Abstract
The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.
Highlights
As the number of mobile terminals has exploded, traditional small-scale multiple-input multiple-output (MIMO) systems cannot meet the requirements of various mobile services for communication rates. erefore, the MIMO technology is gradually developing in the direction of large scale; compared with the traditional MIMO system, the massive MIMO system expands the number of device antennas to dozens or even hundreds, which further improves the performance of the communication system
Proposed a deep neural network, named Detection Network (DetNet) [6, 7]. e DetNet is a network structure that is designed for massive MIMO detection; due to its low complexity and high detection performance, it has been widely studied
We designed a deep learning network for massive MIMO systems and named it MsNet, which is an improvement over DetNet and Sparsely Connected Neural Network (ScNet)
Summary
As the number of mobile terminals has exploded, traditional small-scale MIMO systems cannot meet the requirements of various mobile services for communication rates. erefore, the MIMO technology is gradually developing in the direction of large scale; compared with the traditional MIMO system, the massive MIMO system expands the number of device antennas to dozens or even hundreds, which further improves the performance of the communication system. E DetNet is a network structure that is designed for massive MIMO detection; due to its low complexity and high detection performance, it has been widely studied. We designed a deep learning network for massive MIMO systems and named it MsNet, which is an improvement over DetNet and ScNet. Compared with DetNet and ScNet, MsNet reduces the complexity of the network and greatly improves the detection performance in International Journal of Antennas and Propagation high-order modulation scheme. We design a special activation function for symbol detection to perform multisegment mapping of the input signal, which is suitable for high-order modulation communication scenarios.
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