This paper investigates channel estimation for holographic MIMO (HMIMO) systems that achieve continuous electromagnetic aperture synthesis through ultra-dense configurations of antenna elements within constrained spatial dimensions. We aim to exploit the inherent sparsity characteristics of the HMIMO generated by ultra-dense antenna configurations, thereby reducing the error and complexity caused by the large number of antennas in channel estimation process. Unlike previous algorithms that rely solely on sparsity, the proposed algorithm considers the block sparse structure of the channel, thereby yielding more accurate channel estimation performances. Specifically, we first establish the block sparse representation in the wavenumber domain of the channel for HMIMO systems. Subsequently, the channel estimation problem is formulated as the block sparse matrix recovery problem, incorporating an l2,1norm term in the objective function to promote block sparse structure. The block sparse-based gradient descent (BSBGD) algorithm is proposed to solve the formulated problem. Simulation results show that channel estimation results obtained by the proposed algorithm are more accurate.

In this article, we propose a hybrid-field-aware two-stage beamforming (HFA-TSB) scheme for extremely large-scale MIMO (XL-MIMO) systems. The HFA-TSB scheme can reduce the high pilot overhead associated with channel estimation by leveraging statistical channel state information (SCSI). Utilizing our derived ergodic spectral efficiency (SE), which relies solely on SCSI, we can eliminate the interference resulting from the nonorthogonality of near-field polar domain codewords. Specifically, the HFA-TSB scheme comprises two stages: 1) pre-beamforming and 2) precoding. In the pre-beamforming stage, we use SCSI to design the pre-beamforming matrix, focusing on eliminating interference among asymptotically orthogonal channels. This can establish an equivalent reduced-dimensional channel matrix, which can be estimated using less pilot overhead. Additionally, to address the interference caused by the nonorthogonality of near-field codewords, we derived an ergodic SE that relies solely on SCSI. Using this as an optimization objective, the pre-beamforming matrix design problem is formulated as a 0-1 integer nonlinear programming problem, which is challenging to solve. To address this, we propose a constraint-driven two-phase greedy beam selection algorithm for identifying effective beams. In the precoding stage, we design the precoder based on the estimated equivalent sparse channel to effectively mitigate any residual interuser interference. Simulations validate the superior performance of our proposed HFA-TSB scheme in terms of net SE.

As a potential technology to support the extreme requirements of next-generation communications, holographic multiple-input multiple-output (H-MIMO) refers to arrays with a large number of individually controlled and densely deployed antennas. An important challenge to realize this innovative concept is the need to integrate information theory, electromagnetic theory and circuit theory in modeling, which is also required for electromagnetic information theory (EIT). This paper aims to connect information theory and electromagnetic theory using circuit theory to provide electromagnetically consistent and mutual coupling-aware near-field communication models for H-MIMO systems. Specifically, first, the considered H-MIMO system model is given and represented as a linear multiport network in circuit theory. Unlike the mainstream models that only consider the wireless propagation environment, the circuit-based model is physically consistent and considers the radio frequency components, mutual coupling between different antenna elements, noise, etc. Second, the mutual coupling and propagation effects are represented in the circuit model by the impedance, which requires relating the quantities of the circuit to the fields generated by the antenna. The relationship between the quantities of the circuit and the fields generated by the antenna is given according to the reciprocity and reaction theorems. Furthermore, the method of moments (a computational electromagnetic method) is introduced to calculate the impedance in the line-of-sight case. Finally, the integration of the physical model with the digital model provides a framework for the design of H-MIMO. In the numerical simulations, the H-MIMO performance is provided using the proposed model.

