Dynamic power pricing using distributed resource allocation for large-scale DSA systems

In this paper, we propose dynamic power pricing for distributed resource allocation in large-scale Dynamic Spectrum Access (DSA) systems. The dynamic power pricing is considered to influence the users' spectrum assignment and power allocation in two resource allocation problems. In the first scenario, the objective is to maximize the reward of the obtained throughput over the time window while not exceeding a fixed budget for the power cost. The second problem consists of minimizing the total power cost while guaranteeing a minimum achieved throughput. Since the optimal solutions are of high computational complexity, we propose a distributed two-step algorithm to solve the optimization problems. In the first step, we rely on "learning" to determine the best channel selection for each user. In the second step, we optimize the allocated power to be used for the selected channels. Using simulations, we show that dynamic power pricing models allow achieving better DSA throughput when compared to the case of a static pricing for the same budget. Likewise, it results on power consumption cost's saving when trying to achieve a target throughput.