Hybrid Precoding Architecture Research Articles

Spotting Faults Over the Spectrum: Fast and Online Antenna Array Fault Diagnosis for Multi-Carrier Precoding Systems

We present a new online method to diagnose faulty elements in an antenna array quickly and reliably while a wireless communication system is under regular operation. Existing methods for fault diagnosis do not usually allow simultaneous operation and diagnosis, leading to downtime, which becomes a severe constraint for modern wireless systems. Our approach is based on exploiting an extra degree of freedom available in the frequency domain in modern multicarrier hybrid/digital precoding architectures by allocating a very small part of the available spectrum for diagnosis while the rest is used for regular operation. Measurements can be made in a single shot by using optimized inputs, thereby minimally disrupting regular operation. We address both fully and partially connected RF precoding architectures of hybrid precoders and introduce one and two-stage fault diagnosis procedures, respectively. While the faulty antennas for fully connected hybrid architectures or digital architectures are diagnosed employing ideas from sparse recovery, additional ideas from block-sparse recovery are used in the case of a partially connected architecture. Our results indicate that online array fault diagnosis is possible in both digital and hybrid precoding architectures by introducing simple digital operations into the system and by using a single fixed receiver measurement setup.

Design of bitstream-based adaptive-connected array architecture for mmWave Multiuser MIMO systems

We investigate a bitstream-based adaptive-connected massive multiple-input multiple-output (MIMO) architecture that trades off between high-power full-connected and low-performance sub-connected hybrid precoding architectures. The proposed adaptive-connected architecture which enables each data stream to be computed independently and in parallel, consists of fewer phase shifters (PS) and switches than the other adaptive-connected architectures. With smaller array groups, the proposed architecture uses fewer PS and switches, so that its power consumption gradually decreases in millimeter wave (mmWave) Multiuser MIMO (MU-MIMO) system. To fully demonstrate the performance of the proposed architecture in mmWave MU-MIMO system with practical constraints, we combine the connection-state matrix with the hybrid precoders and combiners to maximize energy efficiency (EE) of the system equipped with the proposed architecture. We then propose the hybrid precoding and combining (HPC) scheme suitable for multi-user and multi-data streams which utilizes the SCF algorithm to obtain the constant modulus of the analog precoder at convergence. In the digital precoding and combining stage, the digital precoder and combiner are designed to reduce the amount of computation by utilizing the singular value decomposition (SVD) of corresponding equivalent channel. In the mmWave MU-MIMO-OFDM system equipped with the proposed architecture, with the increase of the total number of data streams, simulation results demonstrate that we can exploit the proposed HPC scheme to achieve better EE than the traditional hybrid full-connected architecture exploiting some existing schemes.

Adaptive Hybrid Precoding With High Energy Efficiency for Millimeter Wave Communication Systems

Conventional hybrid precoding architectures for millimeter wave systems can be classified into two categories: fully-connected (FC) and subconnected (SC). However, due to the large number of phase shifters (PSs), the conventional hybrid precoding architectures often suffer from high power consumptions. To this end, two novel energy-efficient architectures are proposed, which are, respectively, named as adaptive FC (AFC) architecture and adaptive SC (ASC) architecture. The proposed architectures can adapt the connections between the radio frequency chains and antenna elements according to the channel environment, so that the hardware overhead can be reduced remarkably compared with the conventional counterparts. Afterwards, two novel hybrid precoding algorithms are proposed based on the AFC and ASC architectures. Particularly, in the proposed algorithm, a subspace projection-based branch-and-bound method is developed to derive the switch precoding matrix, thereby the computational burden can be reduced by more than two orders of magnitude in comparison to the traditional branch-and-bound method. Additionally, the problem of PS precoding matrix is reformulated as a positive semidefinite quadratic form via an elegant diagonal loading factor, so that the PS precoding matrix can be obtained easily. Simulation results verify that the proposed architectures and algorithms can offer both high energy efficiencies and spectral efficiencies.

