In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Vermeulen:2021:10.1186/s13638-021-01940-4,
author = {Vermeulen, T and Reynders, B and Rosas, FE and Verhelst, M and Pollin, S},
doi = {10.1186/s13638-021-01940-4},
journal = {Eurasip Journal on Wireless Communications and Networking},
pages = {1--23},
title = {Performance analysis of in-band collision detection for dense wireless networks},
url = {http://dx.doi.org/10.1186/s13638-021-01940-4},
volume = {2021},
year = {2021}
}
TY - JOUR
AB - With the massive growth of wireless networks comes a bigger impact of collisions and interference, which has a negative effect on throughput and energy efficiency. To deal with this problem, we propose an in-band wireless collision and interference detection scheme based on full-duplex technology. To study its performance, we compare its throughput and energy efficiency with the performance of traditional half-duplex and symmetric in-band full-duplex transmissions. Our analysis considers a realistic protocol and overhead modeling, and a measurement-based self-interference model. Our results indicate that our proposed collision detection scheme can provide significant gains in terms of throughput and energy efficiency in large wireless networks. Moreover, when compared to half-duplex and symmetric full-duplex, our analysis shows that this scheme allows up to 45% more nodes in the network for the same energy consumption per bit. These results suggest that this could be an enabling technology towards efficient, dense wireless networks.
AU - Vermeulen,T
AU - Reynders,B
AU - Rosas,FE
AU - Verhelst,M
AU - Pollin,S
DO - 10.1186/s13638-021-01940-4
EP - 23
PY - 2021///
SN - 1687-1472
SP - 1
TI - Performance analysis of in-band collision detection for dense wireless networks
T2 - Eurasip Journal on Wireless Communications and Networking
UR - http://dx.doi.org/10.1186/s13638-021-01940-4
UR - https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-021-01940-4
UR - http://hdl.handle.net/10044/1/90019
VL - 2021
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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