An online learning approach to throughput optimization in wireless networks under dynamic and unknown interference conditions

In this paper, we consider a multi-user communication system with dynamically varying interference on block fading channels. We focus on a multi-antenna receiver, single-antenna transmitters, and the case in which the receiver has no knowledge of the channel state information, interference dynamics, and the variance of the additive noise. Pilot-assisted transmission techniques are employed to enable channel estimation at the receiver. For a given channel coherence length, increasing the number of pilots improves the estimation accuracy, with the tradeoff of reduction in data throughput. Thus, we propose to optimize the pilot content within the data frame to maximize the average data throughput. We employ well-known cross-validation techniques from the machine learning literature to simultaneously improve the estimation accuracy as well as the average throughput. Simulation results with the proposed approach suggest that even when the average number of active interferers is larger than the number of degrees of freedom, at least 85% of the ideal throughput can be achieved with the optimum pilot overhead.