This paper investigates hybrid-field channel estimation for extremely large-scale MIMO (XL-MIMO) systems. Different from the previous algorithms based on sparsity, the proposed algorithm considers both the low-rank structure and the sparsity of the channel, leading to a more accurate estimation performance. Particularly, we formulate the estimation problem of the hybrid-field channel as a sparse matrix recovery problem with a fixed-rank constraint. However, this problem encounters two challenges: the non-smooth$\ell_{1}-\mathbf{norm}$term and the non-convex fixed-rank matrix constraint. To address these challenges, we employ the proximal operator to handle the non-smooth$\ell_{1}-\mathbf{norm}$term and consider the set of matrices with a fixed rank as the fixed-rank matrix manifold. Then a fixed-rank matrix manifold-based proximal gradient (FRM-PG) algorithm is proposed to smoothly search the solution on the fixed-rank matrix manifold, where the descent direction is derived in each iteration. Simulation results demonstrate that the proposed algorithm outperforms the classical channel estimation algorithms.

Abstract This paper proposes a two‐stage beamforming and combining scheme in frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) systems with multi‐antenna users. Specifically, the proposed scheme is a two‐stage scheme, where the pre‐beamforming matrix and pre‐combining matrix are designed using the channel covariance matrix (CCM) in the first stage. The problem of the pre‐beamforming matrix and pre‐combining matrix design are formulated as an 0–1 quadratic integer programming problem. To solve this problem, it is further transformed into an 0–1 mixed linear integer programming problem. In the second stage, the sparse code multiple access and multi‐user precoding and combining are adopted to mitigate the inter‐user interference. Different from the previous transmission scheme using CCM, the proposed scheme consider the multi‐antenna user system, and uses the CCM to design both the pre‐beamforming and pre‐combining matrices to sparsify the effective channel matrix, such that the overhead of pilot and feedback can be reduced. Moreover, the sparse code multiple access can help to better reduce the inter‐user interference. Simulation results validate the good performance of the proposed scheme.

This article proposes a two-stage beamforming (TSB) scheme with user scheduling for FDD massive MIMO. The developed TSB scheme designs the analog prebeamformer and schedules the users using statistical channel state information (S-CSI), reducing the overhead of the pilot and the feedback. Particularly, in the one-ring local scattering channel model, the prebeamformer design and user scheduling problem is formulated as a 0–1 quadratic constrained quadratic programming (QCQP), which is further linearized to a mixed integer linear programming (MILP). In the multiple scattering clusters channel model, we design the prebeamformer and schedule the users based on graph theory, where the chromatic number of the equivalent matrix represents the minimum number of orthogonal pilots. Then, we propose an iterative beam selection and user scheduling (I-BSUS) scheme that approximates the minimum pilot constraint by the maximum vertex degree. Moreover, the net spectrum efficiency (NSE) is improved using a multiuser digital precoder, which depends on the effective instantaneous CSI (EI-CSI). Simulation results validate the superiority of the proposed scheme in enhancing the NSE over the existing schemes.

This paper proposes a manifold optimization-based tensor recovery algorithm for channel estimation (MO-TRACE) in reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) MIMO-OFDM systems. Specifically, considering the inherent sparse scattering characteristics of mmWave channels, the multidimensional cascaded channel in the angular-delay domain is represented by a sparse and low-rank tensor, and we formulate the channel estimation problem as a sparse and low-rank tensor recovery problem. Then, we use the canonical polyadic (CP) decomposition technique to decompose the tensor into multiple factor matrices, where the factor matrices are related to the channel parameters. To account for the sparse factor matrices, we add the sparse regularization terms of the factor matrices to the optimization objective, which can also reduce the number of nonzero columns in factor matrices, i.e., the rank of the tensor. As the concatenation of multiple factor matrices can be seen as a point on a product manifold, we apply MO algorithms to search for the optimal sparse point with enhanced convergence speed. The proposed MO-TRACE algorithm provides a more precise description of channels and ensures that the iteration point always remains within the feasible domain, thereby enhancing solution accuracy. Simulation results validate the superiority of the proposed MO-TRACE in terms of estimation accuracy.

