Hybrid Analog-digital Beamforming Research Articles

Hybrid Beamforming for mm-Wave Massive MIMO Systems with Partially Connected RF Architecture

To satisfy the capacity requirements of future mobile systems, under-utilized millimeter wave frequencies can be efficiently exploited by employing massive multiple input-multiple output (MIMO) technology with highly directive beamforming. Hybrid analog-digital beamforming has been recognised as a promising approach for large-scale MIMO implementations with a reduced number of costly and power-hungry radio frequency (RF) chains. In comparison to fully connected architecture, hybrid beamforming (HBF) with partially connected RF architecture is particularly appealing for the practical implementation due to less complex RF power division and combining networks. In this paper, we first formulate single- and multi-user rate maximization problems as weighted minimum mean square error (WMMSE) and derive solutions for hybrid beamformers using alternating optimization. The algorithms are designed for the full-array- and sub-array-based processing strategies of partially connected HBF architecture. In addition, we propose lower complexity sub-array-based zero-forcing algorithms. The performance of the proposed algorithms is evaluated in two different channel models, a simple geometric model and a realistic statistical model. The performance results of the WMMSE HBF algorithms are meant to reveal the potential of partially connected HBF and serve as upper bounds for lower complexity methods. Numerical results imply that properly designed partially connected HBF has the potential to provide an good compromise between hardware complexity and system performance in comparison to fully digital beamforming.

Versatile Resource Management for Millimeter-Wave Cellular Network: Near Interference-Free Scheduling Methodology

In millimeter-wave (mmWave) cellular network, the highly directional beam generated by large-scale antenna array considerably depresses the inter-cell/intra-cell interferences. To fully exploit this feature in resource management, we introduce the concept of near interference-free (NIF) scheduling, representing a well-designed user scheduling scheme which avoids the possible strong interferences in mmWave network. In this paper, a generalized NIF scheduling methodology is proposed to jointly design user scheduling, beam scheduling and power allocation. The proposed methodology exhibits versatility to handle various optimization objectives, including the maximization of (weighted) sum rate, the maximization of minimum user rate, and the minimization of total transmit power. We take into account 3-dimensional user scheduling (over time, frequency and spatial domain) under the constraints induced by hybrid analog-digital beamforming. The directional beam feature is utilized to simplify the problem, and leads to linearity/convexity in the optimization of resource allocation. For each of the studied optimization objectives, simulation results validate that our proposed NIF scheduling methodology outperforms the state-of-the-art user scheduling schemes.

Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital (AD) beamforming can be used to attain a high directional gain without requiring a dedicated radio frequency (RF) chain for each antenna element, which substantially reduces both the hardware costs and power consumption. While massive MIMO transceiver design typically relies on the conventional mean-square error (MSE) criterion, directly minimizing the symbol error rate (SER) can lead to a superior performance. In this article, we first mathematically formulate the problem of hybrid transceiver design under the minimum SER (MSER) optimization criterion and then develop an MSER-based iterative gradient descent (GD) algorithm to find the related stationary points. We then propose a deep-unfolding neural network (NN). The iterative GD algorithm is unfolded into a multi-layer structure wherein trainable parameters are introduced to accelerate the convergence and enhance the overall system performance. To implement the training stage, we derive the relationship between adjacent layers' gradients based on the generalized chain rule (GCR). The deep-unfolding NN is developed for both quadrature phase shift keying (QPSK) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -ary quadrature amplitude modulated (QAM) signals, and its convergence is investigated theoretically. Furthermore, we analyze the transfer capability, computational complexity, and generalization capability of the proposed deep-unfolding NN. Our simulation results show that the latter significantly outperforms its conventional counterpart at a reduced complexity.

Data-Driven Hybrid Beamforming for Uplink Multi-User MIMO in Mobile Millimeter-Wave Systems

To enable user diversity while balancing tradeoffs between cost and flexibility, we exploit hybrid analog-digital beamforming for multi-user mobile systems. By combining array and spatial signal-processing techniques, highly directional beams can be formed with high beamforming gain to achieve sufficient link budget. In this way, fine selection of codewords for analog beamforming is essential to ensure the uplink rate, which may increase the latency of establishing a reliable communication link, especially for mobile millimeter-wave communication systems. In order to guarantee the reliability of communication, adaptive data-driven beam tracking is proposed to find the suitable beamformer/combiner pair to achieve the given signal-to-interference-plus-noise ratios constraint. Unlike the model-based approach, the proposed approach is dependent only on the real-time measurement data based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Numerical analyses show that the proposed beam tracking algorithm can achieve good tracking performance with lower training overhead compared with traditional schemes.

