Joint Multiple-input Multiple-output Research Articles

Nonorthogonal waveform assisted DOA estimation for joint MIMO sensing and communication

Conventional direction-of-arrival (DOA) estimation methods for multiple-input multiple-output (MIMO) joint sensing and communication system normally pursue high estimation accuracy and resolution by imposing orthogonal waveforms. However, such operation results in a deterioration of communication performance. In this paper, we propose a nonorthogonal waveform assisted DOA estimation algorithm, where an augmented virtual array is derived by exploiting the nonorthogonal MIMO communication waveforms, while a high communication rate can still be maintained. To estimate the round-trip sensing channels of each subcarrier, we utilize the transmitted symbols as pilot symbols, and obtain all the channel coefficients with a minimum mean square error solver. A virtual channel matrix can be formulated with these channel coefficients, which can be regarded as the samples of an augmented virtual array. Based on that, the subspace processing can be conducted for DOA estimation with fully nonorthogonal waveforms. Furthermore, the rank deficiency property of the equivalent signal matrix of the virtual array is analyzed when the distance of targets are identical. To address the problem, a Toeplitz reconstruction method is proposed to restore the rank of the rank-deficient equivalent signal matrix for DOA estimation. Simulations show that the proposed nonorthogonal waveform assisted DOA estimation algorithm outperforms the conventional methods in terms of resolution and accuracy, while maintaining a satisfactory computational efficiency.

Communication-Efficient Federated Learning Over MIMO Multiple Access Channels

Communication efficiency is of importance for wireless federated learning systems. In this paper, we propose a communication-efficient strategy for federated learning over multiple-input multiple-output (MIMO) multiple access channels (MACs). The proposed strategy comprises two components. When sending a locally computed gradient, each device compresses a high dimensional local gradient to multiple lower-dimensional gradient vectors using block sparsification. When receiving a superposition of the compressed local gradients via a MIMO-MAC, a parameter server (PS) performs a joint MIMO detection and the sparse local-gradient recovery. Inspired by the turbo decoding principle, our joint detection-and-recovery algorithm accurately recovers the high-dimensional local gradients by iteratively exchanging their beliefs for MIMO detection and sparse local gradient recovery outputs. We then analyze the reconstruction error of the proposed algorithm and its impact on the convergence rate of federated learning. From simulations, our gradient compression and joint detection-and-recovery methods diminish the communication cost significantly while achieving identical classification accuracy for the case without any compression.

Transmit Design for Joint MIMO Radar and Multiuser Communications With Transmit Covariance Constraint

In this paper, we consider the waveform design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.

Joint Bernoulli Filtering and MIMO Processing for Detection of Moving Targets in Shallow Ocean Environments

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

Joint MIMO Radar and Communication System Using a PSK-LFM Waveform With TDM and CDM Approaches

In this paper, a new technique which enables communication data to be embedded into multiple-input multiple-output (MIMO) radar waveforms is presented. A linear frequency modulated (LFM) signal is used for radar sensing, and multiple phase-shift keying (PSK) symbols or bit sequences are embedded into the LFM signal for communications. Such waveforms are subsequently used for radar sensing with MIMO beamforming. Orthogonality between transmitters is ensured using either a time-division multiplexing (TDM) or code-division multiplexing (CDM) approach. The performance of these novel techniques is demonstrated through both simulation and experimentation.

Joint MIMO Communication and MIMO Radar Under Different Practical Waveform Constraints

A joint transceiver coexistence design of multiple-input multiple-output (MIMO) communication and MIMO radar is addressed under different practical waveform constraints. Without radar waveform constraints, the conventional optimal waveforms can result in large modulus variation and poor range resolution. To tackle these problems, the constant-modulus and/or similarity constraints are added in this work. The proposed constant-modulus waveform constraint is favoured by the cheap and efficient nonlinear power amplifier, and the proposed similarity constraint provides a flexible control on modulus variation, range resolution and transmit power, at the cost of a small extra computational complexity.

MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning

Spatial sparsity of the target space has been successfully exploited to provide accurate range-angle images by the methods based on sparse signal reconstruction (SSR) in Multiple-Input Multiple-Output (MIMO) radar imaging applications. The SSR based method discretizes the continuous target space into finite grid points and generates an observation model utilized in image reconstruction. However, inaccuracies in the observation model may cause various degradations and spurious peaks in the reconstructed images. In the process of the image formation, the off-grid problem frequently occurs that the true locations of targets that do not coincide with the computation grid. In this article, we consider the case that the true location of a target has both range and angle-varying two-dimensional (2D) off-grid errors with a noninformative prior. From a variational Bayesian perspective, an iterative algorithm is developed for joint MIMO radar imaging with orthogonal frequency division multiplexing (OFDM) linear frequency modulated (LFM) waveforms and 2D off-grid error estimation of off-grid targets. The targets during multiple probing pulses are modeled as Swerling II case and a unified generalized inverse Gaussian (GIG) prior is adopted for the target reflection coefficient variance at all snapshots. Furthermore, an approach to reducing the computational workload of the signal recovery process is proposed by using singular value decomposition. Experimental results show that the proposed algorithm is insensitive to noise and has improved accuracy in terms of mean squared estimation error under different computation grid interval.

