Learning Statistically Robust MIMO Detection with Imperfect CSI

Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP). From an optimization viewpoint the BQP is still a computationally hard problem, and we propose two alternatives for dealing with this inherent complexity. First, we consider the semidefinite relaxation (SDR) approach, which leads to a suboptimal, but accurate, blind ML detection algorithm with an affordable worst-case computational cost. We also consider the sphere decoding approach, which leads to an exact blind ML detection algorithm that remains computationally expensive in the worst case, but generally incurs a reasonable average computational cost. For the two algorithms, we study implementation methods that can significantly reduce the computational complexity. Simulation results indicate that the two blind ML detection algorithms are competitive, in that the bit error performance of the two algorithms is almost the same and is noticeably better than that of some other existing blind detectors. Moreover, numerical studies show that the SDR algorithm provides better complexity performance than the sphere decoder in the worst-case sense, and vice versa in the average sense.

Generalized spatial modulation multiple-input multiple-output (GSM-MIMO) is a promising technique to fulfil the ever-growing need for high data rates and high spectral efficiency for 5G and beyond systems. Maximum likelihood (ML) detection achieves optimal performance for GSM-MIMO systems. However, ML detection performs an exhaustive search and hence, ML have intractable exponential computational complexity. Hence, low complexity detection algorithms are needed to be explored for reliable detection in GSM-MIMO systems. In this paper, a novel and robust GSM-MIMO detection algorithm are proposed based on artificial bee colony optimization with mutation operator. Simulation results validate that the proposed algorithm outperforms minimum mean square error detection and achieves near-ML bit error rate performance for GSM-MIMO systems, under both perfect and imperfect channel state information at the receiver.

The accurate performance evaluation of differential amplitude and phase-shift keying (DAPSK) with postdetection equal gain combining (EGC) over generalized fading channels is of great theoretical interest and practical importance. In this paper, by using a decision variable-based moment generating function approach, exact error probability results for DAPSK over generalized Rician and Nakagami fading channels are derived, taking into account the effects of all the system and fading channel parameters. Several maximum likelihood (ML)-based detectors that do not require channel state information (CSI) are proposed for generalized Rician fading channels, and an exact bit error probability (BEP) union bound for the ML detection is derived. Assuming CSI, a performance upper bound for DAPSK with EGC is also presented. Simulation and numerical results show when both detectors have no CSI, the conventional DAPSK detector may perform closely to (though worse than) the ML detector. However, the EGC receiver with CSI performs substantially better than the ML detector without CSI, and very closely to the coherent APSK detector.

In this paper, we consider the optimal design of linear space codes for an indoor multiple input multiple output (MIMO) visible light communication (VLC) system with maximum-likelihood (ML) detection, where we assume that the full knowledge of channel state information (CSI) is available at both the transmitter and the receiver. For such a system with on-off-keying (OOK) modulation, an optimal closed-form linear space code that maximizes the minimum Euclidean distance for ML detection is attained for the scenario with two transmitters and multiple receivers. Our results reveal the fact that when the channel is good, the optimal code has either a diagonal or an anti-diagonal structure, and when the channel is bad, the optimal code is to diagonalize the channel matrix first, then, optimally distribute the total transmission power into each eigen-subchannel, and then, follow a repetition coding (RC) scheme. Computer simulations verify both the accuracy and the validity of our theoretical analysis, and show that our newly proposed linear space code provides significant error performance gains over currently available space modlution (SM) and RC in literature without CSI at the transmitter.

The sphere decoding (SD) algorithm can provide (sub)optimal solutions with reduced computational complexity of maximum likelihood (ML) detection for multi-input multi-output (MIMO) communication systems. In this paper, we propose a novel low complexity symmetric-coded based SD algorithm for short polar codes with low rate. At the encoding stage, the first N/2 sub-channels transmit the frozen bits, while the information bits are selected from the latter N/2 sub-channels. Two symmetric codes are generated due to the mathematical structure of the generator matrix, which is well conditioned to the SD search. At the decoding stage, the presented SD algorithm computes the Euclidean distance value by the combined signals to estimate the latter N/2 input bits. Furthermore, the backtrack operation starts from the earlier (N/2+1)-th bit, which can significantly reduce the average visited nodes (AVN). Simulation results show that, compared to the original SD algorithm, the presented variant of the SD algorithm can reduce the AVN to 0.9% for the polar code P(64, 14) at SNR = 1 dB with a performance loss within 0.2 dB. The presented SD algorithm may find applications in MIMO systems where the complexity of the standar ML detection increases exponentially with the transmitting antennas.

