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This paper investigates distributed MIMO precoding design to maximize the weighted sum rate in a heterogeneous network where the multiple input multiple output full-duplex (MIMO-FD) small cells reuse the downlink spectrum of the macro base-station (BS) to exchange backhaul information. The multi-antenna BS transmits signals to the cellular users using the dirty paper coding technique and the MIMO-FD small cells apply FD precoding structures to effectively balance the received signal, the self-interference (SI), and the co-channel interference (CCI). Since the optimization problem is shown to be nonconvex, obtaining the global optimum is challenging. A low-complexity solution with distributed implementation is introduced with proved convergence. By applying the successive convex approximation technique and the duality between the broadcast channel and the multiple access channel, the original nonconvex problem is decomposed into a sequence of convex subproblems, which can be solved analytically and separately at each small cell and macro BS with limited channel state information exchange. Simulation results confirm the convergence and demonstrate the benefits of the introduced algorithm. It is shown that the SI and CCI can be suppressed effectively with sufficient cancellation power and number of transmit antennas at the FD small cells. [ABSTRACT FROM AUTHOR]

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]

This paper considers multicell scheduling for coordinated multiple-input multiple-output cellular networks. In a multicell scenario, intercell interference, which is the major challenge degrading system performance, can be suppressed via multicell coordination. However, when the number of users is larger than that can be served, user/multimode selection is necessary, and can significantly enhance the performance in such interference-limited environment. Nevertheless, user selection in a multicell scenario incurs high computational complexity and overhead signalling. To this end, in this paper, low complexity greedy algorithms are proposed for user selection. The algorithms are based on the concept of angle between subspaces and intersection of null spaces, and work in partially distributed fashion with limited information exchange. By these algorithms, channel feedback can be greatly reduced with reasonable computational complexity. In addition, multimode scheduling schemes are proposed for this multicell set up, where the spatial streams are adaptively selected. The proposed algorithms are analysed and compared with the optimal solution obtained by exhaustive search. Simulation results show that the algorithms achieve most of the throughput achievable by the optimal solution. [ABSTRACT FROM AUTHOR]

This paper investigates the energy consumption of distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Hence, the problem of grid power consumption minimization has to be solved by efficiently managing the energy delivered from different sources while satisfying the system requirements in terms of users’ quality of service demands. First, this paper solves the optimal offline version of the problem using linear programming. In fact, the main problem is decomposed and heuristically solved in order to deal with the large number of constraints and variables. Since sometimes all the users cannot be served, an iterative link removal algorithm is devised ensuring the feasibility of the problem. Next, we investigate the online energy management problem. We first propose a dynamic programming approach to obtain the optimal online solution but with high complexity. Then, we develop and propose a low complexity heuristic solution based on using the maximal available energy at the batteries. Taking benefits of the characteristics of large-scale MIMO and the modeling of the harvested energy as Markov chains, we devise an efficient online energy management algorithm based on energy prediction. Finally, we propose a heuristic solution for RRH on/off operation with the objective of improving the system energy efficiency. The performance of the proposed algorithms is evaluated by simulations and it is shown that the proposed energy management approaches offer efficient use of nonrenewable energy to compensate the variability of renewable energy in large-scale MIMO systems. [ABSTRACT FROM PUBLISHER]

This paper presents the first network-coded multiple access (NCMA) system prototype operated on high-order modulations up to 16-QAM. NCMA jointly exploits physical-layer network coding (PNC) and multiuser decoding (MUD) to boost throughput of multipacket reception systems. Direct generalization of the existing NCMA decoding algorithm, originally designed for BPSK, to high-order modulations, will lead to huge performance degradation. The throughput degradation is caused by the relative phase offset between received signals from different nodes. To circumvent the phase offset problem, this paper investigates an NCMA system with multiple receive antennas at the access point, referred to as MIMO-NCMA. We put forth a low-complexity symbol-level NCMA decoder that, together with MIMO, can substantially alleviate the performance degradation induced by relative phase offset. To demonstrate the feasibility and advantage of MIMO-NCMA for high-order modulations, we implemented our designs on software-defined radio. Our experimental results show that the throughput of QPSK MIMO-NCMA is double that of both BPSK NCMA and QPSK MUD at SNR = 10 dB. For higher signal-to-noise ratios at which 16-QAM can be supported, the throughput of MIMO-NCMA can be as high as 3.5 times that of BPSK NCMA. Overall, this paper provides an implementable framework for high-order modulated NCMA. [ABSTRACT FROM PUBLISHER]

