# LoRa Resource Allocation Algorithm for Higher Data Rates
## 2 Related Work
Additionally, EWS requires GPS-based location data, RSSI-based distance calculation, or manual distance measurement, adding complexity to its implementation. In contrast, the proposed LRA algorithm avoids offline calculations to maintain a lightweight protocol. [PERSON] et al. [16] proposed Multiple-Input Multiple-Output (MIMO)-LoRa, which enhances BER at higher SFs of 10, 11, and 12 by utilizing multiple SFs for parallel transmission. This approach employs a precoding matrix to optimize the total SNR and peak transmit power to select the appropriate SFs. The LoRa GW chip is designed to decode superimposed signals from different SFs, as proposed in [17]. The demodulator is divided into two groups, handling odd and even SFs separately. To efficiently utilize the MAC layer, which treats all packets from different SFs as a single transmission, strict synchronization across all EDs is required. However, using higher SFs increases the likelihood of network congestion. Since higher SFs result in longer transmission times, they can occupy the channel for extended periods, potentially leading to congestion in networks with multiple devices. As a result, it may be necessary to classify EDs to manage the SF-based packet distribution effectively and avoid congestion-related issues. Some research works have focused on lowering the size of the image by employing encoders and compression techniques to reduce the total data size required for the image transmission [18,19]. Some other works adopted machine-learning-based strategies for dynamic SF allocation. [PERSON] et al. [20] proposed an energy-efficient resource allocation strategy based on Q-Learning, enabling LoRa devices to make autonomous decisions on SF configurations. However, as Q-Learning algorithms require multiple iterations to converge to optimal decisions, in dynamic environments where conditions like interference and device density change frequently, this convergence time could limit performance. [PERSON] et al. [21] focused on mitigating inter-SF interference using a Multi-Armed Bandit (MAB) model and EXP3 (Exponential Weights for Exploration and Exploitation) algorithm, enabling LoRa devices to autonomously select the least-congested SFs. Although the EXP3 algorithm is computationally efficient, it may require time to converge on optimal SF allocations, which could lead to inefficiencies during periods of high network changes or initialization phases. A Deep Reinforcement Learning (DRL)-based SF optimization algorithm that dynamically adjusts SF by considering both node characteristics and channel conditions is proposed in [22]. The algorithm uses retransmission as a proxy for detecting collisions. This indirect method may introduce inaccuracies in environments where packet losses occur due to interference or poor signal conditions rather than collisions. [PERSON] et al. [23] introduced FedLoRa, a federated learning-based optimization scheme that balances resource allocation to minimize FML (Federated Machine Learning) round time and energy consumption by distributing network load across available SFs. A greedy algorithm is proposed to approximate the optimization problem of resource allocation.