Throughput Enhancement of Multicarrier Cognitive M2M Networks: Universal-Filtered OFDM Systems

Mirza Golam Kibria, Gabriel Porto Villardi, Kentaro Ishizu, Fumihide Kojima

We consider a cognitive radio network consisting of a primary cellular system and a secondary cognitive machine-to-machine (M2M) system, and study the throughput enhancement problem of the latter system employing universal-filtered orthogonal frequency division multiplexing (UF-OFDM) modulation. The downlink transmission capacity of the cognitive M2M system is thereby maximized, while keeping the interference introduced to the primary users (PUs) below the pre-specified threshold, under total transmit power budget of the secondary base station (SBS). The performance of UF-OFDM based CR system is compared to the performances of OFDM-based and filter bank multicarrier (FBMC)-based CR systems. We also propose a near-optimal resource allocation method separating the subband and power allocation. The solution is less complex compared to optimization of the original combinatorial problem. We present numerical results that show that for given interference thresholds of the PUs and maximum transmit power limit of the SBS, the UF-OFDM based CR system exhibits intermediary performance in terms of achievable capacity compared to OFDM and FBMC-based CR systems. Interestingly, for a certain degree of robustness of the PUs, the UF-OFDM performs equally well as FBMC. Furthermore, the percentage rate-gain of UF-OFDM based CR system increases by a large amount when UF-OFDM modulation with lower sidelobes ripple is employed. Numerical results also show that the proposed throughput enhancing method despite having lower computational complexity compared to the optimal solution achieves near-optimal performance.

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