Abstract In this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed. The proposed scheme is based on reinforcement learning and alternating optimization algorithm, which can effectively improve communication quality and ensure fairness. The scheme is divided into two parts. In the first part, the multi‐UAV cooperation problem is modeled as a markov decision process. The objective of each UAV is to maximize the minimum user channel gain. To achieve stable strategies for all agents, the Multi‐agent Deep Deterministic Policy Gradient (MADDPG) algorithm is applied to train UAVs trajectories to reach the Nash equilibrium. The MADDPG algorithm is centralized trained at the base station and executed in a distributed manner by each UAV, ensuring efficient and effective coordination among agents. In the second part, an alternating optimization algorithm is formulated to optimize active and passive beamforming. Considering the non‐convexity of the fairness objective, by using auxiliary variables and semi‐definite relaxation method, the problem of maximizing the minimum user achievable rate is transformed into a feasibility problem. Simulation results show that the proposed scheme can effectively train UAVs trajectories and improve the communication performance of all users fairly.

Abstract Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR‐RIS) is capable of achieving full coverage area compared with conventional reflection‐only reconfigurable intelligent surface (RIS). Motivated by this observation, the secrecy performance of STAR‐RIS‐assisted non‐orthogonal multiple access (NOMA) systems over Rayleigh fading channels is investigated, where the incident signals sent by base station are reflected and transmitted to a pair of NOMA users in the presence of two eavesdroppers. To evaluate the secrecy performance of STAR‐RIS‐NOMA systems, the exact and asymptotic expressions of secrecy outage probability (SOP) and secrecy diversity order are derived, in which the correlation of the channels between the elements of each column on STAR‐RIS is taken into consideration. As a benchmark, the secrecy performance of the STAR‐RIS strategy for orthogonal multiple access (OMA) system is also analysed. Numerical results are provided to verify the effectiveness of the analytical results and demonstrate that: The SOP of STAR‐RIS‐NOMA outperforms that of STAR‐RIS‐assisted OMA and conventional cooperative communication systems. Furthermore, the scheme proposed here can obtain full order of secrecy diversity.

To accommodate the stringent requirements of massive connectivity and ultra-high throughput, both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) have been perceived as the key techniques for future communication networks. In this paper, a versatile framework is conceived to boost transmit power efficiency for RIS-enabled multi-group NOMA networks in the presence of coordinated multi-point (CoMP) reception and imperfect successive interference cancellation (SIC). Particularly, a group-level SIC (GSIC) method is proposed to eliminate the decoded group’s interference together with the well-designed transceivers to mitigate the aggregated interference, including the intra-group interference, the residual interference caused by imperfect SIC, and the interference of NOMA users decoded later. According to the novel framework, a power minimization problem is formulated by collaboratively optimizing the transmit power and the phase shifts. To render the problem tractable, an alternating scheme is developed to optimize the transmit power and the phase shifts iteratively. Specifically, the transmit powers for the users in the same group are devised by a parallel iteration algorithm, whilst the phase shifts are optimized by a sequential rotation method. In simulations, it is shown that the proposed scheme requires less transmit power than various benchmark methods under the constraint of each user’s quality of service.

In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices significantly reduces the downlink training length (DTL) and channel feedback. Nevertheless, most of the TSB methods focus on the one-ring channel. In this paper, we consider the multiple scatterer clusters (MSC) channel in massive MIMO systems and propose a TSB method based on cluster group. To reduce the channel state information (CSI) feedback, for each cluster we use a cluster-group-based eigen-prebeamformer to sparsify the effective channel matrix. The DTL is also reduced by a graph-based downlink training design. We further develop a multi-user beamformer to mitigate the inter-user interference. To further reduce the DTL, two other methods are also proposed based on the vertex and edge deletion. Simulation results confirm the efficiency of the proposed schemes in improving the effective spectral efficiency.

