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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]

Wireless network virtualization is a promising technique for future wireless networks. In this paper, different from traditional virtualization approaches by means of resource isolation at the subchannel or time-slot level, we propose a novel framework of heterogeneous cellular network virtualization combined with interference alignment (IA) technology, which utilizes IA to cancel the mutual interference, by aligning the interference from other transmitters into a lower dimensional subspace at each receiver. In this framework, we formulate the virtual resource allocation as a joint virtualization and IA problem, considering the gain not only from interference mitigation introduced by IA but from the sum-rate improvement brought by virtualization as well. In addition, to reduce the computational complexity, with the recent advances in discrete stochastic approximation (DSA), we propose a two-step algorithm to solve the formulated problem. The basic principle is to design IA schemes for each feasible association combination and then to traverse the association space to search for the optimal association combination with the maximum sum rate. Extensive simulations are conducted with different system parameters to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Current and future mobile network access technologies increasingly rely on multiantenna transmission techniques and on cell miniaturization to achieve orders-of-magnitude gains in capacity. In this paper, we propose and evaluate the idea of multiuser multiple-input–multiple-input-based (MU-MIMO) cell reassignments. Dense small-cell deployments make it possible to pair user equipment units (UEs), which were originally assigned to different cells, enabling multiuser MIMO (MU-MIMO) and resulting in performance improvements to both UEs. We discuss how such pairings can be accomplished, and we assess the potential gains in the resulting spectral efficiency and MU-MIMO usage. These are assessed through both discussion of performance bounds and detailed system-level simulation in a realistic outdoor small-cell scenario. [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]

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, we propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, we compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR. [ABSTRACT FROM PUBLISHER]

The main focus of this paper is on multistream multiantenna (multiple-input multiple-output) communications in large-scale heterogeneous random networks. Previous work in this area mainly studies the outage probability from the perspective of a given stream, which is inaccurate as it generally overlooks possible interference-driven cross stream signal-to-interference correlation in each communication link. To tackle this issue, in this paper, we focus on a link-level performance evaluation by characterizing the complementary cumulative distribution function (CCDF) of random mutual information (RMI) under heterogeneous interference. We focus on the scenarios that no coordination among transmitters is persuaded, and the receivers do not attempt to cancel out part of interuser interference. Assume that channel state information is known at both the transmitter and receiver of each communication pair, Rayleigh fading, that the singular value decomposition (SVD) is applied to construct pre-coding/post-coding filters for single-user detection, and that transmitted signals of antennas are i.i.d. Gaussian. We first provide accurate expression for the achievable data rate (ADR) of each link, and show that, regardless of the interference power, when transceivers are equipped with equal number of antennas it grows sublinearly with the number of antennas. We then approximate RMI via Gaussian distribution that its mean is the ADR and its variance is derived based on network's parameters. It is seen that the Gaussian approximation is credible even when two streams of data are transmitted. This paper provides various insights regarding the impact of system parameters, including density of transmitters, path-loss exponent, and the number of antennas, on the ADR and CCDF of RMI. We finally spot tremendous benefits of the SVD scheme over other prominent multiplexing systems, including maximum ratio combining, zero-forcing, and diagonal BLAST superstructure with minimum mean square error receivers. [ABSTRACT FROM PUBLISHER]

Vector perturbation (VP) is a nonlinear precoding technique that achieves near-capacity performance in multiuser multiple-input multiple-output systems at the expense of large complexity due to the search for the optimum perturbation vector. In this paper, we present the hybrid Tomlinson–Harashima VP (TH-VP) algorithm, a novel zero-forcing precoding scheme, which combines TH precoding to remove interuser interference, and VP precoding to equalize each user's multiple spatial streams. We show that the two nonlinear techniques can be integrated in a single optimization and that the proposed algorithm has lower computational requirements than any other. The performance of TH-VP is analyzed and simulation results show that TH-VP outperforms conventional zero-forcing VP and approaches the performance of dirty paper coding. [ABSTRACT FROM AUTHOR]

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]

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 channel feedback overhead for massive multiple-input multiple-output systems with a large number of base station (BS) antennas is very high since the number of feedback bits of traditional codebooks scales linearly with the number of BS antennas. To reduce the feedback overhead, an effective codebook based on channel statistics has been designed, where the required number of feedback bits only scales linearly with the rank of the channel correlation matrix. However, this attractive conclusion was only proved under a particular channel assumption in the literature. To provide a rigorous theoretical proof under a general channel assumption, in this paper, we quantitatively analyze the performance of the channel-statistics-based codebook. Specifically, we first introduce the rate gap between the ideal case of perfect channel state information at the transmitter and the practical case of limited channel feedback, where we find that the rate gap depends on the quantization error of the codebook. Then, we derive an upper bound of the quantization error, based on which we prove that the required number of feedback bits to ensure a constant rate gap only scales linearly with the rank of the channel correlation matrix. Finally, numerical results are provided to verify this conclusion. [ABSTRACT FROM AUTHOR]

