Integration of Cognitive Radio and Ultra-Wideband Technologies for Intelligent Self-Adaptive Wireless Networks

Abstract

This study evaluated whether integrating cognitive radio with ultra-wideband signaling can yield intelligent self-adaptive wireless networks that sustain service in crowded spectrum. A prototype cross-layer architecture combined environment-aware spectrum sensing, reinforcement-learning–driven channel selection, pulse-based waveform agility, and coordinated handoffs to realize dynamic spectrum access. The stack was assessed via hardware-in-the-loop trials and large-scale simulation across three regimes (indoor factory microcell, outdoor V2I corridor, emergency pop-up mesh) in the 5-9 GHz bands under variable occupancy, mobility, and legacy interferers. Relative to single-technology baselines, aggregate throughput rose by 27-38%, median end-to-end delay fell by 25-31%, and packet loss under the heaviest interference was bounded to 2.7%; tail behavior tightened with p95 delays down 19-28% and p99 down 14-22%. Sensing and control were responsive yet conservative toward incumbents: spectrum-opportunity detection averaged 96.4% with a 3.1% false-alarm rate, median handoff latency stayed within 35-60 ms with 90-93 % success, and coexistence impacts remained within masks (≤0.5%). Energy per delivered bit decreased by 18-24%, flow fairness improved (Jain’s index ≈0.91-0.93), adaptation stabilized within 8-12 decision cycles after regime changes, and the system tolerated 57-60 s blackouts without Quality of Service breach via coordinated throttling and staggered resumes. These results indicate that a cognitive ultra-wideband integration delivers predictable Service Level Agreement margins, reduced energy cost, and robust coexistence, recommending the approach for factory cells, roadside units, and rapidly deployed emergency meshes under dense and volatile spectrum conditions.