基于通感一体化技术的自适应调制方案

Adaptive Modulation Design Assisted by Integrated Sensing and Communication

通感一体化(Integrated Sensing and Communication,ISAC)技术能够通过共享频谱资源实现通信与感知功能,进一步提升频谱利用率。介绍了ISAC系统模型,包括传输协议、传感模型和通信模型,提出了一种基于ISAC技术的自适应调制(Adaptive Modulation,AM)方案,利用匹配滤波从回波中提取车辆距离信息,采用深度强化学习(Deep Reinforcement Learning,DRL)算法,自适应选择下一个时刻的调制模式。减少了导频信息、提升了信道容量,并且省去信道预测过程,减少了计算资源消耗。仿真结果表明,采用深度强化学习自适应选择下一时刻调制模式提升了误码率约束下的最大信道容量,并且相比于传统通信,吞吐量有较大的提升。

Integrated Sensing and Communication(ISAC)achieves communication and sensing functions by sharing spectrum resources,improving spectrum utilization.This paper introduces an ISAC system model,including transmission protocols,sensing models and communication models,and proposes an ISAC-assisted Adaptive Modulation(AM)scheme with a Deep reinforcement Learning(DRL)approach where the modulation mode for the next slot is selected based on vehicle distance information extracted from the echo by matched filter.In the proposed ISAC transmission protocol,the number of pilots is reduced to improve the transmission capacity.Additionally,the channel estimation process is eliminated to reduce computing resource consumption.Simulation results show that the maximum channel capacity under Bit Error Rate(BER)constraint is increased by applying DRL for modulation mode selection,and the throughput is significantly increased compared to traditional communication.