Convergence and density evolution of a low-complexity iterative multiple-input multiple-output detection based on belief propagation over a ring-type pairwise graph are presented for binary data. The detection algorithm to be considered is effectively a forward–backward recursion and was originally proposed by Yoon and Chae in a work published in 2014, in which link-level performance, computational complexity, and convergence for Gaussian input were analyzed in detail. This paper presents the convergence proof and the density evolution framework for binary input to give an asymptotic performance in terms of average signal-to-interference-plus-noise ratio and bit error rate (BER) without channel coding. The BER curve obtained via density evolution shows a good match with the simulation results for uncoded BER in the paper by Yoon and Chae verifying the effectiveness of the analysis provided and the performance of the detection algorithm. [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]

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]

Channel state information (CSI) is critical for the performance of cooperative multicell transmission. In this paper, the performances of cooperative multicell precoding under global CSI and local individual CSI are analyzed and compared in the large dimensional regime. Using theoretical results from large random matrix theory, we derive large dimensional approximations of signal-to-interference-plus-noise ratio for both precoding cases. Such approximations depend only on the slow fading channel statistics instead of the fast fading channel realizations. It is easy to quantify the performance loss caused by local individual CSI in the large dimensional regime. Two different massive MIMO scenarios are considered. When the number of antennas and that of users go large at the same rate, there is a constant gap between the two CSI cases. When the number of antennas goes large, while the number of users is fixed, the performances of both CSI cases are the same. This means that the impact of limited local individual CSI vanishes. Numerical results validate the theoretical analysis and show that there is constant performance loss due to the price of limited CSI. Also, the performance of precoding with local individual CSI almost decreases linearly with the increased number of BSs for the first scenario. [ABSTRACT FROM PUBLISHER]

The evaluation of the coverage probability of multistream multiple-input multiple-output (MIMO) communications in HetNets subject to noise, fading, and intercell interference is undoubtedly intricate. Unfortunately, the current literature misses its comprehensive evaluation, as the effects of noise and cross-stream correlation are often overlooked. Furthermore, computationally friendly expressions of the coverage probability allowing engineering insights and adaptive system design are lacking. For multistream MIMO zero-forcing beamforming, in this paper we tackle these issues by considering scenarios where a receiver is in the coverage if all of its data-streams are successfully decoded. Assuming the max signal-to-interference-plus-noise ratio (SINR) cell association (CA), we adopt the stochastic geometry tools to provide an upper bound and an easy-to-compute closed-form lower bound on the coverage probability, while their accuracies are confirmed against extensive simulations. Our contributions are as follows. We prove that full correlation of data streams of a given link slightly reduces the coverage performance. We show that from a coverage probability perspective, the single stream communication is preferable. We exploit our analysis to explore several pertinent design issues, which have not fully discussed in the literature. Our results demonstrate tradeoffs between densification and multiplexing gains. We, further, see that by appropriately designating feedback channel with modest capacity 8 bits per frame per user, the spatial throughput grows by nearly 180 $\%$ over the conventional 1-bit feedback scenario. Finally, We present important extensions of our analysis to underlay spectrum sharing, practical aspects of the max-SINR CA, and a nonhomogeneous path loss environment. [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 uplink device-to-device (D2D) underlay cellular systems, massive multiple-input multiple-output (MIMO) seems promising as the large antenna array at the base station (BS) can nearly null the D2D-to-cellular interference. However, the channel-state information (CSI) from all users is required to obtain this advantage. For the orthogonal training scheme, the pilot overhead increases with the number of D2D users, leading to a loss of the achievable spectral efficiency. In this paper, we propose a pilot scheduling scheme for D2D underlay massive MIMO systems, which allows D2D users to reuse the pilots of cellular users so that the pilot overhead can be reduced. Moreover, a novel interference-aided minimum mean square error (MMSE) detector is proposed to suppress the D2D-to-cellular interference, utilizing the estimated CSI from not only cellular users but D2D users as well. Furthermore, we derive the large-scale approximations of the signal-to-interference-plus-noise ratios (SINRs), which are asymptotically tight and accurate, even for a small number of BS antennas. Based on these SINR approximations, performance analyses are given to provide more insights. Simulation results show that the proposed pilot reuse scheme and detector significantly increase the achievable sum spectral efficiency, as compared with the conventional massive MIMO and other baseline schemes. [ABSTRACT FROM PUBLISHER]

In this paper, resilient transmission in a multicell multiple-input multiple-output (MIMO) interference wiretap channel model is studied. Each base station (BS) transmits confidential messages to its intended legitimate user with multiple antennas in the presence of eavesdroppers that can overhear the transmission. We study the problem of finding the optimal transmit covariance matrices at the BSs to maximize the secrecy sum rate, which is typically nonconvex and intractable to obtain a globally optimal solution. A distributed iterative optimization algorithm based on a novel decomposition framework across all users is proposed. The decomposition framework preserves the convexity of the objective function and linearizes the nonconvex part. At each iteration, the BSs simultaneously solve a sequence of problems that are decoupled convex approximations of the original secrecy sum-rate function. Moreover, the complexity of the algorithm is analyzed. Numerical results are presented to validate the effectiveness of the proposed distributed algorithm. [ABSTRACT FROM PUBLISHER]

This paper studies the impact of a multiple-input–multiple-output (MIMO) relay on the $K$- user multiple-input–single-output (MISO) interference channel (IC) when the relay has delayed feedback (i.e., delayed channel state information (CSI) or delayed channel output feedback) from each receiver, yet each transmitter has no knowledge about CSI. We first develop a new relay-aided retrospective interference alignment (r-RIA) and characterize the optimal sum degrees of freedom (DoFs) for the $K$- user MISO IC with a MIMO relay by providing a new matching outer bound. It is shown that the sum DoF does not decrease, even in the absence of direct links between transmitters and receivers, as long as $K$ is sufficiently large. We next show that a MIMO relay can provide a sum-DoF gain in the $K$- user MISO IC with delayed feedback to the relay in the absence of CSI at transmitter (CSIT), whereas relaying is shown to be not useful with instantaneous CSIT from the DoF perspective. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising transmission technology enabling, essentially, the maximum achievable degrees of freedom (DOF) in $K$-user multiple-input–multiple-output (MIMO) interference channels. The ideal DOF of IA systems have been obtained using independent MIMO channels, which is, usually rarely observed in reality, particularly in indoor environments. Therefore, the data sum rate and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation between MIMO channels decreases the signal-to-noise ratio (SNR) of the received signal after alignment. For this reason, an acceptable sum rate of IA in real MIMO-orthogonal frequency-division multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the distance between network nodes and the separation between the antennas within each node to minimize the spatial correlation. In this paper, we propose to apply transmit antenna selection to MIMO-OFDM IA systems either through bulk or per-subcarrier selection, aiming at improving the sum-rate and/or error-rate performance under real-world channel circumstances, while keeping the minimum spatial antenna separation of 0.5 wavelengths within each node. Two selection criteria are considered: maximum sum rate (Max-SR) and minimum error rate (Min-ER). To avoid subcarrier imbalance across the antennas of each user, which is caused by per-subcarrier selection, a constrained per-subcarrier antenna selection is operated. Furthermore, a suboptimal antenna selection algorithm is proposed to reduce the computational complexity of the optimal algorithm. An experimental validation of MIMO-OFDM IA with antenna selection in an indoor wireless network scenario is presented. The experimental results are compared with deterministic channels that are synthesized using hybrid electromagnetic (EM) ray-tracing models. Our performance evaluation shows that the practical feasibility of MIMO-OFDM IA systems is significantly increased by antenna selection in real-world scenarios. [ABSTRACT FROM AUTHOR]

Massive multiple-input–multiple-output (MIMO) antenna system implies the use of a large number of base station (BS) antennas to serve a relatively small number of user terminals (UTs) for extraordinary spectral efficiency. However, its performance is limited by pilot contamination due to unavoidable reuse of pilot sequences from UTs in different cells. In this paper, we analyze the performance of massive MIMO zero-forcing (ZF) systems using the time-shifted pilot scheme, which was known to combat pilot contamination effectively using conjugate beamforming if there is a very large number of antennas. We derive expressions for achievable sum rates and the signal-to-interference-plus-noise ratios (SINRs) of forward and reverse links if the number of BS antennas is finite. Then, the impact of system parameters, such as cell radius, transmit power, group number (a parameter related to the time-shifted pilot scheme), BS antenna number, etc., on the system performance are revealed. Our model embraces a series of previous works as special cases. Moreover, we compare the performance of conjugate and ZF precoders, from which a simple but effective large-scale fading-based UT scheduling scheme is proposed to enhance the system throughput. [ABSTRACT FROM PUBLISHER]