Abstract This paper considers a relay‐assisted massive multiple‐input multiple‐output (MIMO) downlink system in which a base station (BS) communicates with all terminals via a relay node enabled simultaneous wireless information and power transfer (SWIPT). Both time‐switching (TS) and energy‐splitting (PS) protocols are respectively considered at the relay node for coordinating energy harvesting and information decoding. The quality‐of‐service (QoS) fairness problem among terminals is investigated in the considered systems. In detail, the power allocation coefficients and energy harvesting time duration for TS based SWIPT, and the power allocation coefficients and PS ratio for PS based SWIPT are jointly optimised, aiming at seeking to maximise the minimum achievable rate over all terminals. As the formulated problems are non‐convex and non‐linear, and hard to solve directly, suboptimal heuristic algorithms are proposed for solving them. Additionally, the convergence and complexity of the proposed algorithms are also analysed. Finally, the performance of the proposed algorithms is demonstrated by extensive computer simulations. Results manifest that the proposed algorithms can provide uniformly great QoS for every terminal by resource allocation.

Abstract The paper investigates simultaneous wireless information and power transfer (SWIPT) in massive distributed antenna systems, where the remote radio heads (RRH) equipped with a large number of antennas are arbitrarily distributed over the coverage area and each terminal operates via a power‐splitting (PS) device. Utilising the harvested energy, each terminal transmits its pilot signals for channel estimation. A closed‐form lower bound expression on the downlink ergodic capacity is derived for each terminal and then downlink ergodic sum capacity maximisation problem with respect to RRH‐terminal association, channel estimation duration, PS ratios and power allocation (PA) is designed. The designed problem is proved to be a mixed‐inter nonlinear programming with combinatorial variables, which is generally hard to solve due to its NP‐hardness. To this end, a hierarchical iterative algorithm is developed by utilising decomposition technique. Moreover, the convergence and computational complexity of this algorithm are analysed as well. Numerical results manifest the feasibility and effectiveness of this algorithm, and also show that it can achieve the maximum downlink ergodic sum capacity for massive distributed antenna systems with SWIPT protocol.

Abstract Both cooperative non‐orthogonal multiple access (NOMA) and physical layer security (PLS) are promising techniques for future communication systems. This paper investigates the PLS performance of a cooperative NOMA system where multiple sources transmit a superposed message to two users by utilizing either amplify‐and‐forward (AF) or decode‐and‐forward (DF) relays in both non‐colluding and colluding eavesdropper scenarios. A source‐relay selection scheme is proposed for this system aiming at improving the secrecy performance. Additionally, the closed‐form expression of the secrecy outage probability (SOP) is derived for the proposed source‐relay selection schemes. It is demonstrated that in the proposed schemes, the theoretical results match well with the simulation results. In addition, the proposed schemes outperform the conventional OMA system in terms of SOP.

In this study, the authors investigate the joint design problem of interference alignment (IA) and power splitting (PS) in simultaneous wireless information and power transfer (SWIPT) networks. The existing joint design method is generally divided into two steps. First, IA is performed, and then the PS ratio is optimised. To obtain IA and SWIPT, a new joint design multi‐objective alternating optimisation (MOAO) algorithm is proposed. Different from the existing joint design method, the proposed MOAO algorithm not only maximises the sum harvested power after PS, but also maximises the sum signal‐to‐intereference‐plus‐noise ratio (SINR) by jointly designing the transmitting precoding matrices, the receiving interference suppression matrices, and the PS ratio. For this multi‐objective optimisation problem, a weighted optimisation method is proposed, which compromises between maximising the sum harvested power and maximising the sum SINR. Simulation validates that the sum achievable rate and sum harvested power of the proposed MOAO algorithm are significantly superior to those of the current scheme under the reasonable weighting coefficients.

