  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.
