Low Resolution Phase Shifters Suffice for Full-Duplex mmWave Communications

Fully-dynamic and hardware-efficient analog architecture for terahertz wideband hybrid beamforming with multi-channel low-resolution phase shifters

Economical and energy-efficient analog/digital hybrid beamforming has been widely considered as a promising approach for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. While most hybrid beamforming techniques consider a fully-connected structure with a large number of phase shifters (PSs), the partially-connected structure has drawn more attention recently since it requires much less PSs and can further improve energy-efficiency. However, the impractical assumption of infinite or high resolution of PSs in existing solutions frustrates the real-world deployment of hybrid beamforming designs, and low- resolution PSs are typically adopted to reduce the hardware complexity and power consumption. In an effort to achieve maximum hardware efficiency, this paper focuses on the partially-connected architecture with one-bit (binary) PSs and considers the problem of joint hybrid precoder and combiner design for such mmWave MIMO systems. We propose to successively design the analog beamformers associated with each pair of sub- array, aiming at conditionally maximizing the spectral efficiency. A novel binary analog precoder and combiner optimization algorithm is proposed under a rank-1 approximation of the interference-included equivalent channel with polynomial complexity in the number of antennas. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the advantages of proposed hardware-efficiency hybrid precoder and combiner design.

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

In large-scale multiple-input multiple-output (MIMO) systems, high cost and high power consumption of RF chains typically prohibit the use of traditional baseband beamforming which requires one distinct radio-frequency (RF) chain per antenna. One possible architecture to reduce the number of RF chains is hybrid beamforming in which the overall beamformer consists of a concatenation of an analog RF beamformer implemented using phase shifters (PSs) and a low-dimensional baseband digital beamformer. However, conventional hybrid beamforming designs require high-resolution PSs, which are expensive. In this paper, we consider transceiver design for maximizing the spectral efficiency of a large-scale MIMO system with hybrid beamforming architecture where only finite-resolution PSs are available at both ends. We propose a heuristic transceiver design for the critical case where the number of RF chains is equal to the number of data streams. We show that the proposed hybrid beamforming design can achieve a rate close to that of optimal exhaustive search. We also suggest how to generalize the algorithm for the setting where the number of RF chains exceeds the number of data streams. We show that the generalized algorithm can use the extra RF chains to significantly improve the system performance in the case of low-resolution PSs.

In multiuser millimeter wave (mmWave) massive MIMO systems, hybrid beamforming (HBF) is widely applied to save power and cut down on hardware costs. Hybrid beam- forming can be divided into two stages: analog beamforming in the RF domain usually implemented with phase shifters and digital beamforming in the baseband domain. Constrained by cost, volume and production process, imperfect phase shifters have phase-shifting error and gain error. In this paper, we study the influence of phase-shifting error and gain error upon the performance of the multiuser mmWave massive MIMO systems. We derive the lower bound of the rate loss, which is tight with the large antenna arrays. Both the theoretical results and simulation results show that there is an achievable sum rate ceiling due to phase-shifting error and gain error. The analytic procedure can be applied to many HBF algorithms.

With the growth of the scale of antenna arrays, the full digital beamformer, which requires one dedicated radio frequency (RF) chain per antenna element, is costly and of power consumption. The hybrid beamformer, which offloads part of the beamformer to the analog domain via phase shifters, is an alternative to the full digital beamformer for large-scale antenna arrays. The whole hybrid beamformer is equivalent to the product of a reduced digital beamformer and an analog beamformer. Whereas, the analog beamformer subjects to a constant modulus constraint, because the phase shifters can only control the phase of signals. Due to the non-convex constant modulus constraint, the global optimal hybrid beamformer is infeasible. Therefore, a novel alternating iterative hybrid beamforming algorithm is proposed in this paper. The algorithm divided the analog beamforming matrix and digital beamforming matrix into column blocks and row blocks respectively, only one column of the analog beamforming matrix and one row of the digital beamforming matrix are updated in each processing cycle. The optimal column of the analog beamforming matrix is obtained by the partial derivative of the phase angle of the entries of the analog beamforming matrix, and the corresponding row of the reduced digital beamforming matrix is computed by the least square method. All of the columns of the analog beamforming matrix and rows of the digital beamforming matrix are updated one by one, meanwhile, the residual error of the hybrid beamformer is reduced gradually. Simulation results show that the proposed approach achieves higher achievable data rate compared with the previous works.