In this paper, the spectrum efficiency of multiuser single carrier (SC) transmission is considered. In SC multiuser transmission, multiuser access can be realized by using interference suppression, e.g., SC frequency-domain adaptive antenna array (SC-FDAAA) transceiver, or by orthogonal frequency allocation among users, e.g., SC frequency-division multiple access (SC-FDMA) transceiver. To evaluate the system spectrum efficiency, the signal-to-interference-plus-noise ratio (SINR) at the output of the SC-FDAAA receiver and the SC-FDMA receiver is derived, respectively. The system spectrum efficiency is then evaluated and compared for both transceivers. It is shown that under the assumption of slow transmit power control, the system spectrum efficiency of the SC-FDMA transceiver does not change with the number of users, whereas the spectrum efficiency of the SC-FDAAA transceiver reaches its maximal value by an optimal number of users, which is determined by both the number of receive antennas and the target receive signal-to-noise ratio (SNR). With this observation, an SC-adaptive transceiver is proposed based on unorthogonal frequency allocation and frequency-domain interference suppression to improve the system spectrum efficiency, and the proposed scheme has been shown to be effective by numerical results. [ABSTRACT FROM PUBLISHER]

Recently, heterogeneous network (HetNet) has been introduced as an appropriate strategy to cope with the explosive growth of wireless traffic. To address the issue of severe interference in the HetNet, we can consider interference alignment (IA), which achieves the maximum degrees of freedom (DOFs) in multiple-input–multiple-output (MIMO) interference channels (ICs). However, an anticipated performance of the MIMO IA is difficult to be clarified, particularly with different numbers of antennas across the nodes when designing an appropriate system. In this paper, we thus study antenna configurations for the three-user MIMO IA with respect to the intended data stream tuple, i.e., DOF. For simple analysis, we consider two-tier network with two types of data stream numbers in a system. First, the dimension of the subspace occupied by interference is derived by aligning the interference in the signal space of the MIMO IA. After this, the suitable numbers of antennas are computed depending on the transmitted data stream numbers. Our obtained results provide an insight on how the performance of the MIMO IA is affected as the system configurations change. Furthermore, we discuss how to maximize the performance of such systems when the available numbers of antennas are finite. The actual simulations with an iterative IA confirm the validity of our derived results. [ABSTRACT FROM PUBLISHER]

In this paper, a novel unified transmission framework in the context of both decode-and-forward (DF) and amplify-and-forward (AF) protocols was proposed for a two-hop multiple-input–multiple-output (MIMO) alternate half-duplex (HD) relaying system with $M$ antennas for each node, which can achieve the maximum M$ degrees of freedom (DoFs) of the system, compared with the existing interference alignment (IA) schemes that can only get \3M/\4 DoF. Furthermore, the proposed schemes require only two relays against three relays invoked in the existing IA schemes. Moreover, the proposed schemes are valid for the cases where the number of antennas $M$ can be either even or odd, whereas the existing IA schemes can only apply for an even number of antennas $M$. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new three-user network information flow model, referred to as the triangular multiple-input–multiple-output (MIMO) relay (TMR) channel, which consists of three users and three relays equipped with nU and nR antennas, respectively. Each user sends two independent messages to the other two users via the adjacent relays in two time slots, which are referred to as the multiple-access and broadcast stages. We derive a novel simultaneous signal and interference alignment for the proposed TMR channel in a scenario where there are fewer antennas at each relay than at each user (nR < nU ) . An optimized pseudo-inverse scheme based on an efficient gradient projection algorithm is proposed to solve the simultaneous alignment problem. By deriving a gradient over weighted sum-rate maximization and applying a gradient descent method, the optimal beamforming vectors are obtained to maximize the effective signal-to-noise ratios. Furthermore, to obtain rapid convergence speed and reduce computational complexity, we introduce a quasi-Newton method, which is referred to as the Broyden–Fletcher–Goldfarb–Shanno algorithm, by approximating the Hessian matrix of a pure Newton method. The convergence of the proposed gradient algorithm is guaranteed by proposing a line search algorithm. Finally, a performance evaluation shows that the proposed scheme offers a higher sum rate, produces a better outage probability, and achieves a higher multiplexing gain than the existing schemes. [ABSTRACT FROM PUBLISHER]