Optimal Beamformer Design for Millimeter Wave Dual-Functional Radar-Communication Based V2X Systems

Millimeter wave (MmWave) dual-functional radar-communication (DFRC) technology is believed to hold the ability to alleviate spectrum congestion and inter-radar interference in 5G vehicle-to-everything (V2X) systems. The radar target sizes in V2X system may not be ignored in views of the demands of short-range sensing and ultra-narrow beams supported by massive MIMO mmWave beamforming. Under such scenario, a novel single-target-multi-beams (STMB) radar beam alignment scheme is proposed to acquire more accurate information on estimated ranges and velocities by allocating multiple radar beams to a certain target. For instance, the relative velocity direction can be accurately estimated based on STMB scheme by using a weighted linear estimation methods. Then, the hybrid analog-digital beamforming under STMB scheme is formulated and optimized by maximizing transmission rate subject to radar signal-to-interference-and-noise (SINR) constraints, where a radar beam cancellation algorithm is proposed to adjust adaptively the radar beam number pointing to a certain target, which can guarantee strict radar SINR constraint under different transmission power levels. The numerical results verify the effectiveness and reliability of STMB scheme and show that the proposed beamformer outperforms the benchmark in both spectral efficiency and minimum radar SINR.

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems

In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate.

Multi-UAV Aided Millimeter-Wave Networks: Positioning, Clustering, and Beamforming

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

Joint Design of Hybrid Beamforming and Reflection Coefficients in RIS-Aided mmWave MIMO Systems

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

Low-Complexity and Robust Framework of Precoding for Multi-Panel Massive MIMO

Massive multiple-input multiple-output (m-MIMO) is used to improve the robustness of data transmission and high sum spectral efficiency in 5G systems. For 5G New Radio (NR) systems need to be designed to reduce cost and complexity, most notably hybrid analog-digital beamforming with large-scale antenna array which has become a major research target. In the downlink transmission, we propose a robust and low-complexity framework of precoding design to implement hybrid beamforming (BF) of large antenna arrays without limitation on the size of the antenna. Furthermore, a large number of antenna elements can be coupled into multiple panels m-MIMO for the purpose of reducing costs and saving power. This study proposes to use both azimuth and elevation angles parameters to provide low complexity adaptive beam control in panels m-MIMO system. In contrast to existing works, that aim to eliminate inter-user interference, this work is distinguished by adopting polarization function and space-time block code to the m-MIMO antenna arrays. We also propose a new multi-panel codebook design correspondingly with control parameters. The performance of the proposed framework is analysed with state-of-the-art research comparisons. To this end, the important factor of beamforming which is the Error Vector Magnitude (EVM) will be analyzed and simulated.

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Massive MIMO and mmWave communication are the technologies for achieving 5G design goals. Fortunately, these two technologies share a symbiotic integration. As a result, amalgamating mmWave communications with massive MIMO forms,mmWave-massive MIMO,” which significantly improves spectral and energy efficiency. It also achieves high multiplexing gains and increases mobile network capacity. However, massive MIMO, mmWave communications, and mmWave-massive MIMO systems have been studied independently. Consequently, this article explores the ideas, performances, comparisons, and discussions of these three 5G technologies jointly, considering their precoding and beamforming methods. On the other hand, the complexity of these technologies increases when a large number of antennas and radio frequencies (RFs) are used. Thus, several investigations are going on to search for the appropriate precoding and beamforming strategies with low cost, power, and complexity. Therefore, massive MIMO linear precoding techniques such as zero-forcing, maximum ratio transmission, regularized zero-forcing, truncated polynomial expansion and phased zero forcing are addressed in this work. In addition, the most common non-linear precoding schemes: dirty-paper coding, Tomlinson-Harashima, and vector perturbation, are presented. Furthermore, a detailed discussion of the beamforming techniques called analog, digital, and hybrid analog-digital beamforming schemes is included. We also examine the potential of hybrid analog-digital beamforming with its fully-connected and sub-connected architecture approaches in making mmWave massive MIMO a reality. We evaluate their performance with the parameters: bit error rate, signal to noise ratio, complexity, spectral efficiency, and energy efficiency. According to the analytical and simulation results, the partially-connected hybrid analog/digital beamforming architecture offers better all-over performance for mmWave-Massive MIMO communications by compromising: power consumption, cost, complexity, and performance. Finally, the potential future directions in mmWave-massive MIMO precoding and beamforming challenges are addressed.

