Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Fractional fourier based sparse channel estimation for multicarrier underwater acoustic communication system

Chen, Yixin and Clemente, Carmine and Soraghan, John and Weiss, Stephen (2016) Fractional fourier based sparse channel estimation for multicarrier underwater acoustic communication system. In: 2016 Sensor Signal Processing for Defence, SSPD 2016. Institute of Electrical and Electronics Engineers Inc.. ISBN 9781509003266

[img]
Preview
Text (Chen-etal-SSPD2016-Fractional-fourier-based-sparse-channel-estimation)
Chen_etal_SSPD2016_Fractional_fourier_based_sparse_channel_estimation.pdf - Accepted Author Manuscript

Download (445kB) | Preview

Abstract

This paper presents a hybrid sparse channel estimation based on Fractional Fourier Transform (FrFT) for orthogonal frequency division multiplex (OFDM) scenario to exploit channel sparsity of underwater acoustic (UWA) channel. A novel channel dictionary matrix based on chirp signals is constructed and mutual coherence is adopted to evaluate its preservation of sparse information. In addition, Compressive Sampling Matching Pursuit (CoSaMP) is implemented to estimate the sparse channel coefficients. Simulation results demonstrate a significant Normalized Mean Square Error (NMSE) improvement of 10dB over Basis Expansion Model (BEM) with less complexity.