This paper addresses the design of optimal and near-optimal detectors for a practical interference channel scenario where the transmitters employ discrete modulation schemes. The conventional detectors, which either ignore the interference or successively detect then cancel the interference, typically assume that the desired signal and/or the interference are Gaussian. This paper proposes detectors that explicitly take into account the modulation formats of both the desired signal and the interference. The optimal maximum-likelihood (ML) detector that minimizes the probability of detection error for a given set of modulation schemes is derived first. A joint minimum-distance detector (MD) is then presented as a low-complexity approximation of the optimal ML detector. It is demonstrated by analysis and by simulation that the proposed detectors can significantly outperform their conventional counterparts. In particular, while the interference-ignorant and the successive interference cancellation detectors are both prone to error floors, the proposed optimal ML and joint MD detectors are not.

We investigate the problem of robust joint transmitter and receiver power allocation for uplink multi-input multi-output (MIMO) transmissions. The channel model is assumed to have Rayleigh flat fading. The objective of power allocation is to minimize the total MSE each of which has limited transmit power. The problem is formulated as a non-linear optimization problem. In addition, we investigate robust joint transmitter and receiver power allocation with imperfect channel state information (CSI). The CSI error is assumed to be unknown but bounded by a constant. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results indicate that, with imperfect CSI, the BER performance improvement obtained by taking robustness into account in the joint power allocation process.

In this paper, we design an implementation of a kind of reduced-complexity maximum likelihood (ML) detector for spatial modulation (SM) deployed in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. Based on National Instruments Universal Software Radio Peripheral (NI USRP) platform, two USRP Reconfigurable Input / Output (USRP-RIO) devices are used as the transmitter and the receiver. Error performance comparisons between the reduced-complexity ML detector and conventional ML detector are made in an indoor environment, as well as the comparison between SM scheme and space-frequency block code (SFBC) scheme. 6 different pictures are exploited as transmitted data, and are reconstructed in the receiver side, so the performance of each scheme is taken on the reconstructed pictures. Both of the calculated bit error ratio (BER) and the reconstruction result of each transmitted pictures, validate the highly consistent error performance of HL_ML detector and conventional ML detector, as well as the error performance superiority of SM over SFBC.

In full-duplex orthogonal frequency-division multiplexing systems with IQ imbalances, a frequency-domain least-squares (FD-LS) channel estimator is proposed to estimate both source-to-destination (intended) and destination-to-destination (self-interference) channels. Subsequently, an optimal closed-form pilot matrix is derived to minimize the sum of mean square errors (sum-MSE) of the proposed FD-LS channel estimator. Then, an improved FD-LS estimator is presented and proved to further improve the performance of the FD-LS by exploiting the time-domain property of the channel. In the presence of channel estimation error, an optimal low-complexity maximum likelihood (ML) detector is developed by using eigenvalue and singular value decompositions and whitening the residual self-interference plus noise. Simulation results show that given the proposed FD-LS estimator, the optimal pilot matrix performs much better than those nonsingular pilot matrices with larger conditional numbers (CN). To be specific, the former achieves about 10-dB signal-to-noise ratio (SNR) gain over the latter with CN = 10, the improved FD-LS channel estimator provides about 6-dB SNR gain over the FD-LS estimator at a fixed bit error rate (BER), and the proposed whitening-filter ML detector performs at least 0.6 dB better than the conventional ML detector at a given BER of $10^{-3}$ in the medium-and high-SNR regions.

Recently, lattice-reduction (LR) has been proposed to improve the performance of linear and successive interference cancellation (SIC) detectors and precoders in MIMO systems. It is proved that LR-aided linear detectors collect the same diversity order as that exploited by the ML detector but with much lower complexity when perfect channel state information (CSI) is assumed to be available. However, in practice, CSI is always contaminated by channel estimation error and feedback quantization error. In this paper, we proposed MMSE based LR-aided robust linear and SIC detectors and MMSE based LR-aided (robust) linear precoder and (robust) Tomlinson-Harashima precoder (THP) which are capable of improving the BER performance in the context of imperfect CSI. Furthermore, we find that the performance improvement of LR-aided nonlinear method in SIC detectors at receiver is more pronounced than that in THP at transmitter.

