This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several paths that contribute to EE maximization from all the cascaded paths and define them as dominant paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected dominant paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

The emerging Orthogonal Time Frequency Space (OTFS) technique can achieve stable communication in high-speed mobile scenarios, which combining with multiple-input multiple-output (MIMO) can improve the spectrum and energy efficiency of wireless communication systems. To achieve potential performance gains of OTFS-MIMO systems, this paper considers the precoding design for wideband OTFS-MIMO systems with beam squint effect. We propose a novel precoding structure with compensation modules for eliminating the influence of beam squint and further improving the achievable rate. The simulation results prove that the achievable rate of the wideband OTFS-MIMO system with beam squint has been significantly improved under our proposed scheme.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for improving the spectral- and energy-efficiency of millimeter-wave (mmWave) Internet of Vehicles (IoV) communications, but the conception of their accurate channel estimation poses. Hence, the existing estimation methods mainly focus on time-invariant channels, while ignoring the Doppler effect induced by the high-velocity vehicles, which will lead to significant performance degradation. In this article, we investigate the problem of channel estimation in RIS-aided mmWave IoV systems considering the deleterious Doppler effect. First, we derive the expression of the time-varying cascaded two-hop multiple-path channels, where each delay tap is subject to multiple paths instead of having a simple one-to-one correspondence. In order to decouple the paths, the problem is formulated in the delay-domain by a series of transformations and the cascaded two-hop channel can be estimated at each delay tap. Then, we propose a pair of estimation strategies by considering different hardware constraints depending on the number of receiver antennas at the base station (BS). When a large receiver array is employed at the BS, we can exploit its high angular selectivity for distinguishing each resolvable path at a certain delay tap because they arrive from different directions. However, this cannot be achieved for small arrays, given their more limited angular resolution. Thus, the RIS reflection patterns are delicately designed for distinguishing multiple resolvable paths. After separating the paths, Doppler estimation can be performed by calculating the phase difference of the adjacent symbols. Our simulation results demonstrate the superior performance of the proposed methods within a wide range of Doppler shifts.

Beamforming design with partial channel estimation and feedback for frequency-division duplexing (FDD) reconfigurable intelligent surface (RIS) assisted systems is considered in this paper. We leverage the observation that path angle information (PAI) varies more slowly than path gain information (PGI). Then, several dominant paths are selected among all the cascaded paths according to the known PAI for maximizing the spectral efficiency of downlink data transmission. To acquire the dominating path gain information (DPGI, also regarded as the path gains of selected dominant paths) at the base station (BS), we propose a DPGI estimation and feedback scheme by jointly beamforming design at BS and RIS. Both the required number of downlink pilot signals and the length of uplink feedback vector are reduced to the number of dominant paths, and thus we achieve a great reduction of the pilot overhead and feedback overhead. Furthermore, we optimize the active BS beamformer and passive RIS beamformer by exploiting the feedback DPGI to further improve the spectral efficiency. From numerical results, we demonstrate the superiority of our proposed algorithms over the conventional schemes.

Reconfigurable intelligent surface (RIS) is able to enhance the capacity of wireless communication systems with low overhead. Extremely large (XL)-RIS-aided millimeter-wave (mmWave) communication has become a promising key technique for future 6-th Generation (6G) systems. The performance gain brought in by XL-RIS relies on the accurate channel state information (CSI). However, channel estimation requires huge training overhead and high computational complexity due to the XL number of passive elements at RIS. Moreover, the unknown visual region (VR) infomation caused by the sensitivity of mmWave signal to random blockages makes the channel estimation more difficult. In this paper, we consider the channel estmation for XL-RIS-aided mmWave uplink system. We firstly model the XL-RIS-aided channel as a hybrid one composed of near-field RIS-to-user channel and far-field RIS-to-base station (BS) channel, where the VR issue of XL-RIS has been taken into consideration. Then we formulate the channel estimation problem as a sparse recovery problem. To solve this problem, we propose a two-stage algorithm for joint channel estimation and VR detection. Finally numerical results show that the proposed algorithms outperform the existing benchmark schemes in terms of normalized mean-squared error (NMSE) due to the VR detection and the utilization of shift common-support property among sub-channels.

