Hybrid beamforming structure is widely used in mmWave massive MIMO due to the low hardware complexity. However, its performance is severely degraded by beam squint effects in wideband multiuser mmWave massive MIMO resulting from the non-frequency-specific analog beamforming. To overcome this issue, we develop an integrated user grouping, subcarrier allocation, and hybrid beamforming (IUSH) scheme to compensate for the beam squint effects and maximize the multiuser sum-rate. First, we investigate the time-division and frequency-division analog beamforming for the single radio frequency (RF) chain cases to gain useful insights regarding user grouping and subcarrier allocation. The frequency-division approach achieves superior performance by leveraging the correlations between analog beamforming and user channels, and utilizing subcarrier allocation to dynamically align them. To this end, for multiple RF chain cases, we propose a wideband user grouping strategy that clusters users with close physical channel angles to strengthen the correlations, thereby facilitating efficient subcarrier allocation among users. Then, we develop an alternating minimization-based joint subcarrier allocation and hybrid beamforming (AM-JSH) algorithm to maximize the sum-rate of grouped users. The AM-JSH algorithm alternates between subcarrier allocation and hybrid beamforming, where the subcarrier allocation is formulated as an assignment problem to be solved using the classic Hungarian algorithm, and the hybrid beamforming is implemented with the developed penalty-based two-loop hybrid beamforming algorithm. Simulation results show that the developed IUSH scheme significantly outperforms the existing ones.

Pinching antennas (PAs), a special class of leaky-wave antennas (LWAs), have recently emerged as a promising technology to mitigate blockage and reduce large-scale path loss. However, PAs suffer from performance limitations due to their passive radiation characteristics and mechanical actuation. To overcome these limitations, we introduce reconfigurable LWAs (RLWAs), which enable RLWA beamforming through electronic control of both radiation amplitudes and phases. By integrating RLWAs into the existing PA systems (PASS), we propose a hybrid PASS (H-PASS) architecture, which combines mechanically reconfigurable PA beamforming with electronically reconfigurable RLWA beamforming. Given that PAs in H-PASS can be deployed in discrete-position or continuous-position manners, H-PASS comes in two variants accordingly, and we formulate weighted sum-rate maximization problems for them, respectively. For the discrete-position case, we propose a penalty-based two-loop joint analog and digital beamforming algorithm. For the continuous-position case, we propose an alternating-minimization-based joint position optimization and beamforming algorithm. Simulation results demonstrate that H-PASS can increase the sum-rate of the existing PASS by up to 33%, reduce the performance loss caused by phase quantization in discrete-position PAs by up to 69%, and mitigate the performance loss caused by position errors in continuous-position PAs by up to 90%. Overall, H-PASS significantly improves the performance of existing PASS by leveraging the strengths of RLWAs while mitigating the limitations of PAs.

In this paper, we investigate near-field wideband channel estimation with block sparsity. First, we propose an on-grid total variation-regularized block sparse Bayesian learning (TV-BSBL) algorithm. By constructing sparse representations of near-field wideband channels, we show that the sparse coefficient vectors exhibit both common sparsity across subcarriers and block sparsity in the surrogate distance-angle domain. To promote common sparsity, a Gamma prior combined with subcarrier-adaptive factors is utilized. To encourage block sparsity, TV regularization is incorporated into the prior model. The channel estimation is formulated as a maximum a posteriori (MAP) problem and solved via an expectation-maximization (EM) algorithm. Then, in the M-step, we further propose a primal-dual hybrid gradient-based signal hyperparameter update algorithm, which admits simple primal and dual updates and guarantees convergence to the global optimum. Moreover, we propose an off-grid TV-BSBL algorithm for near-field wideband channel estimation. We introduce additional variables to characterize the deviations between the true channel parameters and their quantized grids. These deviation variables are then integrated into the MAP estimation framework and refined via gradient descent. Simulation results demonstrate that the proposed on-grid and off-grid TV-BSBL algorithms achieve superior estimation performance under various conditions.

