Sum-Throughput Maximization With QoS Constraints for Cooperative WPCNs

Abstract

A wireless powered communication network (WPCN) is considered, which has a hybrid access point (H-AP) and two users with energy harvesting capabilities. The H-AP transfers energy to the users during downlink (DL) transmission, and the users transmit information to the H-AP during uplink (UL) transmission by utilizing the harvested energy. To enhance user fairness, a user cooperation protocol is employed in which a user close to the H-AP relays the message of the other user by partially using its allocated time and available energy resources. By jointly optimizing the time allocation for DL energy transfer and UL information transmission, we maximize the sum-throughput in the considered network subject to a set of quality of service (QoS) requirements. The optimization problem is solved through a divideand-conquer approach along with convex optimization techniques, and thus an analytical solution is derived. In particular, the conditions under which the WPCN benefits from the user cooperation in achieving the maximum sum-throughput are fully characterized. Furthermore, an efficient resource allocation algorithm to find the optimal solution under QoS requirements is presented.

Throughput Maximization for Multi-Cluster WPCN: User Cooperation, Non-linearity, and Channel Uncertainty

“…In [4]- [9], the user cooperation is considered for a three-node WPCN in which all nodes are equipped with single-antenna. In the model proposed in [4] and [5], the user nearer to the HAP transmits the signal of the distant user along with its own signal to the HAP. The sum throughput of the network is maximized by optimal design of time and power allocations.…”

“…Therefore, C det 3 and C det 4 represent non-convex sets. To deal with the aforementioned non-convexity, first we define the sufficiently large parameter 5 ξ k,n such that ∇ 2…”

Sum-Rate Maximization for UAV Aided Wireless Power Transfer in Space-Air-Ground Networks

“…By contrast, the latter is to maximize the minimum achieveable rate of nodes to improve the fairness among the nodes [2]. It is worth noting that the GNs in IoRT are in general deployed with different tasks, which leads to different rate requirements for each GN [23]. With such a motivation, we aim to maximize the system sum rate, subject to the different rate requirements by taking account into the proportional rate constrains (PRC) among the GNs, where PRC can be considered as a fairness-level configuration to guarantee that each GN gets a predetermined proportion of system sum rate [24].…”