In order to reduce computational complexity and hardware cost for reconfigurable intelligent surface (RIS)-aided multiple-input–multiple-output (MIMO) systems, in this article, the subarray partition algorithm designs at RIS are investigated. Without instantaneous channel state information (CSI) of the RIS-related links, the subarray partition algorithms aim at minimizing the number of subarrays while keeping a minimum sum rate requirement. In nature, the subarray partition optimization problem is a combinatorial optimization and NP-hard because of many discrete optimization variables. Three kinds of subarray partition algorithms are proposed. The first one is named as a fixed pattern subarray partition algorithm, in which subarray is arranged in a predefined manner. This algorithm is easy to implement but its performance is far from optimal. To reap the benefits of RIS as much as possible, two dynamic pattern subarray partition algorithms are given as well. The first dynamic pattern algorithm is the greedy dynamic pattern subarray partition algorithm that is more complicated than the fixed pattern one but benefits much better performance. To reduce complexity, the relaxation-based dynamic pattern algorithm is given, which has almost the same performance as the greedy dynamic algorithm but has a much lower complexity. At the end of the whole work, numerical results are given to access the performance of the proposed algorithms.

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and low power consumption. In this article, we propose an RIS-assisted mmWave point-to-point system relying on dynamically configured subarray connected hybrid beamforming structures. More explicitly, an energy-efficient analog beamformer relying on the twin-resolution phase shifters is proposed. Then, we conceive a successive interference cancelation (SIC)-based method for jointly designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized for maximizing each subarray’s rate. Furthermore, a greedy method is proposed for determining the phase shifter configuration of each subarray. We then propose to update the RIS elements relying on a complex circle manifold (CCM)-based method. The proposed dynamic subconnected structure as well as the proposed joint hybrid and passive beamforming method strike an attractive tradeoff between the bandwidth efficiency and power consumption. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.

Wireless powered sensor networks (WPSNs) have evolved as a promising paradigm for energy-efficient communications. Recently, the proliferation of reconfigurable intelligent surface (RIS) has further been envisioned as a cost-effective solution for improving wireless power transfer (WPT) efficiency. In this paper, from the practical perspective of balancing the network sustainability and reliability, we consider a RIS-enhanced partially WPSN that composed of wireless-powered energy receivers (ERs) and battery-powered information receivers (IRs). Assuming the partially WPSN operates in time division multiple access (TDMA) mode, the joint optimization of covariance matrices, downlink/uplink (DL/UL) time allocation and RIS reflecting coefficients are investigated under the minimum DL rate constraint among all IRs for maximizing the achievable UL sum rate. Specifically, the single-IR single-ER (SISE) case is first studied based on the assumption of separate DL/UL RIS reflecting coefficients, in which an alternating optimization algorithm is proposed with semi-closed-form optimal solutions. In order to reduce the hardware overhead and signal processing complexity, we also investigate the case of identical DL/UL RIS reflecting coefficients, in which an iterative optimization algorithm is developed to tackle the coupled DL/UL transmissions. Then, we extend our work to the multiple-IRs multiple-ERs (MIME) case, where both the optimization problems corresponding to separate and identical DL/UL RIS reflecting schemes become more challenging to solve. To circumvent this intractability, we propose a successive convex relaxation (SCA) based alternating optimization algorithm and a low-complexity two-step algorithm. Finally, numerical results demonstrate the superior UL sum rate performance of our proposed algorithms over the adopted benchmarks.

