Abstract The current wireless communication system has higher performance demands for receivers. This paper considers exploiting the natural redundancy (NR) that is widely existed in the transmission sources to improve the receiver's performance of sequence detection. The type of NR discussed in this paper is the inherent redundancy in data caused by the correlation between symbols in the memory sources. Since the correlation between transmitted symbols is too challenging to obtain, deep learning (DL) is introduced into sequence detection, which has a powerful ability to extract complex patterns from data. Specifically, intended for a specific memory source that is convenient for performance analysis, namely the Markov source, an iterative sequence detection algorithm based on the long short‐term memory network is proposed. Simulation results demonstrate that the receiver's performance can be improved by exploiting the NR and the proposed DL‐based sequence detection scheme can obtain optimal bit error rate performance with blind state transition probability of the transmitted Markov source.

A high-performance communication receiver desires a sufficient and accurate recognition of transmitted sources. In this paper, we propose a sequential decoding algorithm for the robust reception of sources with natural redundancy (NR) over the AWGN channel. To fully exploit the abundant NR in the exemplified English text sources, a causal language modeling (CLM) with a powerful Transformer decoder neural network (NN) structure is adopted for modeling the joint probability distribution. For versatility of byte-level tokenization, the UTF-8 encoding scheme is considered for the Wikipedia corpus, namely the English edition of the Wiki-40B dataset. The tree search-based M-algorithm (MA) is integrated with CLM, denoted as CLM-MA algorithm, to synthesize the a priori probability of information sequence and likelihood values of polluted symbols. Both simulation experiments and hardware platform evaluations are conducted to analyze the performance of the CLM-MA algorithm. With an adequately large M value, tremendous performance profit is achievable, especially for high-efficiency modulation levels and extremely noisy conditions. This mechanism of adopting the pre-trained NN model merely desires self-supervised learning architecture and eliminates the requirement of high-cost and accurate labels, which paves a new way for communication receiver design.

In this paper, we propose an optimized cooperative finite-alphabet relaying strategy for two-way relay visible light communication (VLC) systems, in which the relay node has its own information to be transmitted. In the multiple access (MAC) stage, for fixed transmission bit rate of each user, an optimal power control strategy is developed to manage the non-negative finite-alphabet multiuser interference. Under this strategy, it is proved that the received signal constitutes an equally spaced pulse amplitude modulation constellation, which admits low-complexity maximum likelihood detection. In addition, for given overall transmission bit rate of MAC stage, we derive a closed-form solution for optimal bit loading to further improve the energy-efficiency of MAC stage. In the broadcast stage, owing to the fact that each buffer-aided user node knows the signals transmitted to the relay node in the MAC stage, we design a buffer-aided optimal encoding constellation, which can obtain significant performance gains. Intended for this transmitted constellation, we develop two low-complexity detection algorithms. Both the analytical results and computer simulations demonstrate that our proposed design has substantial performance advantage over the currently available time orthogonal transmission scheme for this application.