Efficient jamming attack against MIMO transceiver

Spatial modulation (SM) has been shown to be a promising low-complexity alternative to the state-of-art multiple-input multiple-output (MIMO) schemes due to its novel transmission approach. This paper investigates the performance of SM-MIMO systems in the presence of two practical undesirable effects, namely in-phase (I) and quadrature-phase (Q) imbalance (IQI) and imperfect channel state information (ICSI). An optimum maximum likelihood detection (MLD) method is proposed to tackle the effects of self-interference and signal distortion caused by IQI impairment by adapting the traditional MLD technique in accordance with the asymmetric characteristics of the IQI. More particularly, upper-bounds of the closed-form average pairwise error probability (APEP) and the average bit error rate (ABER) are derived for generalized Beckmann fading channels. As erroneously interpreted channel coefficients at the receiver (Rx) cause the error rate to increase and the detection to fall short, Cramer-Rao bound, which is a lower bound on the variance of the channel estimator, is utilized to assess the estimation accuracy. The system performance is evaluated by analytical derivations that are corroborated with computer simulations. The obtained results show that ICSI and IQI should be seriously considered while designing the future SM-based wireless communication systems.

Multi input multi output (MIMO) system with the multiple antennas at both the transmitter and receiver is the emerging technology in the modern wireless system. MIMO systems are well adopted due to its high spectral efficiency and high data rates. For the detection of MIMO systems, a powerful technique called Lattice Reduction (LR) algorithm are used to obtain the better orthogonality in the channel matrices and is aided with the linear and low complexity detectors such as Zero forcing(ZF) and Minimum mean square error (MMSE) for the reduction in complexity. In this paper we propose a new algorithm called Enhanced lattice reduction algorithm to improve the error performance for the linear detectors with the lesser complexity and also it is aided with the non linear precoding called Tomlinson-Harashima precoding (THP) in the presence of channel state information (CSI). Lattice reduction aided with THP improves the performance linearly. BER performance of the proposed algorithm is determined and it is compared with the other LR aided algorithms in the literature.

A unified approach is developed for the study of multi-input, multi-output (MIMO) systems under fast fading with quantized covariance feedback. In such systems, the receiver computes, using perfect channel state information (CSI), the covariance matrices to be adopted by the transmitter corresponding to the current channel realization, and feeds back a quantized version of this information to the transmitter using finite, say Nf, bits per channel realization. Our analysis is based on a geometric framework we developed in a companion paper for manifolds of positive semi-definite (covariance) matrices with various trace and rank constraints. That analysis applies to MIMO systems in a unified manner to optimal as well as various suboptimal precoding methods for obtaining input covariances. These include, for example, the capacity achieving spatio-temporal water-filling strategy, the incorporation of just spatial water-filling and reduced-rank beamforming with power allocation in both cases, as well as MIMO systems with antenna selection. For a given system strategy, including the one that achieves capacity, the gap between the communication rates in the perfect and quantized covariance cases is shown to be O(2-Nf/N), where N is the dimension of the particular Pn manifold used for quantization. This dimension depends on the precoding strategy adopted and strongly determines how quickly the communication rate with quantized covariance feedback approaches that with perfect CSI at the transmitter (CSIT). Our results show how the choice of covariance quantization manifold can be tailored to the available feedback rate in MIMO system design.

This paper addresses the design of optimal and near-optimal detectors in an interference channel with fading and with additive white Gaussian noise (AWGN), where the transmitters employ discrete modulation schemes as in practical communication scenarios. The conventional detectors typically either ignore the interference or successively detect and then cancel the interference, assuming that the desired signal and/or the interference are Gaussian. This paper quantifies the significant performance gain that can be obtained if the detectors explicitly take into account the modulation formats of the desired and the interference signals. This paper first describes the optimal maximum-likelihood (ML) detector that minimizes the probability of detection error for a given modulation scheme, and the joint minimum-distance (MD) detector, which is a lower-complexity approximation of the ML detector. It is then demonstrated by analysis and by simulation that in an AWGN channel, while interference-ignorant and successive interference cancellation detectors are both prone to error floors, the optimal ML and joint MD detectors are not. This paper further analyzes the performance of joint detection in a Rayleigh fading environment. It is demonstrated that the joint detector can achieve symbol error rates that have the same dependence on the received signal-to-noise ratio (SNR) as if the channel were interference free. Thus, the performance of joint detection is fundamentally limited by the SNR rather than the signal-to-interference ratio (SIR). Moreover, the joint detector enables the use of transmit diversity schemes to achieve the same diversity order as in the absence of interference. These results show that the use of interference-aware detectors can significantly alleviate the effect of interference thereby improving the achievable rates and the reliability of future wireless systems.