Learning-Based Optimization of Wireless-Powered Two-Way Interference Channels With Imperfect CSI

In this article, we consider wireless-powered two-way communication in an
$N$
-user interference channel with imperfect channel state information (CSI). In the system considered, the receivers harvest energy and receive information simultaneously from data signals sent by transmitters using a time switching (TS) policy, before transmitting response signals back to the transmitters in a subsequent phase using the harvested energy. We aim to find the resource allocation that allows the transmit power and TS ratio to be determined jointly to maximize the sum rate of the response links while guaranteeing a predetermined rate requirement for each data link, even in the presence of errors in the estimated CSI. To deal with the nonconvexity of our optimization problem, we first introduce a gradient algorithm with a barrier function that finds suboptimal solutions heuristically. Moreover, to overcome the limitations of the gradient algorithm, e.g., its high computational complexity and vulnerability to channel error, we devise a robust strategy for resource allocation based on deep learning, in which artificially distorted CSI is fed into the deep neural network (DNN) during training to compensate for the incompleteness of the derived solutions caused by channel error. The performances of the considered schemes are examined through simulations, in which the proposed DNN scheme achieves a near-optimal performance with respect to the sum rate of the response links and outage probability under imperfect CSI, which validates its usefulness and robustness.