  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.
