Channel Charting Based Pilot Allocation in MIMO Systems
Shaikh, Bushra; Burguera, Pere Garau; Al-Tous, Hanan; Juntti, Markku; Khan, Bilal Muhammad; Tirkkonen, Olav
IEEE
B. Shaikh, P. G. Burguera, H. Al-Tous, M. Juntti, B. M. Khan and O. Tirkkonen, "Channel Charting Based Pilot Allocation in MIMO Systems," 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Valencia, Spain, 2024, pp. 1-7, doi: 10.1109/PIMRC59610.2024.10817455
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202501301409
https://urn.fi/URN:NBN:fi:oulu-202501301409
Tiivistelmä
Abstract
We consider uplink pilot allocation based on multipoint channel charting (CC) to mitigate pilot contamination in a multi-cell network with spatially correlated MIMO channels. The channel chart is created in an offline phase with full information, i.e. user channel covariance matrices are estimated at multiple base stations (BSs). In the online phase, we assume that only partial information about a user’s channel covariance is known, i.e., it is available only at the serving BS. A machine learning framework is developed to predict the CC locations in the online phase. Pilots are allocated to active users in the online phase based on weighted graph colouring. CC locations are used as proxies of user locations; similarity weights between users are constructed from CC distances. Simulation results show that the CC based approach with partial information in the online phase outperforms a solution based on full angle-of-arrival information, and performs closely to an algorithm with full covariance information. We also consider a partial information machine learning framework to predict the channel covariance matrices at other BSs, which slightly outperforms CC based approach, with the price of a larger communication overhead and computational complexity.
We consider uplink pilot allocation based on multipoint channel charting (CC) to mitigate pilot contamination in a multi-cell network with spatially correlated MIMO channels. The channel chart is created in an offline phase with full information, i.e. user channel covariance matrices are estimated at multiple base stations (BSs). In the online phase, we assume that only partial information about a user’s channel covariance is known, i.e., it is available only at the serving BS. A machine learning framework is developed to predict the CC locations in the online phase. Pilots are allocated to active users in the online phase based on weighted graph colouring. CC locations are used as proxies of user locations; similarity weights between users are constructed from CC distances. Simulation results show that the CC based approach with partial information in the online phase outperforms a solution based on full angle-of-arrival information, and performs closely to an algorithm with full covariance information. We also consider a partial information machine learning framework to predict the channel covariance matrices at other BSs, which slightly outperforms CC based approach, with the price of a larger communication overhead and computational complexity.
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