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Kunnath Ganesan, U., Sarvendranath, R. & Larsson, E. G. (2024). BeamSync: Over-The-Air Synchronization for Distributed Massive MIMO Systems. IEEE Transactions on Wireless Communications, 1-1
Open this publication in new window or tab >>BeamSync: Over-The-Air Synchronization for Distributed Massive MIMO Systems
2024 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, p. 1-1Article in journal (Refereed) Published
Abstract [en]

In distributed massive multiple-input multiple-output (MIMO) systems, multiple geographically separated access points (APs) communicate simultaneously with a user, leveraging the benefits of multi-antenna coherent MIMO processing and macro-diversity gains from the distributed setups. However, time and frequency synchronization of the multiple APs is crucial to achieve good performance and enable joint precoding. In this paper, we analyze the synchronization requirement among multiple APs from a reciprocity perspective, taking into account the multiplicative impairments caused by mismatches in radio frequency (RF) hardware. We demonstrate that a phase calibration of reciprocity-calibrated APs is sufficient for the joint coherent transmission of data to the user. To achieve synchronization, we propose a novel over-the-air synchronization protocol, named BeamSync, to calibrate the geographically separated APs without sending any measurements to the central processing unit (CPU) through fronthaul. We show that sending the synchronization signal in the dominant direction of the channel between APs is optimal. Additionally, we derive the optimal phase and frequency offset estimators. Simulation results indicate that the proposed BeamSync method enhances performance by 3 dB when the number of antennas at the APs is doubled. Moreover, the method performs well compared to traditional beamforming techniques.

National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-201117 (URN)10.1109/twc.2023.3335089 (DOI)
Funder
EU, Horizon 2020, 101013425
Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22
Kunnath Ganesan, U., Björnson, E. & Larsson, E. G. (2022). Bridging the Digital Divide Using SuperCell Massive MIMO. In: 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall): . Paper presented at 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, United Kingdom, 26-29 September, 2022. London, United Kingdom: IEEE
Open this publication in new window or tab >>Bridging the Digital Divide Using SuperCell Massive MIMO
2022 (English)In: 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, United Kingdom: IEEE, 2022, , p. 6Conference paper, Published paper (Refereed)
Abstract [en]

Massive multiple input multiple output (MIMO)emerged as the leading technology for supporting fifth generation(5G) and beyond 5G cellular communication systems. Due to thetremendous increase in data traffic in urban areas and to meetsuch a significant demand, most studies consider macro/micro celldeployments in urban environments. Internet service providers(ISPs) are less interested in providing communication services inrural areas considering the relatively low profits compared to thedeployment and maintenance costs. In this paper, we investigatethe massive MIMO performance in rural scenarios. In particular,we investigate different aspects to consider while designing along-range communication system. We propose to use elevatedbase station (BS) with sectorized antennas with unusually largeaperture and implement a user scheduling algorithm at theBS to provide full digital coverage. We analyze the coveragerange of a massive MIMO system to provide high-rate services.Furthermore, we also analyze the link budget requirements andthe rates users can achieve in such a SuperCell massive MIMOnetwork.

Place, publisher, year, edition, pages
London, United Kingdom: IEEE, 2022. p. 6
Series
IEEE Conference on Vehicular Technology (VTC), ISSN 2577-2465, E-ISSN 1090-3038
Keywords
Massive MIMO; SuperCell; digital divide; scalability; coverage
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-191098 (URN)10.1109/VTC2022-Fall57202.2022.10012724 (DOI)000927580600032 ()9781665454681 (ISBN)9781665454698 (ISBN)
Conference
2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, United Kingdom, 26-29 September, 2022
Note

