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Optimal Receive Filter Design for Misaligned Over-the-Air Computation
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. Stanford University, Electrical Engineering Department, California, USA.ORCID iD: 0000-0002-5761-2580
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0003-4519-9204
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0002-2764-8099
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0001-9810-3478
2023 (English)In: 2023 IEEE Globecom Workshops, GC Wkshps 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1529-1535Conference paper, Published paper (Refereed)
Abstract [en]

Over-the-air computation (AirComp) is a promising wireless communication method for aggregating data from many devices in dense wireless networks. The fundamental idea of AirComp is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals. However, the time-and phase-alignment of these superimposed signals have a significant effect on the quality of function computation. In this study, we analyze the AirComp problem for a system with unknown random time delays and phase shifts. We show that the classical matched filter does not produce optimal results, and generates bias in the function estimates. To counteract this, we propose a new filter design and show that, under a bound on the maximum time delay, it is possible to achieve unbiased function computation. Additionally, we propose a Tikhonov regularization problem that produces an optimal filter given a tradeoff between the bias and noise-induced variance of the function estimates. When the time delays are long compared to the length of the transmitted pulses, our filter vastly outperforms the matched filter both in terms of bias and mean-squared error (MSE). For shorter time delays, our proposal yields similar MSE as the matched filter, while reducing the bias.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 1529-1535
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-350009DOI: 10.1109/GCWkshps58843.2023.10464656Scopus ID: 2-s2.0-85190287853OAI: oai:DiVA.org:kth-350009DiVA, id: diva2:1882185
Conference
2023 IEEE Globecom Workshops, GC Wkshps 2023, Kuala Lumpur, Malaysia, Dec 4 2023 - Dec 8 2023
Note

Part of ISBN 9798350370218

QC 20240704

Available from: 2024-07-04 Created: 2024-07-04 Last updated: 2024-10-11Bibliographically approved
In thesis
1. Function Computation via Electromagnetic Superposition: Estimation Problems
Open this publication in new window or tab >>Function Computation via Electromagnetic Superposition: Estimation Problems
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In wireless communication systems, interference is considered one of the main bottlenecks. Because all devices share the same electromagnetic spectrum, communication protocols generally attempt to separate radio resources to avoid interference. In LTE and 5G, devices are not allowed to transmit at their own behest but must receive an uplink grant that dictates which radio resources to use. In Wi-Fi 5, devices are allowed to transmit without a grant from the router, but they have to listen to the channel and wait until it is quiet before transmitting. While these interference-avoiding methods are quite useful, they face the problem of congestion. If too many devices are communicating data simultaneously, the avoidance of interference leads to insufficient spectrum for each user, and quality of service drops dramatically.

In this thesis, we study a novel form of wireless communication that takes a different approach to sharing electromagnetic spectrum. Rather than using orthogonal resources for each device, it schedules devices on the same communication resources, resulting in electromagnetic superposition of their signals. When the receiver listens, the superimposed signal will contain so much interference that it is difficult to distinguish the individual messages. However, it is possible to compute functions of the transmitted messages. Hence, this method is often referred to as Over-the-Air Function Computation (AirComp).

The challenges of AirComp are fundamentally different from those of orthogonal communication. Well-known results on, e.g., phase acquisition, forward error correction, and modulation do not map directly to the AirComp setting. Because of this, the state-of-the-art literature on AirComp usually does not guarantee error-free function computation with a vanishing probability of error but resorts to imperfect function estimation. We have dedicated this thesis to improving the state of estimation algorithms for AirComp. For example, we have developed a power control scheme that eliminates estimation bias for fast-fading channels, and we have leveraged dimensionality-reduction methods to compute certain functions without error.

Our recent work has focused on incorporating realistic assumptions concerning time synchronization and phase acquisition. In orthogonal communication methods, phase alignment is often achieved by careful correction at the receiver side, using reference signals and phase-locked loops. In AirComp, since we are interested in the coherent superposition of signals, the phase cannot be corrected at the receiver side. Transmitter-side phase correction from UEs is challenging, and therefore we have developed non-coherent AirComp methods that avoid this problem. We also specify non-coherent AirComp schemes for digital communication problems in the sparse regime, outperforming orthogonal methods.