Grey neural network channel estimation and RBFNN hybrid precoding schemes for the multi user millimeter wave massive MIMO

AbstractThe millimeter wave (mm‐wave) multi‐input multi‐output (MIMO) communications are the most promising technology specially developed for next‐generation wireless networks so that the throughput and available spectrum of the network has been massively increased. At base station, it has a large number of antenna arrays with coherent transceiver processing, which helps to increase spectral efficiency. Each antenna in conventional MIMO systems makes use of a single radio‐frequency (RF) chain to boost multiplexing gain. Additionally, due to broader bandwidth, channel frequency is selective and signal‐to‐noise (SNR) is low. A successful channel estimation training phase is thus required. However, the utilization of a huge number of antennas leads to unaffordable hardware costs and excessive power usage. In order to deliver high array gain at a more affordable price, hybrid precoding techniques are applied. Both baseband combinational matrix and baseband precoding matrix must be optimised when transmitter and receiver sides of mm‐wave MIMO systems adopt hybrid precoding architecture. In addition, the combinational matrix and the precoding matrix have to be simulated to obtain the maximum system sum rate. This requires an efficient hybrid precoding approach for generating improved array gain. Therefore, this work uses a novel grey neural network approach for channel estimation and radial basis function neural network (RBFNN) for hybrid precoding. For effective channel estimation, grey wolf optimization (GWO) works in conjunction with artificial neural networks (ANN) to produce optimum results. In mm‐wave MIMO systems, the hybrid precoding RBFNN offers reduced complexity and increased efficiency. As a result, the proposed work adopts neural networks to provide effective channel estimation and hybrid precoding.

Delay-Phase Precoding for Wideband THz Massive MIMO

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlike <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-independent</i> phase shifts, the time delay network introduced in the DPP can realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-dependent</i> phase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.

Switch-and-inverter based adaptive subarrays for hybrid mmWave multi-user MIMO systems

ABSTRACT The hybrid precoding design is considered for a millimeter-wave (mmWave) multi-user (MU) multiple-input multiple-output (MIMO) downlink communication system. A small quantity of more energy-efficient switches and inverters (SIs) are being substituted for a large amount of high-resolution phase shifters (PSs) in the analog precoder of the mmWave MIMO system. This SI-based hybrid precoding architecture reduces significantly both the hardware complexity and energy consumption and thus enhances substantially the transmission energy efficiency (EE). On the basis of the achievable sum-rate (ASR) maximization, the SI-based overlapped subarray (OSA) hybrid precoding is exploited. Compared with the traditional SI-based sub-connected (SC) array configurations, the greater the overlapping antenna elements, the better the transmission ASR performance, and the nearer the fully PS-based precoding configuration. Finally, the SI-based hybrid precoding is equipped with the Gram–Schmidt orthogonalization (GSO) based dynamic subarray mapping and antenna selection (AS) mechanism to improve further the EE measure while maintaining excellent ASR performance.

System-Level Assessment of Low Complexity Hybrid Precoding Designs for Massive MIMO Downlink Transmissions in Beyond 5G Networks

The fast growth experienced by the telecommunications field during the last few decades has been motivating the academy and the industry to invest in the design, testing and deployment of new evolutions of wireless communication systems. Terahertz (THz) communication represents one of the possible technologies to explore in order to achieve the desired achievable rates above 100 Gbps and the extremely low latency required in many envisioned applications. Despite the potentialities, it requires proper system design, since working in the THz band brings a set of challenges, such as the reflection and scattering losses through the transmission path, the high dependency with distance and the severe hardware constraints. One key approach for overcoming some of these challenges relies on the use of massive/ultramassive antenna arrays combined with hybrid precoders based on fully connected phase-shifter architectures or partially connected architectures, such as arrays of subarrays (AoSAs) or dynamic AoSAs (DAoSAs). Through this strategy, it is possible to obtain very high-performance gains while drastically simplifying the practical implementation and reducing the overall power consumption of the system when compared to a fully digital approach. Although these types of solutions have been previously proposed to address some of the limitations of mmWave/THz communications, a lack between link-level and system-level analysis is commonly verified. In this paper, we present a thorough system-level assessment of a cloud radio access network (C-RAN) for beyond 5G (B5G) systems where the access points (APs) operate in the mmWave/THz bands, supporting multi-user MIMO (MU-MIMO) transmission with massive/ultra-massive antenna arrays combined with low-complexity hybrid precoding architectures. Results showed that the C-RAN deployments in two indoor office scenarios for the THz were capable of achieving good throughput and coverage performances, with only a small compromise in terms of gains when adopting reduced complexity hybrid precoders. Furthermore, we observed that the indoor-mixed office scenario can provide higher throughput and coverage performances independently of the cluster size when compared to the indoor-open office scenario.

