# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 2 Related Work  
The ADR algorithm in LoRaWAN [9; 10] dynamically allocates the transmission rate of EDs based on network conditions to reduce power consumption and enhance network capacity and stability. ADR algorithm consists of two main mechanisms: on the ED side, the data rate is lowered to increase communication range and ensure connectivity with the GW; on the network server side, the data rate and transmission power are adjusted according to the maximum signal-to-noise ratio (SNR) of each packet, thereby reducing the power consumption of the EDs. This algorithm analyzes the link status of the EDs and flexibly adjusts communication parameters to ensure communication robustness and reliability. In further optimizing the ADR mechanism, [PERSON] et al. proposed the ADR algorithm [11], which uses the average SNR of the most recent 20 packets instead of the maximum SNR value, improving performance under unstable link conditions. This allows the ED to maintain better communication quality even in fluctuating channel conditions. [PERSON] proposed the FADR algorithm [12], which allocates the spreading factor and transmission power based on RSSI to achieve fairness between EDs and reduce collisions. However, FADR has limitations in network scalability, as the addition or removal of nodes affects fairness, limiting its application in large-scale networks. In addition, [PERSON] et al. proposed the I-SFA algorithm [13], focusing on addressing channel variations by adjusting fixed spreading factors. While effective initially, it lacks the ability to adaptively adjust the data rate, resulting in inadequate performance in dynamic environments. In contrast, [PERSON] et al. proposed the CA-ADR algorithm [14], which reduces collision probability during data transmission by considering the distribution of all EDs, improving packet success rate and network throughput, particularly in large, dense networks. This algorithm reduces conflicts between nodes by assigning different spreading factors and further optimizes data rates through collision awareness. [PERSON] et al. proposed the DRCC algorithm [15], which dynamically adjusts data rates and channel control to support large-scale ED deployments. The algorithm can evaluate channel conditions in real time and reduce collisions through multichannel load balancing, thereby improving network throughput and reducing latency, making it ideal for dense ED scenarios. [PERSON] et al. [16] employed a reinforcement learning method to maximize ED throughput. Although highly scalable, the algorithm's complexity is high, and it does not adequately address the power consumption of EDs, limiting its effectiveness in practical applications. [PERSON] et al. proposed the EF-LoRa algorithm [17], which enhances energy fairness among EDs (EDs) and extends network lifetime by optimizing spreading factors, transmission power, and channel allocation. The algorithm employs a max-min fairness model and uses a greedy algorithm for resource allocation, demonstrating significant advantages in large-scale network simulations. However, the algorithm struggles with dynamic network changes and may lead to performance fluctuations in large-scale networks. Additionally, the algorithm lacks granularity in ED resource allocation, preventing real-time adaptive adjustments, which could result in delays in resource distribution. [PERSON] et al. [18] introduced a channel quality rating and prediction method that incorporates the concept of channel quality scores, offering a more precise evaluation of link quality compared to packet reception rate (PRR). This method uses a gated recurrent unit (GRU) algorithm to build a channel quality rating model, classifying EDs based on their current channel quality scores and spatial channel parameters (SCPs). While this approach improves evaluation accuracy, its complexity can lead to prediction biases. Additionally, the method faces challenges in real-time performance and environmental adaptability. [PERSON] et al.