Energy-efficient transmission is crucial for future wireless communication systems and has attracted much attention. In this paper, we study coordinated beamforming optimization for multicell multiple-input–single-output (MISO) downlink systems using energy efficiency as a criterion, which is still an open problem to the best of our knowledge. The optimization problem of interest is nonconvex and in a fractional form. To solve it, we first reveal that finding the solution of the problem is equivalent to searching for a particular point on the Pareto boundary of the newly defined energy-efficiency rate tuples. Then, we propose to use a set of interference temperature (IT)-constrained beamforming optimization problems to characterize the energy-efficiency rate Pareto boundary. Based on that, two efficient iterative algorithms are developed to reach the Pareto optimality and thus to solve the primary problem. We show that the proposed algorithms can be carried out in a decentralized manner and are guaranteed to converge. Then, these algorithms are extended to consider imperfect channel state information (CSI) using the worst-case design. Numerical results are finally provided to verify the effectiveness of the proposed schemes and they exhibit the great potential of the coordinated beamforming optimization in improving the energy efficiency of the cellular network. In particular, the results also illustrate that our proposed algorithms achieve most of the achievable performance gain with a small number of iterations and thereby with limited backhaul overhead in a time-division duplexing (TDD) system. [ABSTRACT FROM AUTHOR]

The fifth generation communication systems aim to achieve higher capacity and more user access. Full-duplex (FD) communication can achieve twice the spectral efficiency of the conventional half-duplex systems under ideal conditions, and FD relay is a common application of FD technology. In this paper, we propose a modified signal-to-leakage-and-noise ratio (SLNR) precoding scheme for the FD multiuser MIMO relay system, in which the self-interference (SI) of the relay is considered. In contrast to block diagonalization (BD) precoding and zero-forcing (ZF) precoding, SLNR precoding does not have an antenna dimension limitation, which enables it to support more antennas and users. In addition, SLNR precoding balances the interference and noise of a system, whereas BD precoding and ZF precoding only consider the interference. The numerical results show that the proposed SLNR precoding scheme can achieve better performance with low SI. With a reasonable sum capacity loss, the SLNR precoding scheme provides more user access despite a limited number of transmit antennas. [ABSTRACT FROM AUTHOR]

Due to the use of an appropriately designed pulse shaping prototype filter, filter bank multicarrier (FBMC) system can achieve low out of band (OoB) emissions and is also robust to the channel and synchronization errors. However, it comes at a cost of long filter tails, which may reduce the spectral efficiency significantly when the block size is small. Filter output truncation (FOT) can reduce the overhead by discarding the filter tails but may also significantly destroy the orthogonality of FBMC system, by introducing intercarrier interference (ICI) and intersymbol interference (ISI) terms in the received signal. As a result, the signal-to-interference ratio (SIR) is degraded. In addition, the presence of intrinsic interference terms in FBMC also proves to be an obstacle in combining multiple-input multiple-output (MIMO) with FBMC. In this paper, we present a theoretical analysis on the effect of FOT in an MIMO-FBMC system. First, we derive the matrix model of MIMO-FBMC system, which is subsequently used to analyze the impact of finite filter length and FOT on the system performance. The analysis reveals that FOT can avoid the overhead in the time domain but also introduces extra interference in the received symbols. To combat the interference terms, we then propose a compensation algorithm that considers odd and even overlapping factors as two separate cases, where the signals are interfered by the truncation in different ways. The general form of the compensation algorithm can compensate all the symbols in a MIMO-FBMC block and can improve the SIR values of each symbol for better detection at the receiver. It is also shown that the proposed algorithm requires no overhead and can still achieve a comparable bit error rate (BER) performance to the case with no filter truncation. [ABSTRACT FROM AUTHOR]

This paper presents a generalized closed-form beamforming technique that can achieve the maximum degrees of freedom in compounded multiple-input multiple-output (MIMO) broadcast channels with mixed classes of multiple-antenna users. The contribution is first described within the context of a three-cell network and later extended to the general multicell scenario where we also show how to determine the conditions required to align the interference in a subspace that is orthogonal to the one reserved for the desired signals. This is then followed by an analysis of the impact of antenna correlation for different channel state information acquisition models. The proposed scheme is examined under both conventional and large-scale MIMO systems. It will be shown that the proposed technique enables networks with any combination of user classes to achieve superior performance even under significant antenna correlation, particularly in the case of the large-scale MIMO systems. [ABSTRACT FROM AUTHOR]