Abstract [sv]

I trådlösa kommunikationssystem är interferens en av de mest begränsande flaskhalsarna. Eftersom alla enheter delar samma elektromagnetiska spektrum så tenderar kommunikationsprotokol att separera radioresurser för att undvika interferens. I LTE och 5G får enheterna inte ta initiativ till att sända, utan måste vänta på ett tillstånd som bestämmer vilka resurser de får använda. I Wi-Fi 5 får enheterna ta initiativ, men de måste först lyssna på kanalen och vänta tills det är tyst innan de börjar sända. Dessa interferensundvikande metoder är väldigt användbara men de påverkas oundvikligen av problem med bergänsat spektrum. Om för många enheter vill kommunicera samtidigt så kommer undvikningen av interferens leda till otillräckliga resurser för varje enhet, vilket drastiskt minskar kommunikationskvaliteten.

I den här avhandlingen studerar vi en ny metod för trådlös kommunikation som har ett annat tillvägagångssätt för att samarbeta över det elektromagnetiska spektrumet. Istället för att allokera ortogonala resurser till varje enhet så tilldelas många enheter samma kommunikationsresurser, vilket leder till elektromagnetisk superposition av deras signaler. När mottagaren lyssnar på de kombinerade signalerna, så är det så pass mycket interferens att separeringen av individuella meddelanden blir utmanande. Däremot är det möjligt att beräkna funktioner av meddelanderna, vilket är varför metoden ofta kallas Over-the-Air Function Computation (AirComp) på engelska. På svenska föreslår vi att kalla det signalfogning, för att referera till hur signaler fogas till en funktion i spektrumet.

Tekniken för signalfogning är fundamentalt annorlunda från ortogonala kommunikationsmetoder. Välkända resultat inom till exempel, fastrackning, felrättande koder och modulering fungerar inte på samma sätt för signalfogning. Pågrund av detta så brukar signalfogningslitteraturen inte garantera felfri funktionsberäkning, utan arbetar istället med estimering. Den här avhandlingen är dedikerad till att utveckla bättre estimeringsalgoritmer för signalfogning. Till exempel har vi utvecklat en effektallokeringsalgoritm för att ta bort systematiska fel under snabbfädning och vi har utvecklat kompressionsmetoder för att beräkna specifika funktioner helt utan estimeringsfel.

Våra senaste bidrag till litteraturen har fokuserat på att integrera realistiska antaganden gällande tidssynkronisering och fastrackning. Inom ortogonala kommunikationssystem så sker fastrackning ofta genom noggrann korrektion på mottagarsidan, där referenssignaler från sändaren nyttjas i regleralgoritmer för att styra mottagaroscillatorn. Inom signalfogning kan inte fasen korrigeras på mottagarsidan eftersom vi är intresserade av fasriktig superposition i det elektromagnetiska spektrumet. Samtidigt är faskorrigering på sändarsidan utmanande, och därför har vi arbetat med att utveckla signalfogningsmetoder som inte är beroende av någon faskorrigering. Vi specificerar också sådana metoder för digitala kommunikationsproblem med glesa signaler, vilket överträffar otrogonala metoder.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2024. p. 63
Series
TRITA-EECS-AVL ; 2024:78
Keywords
Wireless Communications, Over-the-Air Computation, Compressed Sensing, Non-Coherent, Machine Learning, Histogram Estimation, Federated Learning
National Category
Telecommunications
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-354729 (URN)978-91-8106-074-4 (ISBN)
Public defence
2024-11-04, https://kth-se.zoom.us/j/65644192644, Kollegiesalen, Brinellvägen 6, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20241014

Available from: 2024-10-14 Created: 2024-10-11 Last updated: 2024-10-21Bibliographically approved

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Hellström, HenrikRazavikia, SaeedFodor, ViktóriaFischione, Carlo

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