Deep Learning-Aided Tabu Search Detection for Large MIMO Systems
This addresses efficiency in wireless communication systems by reducing computational complexity, but it is incremental as it builds on existing tabu search and neural network methods.
The study tackled symbol detection in large MIMO systems by proposing a deep learning-aided tabu search algorithm that uses a neural network for initial approximation and adaptive termination, achieving approximately 90% complexity reduction for a 32x32 MIMO system with QPSK while maintaining similar performance.
In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net. Furthermore, in this algorithm, an adaptive early termination algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a $32 \times 32$ MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance.