The invention relates to the technical field of
wireless communication, and discloses a beam forming method for active reconfigurable intelligent surface (RIS)-assisted
wireless energy supply air computing based on deep
reinforcement learning, which comprises the following steps: constructing an active RIS-assisted
wireless energy supply air computing
system; according to the method, multiple constraint conditions such as energy collection and active RIS are comprehensively considered, and an optimization model for joint optimization of a
hybrid access node emission
covariance matrix, a receiving beam vector, an RIS reflection matrix and
Internet of Things equipment
emission power is established by taking minimization of an air calculation
mean square error (MSE) as a target; modeling a beam forming behavior into a Markov
decision process, solving an
optimization problem by adopting a
reinforcement learning algorithm based on depth deterministic policy gradient (DDPG), and improving training convergence by introducing an exponential weighted
moving average mechanism; the effectiveness of the
algorithm is verified through
simulation, and the influence of parameters such as different transmitting powers, the number of RIS units and the number of devices on data aggregation is analyzed. Compared with the prior art, small MSE can be obtained through efficient downlink energy beam forming and
uplink transmission beam forming, and the data aggregation precision of
the Internet of Things is improved.