Picture of boy being examining by doctor at a tuberculosis sanatorium

Understanding our future through Open Access research about our past...

Strathprints makes available scholarly Open Access content by researchers in the Centre for the Social History of Health & Healthcare (CSHHH), based within the School of Humanities, and considered Scotland's leading centre for the history of health and medicine.

Research at CSHHH explores the modern world since 1800 in locations as diverse as the UK, Asia, Africa, North America, and Europe. Areas of specialism include contraception and sexuality; family health and medical services; occupational health and medicine; disability; the history of psychiatry; conflict and warfare; and, drugs, pharmaceuticals and intoxicants.

Explore the Open Access research of the Centre for the Social History of Health and Healthcare. Or explore all of Strathclyde's Open Access research...

Image: Heart of England NHS Foundation Trust. Wellcome Collection - CC-BY.

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.