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Generalized spatial modulation (GenSM) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) has recently received substantial academic attention. However, due to the insufficient exploitation of the transmitter's knowledge of the channel state information, the achievable rates of state-of-the-art GenSM-aided mmWave MIMO systems are far from being optimal. Against this background, a novel analog precoding scheme is proposed in this paper to improve the spectral efficiency (SE) of conventional GenSM-aided mmWave MIMOs. More specifically, we firstly manage to lower-bound the achievable SE of GenSM-aided mmWave MIMO with a closed-form expression. Second, by exploiting this lower bound as a cost function, a low-complexity iterative algorithm is proposed to design the analog precoder for SE maximization. Finally, numerical simulations are conducted to substantiate the superior performance of the proposed design with respect to state-of-the-art GenSM-aided mmWave MIMO schemes. [ABSTRACT FROM PUBLISHER]

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for realizing beyond-fifth generation (5G) networks. In this paper, we study the uplink spectral efficiency (SE) of a practical spatially correlated RISs-aided CF massive MIMO system over Rician fading channels. Specifically, we derive the closed-form expression for characterizing the uplink SE of the system, which shows that increasing the number of RIS elements can improve the system performance. Moreover, the results unveil that the spatial correlation of RIS has a significant impact on the system while leading to the best performance gain at a half wavelength spacing. Finally, the accuracy of our analytical results are verified by Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

A quadrature spatial modulation with the fourth order transmit diversity (FO-QSM) scheme is presented in this paper. In this scheme, two dispersion-matrix (DM) sets are assigned at the transmitter, and $P$ out of $Q$ DMs are activated in each set to extend the real and imaginary parts of $P$ QSM signals respectively, to large number of transmit antennas. The two DM sets are constructed by performing Kronecker product between two simple extension matrices and the DMs of Sezginer-Sari-Biglieri (SSB) code. The framework designs of FO-QSM and the two DM sets make FO-QSM possess the properties: 1) it guarantees the fourth order transmit diversity without requiring any parameter optimization; 2) it has higher spectral efficiency than the newly proposed generalized space-time block coded spatial modulation (GSTBC-SM) scheme. Furthermore, a repeated computation reduced sphere decoding (RCR-SD) is proposed. RCR-SD is very suitable for the schemes constructed by a large number of DMs, including FO-QSM. By sorting the index vectors, the RCR-SD detector can efficiently reduce the repeated computations in the SD searching, which significantly reduces the decoding complexity without sacrificing optimal decoding performance. Simulation results show that FO-QSM has obvious better bit error rate (BER) performance than GSTBC-SM. [ABSTRACT FROM AUTHOR]

The paper proposes a multiple-input multiple-output-based power line communication (PLC) system subject to Rayleigh fading and corrupted by additive cyclostationary Gaussian noise. The proposed coherent receiver structure employs a block reception rule at the reception unit for detecting the space-time block-coded transmitted data symbols. A closed-form expression for the pairwise error probability (PEP) is derived using a characteristic function approach. Exact and asymptotic expressions for the average PEP are obtained for the special case of Alamouti code transmission which show that the PLC system under consideration offers full diversity. Numerical results depict the improvement in the system performance with increase in the number of receive diversity branches and the average signal-to-noise ratio per branch and decrease in the correlation value between the noise samples. [ABSTRACT FROM AUTHOR]

Providing seamless connectivity to unmanned aerial vehicle user equipment (UAV-UE) is very challenging due to the encountered line-of-sight interference and reduced gains of down-tilted base station (BS) antennas. For instance, as the altitude of UAV-UEs increases, their cell association and handover procedure become driven by the side-lobes of the BS antennas. In this paper, the performance of cellular-connected UAV-UEs is studied under 3D practical antenna configurations. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. For both scenarios, the UAV-UE coverage probability is characterized as a function of the system parameters. The effects of the number of antenna elements on the UAV-UE coverage probability and handover rate are then investigated. Moreover, a lower bound on the handover probability is derived as a function of the number of antenna elements and the UAV-UE mobility speed. Results reveal that the UAV-UE coverage probability under a practical antenna pattern is lower than that under a simple antenna model. Moreover, vertically-mobile UAV-UEs are susceptible to altitude handover due to consecutive crossings of the nulls and peaks of the antenna side-lobes. The effect of altitude handover is also shown to be effectively mitigated by serving UAV-UEs cooperatively from multiple ground BSs. [ABSTRACT FROM AUTHOR]

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm. [ABSTRACT FROM AUTHOR]

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and theirassociated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell-free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes. [ABSTRACT FROM AUTHOR]

