Abstract
In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.
Highlights
Multiple-input multiple-output (MIMO) is a technology in which multiple antennas are installed for both transmitter and receiver, and signals are spatially multiplexed
The damping factors trained for each antenna configuration are used in the damped belief propagation (BP) to which the NS method is applied with the same node selection interval as in training
Comparing the cases when the correlation factors match (ρ = 0.3) and not match (ρ = 0.0) between training and test, in the conventional deep neural network-based damped BP (DNN-dBP), the bit error rate (BER) performance when the correlation factors do not match degrades compared to that when they match. This is because the optimal damping factors vary with the channel correlation, and the detection performance deteriorates due to the mismatches of the channel correlations in the conventional DNN-dBP
Summary
Multiple-input multiple-output (MIMO) is a technology in which multiple antennas are installed for both transmitter and receiver, and signals are spatially multiplexed. By transmitting multiple signals simultaneously, high data rates can be realized without increasing the bandwidth and transmission power. Massive MIMO, which uses several tens to hundreds of antenna elements on the transmitter side, is attracting attention as a key technology of 5G wireless communications [1]. Efficiency and large-capacity communication and enables simultaneous connection to a large number of user terminals. In MIMO, different signals are transmitted using the same frequency at the same time. In a massive MIMO system, the base station (BS) communicates with a large number of user terminals at the same time, making accurate signal detection difficult and complexity of detection high
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