Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems
This work provides a more efficient, near-optimal solution for signal detection in large-scale MIMO systems, which is a critical problem for wireless communication engineers.
This paper addresses the NP-hard optimal signal detection problem in large-scale MIMO systems, where existing optimal search algorithms are too complex. The proposed Hyper-Accelerated Tree Search (HATS) algorithm uses a deep neural network to estimate an optimal heuristic, achieving almost optimal bit error rate (BER) performance while bounding memory and visiting nearly the fewest tree nodes.
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 \url{https://github.com/skypitcher/hats}.