This paper investigates novel radar-centric joint collaborative precoding schemes for multiuser, multiple-input multiple-output (MIMO) dual-function radar communications (DFRC) systems. Three base station (BS) collaboration scenarios are considered: 1) collaboration only in radar signals, 2) collaboration only in communication signals, and 3) collaboration in both radar and communication signals. We first present the problem formulation for each considered scenario and then propose a suboptimal algorithm using alternating optimization and quadratic transform. Additionally, a post-processing procedure is proposed to generate the required rank-one solution. Simulation results demonstrated that the proposed radar-centric collaborative schemes can provide significant communication performance gain compared to the benchmark radar-centric non-collaborative DFRC scheme, highlighting the advantages of taking BS collaboration into consideration in DFRC systems.

Tensor train decomposition (TTD) has recently been proposed for high-dimensional signals because it can save significant storage in various signal processing applications. This paper presents a low-rank approximation algorithm of singular value decomposition (SVD) for large-scale matrices in tensor train format (TT-format). The proposed alternating least square block power SVD (ALS-BPSVD) algorithm can reduce the computational complexity by decomposing the large-scale SVD into a low-rank approximation scheme with a fixed-iteration block power method for searching singular values and vectors. Moreover, a low-complexity two-step truncation scheme was proposed to reduce more complexity and facilitate the parallel processing. The proposed ALS-BPSVD algorithm can support the low-rank approximation SVD for matrices with dimension higher than 211× 211. The simulation results show that the ALS-BPSVD achieved up to 21.3 times speed-up compared to the benchmark ALS-SVD algorithm for the random matrices with prescribed singular values.

This paper presents a hybrid precoding processor for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) communication systems. The proposed architecture supports 64 antennas with 4-to-8 RF chains, and 4-bit phase resolution for each phase shifter in the analog beamformer. A polar decomposition (PD) engine is designed to achieve a 2.4-to-$8.6\times $lower latency with 1.5-to-$1.7\times $less normalized gate count compared to the singular value decomposition (SVD)-based implementation for the same functionality. Multiplicands are combined to reduce 55% area by utilizing the distributive property. Approximate phase extraction and low-precision multiplication for quantized phase extraction are adopted, which leads to 43% and 41% area reductions, respectively. The proposed hybrid precoding processor is designed in a 40-nm CMOS technology. It delivers a throughput of up to 10,288 kMat/s and consumes 170.9 mW at 278 MHz. It outperforms the state-of-the-art hybrid precoding processors with 5.5-$13.7\times $higher normalized area efficiency and 6.9-$38.4\times $lower normalized energy.

In the generalized spatial modulation (GenSM) multiple input multiple output (MIMO) system, each block of data bits is mapped to a set of spatially multiplexed (SMX) symbols and an index of transmit antenna combination (TAC) of active antennas. The difficulty for the GenSM MIMO receiver is to detect the SMX symbols and TAC index simultaneously. Recently, we applied the conventional sphere decoding algorithm (SDA) successively to achieve the exact maximum likelihood detection (MLD) of GenSM MIMO signals. The SDA scheme suffers from variable computational complexity and leads to hardware detectors with variable throughput rate. Instead, fixed-complexity tree search algorithms, e.g., reduced fixed sphere decoding (rFSD), with nearly MLD performance are employed to facilitate hardware implementation. Here, we propose to apply the rFSD successively and design a hardware architecture for detecting GenSM MIMO signals under the scenario of 5 transmit antennas, 2 transmit radio frequency chains, 4 receive antennas, and 64-QAM SMX symbols. The VLSI implementation results under the TSMC 90nm CMOS technology reveal that our architecture requires 276.7K gates and provides detection throughput 1.613 Gbps, while operating at 322.6 MHz. Compared with other related architectures, our architecture provides higher detection throughput rate and is of better hardware efficiency.

In this paper we investigate the precoder design problem of a preprocessing-aided spatial modulation multiple-input-multiple-output (PSM-MIMO) system with quadrature-phase-shift-keying (QPSK) signaling that facilitates hardware implementation using one-bit digital-to-analog-converters (DACs) and quantized phase shifters. We formulate the new design problem and propose an efficient algorithm based on a combination of alternating minimization method and mathematical programming with equilibrium constraints (MPECs). The bit-error-rate (BER) performance of the proposed one-bit precoder in comparison to a number of benchmark PSM-MIMO precoders is quantified via numerical simulations.

