Joint Channel Assignment and Stochastic Energy Management for RF-Powered OFDMA WSNs

As an emerging energy replenishment technique, radio-frequency (RF) energy transfer has been considered as a promising solution to power the low-end wireless sensor networks (WSNs). However, in RF-powered orthogonal frequency-division multiple accessing (OFDMA) WSNs, both the energy provisioning and data transmission are easily impacted by the highly dynamic and unpredictable wireless channels, making channel assignment and energy management closely coupled. Furthermore, due to the lack of priori knowledge of stochastic channel conditions, network performance optimization becomes more challenging. In this paper, we investigate joint channel assignment and stochastic energy management to optimize the long-term network utility in RF-powered OFDMA WSNs, by jointly considering the stochasticity in RF energy harvesting rates and channel fading. We formulate the network utility optimization problem (NUOP) as a mixed-integer non-linear stochastic optimization problem. By leveraging Lyapunov optimization to decouple NUOP into two subproblems and address them separately, we propose an online algorithm, named JAMA, to adaptively adjust the sampling rates, data transmission power, transmission rate, and channel assignment according to the ever-changing channel conditions, while guarantee the network stability and sustainability. In addition, theoretical performance analysis is provided to prove that JAMA can achieve near-optimal network utility and satisfy the network stability and sustainability constraints. Extensive simulation results based on the experimental data of an RF-powered WSN testbed verify the effectiveness and efficiency of JAMA and evaluate the impacts of system parameters on network performance.