Adaptive proportional fairness resource allocation for OFDM-based cognitive radio networks

In this paper, we study the resource allocation problem in multiuser Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive radio networks. The interference introduced to Primary Users (PUs) is fully considered, as well as a set of proportional rate constraints to ensure fairness among Secondary Users (SUs). Since it is extremely computationally complex to obtain the optimal solution because of integer constraints, we adopt a two-step method to address the formulated problem. Firstly, a heuristic subchannel assignment is developed based on the normalized capacity of each OFDM subchannel by jointly considering channel gain and the interference to PUs, which approaches a rough proportional fairness and removes the intractable integer constraints. Secondly, for a given subchannel assignment, we derive a fast optimal power distribution algorithm that has a complexity of O(L ² N) by exploiting the problem’s structure, which is much lower than standard convex optimization techniques that generally have a complexity of O((N + K)³), where N, L and K are the number of subchannels, PUs and SUs, respectively. We also develop a simple power distribution algorithm with complexity of only O(L + N), while achieving above 90 % sum capacity of the upper bound. Experiments show that our proposed algorithms work quite well in practical wireless scenarios. A significant capacity gain is obtained and the proportional fairness is satisfied perfectly.