Deep learning based pilot assignment in massive MIMO systems

Abstract. This thesis proposes a solution to the pilot contamination problem in massive multiple-input multiple-output systems by intelligently reusing pilot sequences using deep learning. The considered single-cell network is a massive machine-type communication system that has multiple sectors, each equipped with a uniform linear array antenna. Channels between the base station and the user equipment are modeled as spatially correlated and directive, where the angular domain interference primarily dictates pilot contamination. The main idea behind the proposed solution is that pilot sequences can be shared by a set of user equipment that do not have overlapping angle-of-arrival ranges at the base station, without causing significant mutual interference. The problem is formulated as a regression problem where the loss function represents the total pilot contamination in the network. A deep feedforward neural network architecture is used with the unsupervised learning approach to solve the problem, where the channel covariance matrices estimated at the base station are used as the input. A tailored training approach is proposed that is made up of two strategies as follows. First, the neural network is trained with constrained user equipment locations where the constraint gradually changes as the learning progresses. Second, the input data is rearranged to make the feature extraction easier for the neural network. Numerical experiments show that the proposed solution performs close to the exhaustive search solution when trained on a single network instance. When trained on a batch of training samples and validated on a batch of previously unseen samples, the proposed method generalizes well and subsequently performs on par with existing solutions.Syväoppimiseen pohjautuva pilottien allokointi massiivisissa moniantennijärjestelmissä. Tiivistelmä. Tässä opinnäytetyössä ehdotetaan ratkaisua pilottisekvenssien keskinäisen häiriön vaimentamiseksi massiivisissa moniantennijärjestelmissä pilottisekvenssien älykkäällä uudelleenkäytöllä syväoppimisen avulla. Tarkasteltu yksisoluinen verkko on massiivinen konetietoliikennejärjestelmä, jakaantuen useaan sektoriin, joista kukin toimii lineaarisella ryhmäantennilla. Tukiaseman ja käyttäjälaitteiden väliset kanavat ovat korreloituneita tilatasossa sekä suuntavia, joissa kulmatason häiriö on ensisijainen pilottihäiriön lähde. Ehdotetun ratkaisun pääajatus on, että pilottisekvenssit voidaan jakaa sellaisten käyttäjälaitteiden kanssa, joilla ei ole päällekkäisiä saapumiskulma-alueita tukiasemalla, täten aiheuttamatta merkittäviä keskinäisiä häiriöitä. Ongelma muotoillaan regressio-ongelmaksi, jossa kustannusfunktio edustaa verkon pilottihäiriön kokonaismäärää. Ongelman ratkaisemiseksi käytetään syvää eteenpäin kytkettyä neuroverkkoarkkitehtuuria ohjaamattoman oppimisen kanssa, jossa tulona käytetään tukiasemassa arvioituja kanavakovarianssimatriiseja. Työssä ehdotetaan kahta räätälöityä oppimisstrategiaa. Ensin neuroverkkoa koulutetaan rajoitetuilla käyttäjälaitteiden sijainneilla, joissa rajoitus muuttuu vähitellen oppimisen edetessä. Toiseksi syöttödata järjestetään uudelleen, jotta piirteiden erottaminen neuroverkolle olisi helpompaa. Numeeriset kokeet osoittavat, että ratkaisu on lähes optimaalinen, kun se koulutetaan yhteen verkkorealisaatioon. Kun ehdotettu menetelmä koulutetaan käyttäen harjoitusnäytteitä, ehdotettu menetelmä yleistyy hyvin uusiin näytteisiin sekä antaa yhtä hyvän suorituskyvyn kuin olemassa olevat ratkaisut

Channel charting based beamforming

Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.Comment: Asilomar Conference on Signals, Systems, and Computers, Oct 2022, Pacific Grove, United State

Model-Based Approaches to Channel Charting

We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival theta and the distance between the base station and the user equipment rho by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods with the training-based channel charting algorithms principal component analysis (PCA), Samson's method (SM), and autoencoder (AE). We show that ISQ, LR, and MM outperform all three in performance. The performance of MM is better than LR and ISQ but it is more complex. ISQ and LR have similar performance with ISQ having less complexity than LR. We also compare our algorithm MM with and algorithm from the literature that uses the MUSIC algorithm jointly on theta and rho. We call this algorithm the JM algorithm. JM performs very slightly better than MM but at a substantial increase in complexity. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM and JM algorithms but is less complex due to the avoidance of the eigenvector and eigenvalue analysis and a potential register transfer logic (RTL) implementation.Comment: 28 pages, 13 figures, 6 table

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios

Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust