Throughput Enhancement of Full-Duplex CSMA Networks Using Multiplayer Bandits

This article studies the network-level throughput of a full-duplex (FD)-enabled CSMA network, considering the link's transmit power (TP) control, carrier-sensing threshold (CST) adjustment, and logarithm access intensity (LAI) adaptation. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. We aim to find the best combination of TP, CST, and LAI for each link to maximize the FD-CSMA network throughput. However, adjusting each link's TP and CST will change the network's carrier-sensing relation or contention graph, consequently leading to a computationally intractable network optimization problem. On the other hand, it is difficult to jointly optimize these three parameters in a fully distributed network. To overcome these, we first decompose this network optimization problem into two subproblems: 1) a joint control and scheduling problem in the transport- and media access control (MAC)-layer and 2) a parameter selection problem in the PHY-layer. Then, the multiplayer multiarmed bandit (MPMAB) framework has been introduced to address this problem by solving the two subproblems alternately. We put forth a fully distributed algorithm, named the stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm, to solve the MPMAB problem by taking advantage of the optimization tool and the bandit theory. The numerical results show that the proposed algorithm outperforms the state-of-the-art bandit algorithms and can improve the network throughput by 43% compared with the random selection method.