Reconfigurable intelligent surface based hybrid precoding for THz communications

Benefiting from the growth of the bandwidth, Terahertz (THz) communication can support the new application with explosive requirements of the ultra-high-speed rates for future 6G wireless systems. In order to compensate for the path loss of high frequency, massive Multiple-Input Multiple-Output (MIMO) can be utilized for high array gains by beamforming. However, the existing THz communication with massive MIMO has remarkably high energy consumption because a large number of analog phase shifters should be used to realize the analog beamforming. To solve this problem, a Reconfigurable Intelligent Surface (RIS) based hybrid precoding architecture for THz communication is developed in this paper, where the energy-hungry phased array is replaced by the energy-efficient RIS to realize the analog beamforming of the hybrid precoding. Then, based on the proposed RIS-based architecture, a sum-rate maximization problem for hybrid precoding is investigated. Since the phase shifts implemented by RIS in practice are often discrete, this sum-rate maximization problem with a non-convex constraint is challenging. Next, the sum-rate maximization problem is reformulated as a parallel Deep Neural Network (DNN) based classification problem, which can be solved by the proposed low-complexity Deep Learning based Multiple Discrete Classification (DL-MDC) hybrid precoding scheme. Finally, we provide numerous simulation results to show that the proposed DL-MDC scheme works well both in the theoretical Saleh-Valenzuela channel model and practical 3GPP channel model. Compared with existing iterative search algorithms, the proposed DL-MDC scheme significantly reduces the runtime with a negligible performance loss.

Robust Hybrid Precoding Design for Securing Millimeter-Wave IoT Networks Under Secrecy Outage Constraint

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper's channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper's imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD's information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush-Kuhn-Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

Low-complexity non-iterative hybrid precoding scheme for millimeter wave massive MIMO systems

ABSTRACT Conventional multiple-input multiple-output (MIMO) systems operating at sub-6 GHz frequencies generally employ fully digital precoding which requires dedicated radio frequency (RF) chain for each antenna element. However, this architecture is infeasible for the millimetre wave (mmWave) massive MIMO systems due to excessive power consumption and space constraints posed by large number of RF chains. Hence, hybrid precoding architecture, which utilises small size digital precoder followed by large size analog precoder, is employed for such systems. Due to the constant-magnitude hardware constraint on the phase shifter elements of analog precoder, efficient design of hybrid precoders is a challenge. In this paper, a low-complexity non-iterative hybrid precoder/combiner design is proposed for a single-user mmWave massive MIMO system. The analog RF precoder vectors are selected non-iteratively from the set of transmit array response vectors such that they are ‘aligned’ with the optimal precoder of the channel. Then, for the fixed analog precoder, the digital baseband precoder is evaluated using least square solution. Matrix operations like singular value decomposition, correlation and inversion are required only once. A similar approach is adopted for the design of hybrid combiners of the receiver. Simulation results show effectiveness of the proposed method in terms of spectral efficiency maximisation.

Higher Spectral Efficiency for mmWave MIMO: Enabling Techniques and Precoder Designs

Millimeter wave (mmWave) communications can support huge data rate growth and wide bandwidth in the new 5G era, and a hybrid precoding architecture can effectively handle the high hardware cost and energy consumption problems. A crucial goal is to optimize the system's spectral efficiency (SE) by effectively designing the digital precoder and the analog precoder. In this article, we review the conventional hybrid precoding schemes in mmWave systems, and then investigate several novel systems with index modulation (IM) techniques as well as their hybrid precoder designs. With the advantages of IM, higher SE can be achieved with limited radio frequency chains in mmWave systems. Simulation results demonstrate that two types of IM-aided systems both outperform conventional mmWave systems, which proves that IM is an enabling technique in practical SE-driven mmWave systems. Finally, we discuss the limiting factors and outlook of IM-aided mmWave systems in beyond-5G wireless communications.

Hybrid Precoding in Cooperative Millimeter Wave Networks

In this paper, we study the performance of cooperative millimeter wave (mmWave) networks with hybrid precoding architectures. Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems. We reformulate the analog precoding part as an equal-gain transmission problem, which only depends on the channel information, and the digital precoding part as a relaxed convex semidefinite program subject to per-user quality-of-service constraints that gives the optimal sum power consumption in terms of the BS silence strategy. In order to reduce the complexity of the hybrid precoding algorithm with optimal BS silence strategy, we propose a sub-optimal hybrid precoding algorithm that iteratively put BSs with small power into the silent mode. The simulation results show that, depending on the parameter settings, the power consumption of the PHP may be dominated by the RF transmit power and it may result in a larger power consumption than the FHP. For the cases with 2 BSs and 4 users, implementation of the FHP and the PHP in cooperative networks reduces the required RF transmit power, compared to the case in a non-cooperative network, by 71% and 56%, respectively.

