Soft Spectrum Sensing and Power Adaptation in Multiband Cognitive Radios

We consider joint optimization of spectrum sensing, channel access and power allocation in a multi-band cognitive radio network. Instead of making hard binary decisions as in traditional hypothesis testing spectrum sensing schemes, a soft spectrum sensing concept using the continuous-valued sensing test statistics is considered. The channel access decision about whether to access the channel or not is relaxed into allowing the secondary user to access channels with some probability. This joint optimization problem is aimed at maximizing the secondary users' sum throughput while keeping the interference to primary users under a specified threshold. The problem is shown to be a convex optimization problem and the Lagrangian dual method is employed to obtain the optimal solution. Two heuristic algorithms are also proposed to reduce the complexity while achieving a near optimal performance. Simulation results show that our soft sensing based algorithm significantly outperforms traditional hard decision sensing algorithms.