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In this paper, we propose a distributed beamforming scheme to realize a full-duplex relay (FDR) in multiuser multiple-input–multiple-output (MIMO) relaying systems. In the proposed scheme, each mobile and relay station performs distributed transmit beamforming and receive combining, whereas the base station performs ordinary multiuser MIMO transmission. Using the channel reciprocity, the proposed FDR scheme can be realized in an iterative way with no cooperation among nodes. Numerical results show that the proposed scheme provides substantial performance improvement compared with the half-duplex relay. [ABSTRACT FROM PUBLISHER]

For next-generation network topologies, the distributed wireless ad-hoc network is one of the most challenging issues because of the inherent node intelligence decision. Nevertheless, due to the randomness of node distribution, it is difficult to achieve high efficiency without signaling to neighboring nodes to coordinate random interference among the nodes, which, in turn, leads to high computational overhead in general. In this paper, we propose a framework of distributed multiple-input–multiple-output (MIMO) ad-hoc networks to improve link capacity while achieving computational simplicity without interworking with other links in a fully distributed manner. To achieve this goal, we align three main resource management algorithms, i.e., the distributed link-scheduling algorithm (DLSA), the distributed power allocation algorithm (DPAA), and the distributed cooperative beamforming algorithm (DCBA). In the simulation results, performance has proven to be promising in terms of the average sum rate, compared with that of other conventional schemes. [ABSTRACT FROM PUBLISHER]

This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input–multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems. [ABSTRACT FROM PUBLISHER]

This paper studies the rate maximization problem and designs the beamforming strategy for relay-aided multiuser multiantenna cognitive radio (CR) network that coexists with a primary radio (PR) system via opportunistic spectrum sharing (OSS). The beamforming is characterized by two layers: 1) Cognitive beamforming (CB) is employed by CR base station (BS), CR relay station (RS), and secondary users (SUs) to mitigate the interference to and from the PR. 2) Joint CR-BS and CR-RS beamforming is used to maximize the weighted sum rate of the SUs. Since the optimization turns out to be nonconvex, we apply the classic Blahut-Arimoto algorithm and solve the original problem through an iterative approach. Interestingly, each iterative step involves only convex optimizations whose closed forms can be found. Moreover, we derive an upper bound for the sum rate to assist system analysis. Numerical results show that the proposed approach achieves higher performance, compared with the widely studied diagonally structured beamformer design. [ABSTRACT FROM PUBLISHER]

In this paper, we study the performance of large-scale MIMO network where the base station has a large number of antennas and performs semi-blind channel estimation. We show that one can increase the number of antennas at the user side without increasing the overhead imposed by the channel estimation linearly with the number of user antennas. Specifically, we propose a simple data-aided channel estimation scheme which exploits the quasi-orthogonality of the channel vectors and takes advantage of the conventional pilot-aided channel estimation performed for the first antenna at each user. Two schemes are then analyzed using the extra antenna(s) at the users: data-multiplexing and interference alignment. We obtain achievable rates for the proposed uplink multi-cell MIMO system in both schemes. The rate bounds are tight and the system performance is also analyzed in terms of the outage probability to study the effect of the data length used for channel estimation scheme. [ABSTRACT FROM AUTHOR]

A precoding scheme is proposed for the downlink of multibeam satellite communication in the face of phase perturbations. In order to alleviate the signal-to-interference and noise ratio degradation induced by phase uncertainty and to minimize the transmission power consumption, we formulated the beamforming problem as a chance-constrained optimization. Moreover, we provided a large deviation inequality aided conservative approximation for the chance-constraints, followed by conceiving a tractable solution. Our simulation results showed the accuracy and the efficiency of our proposed approximation algorithm compared to the benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we propose new resource allocation schemes with (or without) adaptive modulation for coordinated multipoint (CoMP) with multiuser multiple-input multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA). In the proposed schemes, a practical linear pre- and postprocessing technique is used to cancel interuser interference and decompose a single-user MIMO channel into parallel noninterfering spatial layers. The transmit power is allocated to spatial layers with (or without) adaptive modulation under the total base station (BS) power constraint and the per-BS power constraint. The set of users for each subcarrier is determined by the exhaustive search user allocation, the sum rate-based user allocation, and the random user allocation. Simulation results show that the proposed schemes enhance the sum rate. [ABSTRACT FROM PUBLISHER]

In downlink multiuser multiple-input–multiple-output (MU-MIMO) systems, users are practically heterogeneous. However, many existing user-scheduling algorithms are designed with an implicit assumption that users are homogeneous. In this paper, we revisit the problem by exploring the characteristics of heterogeneous users from a subspace viewpoint. With an objective of minimizing interference nonorthogonality among users, three new angular-based user-scheduling criteria are proposed. While the first criterion is heuristically determined by identifying the incapability of the largest principal angle to characterize subspace correlation and, hence, interference nonorthogonality between users, the second and third criteria are derived by using the sum rate capacity bounds with block diagonalization and the change in capacity by adding a new user into an existing user subset, respectively. Aiming to capture fairness among heterogeneous users while maintaining multiuser diversity gain, two new hybrid user-scheduling algorithms, whose computational complexities are only linearly proportional to the number of users, are proposed. We show by simulations the effectiveness of our proposed user-scheduling criteria and algorithms with respect to those commonly used in a homogeneous environment. [ABSTRACT FROM PUBLISHER]