Funding: ELLIIT; Swedish Research Council (VR); KAW foundation

Available from: 2023-01-18 Created: 2023-01-18 Last updated: 2023-03-16Bibliographically approved
Gülgün, Z. & Larsson, E. G. (2022). Channel Estimation for Massive MIMO in the Presence of Cauchy Noise. In: ICC 2022 - IEEE International Conference on Communications: . Paper presented at IEEE International Conference on Communications, Hybrid: In-Person and Virtual Conference, Seoul, South Korea, 16–20 May 2022 (pp. 1769-1774). IEEE
Open this publication in new window or tab >>Channel Estimation for Massive MIMO in the Presence of Cauchy Noise
2022 (English)In: ICC 2022 - IEEE International Conference on Communications, IEEE, 2022, p. 1769-1774Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we work on channel estimationtechniques for massive multiple-input multiple-output (MIMO) with Cauchy noise. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal in the base station is projected onto each orthonormal pilot signal. This process is optimum when the noise is Gaussian. In other words, the obtained signal after this processis the sufficient statistic and we do not lose any information. We show that this process is not optimum when the noise is Cauchy. Hence, we propose a channel estimation technique forthe unprocessed received signal. The proposed channel estimation technique is compared with the channel estimates that are obtained from the projected signal.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Conference on Communications proceedings, ISSN 1938-1883
Keywords
massive MIMO; Gaussian noise; Cauchy noise; channel estimation
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-188288 (URN)10.1109/ICC45855.2022.9838699 (DOI)000864709902017 ()9781538683477 (ISBN)9781538683484 (ISBN)
Conference
IEEE International Conference on Communications, Hybrid: In-Person and Virtual Conference, Seoul, South Korea, 16–20 May 2022
Note

Funding: Swedish Foundation for Strategic Research (SSF)

Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2022-12-13
Becirovic, E., Björnson, E. & Larsson, E. G. (2022). Combining Reciprocity and CSI Feedback in MIMO Systems. IEEE Transactions on Wireless Communications, 21(11), 10065-10080
Open this publication in new window or tab >>Combining Reciprocity and CSI Feedback in MIMO Systems
2022 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 21, no 11, p. 10065-10080Article in journal (Refereed) Published
Abstract [en]

Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a “grid-of-beams world” with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Channel estimation, Downlink, Uplink, Base stations, Signal to noise ratio, Massive MIMO, Precoding
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-188498 (URN)10.1109/TWC.2022.3182749 (DOI)000882003900084 ()
Funder
Swedish Research Council, 2019-05068; D0760701Knut and Alice Wallenberg Foundation
Note

Additional funding agencies: Excellence Center at Linköping, Lund in Information Technology (ELLIIT)

Available from: 2022-09-14 Created: 2022-09-14 Last updated: 2022-11-30Bibliographically approved
Vu, T. T., Hien, Q. N., Dao, M. N., Matthaiou, M. & Larsson, E. G. (2022). Data Size-Aware Downlink Massive MIMO: A Session-Based Approach. IEEE Wireless Communications Letters, 11(7), 1468-1472
Open this publication in new window or tab >>Data Size-Aware Downlink Massive MIMO: A Session-Based Approach
Show others...
2022 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 11, no 7, p. 1468-1472Article in journal (Refereed) Published
Abstract [en]

This letter considers the development of transmission strategies for the downlink of massive multiple-input multiple-output networks, with the objective of minimizing the completion time of the transmission. Specifically, we introduce a session-based scheme that splits time into sessions and allocates different rates in different sessions for the different users. In each session, one user is selected to complete its transmission and will not join subsequent sessions, which results in successively lower levels of interference when moving from one session to the next. An algorithm is developed to assign users and allocate transmit power that minimizes the completion time. Numerical results show that our proposed session-based scheme significantly outperforms conventional non-session-based schemes.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022
Keywords
Downlink; Coherence time; Channel estimation; Interference; Signal to noise ratio; Data communication; Power control; Massive MIMO; session-based; zero-forcing
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-187888 (URN)10.1109/LWC.2022.3174829 (DOI)000838382400036 ()
Note

Funding Agencies|U.K. Research and Innovation Future Leaders Fellowships [MR/S017666/1]; FMJH Program PGMO; EDF; Department for the Economy Northern Ireland under the US-Ireland R&D Partnership Programme; ELLIIT; KAW Foundation

Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2022-09-26
Shaik, Z. H., Sarvendranath, R. & Larsson, E. G. (2022). Energy-Efficient Power Allocation for an Underlay Spectrum Sharing RadioWeaves Network. In: ICC 2022 - IEEE International Conference on Communications, Korea, Seoul, 16-20 May 2022: . Paper presented at ICC 2022 - IEEE International Conference on Communications (pp. 799-804). IEEE
Open this publication in new window or tab >>Energy-Efficient Power Allocation for an Underlay Spectrum Sharing RadioWeaves Network
2022 (English)In: ICC 2022 - IEEE International Conference on Communications, Korea, Seoul, 16-20 May 2022, IEEE, 2022, p. 799-804Conference paper, Published paper (Refereed)
Abstract [en]

RadioWeaves network operates a large number ofdistributed antennas using cell-free architecture to provide highdata rates and support a large number of users. Operating thisnetwork in an energy-efficient manner in the limited availablespectrum is crucial. Therefore, we consider energy efficiency(EE) maximization of a RadioWeaves network that shares spectrumwith a collocated primary network in underlay mode.To simplify the problem, we lower bound the non-convex EEobjective function to form a convex problem. We then propose adownlink power allocation policy that maximizes the EE of thesecondary RadioWeaves network subject to power constraint ateach access point and interference constraint at each primaryuser. Our numerical results investigate the secondary system’sperformance in interference, power, and EE constrained regimeswith correlated fading channels. Furthermore, they show that theproposed power allocation scheme performs significantly betterthan the simpler equal power allocation scheme.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Conference on Communications, ISSN 1550-3607, E-ISSN 1938-1883
Keywords
Beyond 5G, RadioWeaves, cell-free massive MIMO, spectrum sharing, energy efficiency, downlink
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-187235 (URN)10.1109/ICC45855.2022.9838491 (DOI)000864709900128 ()9781538683484 (ISBN)9781538683477 (ISBN)
Conference
ICC 2022 - IEEE International Conference on Communications
Note

Funding: REINDEER project of the European Unions Horizon 2020 research and innovation program [101013425]; ELLIIT; KAW

Available from: 2022-08-16 Created: 2022-08-16 Last updated: 2022-12-13
Becirovic, E., Chen, Z. & Larsson, E. G. (2022). Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification. In: IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC): . Paper presented at 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Oulu, FINLAND, jul 04-06, 2022 (pp. 1-5). IEEE
Open this publication in new window or tab >>Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification
2022 (English)In: IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, 2022, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Workshop on Signal Processing Advances in Wireless Communications, ISSN 2325-3789
Keywords
Federated edge learning; best linear unbiased estimator; MIMO; gradient sparsification
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-188499 (URN)10.1109/SPAWC51304.2022.9834030 (DOI)000942520000018 ()9781665494557 (ISBN)9781665494564 (ISBN)
Conference
23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Oulu, FINLAND, jul 04-06, 2022
Note

Funding: KAW foundation; ELLIIT - Linkoping University

Available from: 2022-09-14 Created: 2022-09-14 Last updated: 2023-04-12Bibliographically approved
Manoj, B. R., Santos, P. M., Sadeghi, M. & Larsson, E. G. (2022). Toward Robust Networks against Adversarial Attacks for Radio Signal Modulation Classification. In: 2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC): . Paper presented at 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Oulu, FINLAND, jul 04-06, 2022. IEEE
Open this publication in new window or tab >>Toward Robust Networks against Adversarial Attacks for Radio Signal Modulation Classification
2022 (English)In: 2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC), IEEE , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Deep learning (DL) is a powerful technique for many real-time applications, but it is vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification, with the objective to create DL models that are robust against attacks. Specifically, we introduce three defense techniques: i) randomized smoothing, ii) hybrid projected gradient descent adversarial training, and iii) fast adversarial training, and evaluate them under both white-box (WB) and black-box (BB) attacks. We show that the proposed fast adversarial training is more robust and computationally efficient than the other techniques, and can create models that are extremely robust to practical (BB) attacks.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Workshop on Signal Processing Advances in Wireless Communications, ISSN 2325-3789
Keywords
Adversarial attacks; adversarial training; modulation classification; randomized smoothing; wireless security; UAP
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-192966 (URN)10.1109/SPAWC51304.2022.9833926 (DOI)000942520000025 ()9781665494557 (ISBN)9781665494564 (ISBN)
Conference
23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Oulu, FINLAND, jul 04-06, 2022
Note

