# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 3 Optimization Process and Performance Analysis
### Limitations and Future Work  
While the proposed framework demonstrates significant performance improvements, its limitations under dynamic and multi-user environments must be acknowledged. The current model assumes quasi-static CSI during optimization cycles. However, in real-world PLC systems, rapid channel fluctuations due to load switching or electromagnetic interference may render LSTM-based CSI predictions obsolete. For instance, if the channel coherence time is shorter than the simulated annealing convergence period (\(\approx\)3200 iterations in our experiments), the optimized relay positions and power allocation may become suboptimal, leading to a potential 15-20% increase in outage probability (as observed in [18] for TDI-OFDM systems under similar conditions). Field measurements show that industrial PLC noise exhibits bursty impulses (e.g., 10-100 \(\upmu\)s bursts with 20-30 dB higher power than background), which can corrupt LSTM-based CSI predictions and degrade SIC decoding success rates by up to 40%. The hardware limitations of real-time optimization should be considered when implementing. On low-cost PLC modems (e.g., Broadcom BCM60321, Broadcom Corporation, Palo Alto, CA, USA), the SA-LSTM algorithm requires \(\approx\)2.1 s per optimization cycle, exceeding the 500 ms coherence time of fast-varying channels [18]. In EMI Regulations, the FCC Part 15 limits PLC transmit power to 30 dBm, reducing the effective relay coverage radius from 200 m (simulated) to 50 m in practice. These are all issues that need to be considered in the implementation process, and we will discuss these issues in future research. Although our simulations validate the approach for up to five users, scaling to dense IoT deployments (e.g., 50+ devices) poses challenges. The combinatorial complexity of relay selection grows exponentially as \(O(N^{2})\) increasing the computation time from less than 1 s (for 5 users) to an estimated few minutes (for 50 users) on the same hardware. \begin{table}
\begin{tabular}{c c c} \hline
**Metric** & **Simulation** & **Testbed (Unoptimized)** & **Testbed (Optimized)** \\ \hline Total Power (W) & 44.65 & 52.18 & 47.91 \\ \hline BER (\(\times 10^{-4}\)) & 1.2 & 3.8 & 2.1 \\ \hline \end{tabular}
\end{table} 
Table 6: System performance. \begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Simulation** & **Real-World** & **Error (\%)** \\ \hline Avg. Channel Gain (dB) & \(-\)21.3 & \(-\)21.3 & 11.2 \\ \hline Impulse Noise Rate & 15\% & 22\% & 46.7 \\ \hline \end{tabular}
\end{table} 
Table 5: Channel characteristics comparison. This latency is prohibitive for real-time applications like smart grid fault detection, where sub-500 ms response times are required [3]. The Bernoulli-Gaussian IN model, while analytically tractable, does not capture temporal clustering of impulses observed in industrial PLC environments [12]. Field data from [13] show that bursty impulses (e.g., 10 consecutive corrupted samples) can degrade SIC decoding success rates by 40%, a scenario not fully addressed in our current framework. To address these limitations, we plan to integrate reinforcement learning (RL) for real-time adaptation to dynamic channels, building on the hybrid SA-LSTM framework, develop distributed optimization protocols to decentralize computation in multi-user networks, inspired by [15; 16; 17]'s federated learning approach for VLC-NOMA, and incorporate measured IN datasets from smart grid deployments to refine noise models and enhance SIC robustness. To bridge the gap between theoretical analysis and real-world applications, we propose the following implementation strategies and datasets for validating the proposed framework: The IEEE Powerline Communication Channel Dataset [15] provides measured impulsive noise traces from factory environments, including bursty impulses and time-varying attenuation. Integrating this dataset can refine the Markov chain-based IN model to capture temporal clustering effects.