Design and Performance Evaluation of a Resource-Aware Spectrum Allocation Framework for Cognitive Radio Networks Using NS-3 Simulation: A QoS-Centric Approach

Cognitive Radio Networks (CRNs) have to be efficient in spectrum allocation to meet the Quality of Service (QoS) demands in emergent wireless contexts. This work develops a spectrum allocation framework that incorporates spectrum sensing, adaptive channel-quality scoring, and resource-aware decision-making oriented to QoS. The model evaluated in the ns-3.39 platform with a wireless network of 20 secondary users (SUs) and five primary users (PUs) operating across the 2.4/5 GHz bands with realistic Nakagami-m fading and random waypoint mobility. The simulations demonstrated exceptional sensing performance, with detection probability (Pd) increasing from 78% at -5 dB to 98% at 10 dB and false alarm rates below 5%. Packet-level examination demonstrated that for the smaller packet sizes (512 bytes), there was significantly greater performance (~0.037 Mbps) than larger packet sizes; however, throughput appeared to be the same under varying load conditions. Scalability experiments with respect to the PDR and delay the QoS showed that as SUs increased from 2-20, PDR rose from 13.96% to 28.54%, average latency decreased from 0.0072 s to 0.0036 s and jitter decreased from 0.0027 to 0.0016 s. Given that the proposed framework produced 81% higher throughput, 71% less jitter and 100% PDR under best-case conditions compared to evaluated baseline allocation scheme, these results demonstrate the potential of resource-aware allocation that is QoS-specialised on spectrum utilization, fairness, and user experience in CRNs, as a scalable and energy-efficient means of supporting future cognitive wireless networks.