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arXiv 2112.04075cs.ITcs.LGmath.IT
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通过学习实现通信的主动感知

Active Sensing for Communications by Learning

University of Toronto(多伦多大学)

Foad Sohrabi, Tao Jiang, Wei Cui, Wei Yu

摘要:

本文提出了一种深度学习方法来解决无线通信中的一类主动感知问题,其中代理在预定数量的时间帧内顺序地与环境交互以收集信息,以便执行感知或致动任务以最大化某些效用函数。在这样的主动学习设置中,代理需要基于到目前为止所做的观察来顺序地设计自适应感测策略。为了解决这样一个具有挑战性的问题,其中历史观测的维度随着时间的推移而增加,我们建议使用长短期记忆(LSTM)网络来利用观测序列中的时间相关性,并将每个观测映射到固定大小的状态信息向量。然后,我们使用深度神经网络(DNN)将每个时间帧的LSTM状态映射到下一个测量步骤的设计。最后,我们使用另一个DNN将最终的LSTM状态映射到所需的解决方案。我们研究了无线通信中的自适应信道感知问题的性能所提出的框架。特别是,我们考虑毫米波波束对准的自适应波束形成问题和反射对准的自适应可重构智能表面传感问题。数值结果表明,所提出的深度主动传感策略优于现有的自适应或非自适应传感方案。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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