In this paper, we first study the design of network coding for the generalized multiple-input–multiple-output (MIMO) Y channels, where K users wish to exchange information with each other within two time slots. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and that cross-pair interference can be canceled. In addition, a simple mapping function is proposed to ensure low-complexity detection at the relay. Exact expressions of symbol error rate (SER) are then developed to establish the explicit relationship between the diversity gain and the number of node antennas. Monte Carlo simulation is also provided to demonstrate the performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a two stage hybrid interference cancellation and equalization framework is proposed for interference cancellation in the uplink of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The first stage uses time domain equalization to suppress cochannel interference, mitigate asynchronism, and shorten the postequalization channel response to be no longer than the length of the cyclic prefix. The second stage performs low-complexity single tap equalization and detection in the frequency domain. The framework is developed specifically for spatial multiplexing and is applied to multiuser MIMO-OFDM systems with asynchronism between users as well as to single-user MIMO-OFDM systems. Various equalizer design methods are proposed that determine the coefficients directly from the training data and are compared with methods based on channel estimates. The equalizer coefficients and postequalization channel response are found by solving a joint optimization that maximizes the signal to interference-plus-noise ratio (SINR) in the frequency domain. Simulations compare various training-based methods and show the proposed methods provide good bit error rate (BER) and SINR performance in a variety of interference scenarios. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is widely regarded as a promising interference management technique for wireless networks and its potential is most profound in interference-intensive environments. This motivates us to study IA for multicast communications in multihop MIMO networks, which are rich in interference by nature. We develop a set of linear constraints that can characterize a feasible design space of IA for multicast communications. The set of linear constraints constitutes a simple mathematical model of IA that allows us to conduct cross-layer multicast throughput optimization in multihop MIMO networks, but without getting involved into the onerous signal design at the physical layer. Based on the mathematical model of IA, we formulate a multicast throughput maximization problem and develop an approximation solution that can achieve $(1-\varepsilon)$ -optimality. Simulation results show that the use of IA can significantly increase the multicast throughput in multihop MIMO networks and the throughput gain increases with the volume of multicast traffic and the number of antennas. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the application of simultaneous wireless information and power transfer (SWIPT) in a full-duplex relay network, where the relay node is wirelessly powered by harvesting a portion of the received signal power from the source node. Different from existing work that has employed time-switching-SWIPT, we propose to use power-splitting-SWIPT. As a result, our proposed approach gives rise to a truly full-duplex relay network, where the information reception and transmission take place simultaneously at the relay node all the time. This more thorough exploitation of the full-duplex feature consequently leads to a significant capacity improvement, compared with existing alternatives in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iterative structure for channel estimation and data detection for multiple-input–multiple-output orthogonal frequency-division multiplexing systems (MIMO-OFDM) with insufficient cyclic prefix (CP) and a limited number of pilot subcarriers. Insufficient CP leads to increased intersymbol interference (ISI) and intercarrier interference (ICI). The interference corrupts pilot subcarriers used for channel estimation and affects the detection process. We propose a multistep channel estimation process, which is assisted by a small number of pilot subcarriers. First, the channel covariance matrix and the number of channel paths are acquired from the channel least square estimates at the pilot subcarriers. We then formulate a maximum likelihood process to approximate the time-domain channel. For data detection, a high-performance bidirectional M-algorithm (BDMA) is proposed for trellis-based equalization. Simulation results show that the channel estimate mean square error converges to the Cramer–Rao bound (CRB) after a few iterations. In addition, the achieved bit error rate (BER) can reach that of the sufficient CP case, even when the delay spread is much longer than the CP.1 [ABSTRACT FROM AUTHOR]

The multiuser beamforming (MUBF) scheme has attracted tremendous attention as a suboptimal linear precoding technique to realize the benefits of system capacity in multiple-input–multiple-output (MIMO) systems. Due to mutual interference between cochannel users, the objective of maximizing the system capacity and fair service provisioning to different users should be well balanced in the design of the MUBF scheme. To this end, we propose a framework for the joint multiuser beamforming and partitioning (MUBFP) design problem, which attempts to partition users into a set of disjoint groups, where each group of users is served using the MUBF scheme. By jointly optimizing the grouping number, the user partitioning, and the beamformer design, the average sum capacity can be maximized without sacrificing user fairness. A decomposition approach is presented to decouple this problem into the user partitioning and beamformer design subproblems with the given number of groups, which can be solved using the agglomerative hierarchical clustering (AHC) algorithm and sequential parametric convex approximation (SPCA) approach, respectively. An iterative algorithm is proposed to search the optimal grouping number for the decoupled MUBFP problem by incorporating the algorithms of these two subproblems, and a suboptimal algorithm is developed to reduce the searching times with good approximation performance. Simulation results are provided to show the effectiveness of the proposed schemes in terms of convergence, average sum capacity, and complexity. [ABSTRACT FROM PUBLISHER]

