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Recently, there has been a good deal of interest in the use of chaotic signals for direct sequence code division multiple access (DS-CDMA) communication systems. The capacity of DS-CDMA systems is interference-limited, and can therefore be increased by techniques that suppress interference. This letter is devoted to the evaluation of the impact of blind multiuser detection techniques on chaos based DS-CDMA systems. Blind receivers can suppress multiple access interference and do not require knowledge of the code sequences and propagation channels of the interference. We demonstrate that, for chaotic sequence-based communications, blind multiuser receivers significantly improve the BER with respect to single-user receivers, and that their use is practically essential with a high number of users.

In this Letter, we apply combined linear detector/parallel interference cancellation (PIC) detectors to jointly decode symbols in a multiple access chaotic-sequence spread-spectrum communication system. In particular, three different types of linear detectors, namely single-user detector, decorrelating detector and minimum mean-square-error detector, are used to estimate the transmitted symbols at the first stage of the PIC detector. The technique for deriving the approximate bit error rate (BER) is described and computer simulations are performed to verify the analytical BERs.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimisation (ACO), for synchronous direct sequence code division multiple access systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO-based MUD converges to the optimal bit-error-rate performance in relatively few iterations providing 95% savings in computational complexity. This reduction in complexity is retained even when considering users with unequal received powers.

Large-dimensional (LD) random matrix theory, RMT for short, which originates from the research field of quantum physics, has shown tremendous capability in providing deep insights into large-dimensional systems. With the fact that we have entered an unprecedented era full of massive amounts of data and large complex systems, RMT is expected to play more important roles in the analysis and design of modern systems. In this paper, we review the key results of RMT and its applications in two emerging fields: wireless communications and deep learning. In wireless communications, we show that RMT can be exploited to design the spectrum sensing algorithms for cognitive radio systems and to perform the design and asymptotic analysis for large communication systems. In deep learning, RMT can be utilized to analyze the Hessian, input–output Jacobian and data covariance matrix of the deep neural networks, thereby to understand and improve the convergence and the learning speed of the neural networks. Finally, we highlight some challenges and opportunities in applying RMT to the practical large-dimensional systems.

With the increasingly scarce spectrum, Multiuser Detector is gradually taking the place of the conventional one because of the multiplexing. However, the majority of the researches are focused on the linear modulation case at present. In order to increase the channel capacity and spectral efficiency of nonlinear modulation, a study on multiuser detection in nonlinear modulation case is conducted. Specifically, M-PSK is chosen to be the elementary modulation mode in this paper. Based on it, the general approach to the problem is introduced through an overall analysis using Hypothesis Testing theory and Maximum A Posteriori criterion. In addition to the theoretic deduction, a consistent result from simulation experiment is obtained.