Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks

In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access the licensed spectrum when it is not currently being used by primary users (PUs). In this paper, we study the throughput maximization problem for a multi-channel CRN where each SU can only sense a limited number of channels. We show that this problem is strongly NP-hard, and propose an approximation algorithm with a factor at least $1/2\mu$ where $\mu \in [1,2]$ is a system parameter reflecting the sensing capability of SUs across channels and their sensing budgets. This performance guarantee is achieved by exploiting a nice structural property of the objective function and constructing a particular matching. Our numerical results demonstrate the advantage of our algorithm compared with both a random and a greedy sensing assignment algorithm

Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User

In this paper, we study and analyze cognitive radio networks in which secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We design a random spectrum sensing and access protocol for the SU that exploits the primary link's feedback and requires less average sensing time. Unlike previous works proposed earlier in literature, we do not assume perfect feedback. Instead, we take into account the more practical possibilities of overhearing unreliable feedback signals and accommodate spectrum sensing errors. Moreover, we assume an interference-based channel model where the receivers are equipped with multi-packet reception (MPR) capability. Furthermore, we perform power allocation at the SU with the objective of maximizing the secondary throughput under constraints that maintain certain quality-of-service (QoS) measures for the primary user (PU)