We investigate the heterogeneous cellular network (HetNet) where the macrocell base stations (MBSs) are equipped with very large antenna arrays. Since such a combined massive multi-input multi-output (MIMO) and HetNet system operates in the co-channel time-division duplex mode, to mitigate the pilot contamination effect to channel estimation, the number of users that can be served simultaneously is limited by the available pilot resources in the conventional designs. We propose a pilot reuse scheme for massive MIMO–HetNet to increase the user capacity. Specifically, location-aware channel estimation is employed at the MBSs to mitigate the severe pilot contamination introduced by pilot reuse, and we propose a novel pilot assignment algorithm to reduce the interference in HetNets. Simulation results demonstrate that our proposed scheme can support more users and increase both the uplink and the downlink sum-rate capacity, in comparison to the conventional scheme, particularly when the antenna number at the MBSs is sufficiently large. [ABSTRACT FROM AUTHOR]

This work investigates a robust energy-efficient solution for multiple-input–multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs), considering imperfect interference channel-state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given that the transmission power constraint, the robust interference power constraint, and the minimum transmission rate are satisfied. To solve the optimization problem, we first characterize the uncertainty of CSI by bounding it in a Frobenius-norm-based region and then equivalently convert the robust interference constraint to a linear matrix inequality (LMI). Furthermore, a feasible ascent direction approach is proposed to reduce the optimization problem into a sequential linearly constrained semidefinite program, which leads to a distributed iterative optimization algorithm for deriving the robust and optimal beamforming. The feasibility and convergence of the proposed algorithm is theoretically validated, and the final experimental results are also supplemented to show the strength of the proposed algorithm over some conventional schemes in terms of the achieved EE performance and robustness. [ABSTRACT FROM PUBLISHER]

The users at cell edge of a massive multiple-input–multiple-output (MIMO) system suffer from severe pilot contamination (PC), which leads to poor quality of service (QoS). To enhance the QoS for these edge users, soft pilot reuse (SPR) combined with multicell block diagonalization (MBD) precoding is proposed. Specifically, the users are divided into two groups according to their large-scale fading coefficients, which are referred to as the center users, who only suffer from modest PC, and the edge users, who suffer from severe PC. Based on this distinction, the SPR scheme is proposed for improving the QoS for the edge users, whereby a cell-center pilot group is reused for all cell-center users in all cells, whereas a cell-edge pilot group is applied for the edge users in the adjacent cells. By extending the classical block diagonalization precoding to a multicell scenario, the MBD precoding scheme projects the downlink transmit signal onto the null space of the subspace spanned by the intercell channels of the edge users in adjacent cells. Thus, the intercell interference contaminating the edge users' signals in the adjacent cells can be efficiently mitigated, and hence, the QoS of these edge users can be further enhanced. Our theoretical analysis and simulation results demonstrate that both the uplink and downlink rates of the edge users are significantly improved, albeit at the cost of the slightly decreased rate of center users. [ABSTRACT FROM AUTHOR]

We study the impact of phase noise on the downlink performance of a multiuser multiple-input–multiple-output (MIMO) system, where the base station (BS) employs a large number of transmit antennas M. We consider a setup where the BS employs M_{\mathrm{osc}} free-running oscillators, and M/M_{\mathrm{osc}} antennas are connected to each oscillator. For this configuration, we analyze the impact of phase noise on the performance of zero forcing (ZF), regularized ZF, and matched filter precoders when M and the number of users K are asymptotically large, whereas the ratio M/K=\beta increases, the SINR performance of all considered precoders degrades. On the other hand, the variance of the random phase variations caused by the BS oscillators reduces with increasing M\mathrm{osc}. Through Monte Carlo simulations, we verify our analytical results and compare the performance of the precoders for different phase noise and channel noise variances. For all considered precoders, we show that when \beta is small, the performance of the setup where all BS antennas are connected to a single oscillator is superior to that of the setup where each BS antenna has its own oscillator. However, the opposite is true when $\beta$ is large and the signal-to-noise ratio (SNR) at the users is low. [ABSTRACT FROM AUTHOR]

Tomlinson–Harashima precoded (THP) multiple-input–multiple-output (MIMO) systems are developed to cancel the intersymbol interference (ISI) at the transmitter. However, the THP at least requires perfect channel direction information (CDI) to completely mitigate the interference at the transmitter, which is not practical in the real world. The limited feedback systems are then practically developed to convey the quantized CDI to the transmitter. Using the quantized CDI to conduct the THP, an unavoidable interference degrades the performance at the receiver. In this paper, we investigate the performance degradation resulted from the random vector quantization (RVQ), which is a kind of CDI-based limited feedback mechanism. Here, we measure the performance degradation as the excess mean-squared error (EMSE). Since the formulation of the EMSE is a complicated function of the quantization error, we proposed using the second-order Taylor expansion to derive an analytical result. Simulation results have also verified our proposed analysis, showing that the derived analysis can match the actual EMSE. [ABSTRACT FROM PUBLISHER]

In this paper, we study user selection (US) strategies for a multiple-input–multiple-output (MIMO) cognitive radio (CR) downlink network, where the r-antenna underlay CR secondary users (SUs) coexist with a primary user (PU), and all terminals are equipped with multiple antennas. Two main scenarios are considered: 1) The t-antenna cognitive base station (CBS) has perfect or partial channel state information at the transmitter (CSIT) from the CBS to the PU receiver (RX), and 2) the CBS has absolutely no PU CSIT. For these scenarios, we propose and evaluate multiple SU selection schemes that are applicable to both best-effort PU interference mitigation and hard interference temperature (IT) constraints. The computational complexity of the proposed schemes can be significantly smaller than that of an exhaustive search with negligible performance degradation. For the selection of C SUs out of K, whereas an exhaustive search is on the order of \mathcal{O}(\binom{K}{C}C^4r^3). When t$ and r$ are of the same order, the computational complexity of the proposed scheme can be \binom{K}{C}C^4/K times smaller. Mathematical complexity analysis and numerical simulations are provided to show the advantage of our schemes. [ABSTRACT FROM PUBLISHER]