Millimeter wave (mmWave) massive multiple‐input multiple‐output (MIMO) is a promising technology for next generation wireless communications. The utilisation of traditional full digital precoding techniques for mmWave massive MIMO systems is too costly. Fortunately, the hybrid (analogue/digital) precoding is a better choice. In this study, the authors investigate a near‐optimal iterative approximation hybrid precoding algorithm via cost‐efficient sub‐connected structure for mmWave massive MIMO systems. Based on the minimum mean square error (MSE) criterion, the optimal design of the proposed hybrid precoders is non‐convex. The original problem is reformulated as two optimisation sub‐problems, where one of the sub‐problems is convex and another is non‐convex. First, the authors convert the non‐convex sub‐problem into a convex problem by relaxing the constant modulus constraint from beampattern with interference control method, and eliminating the block diagonal constraint from vector operation with the properties of Kronecker product. Then the two convex sub–problems are solved iteratively. The proposed algorithm can achieve a highly approximate optimal solution, which can be theoretically demonstrated to converge to a Karush‐Kuhn‐Tucker point of the original problem. Simulation results show that the proposed algorithm via both sub–connected and fully connected structure gets favourable performance in terms of MSE and achievable rate.

In this study, the authors propose a simultaneous wireless information and power transfer scheme based on the power‐splitting (PS) protocol for a massive multiple‐input multiple‐output (MIMO) network. On the downlink, the base station transmits the information and energy simultaneously to each terminal and each terminal divides the received signals by its PS device between information decoder and energy harvester (EH). On the uplink, a portion of the energy harvested by EH is drawn for uplink pilot transmission in the coming frame and then the other is used for uplink data transmission in the current frame. Based on this scheme, the closed‐form lower bound expressions are obtained for the achievable downlink rate and uplink rate of each terminal and then are utilised to formulate an optimisation problem that is to maximise achievable sum rate of the whole network while ensuring the minimum required uplink and downlink rates of each terminal. However, as the original optimisation problem is non‐convex and non‐linear and very hard to solve directly, an algorithm is proposed by utilising successive approximation method based on geometric program. Numerical results verify the correctness and feasibility of the proposed algorithm.

In this paper, we propose a joint spatial division and multiplexing (JSDM) beamforming based on a neighbor scheme for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. The neighbor-based JSDM (N-JSDM) can fully utilize signal space, leading to higher spectral efficiency over the conventional JSDMs. The reason is that for the neighbor scheme, neighbors and non-neighbors are classified adaptively by the angles of departure (AoD), and the prebeamformer is designed to mitigate the non-neighbors' interference by the statistical channel state information. The effective channel matrix after the prebeamformer then becomes a band matrix, from which the downlink training length (DTL) and the channel feedback length are much smaller than the number of antennas. Moreover, an optimal prebeamformer which is proved to be able to achieve the same system capacity as the full CSI system is proposed, followed by a suboptimal prebeamformer with constrained DTL, and a DFT-based prebeamformer. On the other hand, the neighbors' interference is mitigated using the banded channel state information. Simulation results validate the good performance of the proposed N-JSDM.

Millimeter wave (mmWave) massive MIMO (Multiple-Input Multiple-Output) systems will enable gigabits per second transmitting data rate owing to the large bandwidth available in mmWave spectrum. It is difficult to utilize conventional lower frequency precoding techniques in mmWave massive MIMO systems because of the hardware constraints. Fortunately, the hybrid precoding can be utilized to extremely reduce the power consumption and high cost. In this paper, we propose the hybrid precoding scheme for downlink multi-user mmWave massive MIMO systems. Our scheme is based on the thought that the inter-user interference can be nullified at the analog step and the transmitting data rate is further maximized at digital step. The analog precoding matrix is derived based on the Equal Gain Transmission (EGT) method. Waterfilling technique is directly employed in the digital precoding matrix based on the almost orthogonal effective channel. Simulation results show that the proposed the hybrid EGT/waterfilling (HEW) scheme which has low complexity outperforms the other existing algorithms in low signal-to-noise ratio (SNR) scenarios.