Multi-user multiple-input multiple-output (MU-MIMO) is an important transmission technique for wireless communication systems, which increases spectral efficiency by transmitting data to several users at the same time and frequency band. The main design issue for the MU-MIMO system is to suppress co-channel interference among all users by using appropriate MIMO precoding technique. Generalized eigenvalue decomposition (GEVD) is an inevitable and high-complexity processing unit in the leakage-based precoding system with multi-antenna users. This paper presents a Givens rotation-based algorithm for hardware implementation, which can be realized by only coordinate rotation digital computer (CORDIC) processors. The proposed GEVD processor was designed for the MU-MIMO mode in the IEEE 802.11ac system. It supports eight transmitting antennas at the base station and two receiving antennas at each of four users. The GEVD processor was designed and implemented by using a TSMC 40-nm process technology. The chip synthesis results show that the proposed GEVD processor achieves a user throughput of 0.98 M matrix/sec/user, equivalent to 0.25 M channel matrices/sec, conforming to the IEEE 802.11ac standard.

The generalized geometric mean decomposition (GGMD) is a recently proposed matrix decomposition which can be viewed as a computationally efficient counterpart of the conventional geometric mean decomposition (GMD). As the GMD is the core algorithm in many high performance precoders and equalizers such as the Tomlinson-Harashima precoder and decision feedback equalizer, GGMD facilitates a more computationally efficient implementation while exhibiting identical performance. This work presents the first GGMD processor in the open literature, supporting various matrix sizes by leveraging the reconfigurable processing element (PE) using coordinate rotation digital computers (CORDICs). The implemented GGMD processor supports matrix sizes of 2nwhich ranges from 2×2 to 16×16, and the throughput performance is maximized through a PE array architecture. The chip integrates 326.9K gates in an area of 1.65 mm2in a 90-nm CMOS technology with the maximum throughput achieving 450K matrices/sec for a 16×16 matrix at 125 MHz. It dissipates 20.7-28.5 mW at 125 MHz from a 1V supply. Compared to previous GMD designs, this work supports a larger MIMO system with lower hardware complexity and power consumption.

This paper addresses the mean-square-error (MSE) based precoder design problems for a new multiple- input-multiple-output (MIMO) system employing the new Receive Quadrature Spatial Modulation (RQSM). We investigate the precoder design problem under the average total power constraint (ATPC), average per-antennna power constraint (APAPC), peak per- antenna power constraint (PPAPC), and the strict per-antenna power constraint (SPAPC). We show that the ATPC precoder can be obtained in closed-form, while the APAPC, PPAPC, SPAPC precoders can be formulated into a unified SOCP framework. The performance of these precoder design have been studied via numerical simulations and compared with the conventional power-backoff assisted approaches.

In this paper, a new generalized space shift keying (GSSK) multiple-input-multiple-output (MIMO) detector is proposed. The proposed detector improves the conventional orthogonal matching pursuit (OMP) detector by a list detection strategy, and admits a regular structure with fixed throughput characteristic. Simulation results show that our proposed list OMP detector can achieve a more favourable performance- complexity tradeoff compared to other GSSK-MIMO detectors.

In this paper, a new transceiver design for multi-user multi-relay MIMO downlink channel is proposed. We derive the closed-form expressions for the gradients of the weighted sum-MSE with respect to the precoders and present a new transceiver design based on the gradient descent algorithm. The algorithm monotonically decreases the weighted sum-MSE and hence guarantees local convergence. Simulation results show that the proposed algorithm can generate good suboptimal solution with much faster convergence rate comparing to other algorithms.

This paper presents an iterative geometric mean decomposition (IGMD) algorithm for multiple-input-multiple-output (MIMO) wireless communications. In contrast to the conventional geometric mean decomposition (GMD) algorithm, the proposed IGMD does not require the explicit Kth root computation in the preprocessing stage but depends on a carefully constructed iterative procedure that generates the GMD in its limit. We prove analytically that the proposed IGMD is guaranteed to converge to the exact GMD under certain sufficient conditions, and propose three different constructions achieving this condition. Both numerical simulations and complexity analysis of the proposed IGMD have been conducted and compared with the conventional GMD. Simulation results show that our new IGMD algorithm effectively reduces the complexity overhead and hence is more advantageous for low-complexity implementations.

This paper presents two new types of transceiver designs for the multi-cell, multi-user, multiple-input-multiple-output (MIMO) interfering broadcast channel (IBC). In contrast to the existing maximum sum rate and max-min fairness transceiver designs, the proposed designs aim at balancing on the two objectives by maximizing the minimum average sum-rate per cell (type-1) or maximizing the minimum average sum-rate within each cell (type-2). Due to the difficulty of these problems, two iterative suboptimal algorithms which ensure local convergence have been proposed. Simulation results show that the proposed algorithms can provide certain level of fairness guarantee without sacrificing much of the overall system performance. As a result, the proposed designs are in some sense more balanced and can be useful alternatives in addition to the existing designs.

This paper presents a new class of Blockwise-Lattice-Reduction (BLR) aided Tomlinson-Harashima precoders (BLR-THPs) for multiuser multiple-input-multiple-output (MU-MIMO) downlink communications with clusters of correlated channels. The proposed BLR-THPs take the clustering information into account by first decoupling the overall downlink channel into multiple subchannels (one for each cluster) before applying the lattice-reduction technique to each subchannel. In comparison to the conventional LR-THPs, the proposed BLR-THPs require much lower complexity as the computationally intensive LR procedure in the LR-THPs is now effectively approximated by multiple LR procedures with much lower dimensionality. We present a number of new precoders that belong to this class and then compared them with the existing THPs with or without LR through extensive computer simulations.

The signal-to-leakage-and-noise ratio (SLNR) based precoding is a promising linear precoding scheme in multiuser (MU) downlink multiple-input-multiple-output (MIMO) communications. It gains its popularity recently as it nicely decouples the design problem and allows for a closed-form solution. In this paper, we extend the idea to nonlinear precoding and propose a closed-form SLNR- based Tomlinson-Harashima precoder. The proposed design is derived from a more general form of the simultaneous diagonalization conditions, allowing the the effective SINR among received streams of the same user to be more balanced. A new user-ordering algorithm is also developed for the proposed design based on the max-min SINR criterion to further improve the average error rate performance and the fairness among users. The performance advantages of the proposed design are quantified through extensive computer simulations.

We consider an amplify-and-forward multiple-input multiple-output relay system with a direct source- destination link. We adopt the minimum-mean-square- error (MMSE) criterion at the destination. The problem of interest is to jointly design source and relay precoders so as to minimize mean square error of transmitted symbols under total power constraints at the source and relay nodes. We propose a method which diagonalizes the MSE matrix using singular value decomposition (SVD) and generalized SVD techniques. The proposed approach based on this diagonalized MSE matrix is suboptimal and aims to reduce the design complexity of the precoders. The solution can be obtained via an iterative water- filling technique. Simulations results show the performance advantages of the proposed approach.

Lattice reduction (LR) has emerged as a popular technique to improve the performance of many low complexity MIMO detectors. Among the existing LR algorithms, the LLL algorithm has been applied to MIMO communications almost exclusively to date. However, the conventional LLL algorithm has variable reduction time, and hence non-constant throughput for the resulting communications system. In order to address this issue, this paper proposed a new variant of the LLL-algorithm (CT-LLL-deep) having constant throughput by introducing the concept of deep insertion into a parallel variant of LLL. Simulation results show that LR-aided detectors using the proposed CT-LLL-deep are capable of achieving lower bit error rate with much less reduction time, comparing to those using other existing LR algorithms.

This paper proposes a space-time shift keying (STSK) modulation scheme, which maps the information onto two dimensions, i.e., the temporal and spatial dimensions. For the proposed modulation, a detector with reduced complexity is also proposed. The complexity of proposed detector is reduced with the slight degradation of performance. The simulation results show that the proposed system achieves good performance. Furthermore, since the STSK utilizes two resources to modulate information bits, the STSK obtains different error rates for the information modulated by different resources. Therefore, the potential of the proposed STSK to be applied in the unequal error protection (UEP) is also discussed.

We consider the data detection in a multiple-input multiple-output (MIMO) broadcasting system where the source simultaneously transmits multiple data streams to single-antenna receivers. To detect multiple data streams, conventional MIMO techniques require the receivers to have multiple receive antennas, the number of which is greater than or equal to the number of transmit antennas. However, user cooperation can be applied to combat this limitation. In this paper, we propose a user decode- and-forward (DF) cooperation scheme and the corresponding detection method. The transmission consists of two phases: source broadcasting and the user cooperation. During the user cooperation phase, a selected leader in the cooperation group collects the detections from the other users. The leader then obtains its final detection by using maximum likelihood (ML) criteria and broadcasts the results to all the other users in the cooperation group. Through simulations we show that the average symbol error performance of the proposed cooperation scheme are affected by several factors including the cooperation group size, the number of users activated for transmission and the length of the cooperation phase. With moderately large group size, the proposed system can potentially outperform the conventional system whose number of receive antennas are equal to the number of transmit antennas.

