Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

TL;DR

This paper tackles energy-efficient transmission in dense LoRa networks by framing channel and transmission power selection as a distributed multi-armed bandit problem. It adopts a lightweight UCB1-Tuned algorithm where end devices learn parameter choices from ACK-based rewards and energy costs, enabling autonomous operation without central control. Empirical results on a real high-density LoRa deployment show the approach outperforms fixed-parameter, ε-greedy, and ADR-Lite in both transmission success rate and energy efficiency, due to more frequent use of lower transmission power without compromising reliability. The work demonstrates a practical, low-complexity solution for resource-constrained IoT devices, with plans to extend the parameter space and analyze computational overhead.

Abstract

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and $ε$-greedy-based methods in terms of both transmission success rate and energy efficiency.

Figures (5)

• Figure 1: System Model
• Figure 2: Experimental Environment
• Figure 3: TP Ratio
• Figure 4: Transmission Success Rate
• Figure 5: Energy Efficiency