Leveraging Massive MIMO Spatial Diversity in Random Access
| dc.contributor.advisor | Sabharwal, Ashutosh | |
| dc.creator | Ahsan, Fatima | |
| dc.date.accessioned | 2019-05-17T16:52:11Z | |
| dc.date.available | 2019-05-17T16:52:11Z | |
| dc.date.created | 2018-12 | |
| dc.date.issued | 2018-11-30 | |
| dc.date.submitted | December 2018 | |
| dc.date.updated | 2019-05-17T16:52:11Z | |
| dc.description.abstract | Random access is a crucial building block for nearly all wireless networks, and impacts both the overall spectral efficiency and latency in communication. In next-generation networks, it is expected that the diverse new services will be served by cellular networks, e.g. connections to Unmanned-Air-Vehicles (UAVs) and Internet-of-Things(IoT) devices, potentially increasing the node density served per base-station. Higher node density implies increased latency in random access operation, due to increased packet collision events. In this thesis, we show via analytical evaluation and monte-carlo simulations that the large spatial degrees of freedom available in massive MIMO systems can potentially be leveraged to reduce random access latency. We show that with large arrays, the spatial channel “codes” of each user are also potentially separable, providing another avenue for the receiver to distinguish overlapping users in the Angle-of-Arrival space. First, using one-ring propagation model, we evaluate how the random access collision probability depends on the aperture size of the array and the spread of user’s signal Angle-of-Arrivals at the base-station, as a function of the user-density and the number of random access codes. Then, in order to practically achieve the analytical performance bounds, we present a simple clustering algorithm inspired by the channel parameters obtained from experimental studies on UAV’s air to base-station channel and on LTE’s 3GPP channel model for ground to base-station traffic. Our numerical evaluations show that depending on the scattering environment and antenna array size, we can attain 2.5x to 6.5x reduction in collision probability. The result of evaluating our algorithm on UAV’s air to base-station channel shows that as a function of node density 1.6x to 3.7x reduction in collision probability is possible with practical array sizes. Moreover, we also show that with parameters from LTE’s 3GPP channel model, nearly 1.7x to 2.5x reduction in collision probability is achievable using our proposed algorithm. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Ahsan, Fatima. "Leveraging Massive MIMO Spatial Diversity in Random Access." (2018) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/105907">https://hdl.handle.net/1911/105907</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/105907 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Massive MIMO | |
| dc.subject | Angle-of-arrival | |
| dc.subject | random access | |
| dc.subject | latency | |
| dc.title | Leveraging Massive MIMO Spatial Diversity in Random Access | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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