This paper considers a novel algorithmic framework for the joint spectral sharing design of a colocated MIMO pulse radar and a MIMO communication system under signal direction mismatch, wherein both radar transceiver and communication transmitter designs are investigated, and the mutual information (MI) is maximized under a number of constraints to suppress the mutual interference of the coexistence system. The designed multi-variate formulation is non-convex, an iterative procedure is derived to compute a sequence of simpler sub-problems which admit a low complexity closed-form solution for each primal variable. The convergence of all devised algorithms is guaranteed. Finally, a thorough performance assessment is conducted to demonstrate the merits of the proposed solutions. [ABSTRACT FROM AUTHOR]

The full-duplex (FD) single-user multiple-input–multiple-output (SU-MIMO) sum-rate maximization problem leads to a nonconvex optimization problem. In this paper, we introduce a maximum self-interference threshold (SIT) constraint to the sum-rate maximization problem to transform the problem into a convex optimization problem and develop the SIT-based FD precoding (FDP-SIT) algorithm. In particular, the FDP-SIT algorithm consists of an inner loop that assumes fixed threshold values and an outer loop that optimizes each node's threshold values. Illustrative examples show how the proposed FDP-SIT algorithm can provide a tradeoff between the separate and joint FDP algorithms. As well, the measured data-based results suggest that the FDP-SIT algorithm can offer increased sum rates when low cancelation power is used. [ABSTRACT FROM PUBLISHER]

This paper proposes a new multiple-input–multiple-output (MIMO) technique called quadrature spatial modulation (QSM). QSM enhances the overall throughput of conventional SM systems by using an extra modulation spatial dimension. The current SM technique uses only the real part of the SM constellation, and the proposed method in this paper extends this to in-phase and quadrature dimensions. It is shown that significant performance enhancements can be achieved at the expense of synchronizing the transmit antennas. Additionally, a closed-form expression for the pairwise error probability (PEP) of generic QSM system is derived and used to calculate a tight upper bound of the average bit error probability (ABEP) over Rayleigh fading channels. Moreover, a simple and general asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results corroborate the accuracy of the conducted analysis and show the significant enhancements of the proposed QSM scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a cognitive-radio-inspired asymmetric network coding (CR-AsNC) scheme is proposed for multiple-input–multiple-output (MIMO) cellular transmissions, where information exchange among users and base-station (BS) broadcasting can be accomplished simultaneously. The key idea is to apply the concept of cognitive radio (CR) in network coding transmissions, where the BS tries sending new information while helping users' transmissions as a relay. In particular, we design an asymmetric network coding method for information exchange between the BS and the users, although many existing works consider the design of network coding in symmetric scenarios. To approach the optimal performance, an iterative precoding design for CR-AsNC is first developed. Then, a channel-diagonalization-based precoding design with low complexity is proposed, to which power allocation can be optimized with a closed-form solution. The simulation results show that the proposed CR-AsNC scheme with precoding optimization can significantly improve system transmission performance. [ABSTRACT FROM PUBLISHER]

The massive multiple-input multiple-output (MIMO) system is one of the most promising techniques for beyond fourth-generation (B4G) wireless communication systems. However, supporting a downlink (DL) frequency-division duplex (FDD) massive MIMO system for backward compatibility might be a bottleneck problem because the number of feedback bits for the FDD system is proportionate to the number of transmit antennas. In this perspective, the well-known generalized space-shift keying (GSSK) can be a suitable candidate to support the DL FDD massive MIMO system. In this paper, the GSSK activating all transmit antennas at once is named as port modulation (PM). PM has enormous potential to utilize the spatial resources of the massive MIMO system by linking precoding and the well-known spatial modulation (SM). In this paper, we propose a codebook design for PM, which makes it possible to improve the average bit error probability (ABEP) of PM without instantaneous channel state information (CSI) at the transmitter (CSIT) or additional efforts to design shaping filters. Simulation results show that when the base station consists of a large number of spatially correlated transmit antennas, the proposed codebook design offers lower ABEP than other limited feedback or open-loop systems, such as spacen‐time-block-coded SM (STBC-SM). [ABSTRACT FROM PUBLISHER]

