The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks (HetNET). We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

In this paper, we present an energy-efficient joint machine learning based beam-user selection and low complexity hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. Massive MIMO system is a key enabler of $5^{th}$ generation (5G) technology which is being used in vehicle-to-everything (V2X) communications and other Internet-of-Things (IoT). The large number of antennas at the base-station side causes high power consumption in the radio frequency (RF) chain. Hybrid beamforming techniques are used to reduce the number of RF chains with minimal performance loss. This paper proposes a low complexity orthogonal hybrid beamforming design with the aid of a machine learning based beam-user selection scheme. The Householder (HH) reflectors are used to generate the orthogonal analog beamforming (ABF) matrix. We use a feedforward neural network (FFNN) beam-user selection scheme. The proposed HH ABF + FFNN scheme provides better energy-efficiency (EE) performance in the ill-conditioned massive MIMO channel. It has been shown that the green SE-EE point (41.35 b/s/Hz , 0.8 b/Hz/J ) can be obtained by the proposed HH ABF + FFNN scheme as compared to the (33.77 b/s/Hz , 0.63 b/Hz/J ) from the state-of-the-art Discrete Fourier transform based techniques.

This paper presents a study on joint radio resource allocation and hybrid precoding in multi-carrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR ≤ 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index.

This paper presents a deep reinforcement learning-based beam-user selection and hybrid beamforming design for the multiuser massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink systems. The conventional hybrid beamforming in massive MIMO provides multiple directional beams, but each beam serves only one user. The integration of NOMA with the massive MIMO enables power domain multiplexing within a beam, hence increasing the system capacity. In this paper, we first design a channel gain and correlation-based users grouping algorithm per beam, and then using the deep reinforcement learning-based beam selection, a beamspace orthogonal analog precoder is obtained. The deep Q-network consists of a main network and target network with Adam optimizer. Finally, optimal power is allocated to the users in each beam. Simulation results show that at transmit SNR of 10 dB, the proposed scheme provides a 42% increase in sum-rate and energy efficiency performance as compared to the state-of-the-art $K$ -means users’ grouping and Stable Matching-based beam selection NOMA scheme.

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices(operating in unlicensed bands), we consider the LTE operation in unlicensed bands based on CSAT, in which ‘ON’ and ‘OFF’ dutycycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6% and 11%) and (8% and 18%) the round-robin and the spectrum-efficient schemes, respectively for two different small cell scenarios. In addition, it is shown that for less CSI estimation errors $\varsigma = 0.02$ , the maximum performance degradation of the proposed scheme is reasonably small (5.5%) as compared to the perfect CSI.

In this paper, we present a machine learning based radio resource management (RRM) scheme for a rank-constrained massive multiple-input multiple-output (MIMO) system. We combine the radio resource management and hybrid beamforming design constraint to desired number of radio frequency (RF) chains. The formulated optimization problem maximizes the sum throughput constraint to total transmit power and the number of RF chains. Being the computationally intensive part of the system, the radio resource allocation in multiuser, multi-carrier massive MIMO system has been replaced by the neural network employing the supervised learning. The proposed design renders the similar sum rate performance given by the CVX-based optimal algorithm, but with 28.72 times less execution time.