Detection For MIMO Systems Research Articles

Optimizing Signal Detection in MIMO Systems: AI vs Approximate and Linear Detectors

Optimizing Signal Detection in MIMO Systems: AI vs Approximate and Linear Detectors

Signal Detection in MIMO Systems With Hardware Imperfections: Message Passing on Neural Networks

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

Compressed Sensing Based Active User Detection in MIMO Systems With One-Bit ADC

We investigate an active user detection for massive machine-type communication systems, in which a massive number of users with sporadic transmission communicate with a base station (BS) with multiple antennas. Due to hardware cost and power consumption, it is assumed that each receiver antenna at the BS is equipped with one-bit analog-to-digital converter (ADC). This problem can be formulated as a new compressed sensing problem with one-bit quantized multiple measurement vectors (termed one-bit CS-MMV). In this paper, we propose a novel algorithm (named Turbo-QISTA) by properly incorporating Turbo principle into quantized iterative shrinkage threshold algorithm (QISTA). Our major contribution is to design two key metrics (i.e., extrinsic and a priori information) of Turbo principle suitable for one-bit CS-MMV problem. Via simulations, we demonstrate the superiority of the proposed Turbo-QISTA.

Spatial spectrum approach for pilot spoofing attack detection in MIMO systems

Spatial spectrum approach for pilot spoofing attack detection in MIMO systems

Architectural implementation of modified K-best algorithm for detection in MIMO systems

We present novel architectures for the modified K-best algorithm and its very-large-scale integration implementation for spatially multiplexed wireless multiple-input multiple-output systems. The objective was to propose a simplified architecture based on the algorithm and to significantly improve the suitability for hardware implementation. Two different architecture designs were proposed: a distributed arithmetic- based tree-search detector and a breadth-first search sphere detector. The implementations were performed to obtain a configurable architectural solution for different antenna configurations and constellations. The synthesis analysis shows that the proposed architectures achieve a throughput of > 500 Mbps with reduced hardware complexity compared to previously reported architectures.

LDMC design for low complexity MIMO detection and efficient decoding

Low-density multiple-input multiple-output code (LDMC) can reduce the complexity of tree-search detection in MIMO systems. In this paper, we present a new modified progressive edge-growth (PEG) algorithm to construct large girth LDMCs, which are referred to as PEG-LDMCs. We analyze the complexity of the LDMC constrained sphere decoding (SD) and show that the LDMC constrained SD detection can be used for high reliability of the data transmission in MIMO systems. Furthermore, we propose two new efficient iterative decoding algorithms for LDMCs, which are high speed serial decoding and fast convergence shuffled decoding. Finally, we compare the bit error rate (BER) performance of PEG-LDMCs to that of the existing LDMCs. The simulation results show that the PEG-LDMCs can achieve better BER performance than that of the existing LDMCs.

Stopping Rule-Based Iterative Tree Search for Low-Complexity Detection in MIMO Systems

Breadth first tree search (BFTS) algorithms are known to provide a close to maximum likelihood (quasi-ML) solution at a low-complexity if the received sequence is detected in the right sequence order. However, finding the right sequence order has an exponential overhead. In view of this, we propose to repeatedly apply a BFTS algorithm to all sequence orders. Since it will test all the orders, it is expected to achieve quasi-ML performance. However, this will increase the complexity because of redundant iterations. The complexity can be reduced if we can stop the iterations as soon as a quasi-ML solution is achieved. For this, we propose two stopping rules, one relies on a constellation based heuristic and the other one uses the distribution of ML cost. It is found that their complexity curves have a cross-over point. Thus, a combination of the two rules provides a quasi-ML error performance at a low-complexity for uncoded as well as coded systems. We further show that the proposed stopping rule can reduce the complexity of depth first tree search algorithms also. Last, for large MIMO systems, compared with existing algorithms, it is found to be exceptionally better in terms of both error performance and complexity.