This paper presents a coherent scheme to optimize an in-band full-duplex multiple-input–multiple-output (MIMO) relay via beamforming and transmit power allocation in a two-hop single-input–single-output (SISO) link under full channel knowledge and perfect hardware assumptions. First, we derive in closed form the optimal pair of transmit power and receive filter for a fixed transmit filter by unifying the minimum-mean-square-error (MMSE) filtering with the SISO-equivalent power allocation, as an iterative approach is not guaranteed to converge to global optimum. Second, we propose a heuristic algorithm to approximate the optimal transmit filter for a fixed receive filter. Furthermore, we study the well-known null-space projection constraint and derive a singular value decomposition (SVD)-based solution for the arbitrary-rank self-interference channel by generalizing the optimal solution under the assumption of rank-1 self-interference channel. Finally, we combine these solutions into a partially iterative algorithm in order to address the global optimization as our observations justify that some of the aforementioned schemes converge to the optimal solution under certain criteria. The numerical analysis of the proposed iterative algorithm demonstrates close-to-optimal performance relative to the theoretical upper bound of the end-to-end link in terms of maximum achievable throughput. [ABSTRACT FROM PUBLISHER]

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes. [ABSTRACT FROM AUTHOR]

This paper focuses on precoding design for sum-rate maximization while considering the effects of residual self-interference for multiuser multiple-input–multiple-output (MU-MIMO) full-duplex (FD) systems. The problem formulation leads to a nonconvex matrix-variable optimization problem, where we develop two efficient sum-rate maximization algorithms using sequential convex programming (SCP), namely, the difference of convex functions (DC)-based and the sequential convex approximations for matrix-variable programming (SCAMP) algorithms. In addition, we derive the achievable sum rate under the effect of residual self-interference. Simulation results show that, even in cases of high self-interference and high estimation error, the SCAMP algorithm provides approximately 20%–30% sum-rate improvements over both conventional optimized half-duplex (HD) transmission and the existing state-of-the-art FD algorithm in a wide range of scenarios. Finally, the convergence results indicate that the DC-based algorithm tends to initially give the best performance; however, at convergence, the SCAMP algorithm tends to significantly outperform the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study the codebook size design strategies in the multiple-input–multiple-output (MIMO) broadcast channel (BC) systems. We adopt the nonlinear Tomlinson–Harashima precoder (THP), which is designed by the imperfect channel direction information (CDI) acquired from the random vector quantization (RVQ) feedback mechanism. Under the limitation of the total feedback rate, we flexibly allocate the size of each user's codebook so that the overall channel capacity is maximized. Since the design problem is not convex, directly finding out the optimum allocation of codebook size is not attainable. However, by sophisticatedly using the discrete majorization theory, we can derive the codebook size allocation strategies for the high- and the low-signal-to-noise-ratio (SNR) regions. Interestingly, the results show that the equal size codebook is preferable in the low-SNR region, whereas the codebook allocates the whole available codebook size to a single user in the high-SNR region. Simulations certify the theoretical result of our design. [ABSTRACT FROM PUBLISHER]

This paper considers limited feedback for a two-cell interfering multiple-input–multiple-output multiple-access channel (MIMO-MAC). We propose a QR decomposition-based interference alignment (QRD-IA) scheme for efficient limited feedback and investigate the conditions for the antenna configuration. We characterize the interference leakage caused by quantization error as a function of the number of feedback bits. To minimize the interference leakage, we further propose a quantized-precoder-based IA (QP-IA) scheme by modifying the proposed QRD-IA scheme. By characterizing the interference leakage of the QP-IA scheme, we investigate the number of feedback bits to maintain the degrees of freedom (DOFs) in the two-cell interfering MIMO-MAC and a feedback bit allocation algorithm to improve the sum rate. From simulation results, we demonstrate that our proposed schemes can provide better sum-rate performance than the existing scheme using the same number of feedback bits, and our feedback bit allocation algorithm provides a higher sum rate compared with equal feedback bit allocation. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a precoding technique that aligns interfering signals at receivers. It is known that IA achieves the maximum degrees of freedom over an interference channel under ideal assumptions. The real-world performance of IA depends on a range of practical issues, such as imperfect synchronization, channel estimation, and feedback. Practical issues have been studied in simulations and prototypes, but fully distributed operation of IA network nodes has not been considered. In this paper, we present the first investigation of real-time IA performance on a fully distributed 2 $\times$ 2 multiple-input–multiple-output (MIMO) prototype system with three physically independent user pairs. Over-the-air algorithms for time and frequency synchronization, as well as analog feedback, are studied and implemented. Sum rates are illustrated as a function of complexity and accuracy of different alignment, synchronization, and feedback algorithms. Corresponding tradeoffs are evaluated using an iterative IA method, the injection of residual frequency offset into synchronization, and analog versus quantization-based limited feedback approaches. We demonstrate that, while considering all possible error sources in estimation, synchronization, and feedback, the theoretical multiplexing gain of IA can be reached in practical systems with a constant sum rate loss that remains within 5 bits/Hz/s compared with an ideal simulation. [ABSTRACT FROM PUBLISHER]

