HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer

Although the LoRaW AN network can support a larger node scale than the LoRa private network, as the number of devices increases, the performance of the LoRaW AN network in terms of network congestion and energy consumption faces significant challenges. The limited spectrum resources and channel congestion will lead to a decrease in the communication efficiency of the netwo rk, which in turn affects the reliability of data transmission. How to achieve efficient and energy - saving resource allocation while ensuring network performance remains a key issue. In order to improve the overall performance of the LoRaW AN network, optim izing the transmission strategy parameters such as the spreading factor, transmit power, and receive window of the uplink and downlink is considered to be an effective means. By reasonably configuring these parameters, network conflicts can be effectively reduced, signal attenuation can be reduced, and signal coverage can be increased, thereby improving network reliability and communication quality. However, most of the existing optimization methods focus on the adjustment of the spreading factor and transm it power of the uplink, and rarely consider the impact of the downlink on network performance. To address this problem, this chapter proposes a History - E nhanced t wo - phase Actor - Critic a lgorithm with a s hared Transformer (HEA T), which aims to improve the resource allocation strategy of the LoRaW AN network and improve the overall performance of the network. This chapter conducts multiple sets of comparative experiments between HEA T and various popular methods under different device densities and traffic int ensities to verify the effectiveness of HEA T. 2 System Model and Problem Representation In order to efficiently verify the effectiveness of various LoRaW AN resource allocation strategies, this section describes and models the LoRa link behavior and the LoRaW AN standard in detail. Subsequently, this section proposes the target problem of LoRaW AN resource allocation and expresses the target problem as a Markov decision process.