Iterative Gaussian-Approximated Message Passing Receiver for MIMO-SCMA System

Sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) are viewed as two key techniques to address the high spectral efficiency requirements for the 5G wireless system. However, the complexity of standard message passing algorithm (MPA) for the MIMO-SCMA detection yet increases exponentially with the degree of resource nodes $d_f$ and the number of transmit antennas $N_t$ . In this paper, to tackle this problem, we design two types of low-complexity MPA over the MIMO-SCMA extended factor graph to perform joint multiuser and MIMO detection. The main idea of the proposed algorithms is approximating the discrete interferences at function nodes (FNs) as the continuous Gaussian messages so that the traversal enumeration at the FN update can be avoided. The symbol level Gaussian approximated MPA (GAMPA-S) updates the symbol likelihood during the detector's inner-loop iterations which is similar to the standard MPA, and this algorithm is compatible to any SCMA codebooks. The bit level Gaussian approximated MPA (GAMPA-B) updates the bit likelihood ratio which shows lower complexity compared to the GAMPA-S. However, the GAMPA-B requires that the SCMA codebooks can satisfy the bit significance expansion property. In the receiver, the proposed detectors are combined with users’ decoders to form the outer-loop structure, in which a critical damping factor (DF) is designed to compress the amplitude of extrinsic information exchanged between the detector and decoders. The extrinsic information transform chart is introduced to optimize the DF. Consequently, the complexity of the proposed receiver just increases linearly with $d_f$ and $N_t$ while its performance is comparable or even better than the conventional receiver. This would effectively boost the practical use of MIMO-SCMA techniques.

Partially Active Message Passing Receiver for MIMO-SCMA Systems

Recently, sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) are viewed as two key techniques to address the high spectral efficiency and massive connectivity requirements for the 5G wireless system. Even though the sparse structure of each user’s codewords has been exploited, the complexity of joint MIMO and multiuser detection (JMMD) in the MIMO-SCMA systems still grows exponentially with the number of antennas and accessed users. In this letter, to tackle this severe problem, based on the Gaussian approximation principle, we propose a partially active message passing receiver for the MIMO-SCMA systems. In this case, a sliding window is introduced to determine which users stay active during each detection and decoding process while others keep silent. By this means, the complexity of JMMD can be efficiently reduced while its performance just suffers a slight loss, which fascinates the practical implementation of MIMO-SCMA systems.

Link Adaptation for MIMO OFDM Visible Light Communication Systems

In this paper, we investigate link adaptation for an orthogonal frequency division multiplexing (OFDM)-based multiple-input multiple-output (MIMO) visible light communication (VLC) system. The proposed adaptive OFDM VLC system supports both repetition coding (RC) and spatial multiplexing (SM) as MIMO modes and allows spatial mode switching based on channel conditions. Regarding to the instantaneous signal-to-noise ratio for both RC and SM modes, the maximum constellation size that can be supported for each MIMO mode on each subcarrier is determined. The MIMO mode that gives the highest spectral efficiency (SE) is then selected. The proposed joint MIMO mode selection and bit loading scheme maximizes the SE while satisfying a target bit error rate. Our numerical results reveal that a peak data rate up to 18.3 Gb/sec can be achieved in a $16 \times 16$ MIMO setting using light emitting diodes with cut-off frequency of 10 MHz in typical indoor environments.

Optimal joint MIMO and modulation order selection for network coded multicast wireless communications

The capacity gain provided by multiple-input multiple-output (MIMO) systems and network coding has been mainly investigated on an individual basis in the existing literature. The aim of this paper is to analyze the advantaged arising by a combined use of MIMO systems and network coding schemes for unicast and multicast communications under Rayleigh fading propagation conditions. In particular a suitable optimization methodology is proposed here in order to achieve the best performance in terms of mean overall energy consumption and delivery delay. In particular, the advantages of the proposed approach are highlighted for the cases of balanced and unbalanced multicast scenarios on the basis of the average group metric paradigm in order to maximize the average group satisfaction.

Joint Antenna Selection and MIMO Symbol De-tection Using Multiobjective Optimization

This study uses the joint receive antenna selection and symbol detection (JRAS/SD) technique to simultaneous- ly improve the bit error rate and channel capacity while reducing the cost of the radio-frequency chains in multiple- input multiple-output (MIMO) systems. Optimal antenna selection methods in the literatureare based on the criteria of maximal capacity or minimal bit error rate. Either of these criteria requires an exhaustive search over all subma- trices in the full channel response matrix, resulting in high complexity. This study proposes a new multiobjective JRAS/SD using the cross-entropy optimizationto reduce the bit error rate while approaching the maximal channel ca- pacity in MIMO systems. Simulation results show that the proposed approach achievesimproved performance in both channel achievable rate and bit error rate performance compared with previous works. 

