基于条件神经过程的星上多用户检测算法

doi:10.11959/j.issn.2096-8930.2021044

Abstract

Abstract:

With the characteristics of all-terrain, all-weather and seamless coverage, satellite communications have become a potentially important part of 6G.An important prerequisite for achieving satellite intelligence is that the satellite have on-board processing capabilities.Multi-user detection (MUD) is a classic method of suppressing multiple access interference (MAI) in wireless communication, such as MMSE, Gaussian processregression (GPR) and other algorithms.Due to the inverse matrix required in the detection process, the algorithm complexity is usually cubic, and it is diff cult to directly apply to satellite platforms because of its limited processing capabilities.The conditional neural process combined the characteristics of the low complexity of the neural network and the data-eff cient of the Gaussian process.The neural network was used to parameterized the Gaussian process to avoided the inversion of the matrix, thereby reduced the computational complexity.The application of conditional neural process in MUD was studied.The simulation results showed that, while reduced complexity, conditional neural process also greatly improved the performance of bit error rate (BER).

Key words: satellite communication, multi-user detection, Gaussian process, conditional neural process, low complexity

CLC Number:

TP393

Yilun LIU, Liang JIN, Jiali LI, Lidong ZHU. On-board Multi-User Detection Algorithm Based on Conditional Neural Process[J]. Space-Integrated-Ground Information Networks, 2021, 2(4): 60-65.

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