In this paper, we investigate physical-layer security for a heterogeneous cellular network consisting of a macro cell and a small cell in the presence of a common eavesdropper that intends to tap both the macro cell and small cell. The orthogonal spectrum sharing (OSS) and non-orthogonal spectrum sharing (NOSS) are considered for the heterogeneous cellular network. The OSS allows the macro cell and small cell to access a given spectrum band in an orthogonal manner, whereas the NOSS enables them to access the same spectrum simultaneously and mutual interference exists. We present two NOSS schemes, namely, the interference-limited NOSS (IL-NOSS) and interference-canceled NOSS (IC-NOSS), where the mutual interference is constrained below a tolerable level through power control in the IL-NOSS, and the IC-NOSS exploits a specially designed signal for canceling out the interference received a legitimate user while confusing the eavesdropper. We derive closed-form expressions for an overall secrecy outage probability of the OSS, IL-NOSS, and IC-NOSS schemes by jointly considering the secrecy of both the macro cell and small cell. Furthermore, we characterize the secrecy diversity of the OSS, IL-NOSS, and IC-NOSS schemes through the asymptotic secrecy outage analysis in the high signal-to-noise ratio (SNR) region. It is proved that the OSS and IL-NOSS schemes obtain the same secrecy diversity gain of zero only, and a higher secrecy diversity gain of one is achieved by the IC-NOSS scheme. In addition, numerical results demonstrate that secrecy outage probabilities of the IL-NOSS and IC-NOSS can be minimized through an optimization of the ratio of the SNR of small cell to the SNR of macro cell, called the small-to-macro ratio (SMR). Moreover, with an optimized SMR, the proposed IC-NOSS scheme significantly outperforms the OSS and IL-NOSS schemes in terms of individual secrecy outage probabilities of both the macro cell and small cell.

In this paper, we investigate the performance of a multi-pair massive MIMO AF relay network, where multiple source users communicate with multiple destination users through a relay equipped with a very large number of antennas, when zero-forcing (ZF) reception/transmission is utilized at the relay. In contrast to prior works, we derive new exact analytical closed-form expressions of the outage probability and the ergodic achievable rate, and these results are valid for any finite number of relay antennas. In particular, the asymptotic performance and power-scaling laws are further analyzed and compared under two different asymptotic cases for the number of relay antennas going to infinity with a fixed and large number of user pairs, respectively. Both analytical results and Mont-Carlo simulations have consistently shown the effects of the number of relay antennas and the number of user pairs on the outage probability, the achievable rate, and power-scaling laws.

This study investigates the spectral efficiency of multi‐pair massive multiple‐input multiple‐output amplify‐and‐forward relay networks by considering the composite channel aging effect. The rational of this work is that the channel aging effect caused by phase noise and node movements is an inevitable practical channel impairment in time‐varying fading channels and substantially degrades the performance. The proposed model comprises multiple sources and multiple destinations, each equipped with a single antenna, which communication via a relay equipped with a very large number of antennas by employing maximal‐ratio combining/maximum‐ratio transmission. Based on this model, the authors first derive a closed‐form lower bound expression for the achievable rate of per source–destination pair. Then, by using the derived expression, they study how the transmitted powers of each source and the relay can be reduced without compromising the spectral efficiency when the number of relay antennas approaches infinity. They also discuss the effect of channel aging coefficients, including Doppler shift and phase noise increment variance, on the asymptotic spectral efficiency under different power scaling laws. Both theoretical analysis and Monte Carlo simulations disclose that the channel aging effect does not affect the power scaling laws but degrades the spectral efficiency.

The decode‐and‐forward multi‐relay system with the presence of an eavesdropper is researched in this study. Two widely used transmission schemes are considered, namely, the best‐relay selection scheme and the all‐relay‐based beamforming scheme. To be specific, the former scheme adopts only the single best relay among all the candidate relays that have succeeded in decoding the source signal to assist the transmission, whereas the all‐relay‐based beamforming scheme allows all the candidates to assist the source simultaneously in a beamforming manner. The authors reveal that in both schemes, if the total transmit power of the two hops is constrained, contradiction exists between the two means of improving physical‐layer security: (i) letting more relays succeed in decoding the source signal; (ii) increasing the transmit power of the selected relay(s). The authors thus analyse the secrecy outage behaviour of the two schemes and derive expressions of secrecy outage probability (SOP), based on which, the optimal power allocation solutions are obtained. It is demonstrated that in both schemes, the analysis results match well with the simulation results. Moreover, the proposed power allocation solutions surpass the conventional average power solution in SOP in both schemes.