In this paper, space-shift keying (SSK) is considered for multihop multiple-input-multiple-output (MIMO) networks. In SSK, only one among ns = \2^{m} available transmit antennas, chosen on the basis of $m$ information bits, is activated during transmission. We consider two different systems of multihop cooperation, where each node has multiple antennas and employs SSK. In system I, a multihop diversity relaying scheme is considered. In system II, a multihop multibranch relaying scheme is considered. In both systems, we adopt decode-and-forward (DF) relaying, where each relay forwards the signal only when it correctly decodes. We analyze the end-to-end bit error rate (BER) and diversity order of both the systems with SSK. For binary SSK (ns = \2), our analytical BER expression is exact, and our numerical results show that the BERs evaluated through the analytical expression overlap with those obtained through Monte Carlo simulations. For nonbinary SSK (ns > \2), we derive an approximate BER expression, where the analytically evaluated BER results closely follow the simulated BER results. We show the comparison of the BERs of SSK and conventional phase-shift keying (PSK) and also show the instances where SSK outperforms PSK. We also present the diversity analyses for SSK in systems I and II, which predict the achievable diversity orders as a function of system parameters. [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]

In this paper, we analyze the diversity performance of source–relay joint precoding in spatial multiplexing (SM) multiple-input–multiple-output (MIMO) amplify-and-forward (AF) relaying systems with channel coding. First, we evaluate the achievable diversity order of singular-value-decomposition-based precoding schemes as a function of a code rate. Then, we show that a significant diversity improvement can be obtained by utilizing signal space diversity. Our analysis provides helpful insights on the tradeoff between the code rate and the diversity order in SM MIMO AF relaying systems. Simulation results demonstrate that our analytical expressions match well with the numerical results. [ABSTRACT FROM PUBLISHER]

A Gibbs distribution based combinatorial optimization algorithm for joint antenna splitting and user scheduling problem in full duplex massive multiple-input multiple-output (MIMO) system is proposed in this paper. The optimal solution of this problem can be determined by exhaustive search. However, the complexity of this approach becomes prohibitive in practice when the sample space is large, which is usually the case in massive MIMO systems. Our algorithm overcomes this drawback by converting the original problem into a Kullback-Leibler (KL) divergence minimization problem, and solving it through a related dynamical system via a stochastic gradient descent method. Using this approach, we improve the spectral efficiency (SE) of the system by performing joint antenna splitting and user scheduling. Additionally, numerical results show that the SE curves obtained with our proposed algorithm overlap with the curves achieved by exhaustive search for user scheduling. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITSs), the communication between the base station (BS) and the vehicles can form a virtual multiple-input multiple-out put (VMIMO) vehicle communication system. This paper addresses the issue of channel estimation in a virtual MIMO system that simultaneously adopt a smart antenna (SA). First, based on quaternion theory, a novel algorithm named quaternion noncircular MUSIC (QNC-MUSIC) is proposed, and direction of arrival (DOA) estimation is achieved for circular and noncircular signals. Then, a dimension reduction procedure is performed for the parameter search by employing the partial derivative of the spectrum function with respect to the noncircular phase; thus, a novel dimensional reduction noncircular MUSIC (DRNC-MUSIC) algorithm with a low computational complexity is proposed for DOA and polarization estimation of circular signals and noncircular signals. Finally, numerical examples are provided to demonstrate the parameter estimation performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Quadrature spatial modulation (QSM) was recently proposed to increase the spectral efficiency of spatial modulation. In QSM, the real and imaginary parts of a symbol are transmitted independently from different antennas. A new spatial modulation technique, termed signed quadrature spatial modulation (SQSM), is introduced in this paper. Unlike QSM, SQSM creates four-dimensional spatial constellation, and hence results in higher system throughput. This technique extends the traditional spatial-constellation dimension into $\pm$ in-phase and quadrature-phase dimensions. The ensuing constellation consists of four antenna indices, which are chosen independently in order to transmit the real and imaginary parts of the modulated symbols and their inverse. The performance of the proposed technique is compared to the most-recent SM techniques, namely, QSM, double SM, and improved QSM (IQSM). Numerical results demonstrate that SQSM provides significant performance gains (3–7 dB), particularly when a large number of transmit antennas is used. This comes with a marginal increase in computational complexity compared to QSM and SM. Also, while IQSM can be competitive with SQSM in some cases, SQSM has a much lower computational complexity, e.g., 46% reduction in complexity in case of 16 transmit antennas and 16 bps/Hz. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is an effective way to improve the spectrum efficiency of multiple-input multiple-output (MIMO) systems, where this advancement can be fully exploited by employing a proper user selection strategy in the multiuser MIMO system. However, for multiuser scenarios, information-theoretic throughput may not be properly used as the performance indicator since the constraints at the receiver (e.g., suboptimal detectors) are not taken into consideration. In this paper, we adopt the error probability (EP) as the performance metric, which is equivalent to the practical throughput efficiency and can take the critical constraints into consideration. We analyze the pair-wise error probability (PEP) performance of different detectors and propose two kinds of user selection methods to minimize the PEP. To facilitate the detection performance evaluation, we further investigate the multiuser diversity of the proposed selection methods and derive a tight lower PEP bound for the case with randomly selected users. From the analytical and numerical results, we demonstrate that the proposed multiuser selection methods can suppress the inter-user interference effectively and achieve better performance than that of the state-of-the-art method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of a single user time-varying millimeter-wave MIMO system. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly in each beam training period to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. [ABSTRACT FROM AUTHOR]

