基于低复杂度加法网络的非正交多址接入短报文多用户检测算法研究

Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network

针对非正交多址接入(NOMA)系统中,接收机使用串行干扰删除算法译码时需要已知干扰用户的调制方式而产生额外的信令开销问题,该文提出一种基于联合星座轨迹图和深度学习的NOMA短包传输干扰用户调制方式盲检测算法。考虑在通信设备部署神经网络时存在计算复杂度高和能量消耗大等不足,将原始卷积神经网络替换为深度加法网络,在调制检测准确率,计算延迟和能耗等方面进行了充分比较,使用时域过采样技术改善低信噪比下的识别率。最后分析并验证了功率分配,数据包长度对检测性能的影响。

A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access(NOMA)systems,which can avoid the required expensive signaling overhead in successive interference cancellation algorithms,especially for NOMA-based short packet transmission.Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network,the original convolutional network is replaced by the adder network.The modulation detection accuracy,computing delay and energy consumption are fully compared for two kinds of network architectures.Meanwhile,time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio.Finally,the influence of power allocation and data packet length on detection performance is analyzed and verified.