Constructive Interference-Based Symbol-Level Precoding Design for Millimeter-Wave Massive Multiuser MIMO Systems With Hardware-Efficient Hybrid Precoding Architecture

This study considers the constructive interference-based symbol-level precoding design problem in millimeter-wave massive multiuser multiple-input multiple-output systems with hybrid analog-and-digital precoding architecture. In contrast to traditional hybrid precoding architecture in which the analog part is implemented by infinite-resolution phase shifters, analog precoders are achieved in this study using low-cost and energy-efficient switches and inverters. Our objective is to design a hybrid precoder that minimizes the Euclidean distance between the hybrid precoder and the optimal fully digital precoder. However, such a design problem is difficult due to the intricate coupling of digital and analog precoder variables. Thus, we apply the alternating minimization (AltMin) framework to decouple the considered problem into digital and analog precoder design problems and then solve for digital and analog precoders in an alternating manner. By adopting the AltMin framework, we obtain a closed-form solution for digital precoder. Nevertheless, solving the analog precoder design problem incurs combinatorial complexity, which we address by proposing a coordinate descent algorithm to sequentially update each element of the analog precoder with a closed-form expression. Simulation results reveal that the application of the proposed algorithm for the proposed hardware-efficient hybrid precoding architecture provides the desired balance among symbol error rate performance, power consumption, and hardware cost.

An Energy-Efficient Hybrid Precoding Design in mmWave Massive MIMO Systems

The hybrid precoding (HP) technique has been widely considered as a promising approach for millimeter wave communication systems. In general, the existing HP structure with a complicated high-resolution phase shifter network can achieve near-optimal spectral efficiency, however, it involves high energy consumption. The HP architecture with an energy-efficient switch network can significantly reduce the energy consumption. To achieve maximum energy efficiency, this paper focuses on the HP architecture with switch network and considers a novel adaptive analog network HP structure for such mmWave MIMO systems, which can provide potential array gains. Moreover, a multiuser adaptive coordinate update algorithm is proposed for the HP design problem of this new structure. Simulation results verify that our proposed design can achieve better energy efficiency than other recently proposed HP schemes when the number of users is small.

The Configurable Hybrid Precoding Processor for Bit-Stream-Based mmWave MIMO Systems

Beamforming technology plays an essential role in the promising millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) communications for fifth generation (5G) new radio system. Specifically, hybrid analog beamforming and digital precoding scheme can be employed to reduce the excessive radio frequency (RF) chains and data converters in the massive MIMO transceiver while still maintaining optimal spectral efficiency. However, traditional hybrid precoding architecture cannot be configured to support different system specifications, such as the number of bit streams or transmit antennas, leading to the limitation to hardware flexibility and efficiency. This article presents a configurable and low-complexity hybrid precoder based on the parallel data-stream processing in respect of system, algorithm, and architecture. The proposed algorithm was designed to avoid signal dependence between data streams so as to realize configurable precoding architecture. The performance and complexity of the proposed algorithm were also simulated and analyzed in detail in this article. Moreover, the hybrid precoding processor chip was designed and implemented based on the proposed algorithm. A novel data-processing flow was designed in the precoder so as to increase the hardware efficiency and configurability. The designed hybrid precoding chip can be configured to support one to four data streams for 16 × 16 mmWave MIMO systems. The designed precoder was implemented by using TSMC 40-nm CMOS technology. The normalized throughput achieved 11.1 M channel-matrices per second at a maximum clock frequency of 300 MHz. The area complexity is 263.5 kGE, and power consumption is 119.1 mW.

Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems.

Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.

A Dynamic Array-of-Subarrays Architecture and Hybrid Precoding Algorithms for Terahertz Wireless Communications

Terahertz (THz) communications are envisioned as a key technology for 6G wireless systems, owing to an unprecedented promised multi-GHz bandwidth. While THz band suffers from huge propagation losses, large arrays of sub-millimeter wavelength antennas can be realized in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the received power and overcome the distance limitation. In this paper, a dynamic array-of-subarrays (DAoSA) hybrid precoding architecture is proposed to reduce the power consumption while meeting the data rate requirement in THz UM-MIMO systems. The connections between RF chains and subarrays are intelligently adjusted through a network of switches. First, to solve the intractable DAoSA hybrid precoding problem, element-by-element (EBE) and vectorization-based (VEC) algorithms are derived. Moreover, to determine the connections of the switches, near-optimal progressive stage-by-stage (PSBS), low-complexity alternating-selection (AS) and block-diagonal-search (BDS) algorithms are developed. Extensive simulation results show that both the EBE and VEC algorithms have higher spectral efficiency than existing hybrid precoding algorithms. Furthermore, the power consumption of the DAoSA architecture is substantially lessened, with PSBS, AS and BDS algorithms, respectively. The developed DAoSA architecture associated with proposed hybrid precoding and switch network design algorithms demonstrates a superior capability on balancing the spectral efficiency and power consumption.

