# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### Practical Implementation Considerations  
The potential implementation of this approach as a real-time monitoring system warrants careful consideration of computational requirements and practical constraints. Some of the recent papers that addressed the issue about real-time implementation of the AI systems can be found in [42; 43; 44; 45]. Our analysis reveals several important aspects that need to be addressed for practical deployment. First, the computational demands of real-time LRP analysis are significant but manageable with modern hardware. The basic implementation requires approximately 8.1 ms of processing time per sample (2.3 ms for LSTM inference and 5.8 ms for LRP computation) and approximately 762 MB of total memory (312 MB for LSTM operations and 450 MB for LRP calculations). These requirements, while substantial, fall within the capabilities of modern GNSS receiver systems, many of which now incorporate powerful embedded processors and substantial memory resources. However, several optimization strategies could significantly improve the efficiency of real-time implementation. One promising approach is selective processing, where LRP scores are computed at reduced frequency or triggered by preliminary anomaly indicators. This could reduce computational overhead while maintaining the system's effectiveness in detecting significant anomalies. Our preliminary analysis suggests that computing LRP scores for every fifth sample, with triggered analysis for suspicious patterns, could reduce computational load by approximately 60% while maintaining detection reliability. Memory optimization presents another crucial consideration. The current implementation requires significant memory resources, primarily due to the storage of intermediate activations for LRP calculations. Techniques such as gradient checkpointing and selective activation storage could potentially reduce memory requirements by 40-50%. Additionally, model compression techniques such as weight quantization and pruning could further reduce both memory footprint and computational complexity. The trade-off between detection accuracy and computational efficiency requires careful balance. Our experiments indicate that while reduced-precision calculations and simplified LRP implementations can significantly improve computational efficiency, they may also impact the sensitivity of anomaly detection. For instance, 8-bit quantization of model weights reduced memory requirements by approximately 75% but introduced a 3-5% decrease in anomaly detection accuracy. Real-time implementation also raises questions about system latency and response time. The current processing time of 8.1 ms per sample suggests that the system could theoretically process about 123 samples per second, which exceeds typical GNSS measurement rates (1-10 Hz). However, this assumes dedicated computational resources and optimizedimplementation. In practice, system integration, data handling overhead, and concurrent processing requirements would likely reduce this theoretical maximum. An important consideration for future development is the potential for hardware acceleration. Modern GNSS receivers increasingly incorporate specialized hardware such as FPGAs or dedicated neural processing units. Such hardware acceleration could potentially reduce processing time by an order of magnitude, making real-time LRP analysis more practical for widespread deployment. Preliminary estimates suggest that FPGA implementation could reduce processing time to less than 1 ms per sample while significantly reducing power consumption.