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power values, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. When the candidates include the ideal beams, the combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and 10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

Multi-User Massive MIMO Systems Based on Hybrid Analog-Digital Beamforming for Millimeter-wave Communications

Multi-User Massive MIMO Systems Based on Hybrid Analog-Digital Beamforming for Millimeter-wave Communications

Secure Hybrid A/D Beamforming for Hardware-Efficient Large-Scale Multiple-Antenna SWIPT Systems

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users' confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption.

A new method for the solution of hybrid analog digital beamforming problems

SummaryBy going to millimeter wave (mmWave) we can use large scale MIMO due to short mmWave wavelength to overcome path loss by using beamforming to focus power of signal to the receiver. System structure of mmWave band is different with conventional MIMO because of large scale MIMO which is leading to use many RF‐chains. For this reason Hybrid structure have been proposed for large Scale MIMO. By going to Hybrid structure a new issue has been created with phase shifter constraint. In this paper we propose a novel solution to make a hybrid precoding and combing to reach high spectral efficiency. Our problem includes a maximizing Frobenius norm of multiplying five complex matrices. As constraints, all elements of some matrices must have unit norm, and also the multiplication of some matrices must have a specific Frobenius norm. A novel solution is proposed for the problem, which is gained in two steps. At the first step, we propose a new decomposition usable for kind of complex matrices which could lead to reduce the dimension of the objective function, and eliminating constraints completely and in the second step, we propose an iterated algorithm for the resulting problem. By proposing another novel technique, we show that in each iteration, the optimal problem is equivalent to a quadratic optimal problem with limitations on the vector norm, and as a result, an optimal problem is obtained quickly. Moreover, some other real examples are included to demonstrate the usefulness and effectiveness of the proposed algorithm.

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

In this paper, we firstly overview the application scenarios and the research progress in the area of communication and radar spectrum sharing (CRSS). We then propose a novel transceiver architecture and frame structure for a dual-functional radar-communication (DFRC) base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna aided user equipment (UE) operating in a mmWave channel, which in the meantime actively detects multiple targets. Note that part of the targets also play the role of scatterers for the communication signal. Given this framework, we propose a novel scheme for joint target search and communication channel estimation relying on the omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design an HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the potential interference imposed on the UE signals by invoking the successive interference cancellation (SIC) technique, even when the radar and communication signals share the equivalent signal-to-noise ratio (SNR). The feasibility and the efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, our discussions are summarized by overviewing the open problems in the research field of CRSS.

Orthogonal Code-Based Transmitted Radiation Pattern Measurement Method for 5G Massive MIMO Antenna Systems

This article presents a novel method to measure the transmitted radiation pattern by using orthogonal encoded signals during the process of hybrid analog-digital (HAD) beamformer development. One of the most important indicators in evaluating the performance of HAD beamforming systems is the measurement of the sub-array radiation patterns. However, as the number of elements increases, the number of combinations that can create sub-arrays increases significantly, and the conventional radiation pattern measurement process becomes exhaustive. However, when an orthogonal coded signal is used for the HAD beamforming system measurement, the magnitude and phase information of each element of the HAD beamforming system, including mutual coupling, can be simultaneously obtained from a single scan. Hence, it is possible to measure all feasible sub-array patterns very efficiently. Therefore, we propose an orthogonal code-based time-domain measurement technique and validate it with full-wave time-domain simulations. The results show that it is possible to receive element pattern information independently and that any sub-array pattern can be reconstructed with an accuracy greater than 99.99%.

Unequally Sub-Connected Architecture for Hybrid Beamforming in Massive MIMO Systems

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.

Hybrid Beamforming With Finite-Resolution Phase Shifters for Multiuser Millimeter-Wave Downlink

This letter considers hybrid analog-digital beamforming with discrete-phase shifters for multiuser multiple-input and single-output (MISO) downlink transmission in millimeter-wave (mm-wave) channels. A max-min signal-to-interference-plus-noise ratio (SINR) fairness problem is formulated. Owing to the discrete-phase constraints, this problem is a mixed integer nonlinear program (MINP) and is generally NP-hard. To handle it, an alternating optimization approach is employed. Specifically, for the digital beamformer, zero-forcing (ZF) beamforming is utilized. For the discrete-phase analog beamformer, we first derive an equivalent continuous reformulation and then solve it by leveraging an inexact majorization-minimization (MM) algorithm, which is built upon the first-order method and MM update. Simulation results show that the proposed design can almost achieve the optimal performance of the exhaustive search method, and can approach the optimal full-digital performance with increasing resolution of the discrete-phase shifters.