Journal article
A MUD-Enhanced Multi-Beam Approach for Increasing Throughput of Dense Wireless Networks
IEEE sensors journal, Vol.21(4), pp.5454-5466
02/15/2021
DOI: 10.1109/JSEN.2020.3035330
Abstract
Uncoordinated random access (URA) wireless communication schemes provide an attractive framework to enable the necessary communication backbone for the advancement of dense sensor systems, many sensors deployed in a relatively small area. By itself, URA is unsuitable for moderately dense networks due to the crippling levels of interference that result from within the communication network. Digital beamforming has been proposed to alleviate intra-URA-network interference with a post-reception formation of multiple simultaneous beams that allow a single receiver to "hear" only the transmissions meant for it and to block out all others. A high density network can result in interference originating from the same direction which cannot be mitigated with beamforming. We propose the addition of post-beamformed multiuser detection (MUD) with the use of direct sequence spread spectrum (DSSS) waveforms to help further combat the interference problem. Motivated by practical design choices such as transceiver weight and size as well as operational expectations of variable and/or evolving node densities, we explore trades between the DSSS time-spreading factor, number of antennas per node, and network node density. Our results show throughput improvements with the addition of MUD, while the most improvements are with the addition of MUD paired with an appropriate choice of spreading factor. Insights from these results provide guidelines for the development of future systems not only aided by MUD and beamforming, but also by on-the-fly adaptation of the DSSS signature waveform time-spreading factor to mitigate the level of interference present at any time.
Details
- Title: Subtitle
- A MUD-Enhanced Multi-Beam Approach for Increasing Throughput of Dense Wireless Networks
- Creators
- Bryan Ehlers - University of IowaAnanya Sen Gupta - University of IowaRachel Learned - Massachusetts Institute of Technology
- Resource Type
- Journal article
- Publication Details
- IEEE sensors journal, Vol.21(4), pp.5454-5466
- Publisher
- IEEE
- DOI
- 10.1109/JSEN.2020.3035330
- ISSN
- 1530-437X
- eISSN
- 1558-1748
- Grant note
- FA8702-15-D-0001 / Department of the Army under Air Force
- Language
- English
- Date published
- 02/15/2021
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197199002771
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