Hybrid precoding based on adaptive RF-chain-to-antenna connection for millimeter wave MIMO systems

Hybrid precoding has recently received significant attentions as a promising alternative to fully digital precoding. The existing hybrid precoding architectures can be classified into two categories: fully-connected (FC) and partially-connected (PC) phase shifter (PS) network structures (FC-PS and PC-PS structures). However, the FC-PS structure involves a large number of PSs which leads to a prohibitive power consumption, and the PC-PS structure suffers from a severe spectral efficiency loss in spite of low power consumption. This paper considers a novel adaptive RF-chain-to-antenna connection based on switch network (AC-S) hybrid precoding architecture, which consists of a digital precoder, an analog switch network and an analog PS network. The advantage of the proposed architecture lies in achieving a satisfactory spectral efficiency while maintaining a relatively low power consumption due to the adaptive connection from a radio frequency (RF) chain to all antennas via a switch network. Based on the aforementioned architecture, an effective hybrid precoding algorithm is presented. In the proposed algorithm, the analog PS precoding matrix, the analog switch precoding matrix and the digital precoding matrix are optimized via a block coordinate descent method, and a stable solution can be obtained by several iterations. Simulation results demonstrate that the proposed algorithm with the AC-S structure not only obtains considerably better spectral efficiency than the PC-PS structure but also offers a lower power consumption than the FC-PS structure.

A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges

Fifth-generation (5G) cellular networks will almost certainly operate in the high-bandwidth, underutilized millimeter-wave (mmWave) frequency spectrum, which offers the potentiality of high-capacity wireless transmission of multi-gigabit-per-second (Gbps) data rates. Despite the enormous available bandwidth potential, mmWave signal transmissions suffer from fundamental technical challenges like severe path loss, sensitivity to blockage, directivity, and narrow beamwidth, due to its short wavelengths. To effectively support system design and deployment, accurate channel modeling comprising several 5G technologies and scenarios is essential. This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies. These technologies induce distinct propagation characteristics and establish specific requirements on 5G channel modeling. To tackle these challenges, we first provide a survey of existing solutions and standards and discuss the radio-frequency (RF) spectrum and regulatory issues for mmWave communications. Second, we compared existing wireless communication techniques like sub-6-GHz WiFi and sub-6 GHz 4G LTE over mmWave communications which come with benefits comprising narrow beam, high signal quality, large capacity data transmission, and strong detection potential. Third, we describe the fundamental propagation characteristics of the mmWave band and survey the existing channel models for mmWave communications. Fourth, we track evolution and advancements in hybrid beamforming for massive MIMO systems in terms of system models of hybrid precoding architectures, hybrid analog and digital precoding/combining matrices, with the potential antenna configuration scenarios and mmWave channel estimation (CE) techniques. Fifth, we extend the scope of the discussion by including multiple access technologies for mmWave systems such as non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA), with limited RF chains at the base station. Lastly, we explore the integration of SWIPT in mmWave massive MIMO systems, with limited RF chains, to realize spectrally and energy-efficient communications.

Symbol-Level Hybrid Precoding in mmWave Multiuser MISO Systems

Recently, symbol-level precoding has emerged as a novel efficient solution in multi-user multi-antenna downlink systems, since it can exploit multi-user interference (MUI) and transform it into useful signal. In this correspondence, we attempt to apply symbol-level precoding into the well-known hybrid precoding architecture in multi-user multi-input single-output (MU-MISO) millimeter wave (mmWave) communication systems. Particularly, we aim to design the symbol-varying digital precoder and the analog precoder with low-resolution phase shifters (PSs) to optimize symbol detection performance under the transmit power constraint and the discrete phase constraint. To handle this difficult joint optimization problem, we propose to minimize the Euclidean distance between the optimal fully-digital precoder and the hybrid precoder. An efficient iterative algorithm is developed to solve this problem. Simulation results demonstrate the advantages of symbol-level precoding and validate the effectiveness of our proposed symbol-level hybrid precoding algorithm.