Hybrid analog/digital precoding in millimeter-wave (mmWave) multi-input multi-ouput (MIMO) systems is capable of achieving the near-optimal full-digital performance at reduced hardware cost and power consumption compared to its full-RF digital counterpart. However, having numerous phase shifters is still costly, especially when the phase shifters are of high resolution. In this paper, we propose a novel twin-resolution phase-shifter network for mmWave MIMO systems, which reduces the power consumption of an entirely high-resolution network, whilst mitigating the severe array gain reduction of an entirely low-resolution network. The connections between the twin phase shifters having different resolutions and the antennas are either fixed or dynamically configured. In the latter, we jointly design the phase-shifter network and the hybrid precoding matrix, where the phase of each entry in the analog precoding matrix can be dynamically designed according to the required resolution. This method is slightly modified for the fixed network's hybrid precoding matrix. Furthermore, we extend the proposed method to multi-user MIMO systems and provide its performance analysis. Our simulation results show that the proposed dynamic hybrid precoding method strikes an attractive performance vs. power consumption trade-off.

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy efficiency of wideband communication systems by using low-cost passive elements for reflecting the impinging signals with adjustable phase shifts. To realize the full potential of IRS-aided systems, having accurate channel state information (CSI) is indispensable, but it is challenging to acquire, since these passive devices cannot carry out transmit/receive signal processing. The existing channel estimation methods conceived for wideband IRS-aided communication systems only consider the channel’s frequency selectivity, but ignore the effect of beam squint, despite its severe performance degradation. Hence we fill this gap and conceive wideband channel estimation for IRS-aided communication systems by explicitly taking the effect of beam squint into consideration. We demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded two-hop channel reflected by the IRS has two peaks, which leads to a pair of estimated angles for a single propagation path, due to the effect of beam squint. One of these two estimated angles is the frequency-independent ‘actual angle’, while the other one is the frequency-dependent ‘false angle’. To reduce the influence of false angles on channel estimation, we propose a twin-stage orthogonal matching pursuit (TS-OMP) algorithm, where the path angles of the cascaded two-hop channel reflected by the IRS are obtained in the first stage, while the propagation gains and delays are obtained in the second stage. Moreover, we propose a bespoke pilot design by exploiting the specific the characteristics of the mutual correlation function and cross-entropy theory for achieving an improved channel estimation performance. Our simulation results demonstrate the superiority of the proposed channel estimation algorithm and pilot design over their conventional counterparts.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Channel feedback is essential for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems to realize precoding and power allocation. Traditional codebooks for channel feedback, where the required number of feedback bits is proportional to the number of base station (BS) antennas, can not scale up with massive MIMO due to the large number of BS antennas. To solve this problem, in this paper, we propose an angle-of-departure (AoD) adaptive subspace codebook to reduce the codebook size and feedback overhead. Specifically, by leveraging the concept of angle coherence time, which implies that the path AoDs vary much slower than path gains, we propose an AoD-adaptive subspace codebook to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed AoD-adaptive subspace codebook, where we prove that the required number of feedback bits only scales linearly with the number of resolvable AoDs, which is much smaller than the number of BS antennas. This quantitative result is also verified by simulations.

Heterogeneous networks (HetNets), which involve densely deployed femtocells underlaid traditional macrocell network, is a promising solution to the extremely high data rate requirements of the future 5G communications. In this paper, we analyze the closed-form optimal deployment of femtocells in HetNets to maximize the network throughput under the outage constraints from both macrocells and femtocells. Specifically, we model the random distribution of macro cell users (MUEs) and femtocell base stations (FBSs) as Poisson Point Processes (PPPs). Then, the closed form expressions for outage probabilities in both uplink and downlink transmissions are derived. Further, we study the network throughput maximization problem under the outage probability constraints. Finally, With the help of convex optimization, the interval of FBS density, which contains the maximum network throughput is obtained in closed form. Simulation results validate the impact of the system parameters on the different optimal FBS density as well as the influence of interference to the maximum network throughput.