The nested array, implemented by nonuniform antenna placement, is an effective approach to achieve $O(N^2)$ degrees of freedom (DOF) with an antenna array of $N$ antennas. Such DOF refers to the number of directions of incoming signals that can be resolved. With the increased number of DOF, an important application of nested array is to nullify interference signals from multiple directions. In this paper, we apply nested array in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) for interference management. With a nested array, a base station (BS) can nullify a certain number of interference signals based on their directions. Then, a key design issue is how to select the set of interference sources to be nullified at each BS. As the DOF of each BS is used to resolve both desired signals and interference, the number of interference signals that can be nullified depends on the number of users served by the BS. Thus, user association is another factor that impacts the system performance and should be jointly considered with interference nulling. We formulate the joint interference nulling scheduling and user association problem as an integer programming problem, aiming to maximize the sum rate of all users subject to BS DOF constraints. We first investigate the case of interference nulling with a given user association, and propose a scheme to solve a relaxed problem as well as derive a performance upper bound. Then, we propose a distributed joint interference nulling and user association scheme based on a poly matching between users and BSs. Simulation results show that the proposed schemes effectively improve the sum rate and achieves a near optimal performance. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the resource allocation problem in two-tier heterogenous networks (HetNets) with massive MIMO, where two frequency bands, cellular frequency band and millimeter wave frequency band, are used for wireless backhaul links. To coordinate the interference among small cells (SCs), SCs are grouped into multiple SC clusters (SCCs), where SC users are jointly served by SC base stations (BSs) (SBSs) belonging to the same SCC. To eliminate the multi-user and inter-tier interference, we propose a block diagonalization based precoding scheme at macro-BS and each SCC, where the transmit precoding matrix for users is projected onto the null space of interference channels. Following this, we formulate a power allocation optimization problem to maximize the downlink sum rate of the system subject to per-SCC power and users’ quality of service requirements. Since the formulated problem is nonconvex, we transform it into a convex one by appropriate approximation, and a constrained concave convex procedure based iterative algorithm is proposed to obtain the solution. Furthermore, we prove that the obtained solution converges to the local optimum. Meanwhile, according to the structure of the original formulated problem, we divide it into multiple subproblems and propose an optimal iterative water-filling algorithm. Next, we extend the original problem with per-SBS power constraint, which results in that the precoding of each SCC has to be redesigned. On this basis, we propose an iterative water-filling algorithm to obtain the optimal power allocation and precoding. Numerical results are presented to verify the performance of our proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel matrix decomposition method, called generalized multi-unitary decomposition (GMUD), and use it to achieve robust multi-antenna multi-user multiple-input–multiple-output (MIMO) precoding with limited and partial channel information feedback. GMUD transforms complex matrix {H} into \bf H = \bf P\theta,\, r{\bf R}r{\bf Q}\theta,\, r^{H}, where \bf Rr is a lower triangular matrix, and \bf P\theta,\, r and \bf Q\theta,\, r are unitary matrices. A unique feature of GMUD is that it gives multiple solutions of the unitary matrices \bf P\theta,\, r and \bf Q\theta,\, r based on two parameters, namely the direction parameter \theta and magnitude parameter r. This property enables the GMUD precoder to steer the precoding vectors of different users to jointly optimize their performance. Compared with regularized-inverse precoding (an existing multi-user MIMO precoding technique that we have extended to handle multiple receive antennas), the advantages of GMUD precoding are that it requires only partial channel state information (CSI) from the users, and its performance is robust to CSI quantization. [ABSTRACT FROM PUBLISHER]

Recently, an adaptive intercell interference (ICI) mitigation technique has been proposed for downlink cellular systems as a means to increase throughput with low system complexity. However, in the prior work, the issue of intercell user scheduling has not been considered. In this paper, we study multiple-input–single-output (MISO) downlink cellular systems that jointly design adaptive ICI cancelation (ICIC) and intercell user scheduling assuming that partial channel state information (CSI) is shared among base stations (BSs). Since the optimal solution would require high complexity, we investigate a new low-complexity algorithm that selects the best users and their beamforming strategies in terms of maximizing the weighted sum rate (WSR). To this end, we first develop a simple threshold criterion for each user to decide the preferred beamforming strategy based on the derivation of the expected signal-to-interference-plus-noise ratio (SINR). Then, according to users' feedback about their decisions, a successive user and beamforming selection algorithm is performed at the BSs. From simulation results, we show that, combined with proportional fair scheduling, the proposed scheme provides excellent throughput performance with very low computational complexity and the amount of inter-BS CSI exchange. Furthermore, we discuss an extension of the proposed scheme to limited feedback systems and observe that our algorithm also provides similar advantages over conventional schemes with quantized feedback. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) has been recognized as a promising technique to obtain large multiplexing gain in multiple-input–multiple-output (MIMO) interference channels. Most existing IA schemes require global channel state information (CSI) at the transmitter to design precoding vectors and, thus, result in significant capacity overhead in the feedback link. To reduce the feedback overhead, in this paper, we investigate the IA scheme employing differential CSI feedback over time-correlated MIMO channels. Specifically, we analyze the achievable sum-rate performance using differential feedback and derive the minimum differential feedback rate to achieve the maximum multiplexing gain. We also provide the upper bound of the sum-rate performance loss due to the limited differential feedback rate. Finally, the analytical results are verified by simulations in a practical IA scheme with differential CSI feedback using Lloyd's quantization algorithm. [ABSTRACT FROM PUBLISHER]