Linear dispersion (LD) based perfect space-time codes (STBCs) are an efficient means of increasing a multiple-input multiple-output (MIMO) system's overall diversity gain while maintaining the same spectral efficiency as a traditional spatial multiplexed (SM) MIMO system. Because the decoding procedure of LD codes traditionally requires the entire code to be received and decoded simultaneously, complexity increases proportional to the square of the MIMO array size. In this paper, we leverage the increased number of spatial and temporal layers at the decoder to dynamically reduce the complexity of a BLAST optimum ordering and successive interference cancellation (SIC) detector based on the instantaneous system capacity and data rate. The novel approach proposed in this paper is channel code and modulation agnostic, meaning the underlying constellation can be HEX or QAM and there is no feedback from a forward error correction (FEC) decoder, which makes the design useful in a wide range of MIMO systems employing LD codes with linear detectors. We investigate the method's bit error rate (BER) using MIMO dimensions up to $8 \times 8$ and bits per channel use (BPCU) up to 32. We analyze the system's run-time complexity and BER performance in software and implement perfect coding along with the novel method presented here in a custom MIMO orthogonal frequency division multiplexing (OFDM) system and test it over-the-air using an Ettus Research X310 software-defined radio (SDR) testbed. [ABSTRACT FROM AUTHOR]

In time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, the reciprocity between the downlink (DL) and the uplink (UL) channels can be utilized to infer the DL channel state information (CSI) with the acquired uplink (UL) CSI. However, the end-to-end UL and DL channel reciprocity are broken when there are mismatches in the radio frequency (RF) analog circuits among different antennas at the base station (BS). Without accurate calibration, there will be severe system performance degradation. This paper studies the internal self-calibration scheme to recover all the unknown full calibration coefficients when different BS antennas are interconnected via hardware transmission lines. On the one hand, we derive the calibration performance for an arbitrary interconnection network in closed-form and obtain closed-form Cramer-Rao lower bound (CRLB) expressions when $(M-1)$ transmission lines are consumed and $M$ denotes the total number of BS antennas. On the other hand, we prove that the star interconnection network is optimal for internal full self-calibration due to its lowest CRLB. Numerical simulation are carried out to verify our theoretical analyses and results. [ABSTRACT FROM AUTHOR]

This paper addresses the channel estimation problem for three-dimensional (3-D) massive multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with a two-dimensional uniform planar array (UPA) to serve a number of user equipments (UEs). To implement with low hardware complexity, the number of available radio-frequency (RF) chains at BS is constrained to be much smaller than the number of antennas. The theoretical analysis of sparse property for 3-D massive MIMO channel reveals that there exists two kinds of sparse structures in beam-domain channel vector, namely the common sparsity structure among sub-arrays of UPA and block sparsity structure per sub-array. Based on this property, a novel structured sparse Bayesian learning framework is proposed to estimate the channel between BS and UE reliably. Moreover, an adaptive spatial interpolation scheme is proposed to further reduce the number of required RF chains at BS while maintaining the estimation performance. The simulation results show that the proposed scheme provides stable estimation performance for a variety of scenarios with different numbers of RF chains, transmit signal-to-noise ratios, Rician factors, and angular spreads, and outperforms the reference schemes significantly. [ABSTRACT FROM AUTHOR]

Large-scale antennas systems have been attracted significant attention to provide considerable array gains for millimeter wave (mmWave) communications systems. However, huge hardware complexity and power consumptions make the conventional precoding architectures impractical to realize. Recently, hybrid precoding structures for mmWave communications systems are widely investigated to save the cost and achieve satisfactory performance. Unfortunately, most existing precoder designs lie in the assumption of infinite or high-resolution phase shifters (PSs), which are also difficult to realize in real-world. Motivated by this fact, a low complexity hybrid precoder and combiner design for multi-user systems with two-bit resolution PSs is proposed in this paper, which provides an appropriate trade-off between energy efficiency and system sum-rate. Specifically, the candidate sets for analog beamforming are constructed by introducing the auxiliary variables, in which the number of elements is the same as that of antennas. Accordingly, the computational complexity of searching optimal beamformer is significantly reduced over proposed codebooks. Then, to avoid high complexity singular value decomposition (SVD) of channel matrix, we propose a rank-1 approximate decomposition algorithm based-on principal component analysis technique. At last, the user scheduling algorithm is presented to further improve the performance in multiuser-scenario. Simulation results verified the effectiveness of the proposed algorithms, the performance of which is close to the optimal unquantized precoder. [ABSTRACT FROM AUTHOR]