Linear decoding of space–frequency block codes (SFBCs) based on the Alamouti structure with orthogonal frequency-division multiplexing (OFDM) requires the channel to be constant across two consecutive tones. However, time-dispersive multipath fading channels exhibit frequency-selective behavior wherein the channel across two tones will vary. This paper provides analytical tools for evaluating the performance of SFBCs with OFDM in frequency-selective channels. The analysis uses the average signal-to-interference-plus-noise ratio (SINR) as a sufficient statistic to describe the performance of SFBCs in frequency-selective channels. A unified formula is derived to characterize the channel correlation between two consecutive tones for various frequency-selective channels. A closed-form solution of the average SINR is obtained, which takes into account imperfect channel estimation and frequency selectivity. An interpolation method of the channel estimate is proposed, which is shown to improve the bit-error-rate performance by 3 dB at a high SNR. The accuracy of the theoretical analyses is verified by numerical simulations under multipath channels such as the International Telecommunications Union (ITU) pedestrian and vehicular models. [ABSTRACT FROM PUBLISHER]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

In multiple-input–multiple-ouput (MIMO) interference channels (IFCs), interference alignment (IA) with zero-forcing (ZF) linear detection can be used to achieve the maximum degrees of freedom (DoFs). However, ZF linear detection usually degrades the performance due to the noise enhancement in practical systems. In this paper, we propose lattice reduction (LR)-aided successive interference cancelation (SIC) in conjunction with IA over lattice for three-user (and extendable to $K$-user) MIMO IFCs in which the interfering signals aligned over the lattices are detected and canceled rather than nulled out. To complete the proposed LR-aided SIC method, the detection ordering and the precoding matrix that minimize the mean square error are also determined. [ABSTRACT FROM PUBLISHER]

To achieve the full multiplexing gain of a $K$-multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, a distributed opportunistic interference alignment (DOIA) technique using a new beamforming algorithm, namely, threshold-based beamforming (TBF), is proposed. The MIMO overlay CR system consists of one primary user (PU) and $K$ secondary users (SUs) where the local channel state information is available at both the transmitters and receivers of SUs. Assuming that the receiver and the transmitter of the PU have perfect knowledge of their own channel matrix, the PU uses a maximum eigenmode beamforming (MEB) scheme for its data transmission to release some of its eigenmodes for the SUs. For this virtual cooperation, the SUs align their transmitted signals to the spatial directions (SDs) associated with the unused PU's eigenmodes to ensure orthogonality between the links of the PU and the SUs. In addition, the MEB scheme overcomes the limitation of the conventional water-filling power allocation (WPA) in releasing the PU's eigenmodes for the SUs at high signal-to-noise ratios (SNRs). A noniterative and distributed power-allocation strategy, namely, TBF, is proposed, in which the SUs' links with a maximum eigenvalue above a certain threshold transmit data at full power, and the rest remain silent. This protocol, along with the MEB scheme, enables the MIMO overlay CR system to enhance the throughput of the network described as the average sum rate of both the PU and SUs. The proposed DOIA-TBF protocol allows the opportunistic SUs to use the same frequency band of a preexisting PU and guarantees that no interference is imposed on the PU's performance for such a MIMO overlay CR system. [ABSTRACT FROM PUBLISHER]