The hybrid beamforming structure is widely used in terahertz (THz) communications due to the low hardware complexity. However, its performance is severely degraded by beam squint effects in wideband THz communications resulting from the non-frequency-specific analog beamforming. To overcome this issue, in this paper, we investigate THz multiuser communications and propose a joint subcarrier allocation and hybrid beamforming (JSH) algorithm to compensate for the beam squint effects and maximize the sum-rate of users. The JSH algorithm alternates between subcarrier allocation and hybrid beamforming. Given the hybrid beamforming, the subcarrier allocation is formulated as an assignment problem and solved using the classic Hungarian algorithm. Given the subcarrier allocation, the hybrid beamforming is solved by developing a penalty-based two-loop hybrid beamforming algorithm. Simulation results show that the proposed JSH algorithm can effectively compensate for the beam squint effects and outperforms the existing ones in terms of the multiuser sum-rate.

This paper proposes a dual-band reconfigurable antenna array (DBRAA), enabling wireless capabilities in both sub-6 GHz (sub-6G) and millimeter wave (mmWave) bands using a single array. For the sub-6G band, we propose a reconfigurable antenna selection structure, where each sub-6G antenna is formed by multiplexing several mmWave antennas, with its position dynamically adjusted using PIN diodes. For the mmWave band, we develop a reconfigurable hybrid beamforming structure that connects radio frequency chains to the antennas via phase shifters and a reconfigurable switch network. We then investigate integrated sensing and communications (ISAC) in sub-6G and mmWave bands using the proposed DBRAA and formulate a dual-band ISAC beamforming design problem. This problem aims at maximizing the mmWave communication sum-rate subject to the constraints of sub-6G communication quality of service and sensing beamforming gain requirements. The dual-band ISAC beamforming design is decoupled into sub-6G beamforming design and mmWave beamforming design. For the sub-6G beamforming design, we develop a fast search-based joint beamforming and antenna selection algorithm. For the mmWave beamforming design, we develop an alternating direction method of multipliers-based reconfigurable hybrid beamforming algorithm. Simulation results demonstrate the effectiveness of the proposed methods.

This paper investigates reconfigurable pixel antenna (RPA)-based electronic movable antennas (REMAs) for multiuser communications. First, we model each REMA as an antenna with a set of predefined and discrete selectable radiation positions within the radiating region. Considering the trade-off between performance and cost, we propose partially-connected and fully-connected RPA-based electronic movable-antenna arrays (PC/FC-REMAA). Then, we formulate a multiuser sum-rate maximization problem subject to the power and hardware constraints of the PC/FC-REMAA. To solve this problem, we propose a two-step multiuser beamforming and antenna selection scheme. In addition, we revisit mechanical movable antennas (MMAs) to establish a benchmark for evaluating the performance of REMA-enabled multiuser communications. Finally, we analyze the performance gap between REMAs and MMAs. Based on Fourier analysis, we derive the maximum power loss of REMAs compared to MMAs for any given position interval. Specifically, we show that REMAs lose at most 3.25% power relative to MMAs when the position interval is one-tenth of the wavelength. Simulation results demonstrate the effectiveness of the proposed methods.

This paper investigates an integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS). Our simultaneous beam training and target sensing (SBTTS) scheme enables the base station to perform beam training with the user terminals (UTs) and the RIS, and simultaneously to sense the targets. Based on our findings, the energy of the echoes from the RIS is accumulated in the angle-delay domain while that from the targets is accumulated in the Doppler-delay domain. The SBTTS scheme can distinguish the RIS from the targets with the mixed echoes from the RIS and the targets. Then we propose a positioning and array orientation estimation (PAOE) scheme for both the line-of-sight channels and the non-line-of-sight channels based on the beam training results of SBTTS by developing a low-complexity two-dimensional fast search algorithm. Based on the SBTTS and PAOE schemes, we further compute the angle-of-arrival and angle-of-departure for the channels between the RIS and the UTs by exploiting the geometry relationship to accomplish the beam alignment of the ISAC system. Simulation results verify the effectiveness of the proposed schemes.