A Chaos MIMO Transmission Scheme for Channel Coding and Physical-Layer Security

SUMMARY In recent wireless communication systems, security is ensured mainly in the upper-layer techniques such as a password or a cryptography processing. However, security needs not be restricted to the upperlayer and the addition of physical-layer security also would yield a much more robust system. Therefore, in this paper, we exploit chaos communication and propose a chaos multiple-input multiple-output (MIMO) transmission scheme which achieves physical-layer security and additional channelcoding gain. A chaotic modulation symbol is multiplied to the data to be transmitted at each MIMO antenna to exploit the MIMO antenna diversity, and at the receiver, the joint MIMO detection and chaos decoding is done by maximum likelihood decoding (MLD). The conventional chaos modulation suffers from bit error rate (BER) performance degradation, while the coding gain is obtained in the proposed scheme by the chaos modulation in MIMO. We evaluate the performances of the proposed scheme by an

Joint maximum-likelihood estimation of frequency offset and channel coefficients in multiple-input multiple-output orthogonal frequency-division multiplexing systems with timing ambiguity

In this study, the authors consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing system operating over unknown frequency-selective fading channels. Based on two different signal models, two maximum-likelihood schemes for joint carrier frequency offset (CFO) and MIMO channel estimation under timing ambiguity have been derived. The first estimator jointly estimates the timing offset, CFO and channel coefficients, but the complexity is high owing to the need of two-dimensional searches. The second estimator has a reduced complexity, but its performance slightly degrades compared with the first one. The performance of proposed estimators was benchmarked with Cramer–Rao bounds (CRB), and investigated by computer simulations. The simulation results show that the proposed estimators achieve almost ideal performances compared with the CRB.

Application of Nonbinary LDPC Cycle Codes to MIMO Channels

In this paper, we investigate the application of nonbinary low-density parity-check (LDPC) cycle codes over Galois field GF(q) to multiple-input multiple-output (MIMO) channels. Two types of LDPC coded systems that employ either joint or separate MIMO detection and channel decoding are considered, depending on the size of the Galois field and the modulation choice. We construct a special class of nonbinary LDPC cycle codes called the parallel sparse encodable (PSE) codes. The PSE code, consisting of a quasi-cyclic (QC) LDPC cycle code and a simple tree code, has the attractive feature that it is not only linearly encodable, but also allows parallel encoding which can reduce the encoding time significantly. We provide a systematic comparison between nonbinary coded systems and binary coded systems in both performance and complexity. Our results show that the proposed nonbinary system employing the PSE code outperforms not only the binary LDPC code specified in the 802.16e standard, but also the optimized binary LDPC code obtained using the EXIT chart methods. Through a detailed complexity analysis, we conclude that for the MIMO channel considered, the nonbinary coded systems achieve a superior performance at a receiver complexity that is comparable to that of the binary systems.

Joint MIMO Channel Tracking and Symbol Decoding Using Kalman Filtering

In this paper, the problem of channel tracking is considered for multiple-input multiple-output (MIMO) communication systems where the MIMO channel is time-varying. We consider a class of MIMO systems where orthogonal space-time block codes are used as the underlying space-time coding schemes. For such systems, a two-step MIMO channel tracking algorithm is proposed. As the first step, Kalman filtering is used to obtain an initial channel estimate for the current block based on the channel estimates obtained for previous blocks. Then, in the second step, the so-obtained initial channel estimate is refined using a decision-directed iterative method. We show that, due to specific properties of orthogonal space-time block codes, both the Kalman filter and the decision-directed algorithm can be significantly simplified. Simulation results show that the proposed tracking method can provide results in a symbol error rate performance that is 1 dB better than that of the differential receiver.

Network Duality for Multiuser MIMO Beamforming Networks and Applications

For any multiple-input multiple-output (MIMO) network with linear beamformers, it is shown that there exists a dual network that attains the same signal-to-interference-plus-noise ratio (SINR) performance. This network duality concept is a generalization of the virtual uplink concept investigated by Rashid-Farrokhi . We first develop the dual relation using a generic duality theory in linear programming. This approach naturally leads to the construction of a dual network, and provides machinery for handling a generalized cost function. We then consider the joint MIMO beamforming and power control problem with individual SINR constraints. We apply the network duality to this problem and propose a high-performance iterative algorithm which, compared with past centralized approaches, has improved convergence behavior. Finally, we propose a simpler distributed version of the algorithm tailored for a cellular downlink. This algorithm can be readily implemented in a distributed fashion, since it does not require information exchange between base stations. The algorithm achieves performance close to that of centralized ones, and outperforms other decentralized approaches available in the literature

Iterative decoding of concatenated layered space-time trellis codes on mimo block fading multipath awgn channel

We propose the building of high rate space-time codes for multiple-input multiple-output (MIMO) block fading multipath additive white Gaussian noise channels by layering small compound space-time trellis codes. To recover part of the spatial diversity between the different layers, we also study their concatenation with an outer code. Naturally, our space-time coding architecture puts the burden on the receiver complexity. As a main contribution of this paper, a near-optimal soft-input soft-output reduced-complexity trellis search algorithm is suggested for joint MIMO detection and inner decoding.