Unified p-norm detection and reliability-weighted fusion for mobility-robust CSS in dynamic clustered 5G NR vehicular network

This paper presents an analytical framework for clustered cooperative spectrum sensing (CSS) in dynamic 5G new radio (NR) vehicular networks, accounting for time-selective Nakagami- $m$ fading, mobility-induced decorrelation, and noisy reporting channels. We derive closed-form moments and detection probabilities for the $p$-norm detector under Jakes’ Doppler model, extending prior static analyses to realistic vehicular mobility. At the cluster head, we compare two fusion schemes: reliability weighting (based on long-term secondary user (SU) performance) and adaptive weighting (based on instantaneous statistics, with finite-sample variance derived via delta method). Results show that adaptive weighting outperforms the benchmark equal-weighting scheme by up to 5 dB in required signal-to-noise ratio (SNR) to achieve probability of detection at the fusion center $P_d^{\mathrm{F}\mathrm{C}}\ge 0.7$ under low-to-moderate SNR ( $<10$ dB) and high-mobility mmWave conditions (carrier frequency $f_c=28$ GHz, velocity $v\ge 110$ km/h), owing to its robustness to Doppler-induced channel de-correlation via self-normalised weight computation. Reliability weighting is superior at high SNR ( $\ge 10$ dB) and under low-mobility, near-AWGN conditions ( $f_c=3.5$ GHz, $m\ge 6$), where its lower-variance fixed-weight statistic yields more stable CFAR threshold calibration. Both proposed schemes consistently outperform the equal-weighting baselines across all three 5G NR configurations (3.5/28 GHz, 30/60/120 kHz sub-carrier spacing (SCS)) and vehicular speeds (70–150 km/h), providing mobility-aware design guidelines for next-generation cognitive radio systems.