Massive MIMO has become a promising key technology for future 5G wireless communications to increase the channel capacity and link reliability. However, with greatly increased number of transmit antennas at the base station (BS) in massive MIMO systems, the pilot overhead for accurate acquisition of channel state information (CSI) will be prohibitively high. To address this issue, we propose a block iterative support detection (block-ISD) based algorithm for channel estimation to reduce the pilot overhead. The proposed block-ISD algorithm fully exploits the block sparsity inherent in the block-sparse equivalent channel impulse response (CIR) generated by considering the spatial correlations of MIMO channels. Furthermore, unlike conventional greedy compressive sensing (CS) algorithms that rely on prior knowledge of the channel sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 87.5% pilot overhead than conventional channel estimation techniques.

Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than standard cyclic prefix OFDM (CP- OFDM), which is achieved by using a known pseudorandom noise (PN) sequence to replace the classical CP. However, due to the interference between the PN sequence and the data block, the performance of TDS-OFDM degrades severely over fast fading channels. To solve this problem, based on the distributed compressive sensing (DCS) theory, we propose an efficient way to realize simultaneous multi-channel reconstruction, which is achieved by using the inter-block-interference (IBI)-free region to reconstruct the high-dimensional sparse multipath channel. Specifically, we propose to utilize the temporal correlation of wireless channels as well as the channel property that path gains change much faster than path delays to simultaneously reconstruct multiple sparse channels. Then, we propose the parameterized channel estimation method based on simultaneous compressive sampling matching pursuit (S-CoSaMP) algorithm to achieve better channel estimation performance in fast time-varying channels. Simulation results demonstrate that the proposed scheme can achieve improved performance than conventional solutions.

For massive MIMO system with hundreds of antennas at the base station (BS), zero forcing (ZF) precoding can achieve the near-optimal capacity due to the asymptotically orthogonal channel, but it involves complicated matrix inversion of large size. In this paper, we propose a Richardson Method (RM) based precoding to avoid the complicated matrix inversion in an iterative way, which can reduce the complexity by one order of magnitude. We also prove that the optimal relaxation parameter to RM can be approached by a simple and quantified value to maximize the convergence rate of RM-based precoding, which only depends on the number of BS antennas and the number of users. Simulation results show that RM-based precoding can achieve the near-optimal performance of ZF precoding with only a small number of iterations.

To fully exploit advantages of massive MIMO, channel state information at the transmitter (CSIT) is essential to obtain the system performance gains. By far, both channel estimation and channel feedback have been proposed for FDD massive MIMO by exploiting the sparsity of CSI, but they are usually separately discussed, which may impair the CSIT acquisition performance and lead to unnecessary complex computation for users. In this paper, we propose the structured-CS based differential CSIT acquisition scheme for massive MIMO systems, where the downlink channel training and uplink channel feedback are jointly considered. Specifically, we first exploit the temporal correlation of time- varying channels to propose the differential CSIT acquisition scheme, which can reduce both the overhead for downlink training and uplink feedback. Then, we propose the structured compressive sampling matching pursuit (S-CoSaMP) algorithm to further reduce overhead by leveraging the structured sparsity of wireless MIMO channels. Moreover, the proposed differential operation and S-CoSaMP can also be used at users for better channel estimation performance if channel state information at the receiver is needed. Simulation results have demonstrated that the proposed scheme can achieve better CSIT acquisition performance than its counterparts.

Conventional linear precoding schemes in massive multiple-input-multiple-output (MIMO) systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we propose a successive overrelaxation (SOR)-based precoding scheme to approximate the matrix inversion by exploiting the asymptotically orthogonal channel property in massive MIMO systems. The proposed SOR- based precoding can reduce the complexity by about one order of magnitude, and it can also approach the classical RZF precoding with negligible performance loss. We also prove that the proposed SOR-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. In addition, to guarantee the performance of SOR-based precoding, we propose a simple way to choose the optimal relaxation parameter in practical massive MIMO systems. Simulation results verify the advantages of SOR-based precoding in convergence rate and computational complexity in typical massive MIMO configurations.