An Adaptive Matching Bridged Resource Allocation Over Correlated Energy Efficiency and AoI in CR-IoT System

We consider a Cognitive Radio based Internet of Things (CR-IoT) system consisting of multiple Primary IoT Devices (PDs) and Secondary IoT Devices (SDs). We focus on the impact of the IoT devices’ heterogeneous traffic pattern on the energy efficiency and on the age of information (AoI) performance, and aim to maximize the sum of energy efficiency of the SDs under their individual constraints of the average age of information (AoI), via optimally deciding the transmit power and channel allocation. We first derive the closed-form expressions of the energy efficiency and the average AoI, and subsequently explore their convexity and monotonicity to the transmit power. Following these characterizations, an optimal transmit power optimization algorithm (TPOA) is proposed for the SDs, and the channel allocation problem is reformulated as a matching game based on the utilities calculated from TPOA. To improve the system performance, we introduce the virtual price charged in each channel. Taking advantage of the correlation between two adjacent matchings, we propose a semi-distributed, low-complexity adaptive matching algorithm to construct an
$\epsilon $
-stable matching, and the effectiveness of the proposed matching algorithm is verified through Monte Carlo simulations.