Optimal Detector for Discrete Transmit Signals in Gaussian Interference Channels

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.

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.

Blind detection in symmetric non-Gaussian noise with unknown PDF using maximum entropy method with moment generating function constraints

In multiple antenna systems employing spatial multiplexing to raise data rates, it is preferable to use maximum likelihood (ML) detection to fully benefit from multiplexing and diversity gain. In this paper, we present a new tree search based detector that uses layer ordering in conjunction with a partial lattice basis reduction. Compared with conventional tree detectors, the error performance of the new detector closely approximates that of the optimal ML detector while achieving a dramatic reduction in complexity.

This paper investigates a backscatter communications system that exploits ambient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pilot</i> symbols used in existing standards based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">orthogonal <i>frequency-division</i> multiplexing</i> (OFDM), such as IEEE802.11, in order to realize ultra-low power communications with longer transmission range and higher data rate than conventional ambient backscatter systems. Two modulation schemes, phase-shift keying (PSK) and a new approach named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay-shift</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">keying</i> (DSK), are investigated for the proposed system, and the optimal design of DSK is provided based on the theoretical upper bound of the symbol error rate (SER) over double frequency-selective channels. An optimal maximum-likelihood (ML) detector is also developed for the proposed system along with a feasible transmission protocol including channel estimation. Computer simulation results reveal that, at a bandwidth efficiency less than 3 bits per channel use (bpcu), PSK achieves the lowest SER while DSK achieves the lowest SER at efficiencies greater than 2 bpcu. The performance of the proposed detector was comparable to that of the conventional joint ML detectors with a lower degree of complexity, even when a limited number of subcarriers were available as pilot symbols.

The overloaded CDMA system, in which the number of users is larger than the dimension of signal space experiences a very serious problem of multiple access interference which is the major limiting factor for the channel capacity. In this letter we used multicarrier communication to resolve the problem of overloading. Computational complexity associated with optimum maximum likelihood detector (MLD) in wideband code division multiple access (WCDMA) systems is also addressed by using the particle swarm optimization (PSO) algorithm. In the proposed scheme, large number of users in an overloaded system is divided in groups. A carrier is modulated with the composite signal of each group. The carriers of each group are orthogonal to each other. Detection of individual users in a particular group is accomplished through PSO algorithm. PSO algorithm lowers the search space compared to MLD. Simulation results show that PSO algorithm avoids being trapped in any local minima and gives a much faster convergence compared to MLD. It also performed better than other suboptimal detectors.

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.

A novel deep neural network based parallel signal detection (DNN-PSD) is proposed for the spatial modulation based orthogonal frequency division multiplexing (SM-OFDM) system. With the purpose to reduce the complexity of the conventional DNN, a uniform small-scale DNN with fewer parameters and less training time is exploited to detect the signals for each subcarrier parallelly. Apart from maximum likelihood (ML) and maximal ratio combining (MRC) detection schemes, the detailed DNN-PSD algorithm and its complexity analysis are presented. Simulation results confirm that the bit error rate (BER) performance of the proposed DNN-PSD is far superior to the MRC detection and similar to the optimal ML detection but with much lower complexity under different scenarios. It has more robustness and achieves a finer compromise between BER performance and complexity.

For multi-input multi-output (MIMO) systems, the detection performance can be severely deteriorated by the channel state information (CSI) uncertainties. In this paper, we propose a learnable robust MIMO detector by taking the statistics of CSI imperfection into account. Specifically, we first formulate a robust maximum likelihood (ML) detection problem and then develop an alternating direction method of multipli-ers (ADMM) based solution, which involves the calculations of closed-form expressions in each iteration. Furthermore, a model-driven neural network is established by unfolding the derived ADMM algorithm whose penalty parameters are learned via offline training. Simulation results demonstrate that the proposed network can considerably outperform the conventional mismatched ML detector and even approach the optimal robust ML detector with only 5 layers.

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.

Multiple input multiple output (MIMO) communication systems have emerged as a key technol-ogy to enhance spectral efficiency and reliability in wireless communications. In recent years, deep neural network (DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection. Among these approaches, the Transformer architecture, known for its effectiveness in capturing long-range dependencies in sequential data, has gained significant attention. Therefore, this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture. This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures, which enables the model to capture both spatial and temporal dependencies in MIMO channels, thereby improving symbol detection accuracy and robustness under varying channel conditions. The proposed scheme's bit error rate (BER) performance is compared with traditional methods through simulations. The results show that the proposed method achieves a signal-to-noise ratio (SNR) gain of nearly 1.5 ​dB against the traditional detection methods, with the optimal maximum likelihood detector (MLD) only outperforming it ​by ​< ​0.5 ​dB. • Transformer-based MIMO signal detection. • Deep learning for wireless communications. • Open access journals on signal processing. • Next-generation wireless communication systems.

In the wireless communication systems, the classic Multi-User Detection (MUD) techniques such as the Minimum Mean Square Error (MMSE) detector have some limitations and imperfections due to Multi-Access Interference (MAI) especially in overloaded scenarios when the number of users is more than the number of receiving antennas. The optimal Maximum Likelihood (ML) detector gives an excellent result to estimate the transmitted data but suffers from a computational complexity that grows exponentially with the number of users. In this paper, we propose a new metaheuristic approach for multiuser detection based on Honey Bees Mating Optimisation (HBMO) hybridised with Tabu Search (TS) for an uplink Space Division Multiple Access-Orthogonal Frequency Division Multiplexing (SDMA-OFDM) system in a flat Rayleigh fading channel. Indeed, the HBMO algorithm provides a good estimation for TS while exploring the largest regions, while the TS algorithm uses this estimation to find the best solution of the problem. The simulation results show that the proposed algorithm HBMO-TS-MUD provides the best trade-off between performance and computational complexity comparing to the conventional detector and the other MUD detectors proposed as Genetic Algorithm hybridised with the Tabu Search (GA-TS).

Efficient jamming attack against MIMO transceiver

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade-offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.

A low complexity maximum likelihood (ML) detection method is proposed for the square quadrature amplitude modulation (QAM) constellation. In order to reduce the implementation complexity of ML detection, set-partition, subset-selection, and subset-shift are recursively applied to the square QAM constellation. The estimated square QAM symbol is finally given in a closed form as a function of the received signal and the estimated channel. Complexity analysis and numerical simulation confirm that the proposed detection method reduces the implementation complexity substantially and achieves exactly the same performance as the conventional ML detection irrespective of the channel estimation error.