  This paper proposes a learning aided gradient descent (LAGD) algorithm to
solve the weighted sum rate (WSR) maximization problem for multiple-input
single-output (MISO) beamforming. The proposed LAGD algorithm directly
optimizes the transmit precoder through implicit gradient descent based
iterations, at each of which the optimization strategy is determined by a
neural network, and thus, is dynamic and adaptive. At each instance of the
problem, this network is initialized randomly, and updated throughout the
iterative solution process. Therefore, the LAGD algorithm can be implemented at
any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does
not require labelled data or training prior to deployment. Numerical results
show that the LAGD algorithm can outperform of the well-known WMMSE algorithm
as well as other learning-based solutions with a modest computational
complexity. Our code is available at https://github.com/XiaGroup/LAGD.
