at: Publisher Site | Google Scholar
  23. Q. Chen, Z. Cai, L. Cheng, and H. Gao, “Low-latency data aggregation scheduling for cognitive radio networks with non-predetermined structure,” IEEE Transactions on Mobile Computing, p. 1, 2020. View at: Publisher Site | Google Scholar
  24. L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,” IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, 2014. View at: Publisher Site | Google Scholar
  25. D. H. Nguyen and T. Le-Ngoc, “MMSE precoding for multiuser MISO downlink transmission with non-homogeneous user SNR conditions,” EURASIP Journal on Advances in Signal Processing, vol. 2014, Article ID 85, 2014. View at: Publisher Site | Google Scholar
  26. P. V. Amadori and C. Masouros, “Low RF-complexity millimeter-wave beamspace-MIMO systems by beam selection,” IEEE Transactions on Communications, vol. 63, no. 6, pp. 2212–2223, 2015. View at: Publisher Site | Google Scholar
  27. W. Croswell, “Antenna theory, analysis, and design,” IEEE Antennas and Propagation Society Newsletter, vol. 24, no. 6, pp. 28-29, 1982. View at: Publisher Site | Google Scholar
  28. Z. Cai, S. Ji, J. He, and A. G. Bourgeois, “Optimal distributed data collection for asynchronous cognitive radio networks,” in 2012 IEEE 32nd International Conference on Distributed Computing Systems, pp. 245–254, Macau, China, 2012. View at: Publisher Site | Google Scholar
  29. Z. Cai, S. Ji, J. He, L. Wei, and A. G. Bourgeois, “Distributed and asynchronous data collection in cognitive radio networks with fairness consideration,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 8, pp. 2020–2029, 2014. View at: Publisher Site | Google Scholar
  30. A. B. Sediq, R. H. Gohary, R. Schoenen, and H. Yanikomeroglu, “Optimal tradeoff between sum-rate efficiency and Jain's fairness index in resource allocation,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3496–3509, 2013. View at: Publisher Site | Google Scholar

Copyright © 2021 Guangyan Liao and Feng Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views354
Downloads330
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.