Transmit Power Optimization for Wireless IoT Networks with Adaptive Quantization

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 mi...

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Bibliographic Details
Published in2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 76 - 81
Main Authors Wang, Hongxin, Wang, Feng
Format Conference Proceeding
LanguageEnglish
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
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DOI10.1109/ICCC55456.2022.9880815

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Summary: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.
DOI:10.1109/ICCC55456.2022.9880815