Transmit Power Optimization for Wireless IoT Networks with Adaptive Quantization

• Published in: 2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 76 - 81
• Main Authors: Wang, Hongxin, Wang, Feng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.08.2022
• Subjects: Adaptive systems; distributed estimation; fractional programming; Minimization; power allocation; Power control; Programming; Quantization (signal); Wireless communication; Wireless sensor networks
• DOI: 10.1109/ICCC55456.2022.9880815

In this paper, by considering adaptive quantization for sensory data, we investigate an energy-efficient transmit power control optimization for distributed estimation in internet-of-things (IoT) networks with co-channel interference. Under the sum-power constraints of multiple sensors, we aim to minimize the mean squared error (MSE) at the access point (AP). By approximating the upper bound of the MSE under consideration, the resultant MSE minimization problem is shown to be a concave-convex fractional programming (FP) problem. As such, we develop an alternating optimization algorithm to iteratively obtain a local-optimal sensor transmit power control solution. Numerical results are provided to show the merit of the proposed scheme as compared to the existing schemes.