Interference Detection of 6G MIMO LANs using Deep Learning

Aug 4, 2026

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A growing mismatch between data demand and limited wireless resources motivates using a local area network (LAN) within the same frequency band as a service provider (SP). When two such networks coexist, mutual interference can degrade reliability and quality of service (QoS). This study introduces a deep-learning-based interference detection method for 6G MIMO LAN coexistence, leveraging physical-layer machine learning to classify interference presence. A binary CNN-based classifier is trained and evaluated with experiments, achieving over 90% accuracy and only 0.126 ms inference latency when the interferer is 500 m away.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Resource demand mismatch and network coexistence

Interference management and detection objective

Deep learning in wireless physical-layer tasks

Interference Detection of 6G MIMO LANs using Deep Learning Chathuri Weragama, Samad Ali, Nandana Rajatheva, and Matti Latva-Aho 6G Flagship, Centre for Wireless Communications, University of Oulu, Oulu, Finland {chathuri.weragama, samad.ali, nandana.rajatheva, matti.latva-aho}@oulu.􀀂 Abstract—A signi􀀂cant challenge in the wirelesscommunication 􀀂eld 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 resourcedemand 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 identi􀀂ed interference. Speci􀀂cally, we employ a binary classi􀀂er 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 classi􀀂er 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. Index Terms—6G, CNN, DL, Interference management, Local area networks, MIMO, ML. I. INTRODUCTION Machine learning (ML) has shown promising results in the telecommunication industry, offering solutions to problems that cannot be solved using the conventional methods [1] . With the unpredictability of the channel in wireless communication, conventional approaches such as convex optimization have not been able to show promising results for some problems in this 􀀂eld [2] . Another signi􀀂cant problem in this industry besides the unpredictability of channels is the exponential growth in user demand compared to the increase in resources. The average monthly smartphone usage as predicted in 2023 is 19 GB and it is anticipated to grow to 46 GB by the end of 2028, which indicates a 242% growth over 􀀂ve years [3] . Today, one of the key challenges faced by researchers in the telecommunication industry is to cater to the growing usage demand of users with the existing resources. The primary objective of this paper is to introduce a novel approach to address the resource demand mismatch by leveraging ML techniques to enable the coexistence of two networks operating in the same frequency and increase spectrum ef􀀂ciency with a state-of-the-art receiver architecture which is able to identify the presence of interference. Unlike the concept of cognitive radio [4], [5] and heterogeneous network (HetNet) [6], [7], these two networks are detached from one another including its access network and core network. The underlying motivation behind the coexistence of two networks in the same frequency is to leverage the service provider’s (SP) spectrum by a local area network (LAN) when the SP spectrum is underutilized. This will allow us to improve spectral ef􀀂ciency, and address the imbalance in user demand and resource availability to some extent by meeting diverse user requirements through the same resources. Interference management becomes a crucial aspect that needs to be addressed in order to maintain the required quality of service (QoS) within the two networks. In this paper, we aim at detecting the presence of interference between the two networks

FAQs

What problem does the paper address in 6G MIMO LAN coexistence?

It addresses interference detection when two local area networks operate in the same frequency spectrum, aiming to support interference management and maintain QoS.

How does the proposed method detect interference?

It uses a binary classifier implemented with a convolutional neural network (CNN) architecture to identify whether interference is present between two networks.

What performance is reported for the interference detector?

Experiments show accuracy exceeding 90% when the interferer is introduced at a 500 m radius, with an inference latency of 0.126 ms.

Aug 4, 2026

Upload by Eden