Funding Agencies|Security-Link; Start-Up Research Grant of IIT Guwahati

Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-10-03Bibliographically approved
Chen, Z., Hu, C.-H. & Larsson, E. G. (2021). Anomaly-Aware Federated Learning with Heterogeneous Data. In: 2021 IEEE International Conference on Autonomous Systems (ICAS): . Paper presented at 2021 IEEE International Conference on Autonomous Systems (ICAS), 11-13 August 2021 (pp. 1-5). IEEE
Open this publication in new window or tab >>Anomaly-Aware Federated Learning with Heterogeneous Data
2021 (English)In: 2021 IEEE International Conference on Autonomous Systems (ICAS), IEEE, 2021, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

Anomaly detection plays a critical role in ensuring the robustness and reliability of federated learning (FL) systems involving distributed implementation of stochastic gradient descent (SGD). Existing methods in the literature usually apply norm-based gradient filters in each iteration and eliminate possible outliers, which can be ineffective in a setting with heterogeneous and unbalanced training data. We propose a heuristic yet novel scheme for adjusting the weights in the gradient aggregation step that accounts for two anomaly metrics, namely the relative distance and the convergence measure. Simulation results show that our proposed scheme brings notable performance gain compared to norm-based policies when the agents have distinct data distributions.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Federated learning, anomaly detection, gradient aggregation rule, fault tolerance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-188282 (URN)10.1109/ICAS49788.2021.9551122 (DOI)978-1-7281-7289-7 (ISBN)978-1-7281-7290-3 (ISBN)
Conference
2021 IEEE International Conference on Autonomous Systems (ICAS), 11-13 August 2021
Note

Funding agencies: This work was supported in part by Centrum for Industriell Information- ¨steknologi (CENIIT), Excellence Center at Linkoping - Lund in Information ¨Technology (ELLIIT), and Knut and Alice Wallenberg (KAW) Foundation

Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2024-01-02
Kunnath Ganesan, U., Sarvendranath, R. & Larsson, E. G. (2021). BeamSync: Over-The-Air Carrier Synchronization in Distributed RadioWeaves. In: : . Paper presented at 25th International ITG Workshop on Smart Antennas (WSA 2021). IEEE
Open this publication in new window or tab >>BeamSync: Over-The-Air Carrier Synchronization in Distributed RadioWeaves
2021 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In a distributed multi-antenna system, multiple geographically separated transmit nodes communicate simultaneously to a receive node. Synchronization of these nodes is essential to achieve a good performance at the receiver. RadioWeaves is a new paradigm of cell-free massive MIMO array deployment using distributed multi-antenna panels in indoor environments. In this paper, we study the carrier frequency synchronization problem in distributed RadioWeave panels. We propose a novel, over-the-air synchronization protocol, which we call as BeamSync, to synchronize all the different multi-antenna transmit panels. We also show that beamforming the synchronization signal in the dominant direction of the channel between the panels is optimal and the synchronization performance is significantly better than traditional beamforming techniques.

Place, publisher, year, edition, pages
IEEE, 2021
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-182599 (URN)978-3-8007-5686-5 (ISBN)
Conference
25th International ITG Workshop on Smart Antennas (WSA 2021)
Available from: 2022-01-31 Created: 2022-01-31 Last updated: 2023-01-12
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7599-4367

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