Near-Optimal and Practical Jamming-Resistant Energy-Efficient Cognitive Radio Communications

This paper studies the problem of jamming-resistant spectrum aggregation and access (SAA) for energy-efficiency (EE) cognitive radio communications. We consider various jamming behaviors, where jammers may attack all available channels with arbitrarily changing strategies over time, attack a subset of the channels at certain time slots, or have different intelligence, i.e., oblivious or adaptive adversary, and so on. Without any priori knowledge about the channels and jammers, it is very challenging to design an efficient and practical jamming-resistant SAA algorithm to reach the optimal EE goal. In this paper, we utilize the advanced martingale concentration inequalities in an multi-armed bandits-based online learning framework to facilitate the optimal detection of various jamming behaviors. We first define a novel EE model for discontiguous orthogonal frequency division multiplexing to facilitate scalable SAA over distributed spectrum pools in practice. Then, the jamming-resistant dynamic channel access problem is formulated as a regret minimization problem. Meanwhile, an online stochastic gradient descent with bandit feedback procedure is adopted to allocate the transmit power. The proposed algorithm can autonomously detect the environmental features and find a near-optimal solution in each attacking scenario. Our algorithm is implemented with low complexity and with multiple users under some practical jamming scenarios. Extensive numerical studies show that under some practical jamming scenarios, our algorithm has an EE improvement of 45.3% over a fixed learning period, and an improvement of 82.5% in terms of learning duration compared with existing approaches.