We consider linear precoding in the downlink of a multiuser multiple-input–single-output (MISO) overlay cognitive radio (CR) network, wherein a primary base station (PBS) and a cognitive base station (CBS) share the same frequency band to transmit to a number of primary users (PUs) and secondary users. Conventionally, linear precoding techniques in a CR network aim to limit or completely cancel interference to the PUs while achieving maximum downlink throughput of the CR system. In this paper, we investigate novel adaptive linear precoding techniques at the CBS to exploit the interference to the primary and secondary systems, instead of its cancellation, by exploring the potential of making use of interference energy when the interference between the two systems is mutually constructive on an instantaneous basis. By doing so, the received signal-to-noise ratio (SNR) is enhanced by the signal energy gleaned from the constructive interference. In this direction, constructive-interference-based adaptive linear precoding techniques are proposed for the CR network, assuming a simple zero-forcing precoder at the PBS and noncausal knowledge of the primary message at the CBS. The presented analysis and simulations show that the proposed precoding techniques outperform conventional techniques, as the CR transmission actively enhances the performance of the PUs through constructive interference while achieving significant throughput for its own transmission. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a sequential and incremental precoder design for downlink joint transmission (JT) network multiple-input–multiple-output (MIMO) systems with imperfect backhaul links. The objective of our design is to minimize the maximum of the substream mean square errors, which dominates the average bit error rate (BER) performance of the system. In the proposed scheme, we first optimize the precoder at the serving base station (BS) and then sequentially optimize the precoders of nonserving BSs in the JT set according to the descending order of their probabilities of participating in the JT. The BS-wise sequential optimization process can improve system performance when some BSs have to temporarily quit the JT operations because of poor instant backhaul conditions. In addition, the precoder of an additional BS is derived in an incremental way, i.e., the sequentially optimized precoders of the previous BSs are fixed, and thus, the additional precoder plays an incremental part in multi-BS JT operations. An iterative algorithm is designed to jointly optimize the substream precoder and substream power allocation for each additional BS in the proposed sequential and incremental optimization scheme. Simulations show that, under the practical backhaul link conditions, our scheme significantly outperforms the autonomous global precoding scheme in terms of BER performance. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the performance analysis of the maximum eigenmode beamforming (MEB) scheme with equal power allocation (EPA) over practically coded multiple-input multiple-output (MIMO) multiple-access channels (MACs). Both the scenarios of perfect and imperfect channel state information (CSI) at the transmitters (CSIT) are considered. Analytical and numerical results show that MEB with EPA can achieve significant power savings and is less sensitive to CSIT error than other alternatives. [ABSTRACT FROM PUBLISHER]

This paper analyzes the average bit error rate (BER) of multiple-input–multiple-output (MIMO) systems in transmit-correlated Rayleigh flat-fading channels. The receiver scheme is based on the minimum mean square error (MMSE) criterion, and the input may be precoded to optimize the communication. Accurate closed-form formulations for the average BER are derived. [ABSTRACT FROM PUBLISHER]

Multicell cooperative processing (MCP) has been recognized as a promising technique for increasing the spectral efficiency of future wireless systems. Unfortunately, the provided benefits of MCP come at the cost of increased radio feedback and backhaul overhead; for downlink transmission in frequency-division duplexing (FDD) systems, users need to feed back their channel-state information (CSI) to the MCP scheduler, and user data need to be exchanged between all cooperating base stations (BSs) through the backhaul network. In the context of conventional noncooperative networks, it has been suggested that radio feedback load could be reduced by preventing users with low-quality channels from feeding back their CSI (concept of selective feedback), at the cost of a small fraction of the multiuser diversity gain. In this paper, we investigate the translation of this selective feedback concept to MCP systems. According to this technique, users with weak interference links are prevented from feeding back their full CSI to the MCP scheduler. Although efficient, this technique alone cannot mitigate the backhaul overhead related to routing user data possibly to several BSs. To overcome this condition, we propose two schemes: one scheme based on Media Access Control (MAC)-layer scheduling and the other scheme based on physical-layer precoding. These schemes, combined with feedback load reduction, allow for a substantial mitigation of the MCP overheads related to feedback and backhaul load. We analyze the improvements in terms of both user-to-BS feedback reduction and backhaul load reduction, and we show that this framework results in a good tradeoff between performance and overhead for multicell cooperative networks. [ABSTRACT FROM PUBLISHER]

