Adaptive power control is a critical challenge in dense radio frequency identification(RFID)environments,where uncontrolled power levels can lead to excessive interference,energy inefficiency,and reduced system perfor...Adaptive power control is a critical challenge in dense radio frequency identification(RFID)environments,where uncontrolled power levels can lead to excessive interference,energy inefficiency,and reduced system performance.This paper presents a robust and scalable adaptive power control framework that dynamically adjusts transmit power levels to optimize energy efficiency,minimize interference,and enhance system throughput.The proposed framework leverages an optimization-driven approach based on real-time environmental feedback,ensuring compliance with regulatory constraints while maintaining optimal performance.A multi-objective optimization strategy is employed to balance several key metrics,including throughput,energy consumption,and fairness,with a Pareto front analysis demonstrating superior trade-offs compared to fixed power strategies.The effectiveness of the proposed approach is validated through extensive simulations and real-world experiments using universal software radio peripheral(USRP)devices in dense RFID deployments.The results show that our framework achieves a 34%reduction in cumulative interference,a 15%improvement in energy efficiency,and a 20%increase in throughput compared to baseline fixed power methods.Furthermore,it converges rapidly,even in dynamic and high-density networks.These improvements make it highly scalable and adaptable to varying reader densities,ensuring reliable performance in large-scale RFID applications such as supply chain management and industrial automation.展开更多
基金supported in part by the United States National Science Foundation under Grants CCSS-2245607 and CCSS-2245608the Wireless Engineering Research and Education Center and RFID Lab at Auburn University.
文摘Adaptive power control is a critical challenge in dense radio frequency identification(RFID)environments,where uncontrolled power levels can lead to excessive interference,energy inefficiency,and reduced system performance.This paper presents a robust and scalable adaptive power control framework that dynamically adjusts transmit power levels to optimize energy efficiency,minimize interference,and enhance system throughput.The proposed framework leverages an optimization-driven approach based on real-time environmental feedback,ensuring compliance with regulatory constraints while maintaining optimal performance.A multi-objective optimization strategy is employed to balance several key metrics,including throughput,energy consumption,and fairness,with a Pareto front analysis demonstrating superior trade-offs compared to fixed power strategies.The effectiveness of the proposed approach is validated through extensive simulations and real-world experiments using universal software radio peripheral(USRP)devices in dense RFID deployments.The results show that our framework achieves a 34%reduction in cumulative interference,a 15%improvement in energy efficiency,and a 20%increase in throughput compared to baseline fixed power methods.Furthermore,it converges rapidly,even in dynamic and high-density networks.These improvements make it highly scalable and adaptable to varying reader densities,ensuring reliable performance in large-scale RFID applications such as supply chain management and industrial automation.
