Freshness-aware Resource Allocation for Non-orthogonal Wireless-powered IoT Networks
Yunfeng Chen, Yong Liu, Jinhao Xiao, Qunying Wu, Han Zhang, Fen Hou
TL;DR
This work tackles minimizing the expected weighted sum AoI ($EWSAoI$) in a wireless-powered IoT network with a central Hybrid Access Point (HAP) that can deliver downlink wireless energy transfer and uplink information transfer concurrently. It casts the scheduling problem as a Markov decision process (MDP) where the state comprises the instantaneous AoI and device battery levels, and the action space includes four transmission schemes (WET, OMA, NOMA, WET+OMA) with discrete power allocations; policy iteration yields an optimal adaptive scheme that switches decisions based on current AoI and energy. The study derives outage probability expressions for each scheme and demonstrates, via numerical results, a distinct boundary-switching property of the optimal policy and clear performance gains over fixed-scheme strategies. The findings offer practical insights for designing energy-sustainable, timely IoT systems by balancing energy harvesting and interference effects across schemes, especially as the wireless channel quality (SNR) varies.
Abstract
This paper investigates a wireless-powered Internet of Things (IoT) network comprising a hybrid access point (HAP) and two devices. The HAP facilitates downlink wireless energy transfer (WET) for device charging and uplink wireless information transfer (WIT) to collect status updates from the devices. To keep the information fresh, concurrent WET and WIT are allowed, and orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are adaptively scheduled for WIT. Consequently, we formulate an expected weighted sum age of information (EWSAoI) minimization problem to adaptively schedule the transmission scheme, choosing from WET, OMA, NOMA, and WET+OMA, and to allocate transmit power. To address this, we reformulate the problem as a Markov decision process (MDP) and develop an optimal policy based on instantaneous AoI and remaining battery power to determine scheme selection and transmit power allocation. Extensive results demonstrate the effectiveness of the proposed policy, and the optimal policy has a distinct decision boundary-switching property, providing valuable insights for practical system design.