Throughput Enhancement of Multicarrier Cognitive M2M Networks: Universal-Filtered OFDM Systems
Mirza Golam Kibria, Gabriel Porto Villardi, Kentaro Ishizu, Fumihide, Kojima

TL;DR
This paper investigates throughput enhancement in cognitive M2M networks using UF-OFDM, proposing a resource allocation method that balances performance and complexity, and compares it with OFDM and FBMC systems.
Contribution
It introduces a near-optimal, low-complexity resource allocation approach for UF-OFDM in cognitive M2M networks, demonstrating its competitive performance.
Findings
UF-OFDM achieves intermediary capacity compared to OFDM and FBMC.
UF-OFDM with lower sidelobes ripple improves rate gain.
Proposed method attains near-optimal throughput with reduced complexity.
Abstract
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…
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