Le He;Lisheng Fan;Xianfu Lei;Xiaohu Tang;Pingzhi Fan;Arumugam Nallanathan
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引用次数: 19
Abstract
In this paper, we investigate signal detection in emerging dynamic spatial modulation (DSM) based MIMO systems, where the existing mapping and detection methods do not work efficiently. In order to address this issue, we begin by proposing a combinatorial mapping-based DSM (CM-DSM) scheme in this paper. The proposed CM-DSM scheme employs a combinatorial 3D mapping to address the detection ambiguity by leveraging the combinatorial nature of DSM. Additionally, this mapping helps construct an appropriate decision tree for the optimal signal detection. By leveraging the resulting tree, we further propose a memory-bounded tree search (METS) algorithm, which efficiently finds the maximum likelihood (ML) estimate. To further enhance detection efficiency, we propose a deep learning boosted version of METS (DL-METS), which efficiently reduces the computational complexity via estimating the optimal heuristic function. Simulation results show that both the proposed METS and DL-METS work well in the considered system. In particular, the proposed DL-METS achieves nearly optimal detection performance while maintaining almost the lowest expected computational complexity, which strongly validates the effectiveness of the proposed algorithm.
期刊介绍:
The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.