Comparative Analysis of GFDM and UFMC Modulation Techniques for Cognitive Radio in Dispersive Wireless Channels

Abstract

Cognitive radio (CR) systems demand highly spectrally-efficient and adaptable waveform designs to operate effectively under dynamic spectrum conditions and dispersive channels. Generalized Frequency Division Multiplexing (GFDM) and Universal Filtered Multicarrier (UFMC) are two prominent multicarrier candidates aiming to improve spectral containment and flexibility beyond OFDM. This paper presents a detailed comparative analysis of GFDM and UFMC within cognitive radio contexts under dispersive channels. Building on existing simulation studies and deployment frameworks, we assess performance metrics such as bit error rate (BER), peak-to-average power ratio (PAPR), out-of-band (OOB) emissions, channel robustness, and waveform complexity. A dual setup is considered: first, their resilience to multipath fading in cognitive radio scenarios, and second, system-level metrics drawn from MATLAB/Simulink implementations. Findings reveal that UFMC offers lower PAPR and better spectral localization, reducing interference with primary users—an essential trait for CR environments. GFDM provides higher flexibility and OOB suppression, especially when optimized with windowing, which is advantageous in congested spectrum allocation. However, GFDM’s block processing introduces latency and complexity compared to UFMC. We outline a structured evaluation methodology—covering waveform generation, channel modeling, performance measurement, and parameter optimization—to guide further analysis. Also discussed are the trade-offs between spectral efficiency, robustness, and implementation complexity. Entities deploying CR systems must weigh these factors: UFMC suits lowlatency, power-sensitive applications requiring agile access to spectrum holes, whereas GFDM may yield higher spectral packing and adaptability when overhead is manageable. Future work should explore adaptive hybrid schemes, MIMO augmentation, and cognitive-aware dynamic filter tuning to fully harness both techniques’ potential.