In this paper, a low-complexity detection scheme is proposed for an underdetermined multiple-input–multiple-output (UD-MIMO) wireless communication system that employs $N$ transmit antennas and M < N$ receive antennas. The proposed scheme combines a simplified parallel interference cancelation (S-PIC) with the block decision feedback equalization (BDFE) algorithm. To account for the extra (N - M)$-dimension in the transmitted signal, the UD-MIMO system is partitioned into two subsystems: one with (N - M)$ transmit antennas and the other one with M sequences in the first subsystem, with $Q$ being the constellation size, the proposed scheme explores only a small number of candidate sequences in the first subsystem; thus, it achieves significant complexity reduction. Two new candidate sequence selection methods are proposed. In the first iteration, the candidate sequences used for PIC are selected by exploring the statistical properties of the received signals. In the second iteration and beyond, the set containing the candidate sequences is constructed by utilizing the soft information generated from the previous iteration. The proposed scheme provides a balanced tradeoff between computational complexity and bit error rate (BER) performance. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel low-complexity beamforming algorithm for multiuser successive maximum ratio transmission (MS-MRT) toward space-division multiplexing to achieve video quality maximization in the downlink of a multiuser (MU) multiple-input–multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA)-based fourth-generation (4G) wireless network. We compare the performance of this algorithm in terms of system throughput and video quality with the two popular precoding techniques, namely, block diagonalization (BD) and successive optimization (SO). We also compare its performance with the generalized versions of these two algorithms termed coordinated transmit–receive processing (CTR)-BD and CTR-SO algorithms, respectively. Further, we propose an extension of the MS-MRT scheme to broadcast scenarios, termed broadcast successive maximum ratio transmission (BS-MRT), which computes the optimal broadcast beamforming vector to maximize the video quality at each of the broadcast group members while maintaining orthogonality to the previously scheduled user groups. We also demonstrate that the proposed MS-MRT and BS-MRT schemes can be naturally adapted to the context of various MU OFDMA scheduling algorithms such as proportional fairness (PF) and round robin (RR), to name a few, to maximize video quality through optimal scheduling in the presence of a large number of users. We discuss implementations of the proposed MS-MRT scheme in association with these scheduling algorithms for MU unicast and broadcast video transmission scenarios. The simulation results presented in this work rely on the rate models derived using the Joint Scalable Video Model (JSVM) software developed by the Joint Video Team (JVT) and are, thus, readily applicable in practice. The presented results clearly demonstrate the ability of the proposed algorithm to maximize the video quality in comparison with the other competing MU MIMO precoding techniques. Further, employing the proportional fair scheduling algorithm in conjunction with the MS-MRT results in an overall enhancement in the received video quality compared with the RR and max-rate algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of spectrum leasing in cognitive radio (CR) networks, while incorporating two advanced physical-layer technologies, i.e., multiple-input multiple-output (MIMO) and distributed interference alignment. We present a cooperative spectrum leasing scheme for primary and secondary users to balance the tension between data transmission and revenue collection/payment. A Stackelberg game is formulated, where the primary user is the leader, and secondary users are followers. With backward induction, we derive the optimal strategies for primary and secondary users that can achieve the unique Stackelberg equilibrium, where no player can gain by a unilaterally changing strategy. We find that spectrum leasing is always beneficial to enhancing the utilities of primary and secondary users. The proposed scheme outperforms a no-spectrum-leasing scheme and a cooperative scheme presented in the literature with considerable gains, which demonstrate the benefits of spectrum leasing and distributed interference alignment and validates the efficacy of the proposed scheme. [ABSTRACT FROM PUBLISHER]

The maximum–minimum-throughput-based power assignment (MMTPA), which results in the maximum–minimum throughput among all secondary users (SUs) while assuring the quality of service (QoS) of each primary user (PU), is proposed for multiple-input–multiple-output (MIMO) systems in cognitive radio (CR) networks. We first derive the capacity bound of the MIMO system under cochannel interference and use this capacity bound to quantify the throughput. Instead of employing the traditional optimization technique, we present a proposition to simplify the optimization of the power assignment. In particular, in the uplink of the infrastructure-based CR network, the problem can be reduced so that the MMTPA only has to optimize the transmit power of the representative SU and keep the ratio of the transmit power of each SU to that of the representative SU constant. For comparison, we also propose two other power assignments, namely, the equal power assignment (EPA) and the iterative power control (IPC). As expected, we found from simulation results that MMTPA outperforms EPA and IPC in the minimal throughput among all SUs. [ABSTRACT FROM PUBLISHER]

In downlink multiuser multiple-input–multiple-output (MIMO) systems, block diagonalization (BD) is a practical linear precoding scheme that completely eliminates interuser interference. However, its sum rate performance is rather poor in practical signal-to-noise ratio (SNR) regions due to the transmit power boost problem. In this paper, we propose a modified BD precoding scheme to improve its low-to-medium SNR performance with the so-called “effective SNR enhancement” technique. The proposed transmit covariance matrices are obtained by first solving a power minimization problem subject to the minimum rate constraint achieved by BD and then properly scaling the solution until the power constraint is active. The power minimization problem is nonconvex in general. We therefore propose an efficient algorithm that approximately solves the problem. Simulation results demonstrate significant sum rate gains over BD and existing minimum mean square error (MMSE)-based precoding schemes. [ABSTRACT FROM PUBLISHER]

We consider a multicast scenario that involves an ad hoc network of cochannel multiple-input–multiple-output (MIMO) nodes in which a source node attempts to share a streaming message with all nodes in the network through some predefined multihop routing tree. The message is assumed to be broken down into packets, and the transmission is conducted over multiple frames. Each frame is divided into time slots, and each link in the routing tree is assigned one time slot in which to transmit its current packet. We present an algorithm for determining the number of time slots and the scheduling of the links in these time slots to optimize the connectivity of the network, which we define to be the probability that all links can achieve the required throughput. In addition to time multiplexing, the MIMO nodes also employ beamforming to manage interference when links are simultaneously active, and the beamformers are designed with the maximum connectivity metric in mind. The effects of outdated channel-state information are taken into account in both the scheduling and the beamforming designs. We also derive bounds on the network connectivity and sum transmit power to illustrate the impact of interference on network performance. Our simulation results demonstrate that the choice of the number of time slots is critical in optimizing network performance and illustrates the significant advantage provided by multiple antennas in improving network connectivity. [ABSTRACT FROM PUBLISHER]

We consider the detection of multiple cochannel single-carrier spatially multiplexed data streams. To this end, we propose a hybrid time–frequency-domain decision feedback equalizer, which performs parallel detection of the data streams. A practical parameter estimation algorithm for these receivers is developed, which estimates the MIMO channel in parallel as a set of effective channel vectors. To avoid interference cancellation, we develop a two-stage frequency-domain (FD) forward section that implements linear cochannel interference (CCI) suppression in cascade with equalization utilizing estimated channel-state information. The resulting receiver architecture outperforms interference-cancellation-based equalizers in the presence of channel estimation errors. Furthermore, all transmitted data streams achieve equal diversity gains. [ABSTRACT FROM PUBLISHER]