This paper analyzes the performance of a 5G massive MIMO network based on hybrid beamforming (HBF) on geometric channels of 3GPP TR 38.901. The evaluation is carried out through link/system-level simulations at 28 GHz (OFDM 400 MHz), BS 128 antennas, 8 UEs, HBF architecture with N_RF.∈{12,16} and a 3–4 bit phase shifter, covering both UMi street canyon and indoor office (LOS/NLOS) scenarios. Channel estimation and precoder/combiner design follow the framework of compressed/hierarchical beam training and factorization of fully digital solutions; each configuration is evaluated on ≥1000 Monte Carlo drops. Results show that HBF (N_RF=16, 4-bit) achieves ≈90–95% sum spectral efficiency (SE) over fully digital with an average gap of 3–6 bps/Hz, while N_RF=12 reduces SE by an additional ~1–2 bps/Hz but provides the highest energy efficiency (EE). Compared to fully digital, HBF improves EE by ~4–5× due to the RF-chain reduction despite a slight SE decrease. Robustness tests against angle misestimation show SE degradation slopes of approximately 0.5%/° (fully digital), ~1.2%/° (4-bit HBF), and ~1.6%/° (3-bit HBF). The differences between scenarios highlight the sensitivity of HBFs to wider angular spread indoors. Overall, HBFs with ≥4-bit and N_RF selection proportional to the number of user flows provide the best SE-EE compromise, making them feasible for 5G mmWave implementations. Future directions include beam-squint mitigation in broadband, more detailed hardware impairment modeling, and robust machine learning-based HBF design.

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.

The recent studies on hybrid beamformers with a combination of switches and phase shifters indicate that such methods can reduce the cost and power consumption of massive multiple-input multiple-output (MIMO) systems. However, most of the works have focused on the scenarios with frequency-flat channel models. This letter proposes an effective approach for such systems in frequency-selective channels and presents the closed-form expressions of the beamformer and the corresponding sum-rates. Compared to the traditional subconnected structures, our approach with a significantly smaller number of phase shifters results in a promising performance.

In the context of a massive MIMO system, the number of RF chains increases prohibitively. To solve the problem, hybrid beamforming (BF) has been proposed. Nevertheless, the amplitude constraint of the analog part in hybrid BF limits achieving the optimal performance given by the optimal digital BF. Some studies have been conducted recently to relax the amplitude constraint by deploying multiple phase shifters and switches (that select phase shifters) in the hybrid beamforming structures. The tremendous number of switches in the structure makes the problem of switch state selection very complex. In this paper, we propose an efficient low-complexity algorithm to obtain the optimal switch states. In the proposed algorithm, we decompose the main problem into sub-ones to reduce the complexity and solve the problem using a finite search space. The results of our study suggest that the optimal solution will be attainable by searching through less than 1% of the search space, if the number of phase levels exceeds 14. Moreover, the proposed algorithm has the potential to perfectly implement linear beamformers, designed for fully digital architectures, in a hybrid structure, while applying the minimum number of RF chains. Finally, the simulation results prove the efficiency of the algorithm and confirm the sufficiency of the analog part of the BF to achieve the optimal digital BF performance just by applying a few number of fixed phase shifters.

The study of hybrid precoder has been focused on the application of the low-resolution phase shifters (PSs) since they can reduce the power consumption of RF chains. In this paper a heuristic iteration algorithm is proposed to design hybrid precoder and combiner in mmWave massive MIMO systems to improve the PSs quantization performance using MMSE criterion. This heuristic algorithm first builds a cost function, which can continuously converge to the optimal solution of the quantized shifter phases with constant modulus constraints. Simulation results show that the proposed design of hybrid precoder can achieve significant performance with low-complexity in mmWave massive MIMO systems.

Self-interference suppression improvement by employing circular polarized antennas

In this paper, we delve into the maximized spectral efficiency (SE) of millimeter-wave (mmWave) multicell multiuser massive MIMO Systems for uplink transmission with low-resolution phase shifters (LRPSs). Millimeter-wave massive multiple-input multiple-output (mMIMO) is an important technology for upcoming cellular networks which will provide higher bandwidth and throughput than current wireless systems and networks. LRPSs are commonly used to minimize power consumption, maximize spectral efficiency and diminish the complexity of hybrid precoder and combiner. In this paper, we consider a hybrid analog-digital precoder and combiner design with LRPSs for mmWave multi-cell multiuser mMIMO systems for uplink transmission to spectral efficiency in terms of iterations. The proposed technique outperforms when compared to traditional optimization approaches concerning spectral efficiency and bit error rate (BER). We show through simulation results that our designs with LRPSs outperform standard iteration procedures.

The mmWave cell-free massive MIMO (CFmMIMO), combining the advantages of wide bandwidth in the mmWave frequency band and the high- and uniform-spectral efficiency of CFmMIMO, has recently emerged as one of the enabling technologies for 6G. In this paper, we propose a novel framework for energy-efficient mmWave CFmMIMO systems that uses low-resolution digital–analog converters (DACs) and phase shifters (PSs) to introduce lowcomplexity hybrid precoding. Additionally, we propose a heuristic pilot allocation scheme that makes the best effort to slash some interference from copilot users. The simulation results show that the proposed hybrid precoding and pilot allocation scheme outperforms the existing schemes. Furthermore, we reveal the relationship between the energy and spectral efficiencies for the proposed mmWave CFmMIMO system by modeling the whole network power consumption and observe that the introduction of low-resolution DACs and PSs is effective in increasing the energy efficiency by compromising the spectral efficiency and the network power consumption.

SummaryThe advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.