In this correspondence paper, a novel code-index modulation (CIM) aided quadrature spatial modulation (QSM) scheme, called CIM-QSM, is proposed for highly spectrum and energy efficient multiple-input multiple-output (MIMO) systems. The CIM-QSM scheme transmits information over three different transmit entities: modulated symbols, activated antenna indices, and spreading code indices. Hence, a higher efficiency in terms spectrum and energy use, and better error performance compared to the conventional direct sequence spread spectrum, spatial modulation (SM), QSM, and CIM aided SS (CIM-SS) schemes are obtained by the CIM-QSM technique for MIMO communication systems. Analytical expressions for the average bit error rate, energy efficiency, and throughput of the CIM-QSM system are performed and the theoretical results are validated with Monte Carlo simulation results. [ABSTRACT FROM AUTHOR]

This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt). [ABSTRACT FROM AUTHOR]

In this paper, a 3D geometry-based double-spherical terahertz (THz) channel model is proposed for ultra massive multiple-input multiple-output (MIMO) communications. To reflect real THz ultra massive MIMO communications, the characteristic of nano-material array antenna and the high loss of THz bands are considered in channel modeling. Based on the proposed geometry-based stochastic model (GBSM), power delay profile (PDP), space-time-frequency correlation function, and the Doppler power spectral density are derived and analyzed. Furthermore, the proposed model is validated by comparing the simulated results with the measurement reference. Influences of channel parameters, namely, antenna element spacing, carrier frequency, number and location of clusters, on the correlation functions are investigated. The results indicate that antenna element spacing and number of clusters have significant impacts on channel correlation, and the decreasing of correlation function becomes slower when antenna element spacing decreases. Moreover, location of clusters also affects the drop rate of correlation function. The influences of the number of clusters and concentration of intra-cluster multipath components (MPCs) on the Doppler power spectral density are analyzed. It is found that a larger number of clusters and a more concentrated distribution of intra-cluster MPCs increase the degree of Doppler spectrum variation. The proposed channel model and the corresponding statistical properties are insightful for designing and realizing THz ultra massive MIMO systems for 6G and beyond. [ABSTRACT FROM AUTHOR]

Identification of communication parameters, a major task of intelligent receivers, has important applications in intelligent systems, especially cognitive radio systems. Multiple antennas make the identification problem more challenging. In this paper, we focus on the problem of detecting the number of transmit antennas in multiple-input multiple-output (MIMO) cognitive radio systems. A novel identification algorithm is proposed to determine the number of transmit antennas for MIMO systems in the presence of alpha-stable noise. We first introduce the correlation matrix based on the fractional lower order statistics (FLOS) and provide a particular structure of FLOS-based correlation matrix. Then, the eigenvalues of the FLOS-based correlation matrix are employed to construct a test statistic and the central limit theorem is exploited to obtain the decision threshold. Finally, the transmit-antenna number is detected using a serial binary hypothesis test. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number detection algorithm for MIMO systems in the presence of alpha-stable noise. [ABSTRACT FROM AUTHOR]

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease. [ABSTRACT FROM AUTHOR]

This paper presents a new class of precoder designs, namely, blockwise-lattice-reduction-aided Tomlinson–Harashima precoders (BLR-aided THPs), for multiuser multiple-input–multiple-output (MU-MIMO) downlink communications in the presence of clusters (CLs) of correlated channels. By combining the techniques of leakage-based preprocessing and lattice reduction (LR), the proposed BLR-aided THPs decompose the computationally intensive LR procedure into multiple subprocedures, resulting in reduced complexity. A number of precoder designs along with a computationally efficient intercluster ordering algorithm have been proposed. Complexity analysis and simulation results show that the proposed BLR-aided THP designs stand out as useful alternatives in balancing the tradeoffs between complexity and performance in the considered scenario. [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]

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes. [ABSTRACT FROM AUTHOR]

