Sensing-Transmission Tradeoff for Multimedia Transmission in Cognitive Radio Networks

Efficient probing spectrum holes is one of the most challenging tasks for the secondary user (SU) in a cognitive radio (CR) network. In this paper, we introduce a novel spectrum sensing framework where the duration for sensing at each time slot is variable. Sensing more channels increases the probability of finding a spectral hole, however, it would spend more time for sensing inevitably, which reduces the time for data transmission at a given time slot. Considering the sensing-transmission tradeoff, the optimization goal of spectrum sensing strategy is set to maximize the expected achievable throughput of the SU, which is formulated as a partially observable Markov decision process (POMDP). Finding an optimal solution to this optimization problem is computationally expensive due to its large state space, as well as large action space. We develop a novel spectrum sensing strategy based on deep reinforcement learning, which converges fast and can deal with complex scenario. Numerical results show that our proposed strategy can improve system throughput significantly.