Schwarz Information Criterion Aided MAB for Resource Allocation in Dynamic LoRa System

TL;DR

This paper introduces a lightweight, adaptive resource allocation method for LoRa networks that combines UCB1-tuned with Schwarz Information Criterion to quickly respond to environmental changes, improving success rate and energy efficiency.

Contribution

It integrates SIC with UCB1-tuned for rapid adaptation in dynamic environments, suitable for resource-constrained LoRa devices.

Findings

Enhanced transmission success rate in dynamic conditions

Improved energy efficiency over traditional methods

Faster adaptation to environmental changes

Abstract

This paper proposes a lightweight distributed learning method for transmission parameter selection in Long Range (LoRa) networks that can adapt to dynamic communication environments. In the proposed method, each LoRa End Device (ED) employs the Upper Confidence Bound (UCB)1-tuned algorithm to select transmission parameters including channel, transmission power, and bandwidth. The transmission parameters are selected based on the ACKnowledgment (ACK) feedback returned from the gateway after each transmission and the corresponding transmission energy consumption. Hence, it enables devices to simultaneously optimize transmission success rate and energy efficiency in a fully distributed manner. However, although UCB1-tuned based method is effective under stationary conditions, it suffers from slow adaptation in dynamic environments due to its strong reliance on historical observations. To address this limitation, we integrate the Schwarz Information Criterion (SIC) to our proposed method. SIC is adopted because it enables low-cost detection of changes in the communication environment, making it suitable for implementation on resource-constrained LoRa EDs. When a change is detected by SIC, the learning history of UCB1-tuned is reset, allowing rapid re-learning under the new conditions. Experimental results using real LoRa devices demonstrate that the proposed method achieves superior transmission success rate, energy efficiency, and adaptability compared with the conventional UCB1-tuned algorithm without SIC.