This paper investigates beam training for extremely large-scale multiple-input multiple-output systems. By considering both the near field and far field, a triple-refined hybrid-field beam training scheme is proposed, where high-accuracy estimates of channel parameters are obtained through three steps of progressive beam refinement. First, the hybrid-field beam gain (HFBG)-based first refinement method is developed. Based on the analysis of the HFBG, the first-refinement codebook is designed and the beam training is performed accordingly to narrow down the potential region of the channel path. Then, the maximum likelihood (ML)-based and principle of stationary phase (PSP)-based second refinement methods are developed. By exploiting the measurements of the beam training, the ML is used to estimate the channel parameters. To avoid the high computational complexity of ML, closed-form estimates of the channel parameters are derived according to the PSP. Moreover, the Gaussian approximation (GA)-based third refinement method is developed. The hybrid-field neighboring search is first performed to identify the potential region of the main lobe of the channel steering vector. Afterwards, by applying the GA, a least-squares estimator is developed to obtain the high-accuracy channel parameter estimation. Simulation results verify the effectiveness of the proposed scheme.

In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

Beam refinement is a key technology to overcome the problem of limited resolution in beam training. However, most existing works on beam refinement are not suitable for the emerging extremely large-scale multiple-input-multiple-output (XL-MIMO) due to the differences in the channel characteristics. To fill in the gap, in this paper, beam refinement for XL-MIMO systems is investigated. Inspired by the similarities between the Taylor series of the Gaussian function and that of the beam gain, we propose to approximate the beam gain by the Gaussian function. Then, a low-complexity beam refinement based on the Gaussian approximation (BRGA) scheme, which quantizes the narrowed intervals after beam training into several samples and performs additional channel tests on the quantized grids, is proposed to improve the estimation accuracy of the beam training. Based on the measurements in the beam refinement stage, the BRGA-based least square (BRGA-LS) estimator is developed for high-resolution channel parameter estimation. To avoid the noise amplification effects of the BRGA-LS, the BRGA-based weighted least square (BRGA-WLS) estimator is further developed. Simulation results verify the effectiveness of the proposed scheme and show that the proposed BRGA scheme can greatly improve the accuracy of beam training with only a few additional channel tests.

In this paper, simultaneous beam training and target sensing for reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems is investigated. The sensing ability of base station (BS) is exploited to acquire the channel state information of the RIS-aided ISAC. Based on our findings that the energy of the echoes from the RIS can be accumulated in the angle-delay domain while the energy of the echoes from the targets can be accumulated in the Doppler-delay domain, we can distinguish the RIS from the targets. Then we propose a simultaneous beam training and target sensing scheme, which enables the BS to perform the beam training with the RIS and to sense the targets simultaneously based on the mixed echoes from the RIS and the targets, and also enables the user equipment (UE) to perform collaborative sensing to figure out their position. Moreover, the beam alignment between the BS and the UE via the RIS can be directly computed without additional beam training overhead. Simulation results verify the effectiveness of the proposed scheme and show that it outperforms the existing schemes with much smaller training overhead, which benefits from the integration of the sensing units.

In this paper, radar-aided multiuser beam tracking and target detection are investigated for integrated sensing and communication (ISAC). A multiuser beam tracking scheme based on the collaboration of radar sensing and uplink communication is proposed. It is first proved that the echoes of each communication signal can be extracted from the mixed echoes of multiple communication signals, by performing point-wise division and two-dimensional discrete Fourier transform. Based on it, the scheme enables the road side unit (RSU) to perform multiuser beam tracking and target detection with only two radio frequency chains, no matter how many users are served by the RSU. To distinguish the users from the targets and further improve the beam tracking by extended Kalman filtering, uplink pilots from the users to the RSU are employed. Then the digital beamformer can be designed to mitigate the multiuser interference. Simulation results show that the proposed scheme outperforms the conventional beam tracking scheme and can approach the performance of independent beam tracking of each user.

