Throughput Fairness Optimization for Cluster-Based Collaboration

This article considers a cognitive wireless-powered communication network (WPCN) (C-WPCN), which overlays to a primary peer-to-peer communication link and constitutes a hybrid access point (HAP) with multiple antennas. The HAP first transmits wireless power to an array of wireless devices (WDs) with low-power consumption and then receives sensing data from these WDs. In particular, user cooperation is proposed among the cluster of WDs to enhance the throughput fairness. We consider that the HAP assists primary transmission by cooperatively transmitting the primary message, and at the same time cancels the interference from the primary transmission. However, the mutual interference between the WDs and the primary link cannot be fully mitigated. To guarantee throughput fairness and protect the primary communication link, the minimum achievable data rate among the WDs (max-min throughput) is maximized under a primary rate constraint by optimizing the C-WPCN's transmission time allocation. To settle this nonconvex issue, a high-efficiency algorithm is put forward on the ground of the technique of sequential convex approximation. Furthermore, we run simulations in real network settings and the simulation results prove that our proposed approach can validly raise the secondary WPCN's throughput fairness under a constraint of primary rate.