Distributed Interference Compensation for Multi-Channel Wireless Networks

We present distributed power control algorithms for a wireless peer-to-peer network with multiple channels per user. Users exchange “price ” signals that indicate the negative effect of interference at the receivers in each channel. Given this set of prices, each transmitter chooses a power allocation across channels to maximize its net benefit (utility minus cost), subject to a total power constraint. We consider two specific algorithms for power and price updates, and establish global convergence for both algorithms to the unique globally optimal power allocation for a class of concave user utility functions. When the utility functions represent achievable rates, global convergence is not guaranteed; however, we show numerically that the proposed power control algorithms achieve much better performance than iterative water-filling, in which users maximize their own rates without exchanging price information.