We investigate weighted sum-rate maximization and rate-balancing problems for spectrum-sharing multiple-input–multiple-output orthogonal frequency-division-multiplexing (MIMO OFDM) based broadcast channels. The problem is solved by converting the MIMO OFDM channel into a block-diagonal form and applying the principle of multiple-access channel–broadcast channel (MAC-BC) duality. The algorithm in its dual form is solved using subgradient methods. The simulation results demonstrate the convergence of the algorithms. [ABSTRACT FROM PUBLISHER]

We examine the application of transmit precoding in multiuser multi-input–multi-output (MIMO) communication systems with maximum ratio combining (MRC) receivers. In many multiuser applications, the maximum-likelihood or minimum mean-square error (MMSE) receivers can be prohibitive to implement due to their high implementation complexity. We examine the performance of the system with simple MRC receivers and carefully selected precoders, which are designed to compensate the lack of high-complexity receivers, at the transmitter side. We examine the sum MSE minimization and signal-to-interference-plus-noise ratio (SINR) balancing frameworks for the selection of precoders. The performance of two frameworks with MRC receivers are compared between themselves as well as with their counterparts implementing MMSE receivers. It has been observed that the SINR balancing framework with simple MRC receivers has little performance loss in comparison with the MMSE receivers with a proper selection of precoders. [ABSTRACT FROM PUBLISHER]

Different-complexity multicell preprocessing (MCP) schemes employing distributed signal-to-interference leakage-plus-noise ratio (SILNR) precoding techniques are proposed, which require reduced back-haul data exchange in comparison with the conventional MCP structure. Our results demonstrate that the proposed structures are capable of increasing the throughput achievable in the cell-edge area while offering different geographic rate profile distributions, as well as meeting different delay requirements. [ABSTRACT FROM PUBLISHER]

Although the widely linear least mean square error (WLMMSE) receiver has been an appealing option for multiple-input-multiple-output (MIMO) wireless systems, a statistical understanding on its pose-detection signal-to-interference-plus-noise ratio (SINR) in detail is still missing. To this end, we consider a WLMMSE MIMO transmission system with rectilinear or quasi-rectilinear (QR) signals over the uncorrelated Rayleigh fading channel and investigate the statistical properties of its SINR for an arbitrary antenna configuration with $N_t$ transmit antennas and $N_r$ receive ones. We first derive an analytic probability density function (PDF) of the SINR in terms of the confluent hypergeometric function of the second kind, for WLMMSE MIMO systems with an arbitrary $N_r$ and $N_t=2, 3$. For a more general case in practice, i.e., $N_t>3$ , we resort to the moment generating function to obtain an approximate but closed form PDF under some mild conditions, which, as expected, is more Gaussian-like as $2N_r-N_t$ increases. The so-derived PDFs are able to provide key insights into the WLMMSE MIMO receiver in terms of the outage probability, the symbol error rate, and the diversity gain, all presented in closed form. In particular, its diversity gain and the gain improvement over the conventional LMMSE one are explicitly quantified as $N_r-(N_t-1)/2$ and $(N_t-1)/2$ , respectively. Finally, Monte Carlo simulations support the analysis. [ABSTRACT FROM AUTHOR]

It is known that interference alignment (IA) plays an important role in improving the degree of freedom (DoF) of multi-input and multi-output (MIMO) systems. However, most of the traditional IA schemes suffer from the high computational complexity and require the global and instantaneous channel state information (CSI), both of which make them difficult to be extended to cellular MIMO systems. To handle these issues, two new IA schemes, i.e., the retrospective interference regeneration (RIR) scheme and the beamforming based distributed retrospective interference alignment (B-DRIA) scheme, are proposed for cellular $K$ -user MIMO downlink networks. For the RIR scheme, it adopts interference elimination algorithm to erase redundant symbols in inter-cell interference (ICI) signals, and then uses interference regeneration algorithm to avoid secondary interference. The RIR scheme obtains greater DoF gain than the retrospective interference alignment (RIA) scheme, but incurs performance degradation when the transceiver antennas ratio (TAR) approaches 1. Therefore, the B-DRIA scheme is further proposed. For the B-DRIA scheme, the cellular beamforming matrix is introduced to eliminate the ICI, and meanwhile distributed RIA algorithm is adopted to align inter-user interference (IUI). The simulation results show that the B-DRIA scheme obtains larger DoF than the RIR scheme locally. Specifically, when TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2, the DoF of the B-DRIA scheme is superior than the RIR scheme. [ABSTRACT FROM AUTHOR]