The implementation of uplink hybrid automatic repeat request (HARQ) in a cloud-radio access network (C-RAN) architecture is constrained by the two-way latency on the fronthaul links connecting the remote radio heads (RRHs) with the baseband units (BBUs) that perform decoding. To overcome this limitation, this paper considers an architecture based on the separation of control and data planes, in which retransmission control decisions are made at the edge of the network, that is, by the RRHs or user equipment (UE), while data decoding is carried out remotely at the BBUs. This solution enables low-latency local retransmission decisions to be made at the RRHs or UE, which are not subject to the fronthaul latency constraints, while, at the same time, leveraging the decoding capability of the BBUs. A system with a BBU Hoteling system is considered first in which each RRH has a dedicated BBU in the cloud. For this system, the control-data separation leverages low-latency local feedback from an RRH to drive the HARQ process of a given UE. The throughput and the probability of error of this solution are analyzed for the three standard HARQ modes of Type-I, chase combining, and incremental redundancy over a general fading multiple-input multiple-output (MIMO) link. Then, novel user-centric low-latency feedback strategies are proposed and analyzed for the C-RAN architecture, with a single centralized BBU, based on limited “hard” or “soft” local feedback from the RRHs to the UE and on retransmission decisions taken at the UE. The analysis presented in this paper allows the optimization of the considered schemes, as well as the investigation of the impact of system parameters such as HARQ protocol type, blocklength, and number of antennas on the performance of low-latency local HARQ decisions in BBU Hoteling and C-RAN architectures. [ABSTRACT FROM PUBLISHER]

Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood detector has the high complexity, whereas state-of-the-art low-complexity detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM signals, we propose a low-complexity signal detector based on structured compressive sensing (SCS) to improve the signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM signals by leveraging the structured sparsity. In addition, we also propose the SM signal interleaving to permute SM signals in the same transmission group, whereby the channel diversity can be exploited to further improve signal detection performance. Theoretical analysis quantifies the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output (MIMO) full-duplex relaying (FDR) has been considered an efficient technique to provide coverage to users where their direct links from the base station (BS) are too weak for reliable signal reception. However, when multiple MIMO full-duplex relays are deployed in a network, the signal reception quality relies on the effective suppression of multiple types of interference. In this paper, the distributed beamforming is studied for the MIMO FDR network by formulating a power minimization problem with a nonstrict convex objective function (total transmit power of both the BS and all the relays in the network) under individual user rate constraints. We come up with two iterative distributed beamforming algorithms, Algorithm 1 for relays equipped with single receive antenna and Algorithm 2 with multiple receive antennas. The former can yield a global optimal solution of the power minimization problem, whereas the latter can yield only a local optimal solution due to conservative successive convex approximations (SCAs) performed at each iteration, and a rigorous analysis on the upper bounds of step sizes is proposed to guarantee their convergence. The proposed two algorithms only require local information exchange between relays and hence are scalable for different network sizes and topologies. An “early termination” strategy in the operation of the proposed two algorithms is also presented to acquire an acceptable transmit power solution with less computation time consumption and thus suitable for realistic applications. Finally, some simulation results are provided to demonstrate that the proposed two algorithms perform well and significantly better than the existing state-of-the-art scheme reported in the work of Lee and Shin. [ABSTRACT FROM PUBLISHER]

Space shift keying (SSK) modulation is an emerging transmission technique for multiple-input multiple-output (MIMO) wireless channels, which exploits the spatial domain to convey information. In this paper, we present a layered space shift keying (LSSK) modulation scheme to fully exploit the spatial domain to transmit information bits, where a layered architecture is developed to achieve spatial multiplexing transmission in an SSK system. Specifically, LSSK leverages the rotated signals predetermined at the transceiver to identify different layers and improve the bit-error-rate (BER) performance. The layered signals are directly generated by the proposed LSSK modulation method with a low computational overhead. Furthermore, we propose a layered-and-joint (LJ) near-optimal detection algorithm based on the layered architecture of LSSK to reduce the detection complexity. In LJ detection, layered detection is performed to find a set of detection candidates for each layer, and then, joint detection is performed with these candidates. We show that the performance of LJ detection is quite close to that of optimal maximum-likelihood detection with significantly reduced detection complexity for high-spectrum-efficiency scenarios. Results demonstrate that the proposed LSSK scheme substantially improves the spectrum efficiency of an SSK system and outperforms other existing MIMO schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we consider energy-efficiency (EE) optimization problems with nonideal power amplifier (PA) models, per-antenna power constraints, and minimal spectral efficiency (SE) requirements in block-diagonalization-based multiuser multiple-input–multiple-output (MU-MIMO) systems. In the problem formulation, a joint optimization of the transmit covariance and the active transmit antenna set at the base station is considered. In general, the problem is a mixed-integer fractional programming problem, and it is difficult to solve it globally optimally. By reformulating the original problem as a sparse beamforming design problem and using a successive convex approximation method, we propose an iterative algorithm to solve it locally. In each iteration step, a concave fractional programming is solved, and the solution can be expressed in closed form with the help of the Lagrange dual method. Simulation results are used to verify the performance of our proposed algorithms. We also study the EE–SE tradeoff for the considered system. In fact, the relationship between EE and SE under an ideal PA model is not accurate for realistic systems. We characterize the EE–SE tradeoff under nonideal PA models by solving the EE maximization problems under different SE values. In this way, we can also shed light on the design of the system parameters to optimize the EE–SE curve. We find that with nonideal PA considered, the SE corresponding to the optimal EE will increase, compared with the ideal case. If we design the system parameters according to the optimal SE under an ideal PA model to maximize the EE, both the EE and the SE of the realistic system will decrease. We also find that when the proposed antenna selection algorithm is applied, the achieve region of the EE and the SE is expanded, particularly when the SE is low. In other words, the EE–SE win–win region is enlarged, and the EE–SE tradeoff is improved. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate an efficient numerical approach for solving higher order statistical methods for blind and semiblind signal recovery from nonideal channels. We develop numerical algorithms based on convex optimization relaxation for the minimization of higher order statistical cost functions. The new formulation through convex relaxation overcomes the local convergence problem of existing gradient-descent-based algorithms and applies to several well-known cost functions for effective blind signal recovery, including blind equalization and blind source separation in both single-input–single-output (SISO) and multiple-input–multiple-output (MIMO) systems. We also propose a fourth-order pilot-based cost function that benefits from this approach. The simulation results demonstrate that our approach is suitable for short-length packet data transmission using only a few pilot symbols. [ABSTRACT FROM PUBLISHER]

