# Analysis and Rate Optimization of GFDM-based Cognitive Radios

**Authors:** A. Mohammadian, M. Baghani, C. Tellambura

arXiv: 1803.03298 · 2018-03-12

## TL;DR

This paper explores GFDM's application in cognitive radios, optimizing power allocation to maximize data rates while minimizing interference, and demonstrates GFDM's significant capacity advantage over OFDM in congested spectrum scenarios.

## Contribution

It introduces a convex optimization approach for power allocation in GFDM-based cognitive radios and compares its performance with OFDM.

## Key findings

- GFDM achieves up to 400% higher capacity than OFDM under certain interference constraints.
- Optimal power allocation effectively manages interference and enhances data rates.
- GFDM's low OOB emission makes it suitable for spectrum sharing in cognitive radio networks.

## Abstract

Generalized frequency division multiplexing (GFDM) is suitable for cognitive radio (CR) networks due to its low out-of-band (OOB) emission and high spectral efficiency. In this paper, we thus consider the use of GFDM to allow an unlicensed secondary user (SU) to access a spectrum hole. However, in an extremely congested spectrum scenario, both active incumbent primary users (PUs) on the left and right channels of the spectrum hole will experience OOB interference. While constraining this interference, we thus investigate the problem of power allocation to the SU transmit subcarriers in order to maximize the overall data rate where the SU receiver is employing Matched filter (MF) and zero-forcing (ZF) structures. The power allocation problem is thus solved as a classic convex optimization problem. Finally, total transmission rate of GFDM is compared with that of orthogonal frequency division multiplexing (OFDM). For instance, when right and left interference temperature should be below 10 dBm, the capacity gain of GFDM over OFDM is 400 %.

## Full text

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## Figures

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## References

28 references — full list in the complete paper: https://tomesphere.com/paper/1803.03298/full.md

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Source: https://tomesphere.com/paper/1803.03298