In this paper, a MIMO detection scheme is proposed based on a combination of Monte Carlo technique and list detection. Specifically, a list of Gaussian samples are first generated to determine the search range of constellation points in which the transmitted symbol is most likely to locate. Linear equalizations are then applied to equalize the effect caused by the channel mixing, and a list detector is used to search within the determined search range. By varying the parameters in the Monte Carlo method, different symbol error rate (SER) versus complexity tradeoff can be obtained to account for different system design requirements. Simulation results also show that near-ML SER performance with considerably less computational complexity can be achieved by the proposed scheme compared to the exhaustive search.

Multimode antenna selection (MAS), the technique of jointly selecting both the number of spatial streams and antenna subsets at the transmitter, is a promising scheme for improving the performance of spatial multiplexing MIMO systems. In this paper, we investigate the problem of multimode antenna selection for the Vertical Bell Laboratories Layered Space-Time (V-BLAST) system with a goal to minimize the system's error rate. As the error rate of a communications system varies dramatically with the transceiver architecture, the optimal MAS algorithm for V-BLAST should explicitly take the ordered-successive interference cancellation (OSIC) detection structure into account. A computationally efficient MAS algorithm is therefore proposed to exploit the recursive structure of mean-square-error matrices between consecutive OSIC detection stages and achieve near optimal error rate performance with significantly lower complexity as verified by the computer simulations.

Lattice reduction (LR) has recently emerged as a promising technique for improving the performance of suboptimal multiple-input-multiple-output (MIMO) detectors. For LR-aided MIMO detection, the Lenstra-Lenstra-Lovász (LLL) and Seysen's algorithm (SA) have been considered almost exclusively to date. In this paper, we introduced a new LR algorithm for LR-aided linear detection (LD). In contrast to the LLL and SA, which are targeted to search for bases with relatively short basis vectors, the proposed algorithm has been designed to improve the minimum Euclidean distance of the LR-aided linear detector, thus exhibiting improved error rate at high SNR. The error-rate performance of the proposed algorithm as well as the required complexity has been demonstrated through extensive computer simulations.

Lattice-reduction (LR) has recently emerged as a promising technique for MIMO detection. Through lattice reduction, the original MIMO channel can be transformed into an equivalent one with better condition and result in the improved performance. Since all the LR-aided detectors require the prior knowledge of the instant channel state information (CSI) which can only be estimated in practice, the accuracy of the estimated CSI directly affects the attainable performance. In this paper, we present a new class of LR-aided MIMO detectors (ICSI-LRAD) which is capable of improving the performance of the conventional LRADs by exploiting the second-order-statistics of the channel estimation. The performance of the proposed ICSI-LRADs under different amount of prior information has been demonstrated and quantified through extensive computer simulations.

This paper deals with the problem of estimating the directions-of-arrival (DOA) of multiple wideband sources using an array of sensors. While a variety of estimation techniques have been proposed in the literature, the maximum-likelihood (ML) DOA estimator has been shown to have superior performance under many challenging environments. In this paper, we propose a novel implementation for ML DOA estimator based on the cross-entropy (CE) method. Simulation results show the CE algorithm converges to the Cramer-Rao bound (CRB) in all scenarios within several iterations and the convergence speed is insensitive to the coherence of sources.

We propose a novel algorithm employing particle filters for acoustic source tracking in a reverberant environment. By incorporating the likelihood function computed through approximate maximum-likelihood (AML) method, the proposed algorithm is applicable to wideband sources and can be implemented for multiple sources tracking. Both computer simulation and experimental results show the effectiveness of the proposed algorithm

Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous, unmanned monitoring. In this paper, we investigate the design, analysis, and testing of acoustic arrays for localizing bird vocalizations of different species. The spectra of the bird waveforms affect the desired dimension of the array. Microphones are placed in a uniform circular array and are finely synchronized within a few microseconds. We apply the Approximate Maximum Likelihood (AML) method to estimate the source direction-of-arrival (DOA) and perform beamforming for signal enhancement. The crossing of the distributed DOA estimated bearings is used to localize the birds, and the enhanced signals are used for training and estimation for the classification of the birds. The experimental results demonstrate the practicality and robustness of our array design.