Reference : Toward Optimally Efficient Search With Deep Learning for Large-Scale MIMO Systems
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Toward Optimally Efficient Search With Deep Learning for Large-Scale MIMO Systems
He, Le mailto [Guangzhou University > School of Computer Science and Cyber Engineering]
He, Ke mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom > ; Guangzhou University > Computer Science and Cyber Engineering]
Fan, Lisheng mailto [Guangzhou University > Computer Science and Cyber Engineering]
Lei, Xianfu mailto [Southwest Jiaotong University > School of Information Science and Technology]
Nallanathan, Arumugam mailto [Queen Mary University of London > School of Electronic Engineering and Computer Science]
Karagiannidis, George mailto [Aristotle University of Thessaloniki > Wireless Communications and Information Processing Group (WCIP)]
IEEE Transactions on Communications
Institute of Electrical and Electronics Engineers
United States
[en] integer least-squares ; sphere decoding ; MIMO
[en] This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at
Natrual Sicence Foundation of China
Researchers ; Students
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