Optimal action decision of secondary users in light-handed cognitive radio networks

In traditional cognitive radio (CR) network, secondary users (SUs) are always assumed to obey the rule of “introducing no interference to the primary users (PUs)”. However, this assumption may be not realistic as the CR devices becoming more and more intelligent nowadays. Light-handed CR is proposed to deal with this problem by introducing “punishment” to illegal CR transmissions. In this network framework, the action decisions of SUs are modeled as a partially observable Markov decision process (POMDP), and are optimized with the objective of maximizing their reward. Furthermore, extensive simulation results show that the proposed scheme improves the SUs´ reward significantly compared to the existing scheme.