Channel state information at the transmitter (CSIT) plays a key role in achieving potential gain of massive multiple-input–multiple-output (MIMO) systems. In frequency-division duplex (FDD) systems, CSIT can be obtained through feedback from the receiver. Conventional limited feedback schemes, which have been designed for small-scale MIMO systems, suffer from a prohibitive amount of feedback requirement and encoding complexity when the number of transmit antennas becomes massive. In this paper, a projection-based differential feedback (PBDF) protocol is proposed for FDD massive MIMO systems. In the PBDF framework, the difference between original and predicted vectors is projected, and quantization is performed in a smaller dimensional subspace. With an appropriate projection exploiting spatial and temporal correlation of massive MIMO channels, the feedback amount and encoding complexity can be significantly reduced. The simulation results show that the proposed scheme achieves a large portion of potential throughput gain of massive MIMO systems with a small amount of channel feedback. [ABSTRACT FROM PUBLISHER]

To achieve an excellent error-correcting capability, the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) employs turbo codes as its forward error correction (FEC) standard. In addition, to achieve higher throughput, the LTE also implements multiple-input–multiple-output (MIMO) systems. Although a conventional turbo decoding scheme gives satisfactory performance in ideal MIMO systems, significant performance degradation occurs in an overloaded MIMO system when the number of transmit antennas is larger than that of receive antennas. In this paper, a joint decoding scheme for turbo codes, i.e., so-called joint turbo decoding, is proposed. In joint turbo decoding, calculations of soft information are conducted for each combination of bits from all streams instead of separately between each stream. In addition, to combine the trellis diagrams from all the streams, a super-trellis diagram is employed. The numerical results obtained through computer simulation show that the proposed scheme performs better than the conventional scheme, particularly in the case of an overloaded MIMO system. In the MIMO system with four transmit and two receive antennas, up to 2-dB performance enhancement can be attained at a bit error rate (BER) of 10−5. The proposed scheme also obtains improved throughput with a throughput/(SNR/stream) ratio close to the theoretical capacity of overloaded MIMO systems. [ABSTRACT FROM PUBLISHER]

Joint source/relay precoding has been considered an effective method to improve the system performance of dual-hop amplify-and-forward (AF) multiple-input–multiple-output (MIMO) relay systems. For practical implementations, the codebook-based limited-feedback precoding is widely exploited since the feedback bits can be effectively limited. As known, system performance strongly depends on the codeword selection criterion. In this paper, we will explore how to design the selection criterion when successive interference cancellation (SIC) receivers are adopted at the destination. Starting from the QR-SIC receiver, we first propose a centralized selection scheme by maximizing the minimum postprocessing signal-to-noise ratio (SNR). Next, a decentralized selection scheme is further developed to reduce the computational complexity. Furthermore, we demonstrate that our methods can be directly extended to the system with the minimum mean square error (MMSE)-SIC receiver. Simulation results show that the proposed designs can significantly improve system performance. [ABSTRACT FROM PUBLISHER]