# Optimizing Throughput Fairness of Cluster-based Cooperation in Underlay Cognitive WPCNs

## TL;DR

This paper proposes a joint optimization approach for cluster-based cooperation and energy beamforming in underlay cognitive wireless powered communication networks to enhance throughput fairness while protecting primary system performance.

## Contribution

It introduces a novel joint optimization framework for energy beamforming, time, and power allocation to maximize minimum throughput fairness in secondary WPCNs under interference constraints.

## Key findings

- Effective throughput fairness improvement demonstrated through simulations.
- The joint optimization problem is transformed into a convex problem for efficient solving.
- The method guarantees primary system performance while enhancing secondary network fairness.

## Abstract

In this paper, we consider a secondary wireless powered communication network (WPCN) underlaid to a primary point-to-point communication link. The WPCN consists of a multi-antenna hybrid access point (HAP) that transfers wireless energy to a cluster of low-power wireless devices (WDs) and receives sensing data from them. To tackle the inherent severe user unfairness problem in WPCN, we consider a cluster-based cooperation where a WD acts as the cluster head that relays the information of the other WDs. Besides, we apply energy beamforming technique to balance the dissimilar energy consumptions of the WDs to further improve the fairness. However, the use of energy beamforming and cluster-based cooperation may introduce more severe interference to the primary system than the WDs transmit independently. To guarantee the performance of primary system, we consider an interference-temperature constraint to the primary system and derive the throughput performance of each WD under the peak interference-temperature constraint. To achieve maximum throughput fairness, we jointly optimize the energy beamforming design, the transmit time allocation among the HAP and the WDs, and the transmit power allocation of each WD to maximize the minimum data rate achievable among the WDs (the max-min throughput). We show that the non-convex joint optimization problem can be transformed to a convex one and then be efficiently solved using off-the-shelf convex algorithms. Moreover, we simulate under practical network setups and show that the proposed method can effectively improve the throughput fairness of the secondary WPCN, meanwhile guaranteeing the communication quality of the primary network.