Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

TL;DR

This work addresses energy efficiency in multi-GW LoRa networks by developing an analytical EE model that accounts for duty cycling, capture effects, and imperfect SF orthogonality, and then solving a joint CH/SF/TP allocation problem with a two-stage MMALoRa framework. The first stage uses a matching-based approach to assign EDs to CHs, incorporating externalities and swap stability; the second stage employs a MAAC-based reinforcement learning algorithm with multihead attention to allocate SF and TP within CH groups, enabling fast convergence. Results show accurate EE prediction, rapid convergence (about 50 episodes), and significant EE improvements over baseline schemes under varying network sizes and PDR constraints. The proposed framework provides a scalable, data-efficient method to optimize uplink performance in realistic multi-GW LoRa deployments, with potential impact on IoT energy budgets and network efficiency.

Abstract

Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access control, which may suffer from severe packet collisions as the network scale expands, consequently reducing the system energy efficiency (EE). To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED). Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem, with the objective of optimizing the system EE for uplink transmissions. Due to the NP-hard complexity of the problem, the optimization problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to address the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH, which reduces the number of learning agents to achieve fast convergence. The simulation outcomes indicate that the proposed approach converges quickly under various parameter settings and obtains significantly better system EE than baseline algorithms.

Figures (8)

• Figure 1: Packets from the target ED and interferer with interference time interval and preamble details.
• Figure 2: The workflow of the MMALoRa framework.
• Figure 3: The MAAC-based algorithm architecture for SF/TP allocation.
• Figure 4: Locations of the GWs and EDs.
• Figure 5: MAE between the proposed analytical model and simulations for various (a) numbers of EDs, (b) numbers of GWs and (c) transmission parameter settings.