Joint Directional LBT and Beam Training for Channel Access in Unlicensed 60GHz mmWave

Li, Pengru; Liu, Danpu; Yu, Feng

doi:10.1109/glocomw.2018.8644418

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…As stated above, the use of directional CCS in mmWave raises fundamental differences in the overall system and it is important to evaluate such systems. Some recent works [15]- [19] have taken the simulation-based approach to discuss the importance of directional sensing in the cognitive mmWave network. The work in [15] addressed the role of directional CCS in improving the coverage performance from the perspective of secondary transmitters only.…”

Section: A Related Workmentioning

confidence: 99%

“…Authors in [16], [17] have studied circumstances when potential interfering secondary links can be shut down based on the exact location information of the associated secondary receivers. Authors in [18], [19] have shown that directional CCS performed at both ends of the secondary link has the advantage of eliminating hidden sources of interference to the primary link. However, a systematic approach to an analytical evaluation of the gain that directional CCS can provide in mmWave cognitive networks in comparison to that of omnidirectional CCS is not available in the literature.…”

Section: A Related Workmentioning

confidence: 99%

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Coverage Analysis of Cognitive mmWave Networks with Directional Sensing

Tripathi

Gupta

Amuru

2021

2021 55th Asilomar Conference on Signals, Systems, and Computers

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Millimeter-waves' propagation characteristics create prospects for spatial and temporal spectrum sharing in a variety of contexts, including cognitive spectrum sharing (CSS). However, CSS along with omnidirectional sensing, is not efficient at mmWave frequencies due to their directional nature of transmission, as this limits secondary networks' ability to access the spectrum. This inspired us to create an analytical approach using stochastic geometry to examine the implications of directional cognitive sensing in mmWave networks. We explore a scenario where multiple secondary transmitterreceiver pairs coexist with a primary transmitter-receiver pair, forming a cognitive network. The positions of the secondary transmitters are modelled using a homogeneous Poisson point process (PPP) with corresponding secondary receivers located around them. A threshold on directional transmission is imposed on each secondary transmitter in order to limit its interference at the primary receiver. We derive the medium-access-probability of a secondary user along with the fraction of the secondary transmitters active at a time-instant. To understand cognition's feasibility, we derive the coverage probabilities of primary and secondary links. We provide various design insights via numerical results. For example, we investigate the interference-threshold's optimal value while ensuring coverage for both links and its dependence on various parameters. We find that directionality improves both links' performance as a key factor. Further, allowing location-aware secondary directionality can help achieve similar coverage for all secondary links.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Coverage Analysis of Cognitive mmWave Networks with Directional Sensing

Tripathi

Gupta

Amuru

2021

2021 55th Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…Several research works have exploited the corresponding resource allocation technologies for mmWave systems. In [16], a channel access mechanism with joint directional LBT(Listen before talk) and beam training has been proposed to cope with the beam selection problem when 5G and WiFi coexisted at 60 GHz. In [17], a two‐stage trajectory prediction and channel monitoring aided fast beam tracking scheme was proposed to optimise the beam pairs used in the process of terminal movement to minimise the latency and signalling overhead.…”

Section: Introductionmentioning

confidence: 99%