Interference Detection of 6G MIMO LANs using Deep Learning
Weragama, Chathuri; Ali, Samad; Rajatheva, Nandana; Latva-Aho, Matti (2024-08-15)
IEEE
15.08.2024
C. Weragama, S. Ali, N. Rajatheva and M. Latva-Aho, "Interference Detection of 6G MIMO LANs using Deep Learning," 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, 2024, pp. 354-359, doi: 10.1109/ICMLCN59089.2024.10624949.
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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-202504022345
https://urn.fi/URN:NBN:fi:oulu-202504022345
Tiivistelmä
Abstract
A significant challenge in the wireless-communication field revolves around the growing demand for data usage, all while dealing with the limitations of available resources. One potential solution lies in leveraging a local area network (LAN) within the same frequency band as a service provider (SP) especially when the SP’s bandwidth is underutilized. This approach aims to minimize the resource-demand mismatch. This paper focuses on addressing the issue of interference detection when two such networks coexist within the same frequency spectrum. Our study introduces an innovative methodology that harnesses machine-learning techniques to tackle this challenge. We have delved into various ML methods used in the physical layer of wireless communication for similar purposes. As a result, we have developed a deep-learning model designed to identify the presence of interference. This, in turn, enhances the quality of service (QoS) for both networks by effectively mitigating any identified interference. Specifically, we employ a binary classifier utilizing a convolutional neural network (CNN) architecture to detect interference between two networks operating at the same frequency. To evaluate the effectiveness of this binary classifier in identifying interference, we conducted a series of experiments. Our results have demonstrated an accuracy exceeding 90% when the interferer has been introduced at a 500 m radius from the local base station, but it has done so by adding only an inference latency of 0.126 ms.
A significant challenge in the wireless-communication field revolves around the growing demand for data usage, all while dealing with the limitations of available resources. One potential solution lies in leveraging a local area network (LAN) within the same frequency band as a service provider (SP) especially when the SP’s bandwidth is underutilized. This approach aims to minimize the resource-demand mismatch. This paper focuses on addressing the issue of interference detection when two such networks coexist within the same frequency spectrum. Our study introduces an innovative methodology that harnesses machine-learning techniques to tackle this challenge. We have delved into various ML methods used in the physical layer of wireless communication for similar purposes. As a result, we have developed a deep-learning model designed to identify the presence of interference. This, in turn, enhances the quality of service (QoS) for both networks by effectively mitigating any identified interference. Specifically, we employ a binary classifier utilizing a convolutional neural network (CNN) architecture to detect interference between two networks operating at the same frequency. To evaluate the effectiveness of this binary classifier in identifying interference, we conducted a series of experiments. Our results have demonstrated an accuracy exceeding 90% when the interferer has been introduced at a 500 m radius from the local base station, but it has done so by adding only an inference latency of 0.126 ms.
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