Lattice-Reduction-Aided Conditional Detection for MIMO Systems

We introduce a low-complexity detector with near-optimal performance for transmission over multi-antenna systems. By using lattice basis reduction for generating almost orthogonal channel submatrices, we enhance the conditional optimization technique to implement a fast yet efficient detector. The lattice-reduction-aided (LRA) conditional method is presented as a general detection technique over fading channels to yield significant saving in computational complexity while achieving close to Maximum Likelihood (ML) error performance. By employing the orthogonality defect factor as a universal measure to select a near-orthogonal channel submatrix for conditional detection, we implement efficient detectors for MIMO systems. In particular, an almost optimal decoder with linear complexity for the Golden code is presented over quasi-static channels.

Gram-Schmidt Combined LLL Lattice-Reduction Aided Detection in MIMO Systems

This paper proposes an improved lattice-reduction aided (LRA) MMSE detection scheme, based on the Gram-Schmidt (GS) procedure. The proposed scheme reduces the column vectors of the MIMO channel matrix, by using the LLL algorithm followed by the GS procedure in order to transform the channel matrix into a new one which has mutually purely orthogonal column vectors. Compared to the conventional LRA MMSE detector, the proposed detector achieves a very good BER performance, almost equivalent to those using the ML detector in the 4 × 4 MIMO system at the cost of a slightly larger computational complexity.

Performance Analysis of ML Detection in MIMO Systems with Co-Channel Interference

In this letter, we analyse a receiver technique for MIMO systems with flat fading channel, in which the Co-Channel Interference (CCI) along with white Gaussian noise is modelled as correlated Gaussian noise. This correlated Gaussian noise is whitened and then Maximum likelihood detection (MLD) is performed over the desired user's streams. An upper bound on the bit error rate (BER) is derived to understand the performance of this approach. Using the upper bound, we show that the covariance matrix of the interference has to be rank deficient for the BER to reduce with SNR. Monte Carlo simulations are performed to verify the diversity order predicted by the upper bound.

Detection of Gaussian constellations in MIMO systems under imperfect CSI

This paper considers the problem of Gaussian symbols detection in MIMO systems in the presence of channel estimation errors. Under this framework we develop a computationally efficient approximations of the MAP detector. The new detectors are based on a relaxation of the discrete nature of the digital constellation and on the channel estimation error statistics. This leads to a non-convex program that is solved efficiently via a hidden convexity minimization approach. Additionally, we show that using a Bayesian EM approach, comparable BER performance to that of the MAP detector can be achieved. Next we extend the detection scheme to the case where the noise variance is unknown. We present a modified Bayesian EM approach with annealed Gibbs sampling to perform joint noise variance estimation and symbols detection. Simulation results in a random MIMO system show that the proposed algorithm outperforms the linear MMSE receiver in terms of BER.

Maximum-likelihood detection based on branch and bound algorithm for MIMO systems

Maximum likelihood detection for MIMO systems can be formulated as an integer quadratic programming problem. In this paper, we introduce depth-first branch and bound algorithm with variable dichotomy into MIMO detection. More nodes may be pruned with this structure. At each stage of the branch and bound algorithm, active set algorithm is adopted to solve the dual subproblem. In order to reduce the complexity further, the Cholesky factorization update is presented to solve the linear system at each iteration of active set algorithm efficiently. By relaxing the pruning conditions, we also present the quasi branch and bound algorithm which implements a good tradeoff between performance and complexity. Numerical results show that the complexity of MIMO detection based on branch and bound algorithm is very low, especially in low SNR and large constellations.

Fuzzy adaptive partial parallel multistage detection for MIMO systems

Vertical Bell Laboratories Layered Space-Time (V-BLAST) detection followed by a full parallel interference cancellation (full PIC) or a constant-weight partial parallel interference cancellation (CW-PPIC) scheme has been proposed. However, its performance degrades when the received power of the antenna is unbalanced and/or the system is heavily loaded. Therefore, V-BLAST is proposed, followed by fuzzy adaptive partial parallel multistage detection for MIMO systems. The simulation results show that the error probability of this proposed method is better than that of V-BLAST followed by full PIC or CW-PPIC schemes.