In this paper we investigate the uplink collaborative sensing in the mmWave MIMO integrated sensing and communication system. Since the vehicles periodically transmit pilot sequences to the road side unit (RSU) and the pilot sequences are a priori knowledge of the RSU, the RSU can perform collaborative sensing and parameter estimation for the vehicles. A two-dimensional fast Fourier transform (FFT) and estimating-signal-parameter-via-rotational-invariance-techniques (ESPRIT)-based scheme is proposed for parameter estimation in the RSU, where the FFT is used to estimate the distance and relative radial velocity, and the ESPRIT is used to estimate the angle-of-arrival and angle-of-departure of the channel line-of-sight path. Then these estimated parameters are exploited by an extended Kalman filtering model to achieve the efficient beam tracking. Simulation results verify the effectiveness of the proposed scheme and show that it can save the estimation resources while ensuring the estimation accuracy.

In this letter, we conduct an insightful study on protograph-low-density parity-check (PLDPC)-code-assisted deterministic measurement matrices for compressed sensing applications. As is well known, the recovery performance of conventional PLDPC sparse matrices (PLDPC-SM) will be dramatically degraded as the ratio of N to M increases, where M × N is the size of the matrices. To address the above issue, we propose a novel construction method to formulate a class of extended PLDPC-SM (EPLDPCSM) by intelligently inserting part of Hadamard matrices into the conventional PLDPC-SM. The proposed EPLDPC-SM not only can realize more flexible sizes with respect to the existing counterparts, but also can be amenable to lower coherence without costing more storage resources. Both coherence analyses and experiment results demonstrate that the proposed EPLDPC-SM are superior to the well-performing deterministic measurement matrices (i.e., PLDPCSM) and random matrices (i.e., random Gaussian matrices (R-GM) and random sparse matrices (R-SBM)) for various values of N/M1.

In this article, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneously performs beam training with multiple user equipments (UEs). For the UEs, an alternative minimization method with a closed-form expression (AMCF) is proposed to design the hierarchical codebook under the constant modulus constraint. To speed up the convergence of the AMCF, an initialization method based on Zadoff-Chu sequence is proposed. For the BS, a simultaneous multiuser beam training scheme based on an adaptively designed hierarchical codebook is proposed, where the codewords in the current layer of the codebook are designed according to the beam training results of the previous layer. The codewords at the BS are designed with multiple mainlobes, each covering a spatial region for one or more UEs. Simulation results verify the effectiveness of the proposed hierarchical codebook design schemes and show that the proposed multiuser beam training scheme can approach the performance of the beam sweeping but with significantly reduced beam training overhead.

In this paper, a simultaneous multiuser hierarchical beam training scheme for multiuser mmWave massive MIMO systems is proposed based on the designed adaptive hierarchical codebook. Different from the existing work sequentially performing the beam training for different users with the same hierarchical codebook, in our work the hierarchical codebook is designed in an adaptive manner, where the codewords in the current layer are designed according to the beam training results of the previous layer. In particular, multi-mainlobe codewords are designed for simultaneously beam training with all the users, where each mainlobe of the multi-mainlobe codeword covers a spatial region that one or more users are probably in. Except for the bottom layer, there are only two codewords at each layer in the designed adaptive hierarchical codebook, which only requires two times of simultaneous beam training for all the users no matter how many users the BS serves. Simulation results verify the effectiveness of our scheme and show that our scheme can approach the performance of the beam scanning but with considerable reduction in training overhead.

In this paper, beam design for millimeter wave (mmWave) massive MIMO systems is studied regarding quantized phase shifters and different number of RF chains. Given the objective beam, the beam design problem is formulated as a hybrid optimization problem involving continuous variables as well as discrete variables. To reduce the difficulty in solving this problem, it is converted into several discrete optimization subproblems. Then beam design methods are proposed for the system with only one RF chain and two RF chains, respectively. For the general system with more than two RF chains, the parameter estimation is incorporated into the random search. Based on these findings, a partial random search (PRS) based beam design algorithm is proposed. To further improve the convergence speed, a fast search (FS) based beam design algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithms and show that the beam pattern using the proposed PRS and FS based algorithm can well approach the objective beam with much less RF chains than OMP.