This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes.
翻译:本文建议对无线通信中的一类主动感测问题采取深层次的学习方法,在这类问题上,一种物剂在预定的时间范围内与环境相依地在一定的时间范围内收集信息,以便执行感测或触动任务,最大限度地发挥某些实用功能。在这种积极的学习环境中,物剂需要根据迄今的观测结果,设计一个适应感测战略。要解决历史观测层面随着时间推移而增加的这一具有挑战性的问题,我们建议使用一个长期的短期内存(LSTM)网络,以利用观测序列中的时间相关性,并将每次观测映射成固定规模的信息矢量。然后我们用一个深神经网络(DNNN)在每一个时间框架内绘制LSTM状态图,以设计下一个测量步骤。最后,我们使用另一个DNNN来绘制最终LSTM状态图,以达到理想的解决方案。我们调查了无线通信中适应感测频道感测问题拟议框架的绩效。我们特别考虑对毫米瓦夫对准和适应性智能表面感测表面感测问题进行适应性调整,并演示现有感测战略。Nummeralalalimalaling 计划,以显示现有的不进行反射为反向的反射。