Layered maximum likelihood detection for MIMO systems in frequency selective fading channels

By transmitting different substreams in different antennas simultaneously, a multiple element antenna array system provides increased capacity that grows linearly with the number of transmit antennas. Layered space-time processing that performs nulling, detection and cancellation for each substream can be used for reception, with a linear growth in receiver complexity. This paper considers this multi-input multi-output system over a slow time-varying frequency selective Rayleigh fading channel environment. With the equivalent channel tap delay line model, each delayed tap in every transmit-receive antenna pair can be considered as an imaginary antenna transmitting a delayed version of the substreams. Based on this idea, we propose a layered maximum likelihood detection (L-MLD) scheme which performs layered processing and maximum likelihood detection for each substream and its delayed elements. To further improve the performance, a group maximum likelihood detection (G-MLD) scheme is also proposed by grouping the substreams and performing layered processing in groups and maximum likelihood detection within the group. However, both schemes increase the required number of receiving antennas, which increases hardware cost and size. To reduce this requirement, we propose the use of oversampling technique to increase the dimension of the received signal. Simulation results show that the L-MLD scheme achieves frequency diversity and outperforms existing schemes such as the V-BLAST system with OFDM and multi-input multi-output decision feedback equalizers (MIMO-DFE). Moreover, the G-MLD scheme performs better than L-MLD with an increased detection complexity. In addition, it was showed that further increasing the oversampling rate beyond the minimum requirement does not improve the performance.

Analogue MIMO Detection

In this contribution we propose an analogue receiver that can perform turbo detection in MIMO systems. We present the case for a receiver that is built from nonlinear analogue devices, which perform detection in a free-flow network (no notion of iterations). This contribution can be viewed as an extension of analogue turbo decoder concepts to include MIMO detection. These first analogue implementations report reductions of few orders of magnitude in the number of required transistors and in consumed energy, and the same order of improvement in processing speed. It is anticipated that such analogue MIMO decoder could bring about the same advantages, when compared to traditional digital implementations.

Probabilistic data association for symbol detection in MIMO systems

The application of the probabilistic data association (PDA) algorithm to the symbol detection in narrowband spatial multiplexed multiple input multiple output systems with known channel state information is investigated. The performance of a new proposed complex formulation of PDA algorithm, which matches the full parameters of the complex Gaussian distribution (which are the mean vector, covariance matrix and the pseudo-covariance matrix), is compared with the real vector formulation of a generalised PDA algorithm and a PDA implementation, which employs complex Gaussian approximations with matched mean and covariance only.

An iterative singular vectors estimation scheme for beamforming transmission and detection in MIMO systems

An iterative singular vectors estimation scheme for beamforming transmission and detection in MIMO systems

Optimal Diversity-Multiplexing Tradeoff With Group Detection for MIMO Systems

Optimal Diversity-Multiplexing Tradeoff With Group Detection for MIMO Systems

Blind detection in MIMO systems via sequential Monte Carlo

We provide a novel sequential estimation and detection approach for multiple-input-multiple-output (MIMO) systems. The basic idea is to design a probabilistic approximation method for the computation of the maximum a posterior distribution (MAP) via the sequential Monte Carlo methods (SMC). The SMC method has two advantages over the other methods in that it is a blind method and can be computed in parallel. Furthermore, the SMC has characteristics of soft-input and soft-output in nature and, thus, it can be employed as the first stage demodulator in a turbo receiver for a coded MIMO system. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the so-called extrinsic information with the MAP outer channel decoder. Finally, the performance of the proposed sequential Monte Carlo receiver is demonstrated through computer simulations for MIMO systems over single-path and multipath fading channels.