# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
###### Abstract
For example, slot-based ALOHA schemes, though simple, are effective, and multi-channel ALOHA has been applied and analyzed in cognitive networks. However, traditional access protocols such as ALOHA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) are limited by the number of subchannels, preventing them from supporting large-scale user access. Fortunately, applying NOMA in Medium Access Control (MAC) layer protocols allows multiple users to share the same channel, improving system throughput and reducing collision rates [14; 15; 16]. In summary, PLC systems combining NOMA, OFDM, and relay technologies show great potential in enhancing system capacity, extending transmission distance, improving signal quality, and reducing power consumption. The integrated application of these technologies provides effective solutions for future smart grid and IoT applications and guides further research and development of PLC systems. Similarly, in Visible Light Communication (VLC) networks, [PERSON] et al. [17] proposed an Exponential Gain Ratio Power Allocation (EGRPA) strategy for MIMO-NOMA systems, achieving a 38.7% sum rate improvement compared to traditional methods. These studies highlight the critical role of adaptive resource allocation and noise mitigation in heterogeneous communication environments [15]. IN suppression techniques have evolved from traditional filtering to advanced signal processing. In PLC, [PERSON] et al. [10] employed cyclic redundancy checks (CRCs) to detect and retransmit corrupted packets, while [PERSON] et al. [18] leveraged TDI with MMSE equalization to reduce BER in NOMA-OFDM systems by 20 dB under IN. For VLC, [PERSON] et al. [16] integrated chaotic encryption with OFDM-NOMA to enhance security and PAPR performance. These approaches, however, often trade computational complexity for performance gains, necessitating low-complexity alternatives for resource-constrained IoT devices. The integration of heuristic algorithms and machine learning has gained traction. [PERSON] et al. [17] focused on exponential power allocation in MIMO-VLC but omitted relay optimization. [PERSON] et al. [8] highlighted the potential of hybrid GA-SA algorithms in bidirectional PLC systems, yet their work lacks scalability for multi-user scenarios. Despite significant progress in improving PLC system performance, some shortcomings remain. First, most studies consider fixed relay positions and power allocation schemes, without considering dynamic channel conditions and real network environments. Second, existing optimization algorithms are mostly based on traditional mathematical models and do not fully utilize modern artificial intelligence technologies to enhance system performance. Additionally, most studies consider systems with only two or three users, with limited research on multi-user scenarios. This paper proposes a multi-user PLC system combining NOMA and OFDM technologies to address these issues. In this system, we design a communication network with multiple users and relays, where suitable users act as relays to communicate with remote users. Specifically, this paper proposes a method combining deep learning models and improved simulated annealing algorithms to optimize relay positions and total system power. The main contributions of this paper are as follows:  
1. Constructing a multi-user NOMA PLC system model that combines frequency division multiplexing technology, selecting suitable users as relays to communicate with remote users. This model enhances spectral efficiency, extends transmission distance, and improves signal quality through relay technology. 2. LSTM networks in deep learning models to predict future Channel State Information (CSI). LSTM is a neural network model capable of capturing long-term dependencies in time series data, making it highly suitable for predicting time series CSI. By training on historical channel data, the LSTM model can accurately predict future CSI, providing precise input data for system optimization. 3.