SD-Based Low-Complexity Signal Detection Algorithm in Massive MIMO Systems

Steepest descent algorithm (SD) itself can find a better convergence direction, but its own convergence speed is relatively slow, resulting in multiple iterations to approach the true solution. In order to speed up its convergence, this paper proposes the SD-NM algorithm first, whose principle is to improve the approximate solution obtained after each execution of the steepest descent algorithm. In order to further reduce the computational complexity and improve the detection performance of the SD-NM algorithm., SD-NM-RC detection algorithm and local optimal LO-SD-NM-RC detection algorithm are proposed in this paper. The principle is to divide the constellation into three regions using the idea of regional division. The estimated value that fall into the reliable area are directly extracted for judgment, and the estimated value are no longer involved in subsequent iterations. The estimated value that fall into the unreliable region or the normal iteration region are not processed during the iteration. After the iteration, the constellation points around the estimated value that fall into the unreliable region are traversed. Simulations show that when the number of iterations of the SD-NM algorithm is consistent with that of SD algorithm, the detection performance of SD-NM algorithm is one order of magnitude higher than that of the SD algorithm. The bit error rate (BER) of the LO-SD-NM-RC algorithm is lower than the BER of the minimum mean square error (MMSE) algorithm when the signal-to-noise ratio is greater than 4dB and the number of iterations is 2. When the number of iterations is 2, the BER of the SD-NM-RC algorithm is close to that of the SD-NM algorithm but the computational complexity of the SD-NM-RC algorithm is only 79% of that of the SD-NM algorithm, the computational complexity of the SD-RC algorithm will be further reduced as the number of iterations increases.