Recently, lattice reduction (LR)‐aided linear detectors (LDs) are shown to be very effective in multi‐input–multi‐output systems for low‐complexity and small bit‐error‐rate (BER) performance. However, the LR algorithms in the previous LR‐aided LDs mainly aim to improve the orthogonality of the channel matrix where only channel state information is used. In this study, the authors design a novel LR algorithm to enhance the BER performance of the LR‐aided LDs. Unlike the previous LR algorithms, the proposed LR algorithm aims to decrease the modified Euclidean distance (MED) in the LR‐aided LDs, whereas the MED utilises the received signal as well as the channel matrix. Note that the MED is directly related to the BER performance of the LR‐aided LDs, thus decreasing the MED in the LR‐aided LDs can help to reduce the BER of the LR‐aided LDs. In the proposed LR algorithm, the partial column addition operation is used. The simulation results indicate that their LR‐aided LDs exhibit smaller BER than the previous LR‐aided LDs. Moreover, the computational complexity is also shown in the simulation results.

Lattice reduction (LR) is a powerful technique for improving the performance of linear multiple‐input–multiple‐output detection methods. The efficient LR algorithms can largely improve the performance of the linear detectors (LDs). Note that the ordered successive interference cancellation (OSIC) system can decrease the interference between antennas and provide performance gain of the LDs. In this paper, a novel LR‐aided algorithm called NLR‐OSIC improving the performance of the OSIC system has been proposed. Most existing LR algorithms are designed to improve the orthogonality of channel matrices, which is not directly related to the error performance of the OSIC system. While the authors’ algorithm maximises the signal‐to‐interference‐plus‐noise ratio (SINR) of the detected symbol in each stage of the OSIC system, thus exhibiting improved error rate than the previous LR‐aided LDs and their corresponding OSIC algorithms. In each stage, the authors verify that maximising the SINR of the detected symbol can be formulated as a shortest vector problem which is solved by a suboptimal algorithm in this study. In the end of this study, the error rate performance of the proposed algorithm as well as the required complexity has been demonstrated through extensive computer simulations.

Lattice reduction (LR) is a powerful technique for improving the performance of linear MIMO detection methods. The efficient LR algorithms can largely improve the performance of the linear detectors (LDs). However, there are two problems involved in the LR-aided detection algorithms. One is that, the reduced basis is still not orthogonal, and most existing LR-aided algorithms devote to find a near orthogonal matrix. The other one is that the original constellation is no longer the feasible set of the detected symbol, and the elements of the new symbol vector are correlated. In this paper, an improved LR-aided algorithm (ILRA) considering the feasible set of the new symbol has been proposed. We verify that the feasible set is the integer set in a polyhedron. Eliminating the integer constraint, we simplify the detection to a convex optimization problem. Finally, we get the new detected symbol by solving the convex optimization problem. The simulation results show that our algorithm can achieve significant performance gain over the previous LR-aided LDs, especially in the low-order QAM system.

This paper studies the asymptotic capacity of large MIMO amplify-and-forward (AF) relay networks with one source-destination pair equipped with M antennas and K single-antenna relays. In contrast to prior works, we consider not only the multipath fading but also the path loss effect. By using random matrix theory, we first derive the asymptotic eigenvalue distribution (A.E.D.) of the channel covariance matrix between the source and the destination as M and K approach infinity while their ratio remains constant. Based on the resulting A.E.D., the system capacity is then obtained. More specifically, a linear relay distribution is considered to evaluate the asymptotic capacity, which can realistically model the large MIMO AF relay networks. Both theoretical analysis and Mont-Carlo simulations have consistently shown the impact of path loss parameters on the asymptotic capacity.

In this paper, we propose a joint relay and destination optimization design for two-way half-duplex amplify-and-forward (AF) relay systems with multiple relays, each with multiple antennas. By using the sum mean-squared error (MSE) criterion and Wiener filtering principle, the joint relay and destination design is formulated as an optimization problem of the relay precoding matrix under the constraint of total relay transmit power. Then, constructing a virtual point to point MIMO channel and using the singular-value-decomposition (SVD), the optimization problem is simplified to a convex minimization of an upper bound of the sum MSE through a diagonalization process. Finally, a suboptimum scheme is proposed to solve the simplified convex optimization problem. Monte-Carlo simulation shows that the proposed suboptimal scheme gives a better MSE performance than the existing gradient descent algorithm does, and moreover, our method becomes more advantageous when the number of relays or the number of relay antennas increases.

This paper focuses on relay selection, subcarrier selection and power allocation (PA) in two-hop multi-relay networks in frequency-selective (FS) fading channel. The multiple relays adopt amplify-and-forward (AF) protocol and are subject to an aggregate power constraint. In order to maximize the instantaneous information rate of the system, both parallel mode (all the relays transmit signals simultaneously) and non-parallel mode (the relays transmit signals sequentially) are analyzed. In the parallel mode, compared with some other previous optimal PA work, our proposed optimal subcarrier selection (OSS) scheme and optimal relay selection (ORS) scheme both can get a better performance in terms of the instantaneous information rate and have a lower complexity. In the non-parallel mode, though our OSS scheme gets a little worse performance than the previous optimal scheme in terms of the rate, it has a much lower complexity. Thus, as for the non-parallel mode our OSS scheme is more suitable for some real-time transmissions. The simulations are also provided to validate the proposed schemes.

This paper presents a joint linear processing scheme for two-hop and half-duplex distributed amplify-and-forward (AF) relaying networks with one source, one destination and multiple relays, each having multiple antennas. Under the perfect CSI (channel state information) assumption, a joint relay and destination design is proposed to minimize the mean-square error (MSE) of the output signal at the destination. By the Wiener filter principle, it is formulated as an optimization problem with respect to the relay precoding matrix under the constraint of a total relay transmit power. And then, the constrained optimization with an objective to design the relaying block-diagonal matrix is simplified to an equivalent problem with scalar optimization variables. Moreover, it is revealed that the scalar-version optimization is convex when the total relay power or the second-hop SNR (signal to noise ratio) is above a certain threshold. The underlying optimization problem, which is non-convex in general, is solved by the complementary geometric programming (CGP). Simulation results show that the proposed scheme outperforms the existing relaying method in the case of relatively large second-hop SNR.

This paper presents a relay precoding scheme under the imperfect channel state information (CSI)assumption for two-hop amplify-and-forward based MIMO relay networks with one source, one destination and multiple relays. The forward channel mean and covariance available at the relays is assumed to be for the robust design of the linear relay precoder. By using the average mutual information criterion along with a total relay transmitting power constraint, an optimization problem is formulated by assuming the imperfect CSIT(CSI at the transmitter) at the relays. We first derive the approximation of the average mutual information rate, and then simplify the constrained optimization problem into the equivalent problem involving scalar optimization variables. Through an in-depth study of the problem formulation, it is revealed that the objective function is convex only when each optimization variable is larger than a certain threshold, which can be determined mainly by the system configurations and the channel condition. The effectiveness of the proposed relay precoder is validated by Monte-Carlo simulations with comparison to that under perfect CSI assumption.

In this paper, we first derive an optimal precoder for amplify-and-forward (AF) multiple-antenna relay systems with single-antenna source and destination terminals by maximizing the output signal-to-noise ratio (SNR). Then, the resulting maximum SNR expression and its approximation at high transmission SNR are analyzed to obtain their statistical properties for independently identically distributed (i.i.d.) Rayleigh fading channels. Based on the properties of the approximate maximum SNR, the average symbol error probability (ASEP) is investigated, leading to an explicit diversity order and array gain. The ergodic achievable rate is also studied, giving a tight closed-form upper bound. The theoretical analysis is finally validated by Monte Carlo simulations.