In this work, we investigate the power allocation needed to maximize the energy efficiency (EE) of a massive multiple-input-multiple-output (MIMO) system. We first derive a closed-form expression for the user rate with the presence of pilot contamination caused by inter-cell interference during the channel estimation phase. From the definition of system EE, we propose a maximization problem with two constraints: limited power for pilot and data signals and a minimum value for every user rate. The proposed maximization problem is a fractional programming problem and is very hard to solve directly. Therefore, we propose the first iterative algorithm to transform the original problem into subtractive form. Moreover, we propose the second iterative algorithm to solve the subtractive form maximization problem by approximating it as a convex problem. The convergence and feasibility of the two proposed algorithms are also discussed. Finally, the effectiveness of our scheme is shown by simulation results. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) can significantly improve the throughput and spectral efficiency by equipping multi-antenna at both the base station (BS) and the users, which is greatly promising technologies for wireless communication systems. However, the large hardware requirement and high energy consumption are great hindrances. To effectively reduce the hardware and computational complexity and achieve a better trade-off between system complexity and performance, we propose an energy-efficient split sub-connected architecture based multi-user hybrid block diagonalization (BD) precoding and combining (Sub MU-HBD) scheme. The proposed Sub MU-HBD scheme employs the split sub-connected architecture to reduce the hardware complexity and improve spectral efficiency. Additionally, to significantly reduce the inter-user interference and further lessen the computational complexity, a low-dimensional digital precoding and combining scheme based on the BD is proposed. Furthermore, we propose a proportional water-filling power allocation algorithm to ensure optimal power distribution to users and improve energy efficiency. The simulation results demonstrate that the proposed Sub MU-HBD scheme can achieve near-optimal, and at times even better performance than some existing schemes. [ABSTRACT FROM AUTHOR]

As the demand to alleviate the high cost and energy consumption problem in cellular communication systems grows, research on ways to compensate the hardware impairments for introducing low-cost devices into multi-input multi-output (MIMO) systems is becoming important. In particular, the nonlinearity of power amplifiers may severely degrade the performance of MIMO systems by distorting the precoded signals. In this work, we study the performance and precoding strategy of downlink multiuser MIMO systems with nonlinear power amplifiers. First, we analyze the signal-to-interference-plus-noise ratio (SINR) of nonlinear systems to establish the relationship between the transmit power and SINR. We find that the condition for maximizing the SINR depends on the average channel gain. Specifically, the maximum SINR is determined by the uncorrelated distortion when the average channel gain is large and by the distortion of precoding vector when the average channel gain is small. Motivated by the analysis, we propose an iterative algorithm, which alternately updates the precoding and power allocation by resolving the complicated coupling factors in the SINR expressions of nonlinear system. Furthermore, the proposed algorithm implicitly controls the total transmit power so that the desired signal, interference, and distortion are properly balanced. By the simulation results, we verify the analysis results and show that the proposed scheme outperforms the conventional schemes and achieves a near-optimal sum rate. [ABSTRACT FROM AUTHOR]

The conventional zero-forcing (ZF) precoding and the two dimensional (2D) ZF precoding are two popular schemes to cancel the interference in the downlink of the full-dimensional (FD) massive MIMO systems. However, the conventional ZF is of high complexity as a result of ignoring the structure of the FD massive MIMO channels while the 2D ZF has poor performance due to neglecting the elevation angular spread. In this correspondence, by exploiting the spatial correlation of the FD massive MIMO channels, we propose a dominant path zero-forcing precoding (DPZF) scheme based on the low-rank matrix approximation. The proposed DPZF scheme includes two stages. In the first stage, we obtain the elevation and the azimuth components of r dominant paths for each user's channel by solving the rank-r channel matrix approximation. In the subsequent stage, we reformulate the Gram matrix with the obtained components so as to zero force the most dominant path of each user's channel with low complexity. For the proposed DPZF, the upper bound of the performance gap to the conventional ZF is derived and the complexity is analyzed. The analysis and simulation results indicate that the proposed DPZF can yield a more elegant performance-complexity tradeoff than the existing precoding schemes. [ABSTRACT FROM AUTHOR]

A multiple relay-aided downlink (DL) multi-user massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated to support simultaneous connectivity to cell-edge users. The estimated uplink channel state information (CSI) is used for precoder designs, and statistical DL CSI is adopted for successive interference cancellation (SIC) at the users. The achievable rates are derived, and thereby, the deleterious effects of CSI uncertainty, imperfect SIC, inter-relay interference and intra-cluster pilot contamination are quantified. Fundamental trade-offs between the achievable sum rate and system scalability are explored via two pilot assignment schemes. The performance of the proposed system is compared with that of the orthogonal multiple access (OMA) counterpart. We reveal that the latter performs better in low-user regime because the former's achievable rates are hindered by intra-cluster pilot contamination and imperfect SIC.However, the proposed model provides better system scalability and outperforms OMA in the large-user regime. The performance of zero-forcing (ZF)/null-space (NS) precoding is compared with maximum ratio transmission (MRT). We show that NS-based ZF precoding can boost the sum rate by mitigating inter-cluster interference at the expense of a higher computational complexity than that of MRT. [ABSTRACT FROM AUTHOR]

In practical multi-antenna systems, the design of precoding and power control is not straightforward even for multi-input single-output (MISO) systems because of the distortion caused by nonlinear power amplifiers. Through analysis of the signal-to-interference-plus-noise ratio (SINR), we found that the equal gain transmission scheme can achieve the maximum SINR of a nonlinear MISO system only at the point of local optimal transmit power. Then we derived an approximated optimal transmit power for the nonlinear MISO system. To maximize the SINR in all transmit power region, we propose a precoding and power control algorithm for a MISO system with nonlinear power amplifiers under power constraint. By simulation, we show that the proposed scheme can achieve the local optimum SINR, and outperform other baseline precoding techniques. [ABSTRACT FROM AUTHOR]

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences. [ABSTRACT FROM AUTHOR]