Efficient channel feedback is crucial for optimizing Wireless Local Area Networks (WLANs), where traditional channel sounding often incurs substantial overhead, primarily from beamforming feedback. This work presents an innovative Adaptive Quantization Model (AQM) leveraging Artificial Intel - ligence and Machine Learning (AIML) to optimize the channel sounding feedback mechanism. The AQM intelligently adjusts feedback quantization levels, achieving a balanced quantization accuracy while minimizing resource consumption. It dynamically selects the optimal number of quantization bits for feedback angle vectors, tailored to current channel states and device operational parameters. Notably, our method achieves a signif - icant reduction in overhead, safeguarding Packet Error Rate (PER) and thus ensuring the integrity of communication qual - ity. Through rigorous simulations, the AQM has demonstrated marked improvements in throughput and a notable decrease in channel sounding overhead. The findings also suggest potential avenues for incorporating such AIML - based approaches into future WLAN protocol standards, charting a course for more sophisticated and resource - efficient wireless communications.

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e., sequence-to-sequence mapping). Recent works replace the linear reference sequence with a graph-based representation of the reference genome, which captures the genetic variations and diversity across many individuals in a population. Mapping reads to the graph-based reference genome (i.e., sequence-to-graph mapping) results in notable quality improvements in genome analysis. Unfortunately, while sequence-to-sequence mapping is well studied with many available tools and accelerators, sequence-to-graph mapping is a more difficult computational problem, with a much smaller number of practical software tools currently available.