Channel estimation is critical to the performance of a multiple-input multiple-output and orthogonal frequency-division-multiplexing (MIMO-OFDM) communication system. Current MIMO-OFDM channel estimation techniques suffer from either high complexity or poor performance. In fact, this issue has become one of the bottlenecks of a practical MIMO-OFDM system. This paper solves the problem by introducing a new technique that has minimum complexity and yet provides superior channel tracking performance close to a minimum mean square error (MMSE) estimate over a wide range of channel selectivity and noise variance. The performance was evaluated via numerical simulations. [ABSTRACT FROM PUBLISHER]

Full-duplex (FD) multiple-input multiple-output relaying has been considered an effective scheme to increase the spectral efficiency for wireless communications. As known, the main problem for the FD system is the cancellation of loop interference (LI). In this paper, we propose using the joint source/relay precoding to reduce the influence of LI. Therein, linear precoders are used at the source and relay, while the minimum mean-squared-error receiver is adopted at the destination. The joint precoder design is complicated when spatial multiplexing is exploited for signal transmission. To solve the problem, we propose an iterative method in which the original problem is split into two subproblems. With some matrix properties, we then show that each subproblem can be formulated as a convex optimization. Finally, a closed-form solution can be obtained with the Karush–Kuhn–Tucker conditions. Using a mean-squared-error upper bound, we also propose a low-complexity method to reduce the computational complexity. The proposed precoders have closed-form expressions, which is a great advantage in real-world implementation. Simulation results show that the proposed methods significantly outperform existing ones. [ABSTRACT FROM AUTHOR]

This paper considers a multipair two-way amplify-and-forward (AF) relaying system, where multiple pairs of full-duplex users are served via a full-duplex relay with massive antennas, and the relay adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) processing. The orthogonal pilot scheme and the least square method are first exploited to estimate the channel state information (CSI). When the number of relay antennas is finite, we derive an approximate sum rate expression which is shown to be a good predictor of the ergodic sum rate, especially with a large number of antennas. Then, the corresponding achievable rate expression is obtained by adopting another pilot scheme which estimates the composite CSI for each user pair to reduce the pilot overhead of channel estimation. We analyze the achievable rates of the two pilot schemes and then show the relative advantages of the two methods. Furthermore, power allocation strategies for users and the relay are proposed based on sum rate maximization and max-min fairness criterion, respectively. Finally, numerical results verify the accuracy of the analytical results and show the performance gains achieved by the proposed power allocation. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) in conjunction with multiple-input multiple-output (MIMO) orthogonal frequency-division multiple access is a candidate technology for future mobile radio networks. The short communication range of underlay CR systems is commonly a major limiting factor. In this paper, we propose a computationally and spectrally efficient resource allocation scheme for multiuser MIMO orthogonal-frequency-division-multiplexing-based underlay CR networks to provide good spectral efficiency gain and, therefore, increased communication range. Since the formulated optimization problem defines a mixed-integer programming (combinatorial task) that is hard to solve, we propose a two-phase scheme to produce efficient solutions for both the downlink and the uplink. Particularly, the first procedure elaborates on an adaptive precoding that is characterized by spectral efficiency due to the degrees of freedom it can provide. The second procedure develops a fast subcarrier mapping algorithm, which can be worked out through optimal power distribution among the CR users. The proposed scheme is optimal for the downlink but, however, near-optimal for the uplink. Simulation results demonstrate the bandwidth and computational efficiencies of the proposed scheme compared with the state of the art. [ABSTRACT FROM AUTHOR]

We investigate coordinated regularized zero-forcing (RZF) precoding for limited feedback multicell multiuser (MU) multiple-input–single-output (MISO) systems. We begin by deriving an approximation to the expected signal-to-interference-plus-noise (SINR) ratio for the proposed scheme with perfect channel direction information (CDI) at the base station (BS). We also derive an expected SINR approximation for limited feedback systems with random vector quantization (RVQ)-based codebook CDI at the BS. Using the expected interference result for the RVQ-based limited feedback CDI, we propose an adaptive feedback bit allocation strategy to minimize the expected interference by partitioning the total number of bits between the serving and out-of-cell interfering channels. Numerical results show that the proposed adaptive feedback bit allocation method offers a spectral efficiency gain over the existing coordinated zero-forcing (ZF) scheme. [ABSTRACT FROM PUBLISHER]

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]

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]