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Zaher, M., Björnson, E. & Petrova, M. (2024). Near-Optimal Cell-Free Beamforming for Physical Layer Multigroup Multicasting. In: GLOBECOM 2024 - 2024 IEEE Global Communications Conference: . Paper presented at 2024 IEEE Global Communications Conference, GLOBECOM 2024, Cape Town, South Africa, Dec 8 2024 - Dec 12 2024 (pp. 4082-4087). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Near-Optimal Cell-Free Beamforming for Physical Layer Multigroup Multicasting
2024 (English)In: GLOBECOM 2024 - 2024 IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 4082-4087Conference paper, Published paper (Refereed)
Abstract [en]

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
cell-free massive MIMO, convex optimization, downlink beamforming, Multicast, semidefinite relaxation
National Category
Signal Processing Telecommunications
Identifiers
urn:nbn:se:kth:diva-361982 (URN)10.1109/GLOBECOM52923.2024.10900985 (DOI)2-s2.0-105000826381 (Scopus ID)
Conference
2024 IEEE Global Communications Conference, GLOBECOM 2024, Cape Town, South Africa, Dec 8 2024 - Dec 12 2024
Note

Part of ISBN 9798350351255

QC 20250409

Available from: 2025-04-03 Created: 2025-04-03 Last updated: 2025-04-09Bibliographically approved
Zaher, M., Björnson, E. & Petrova, M. (2024). Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks. IEEE Transactions on Wireless Communications, 23(6), 6124-6138
Open this publication in new window or tab >>Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks
2024 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 23, no 6, p. 6124-6138Article in journal (Refereed) Published
Abstract [en]

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Cell-free massive MIMO, cluster formation, Clustering algorithms, Distributed algorithms, handover, Handover, Heuristic algorithms, Massive MIMO, Millimeter wave communication, mobility management, pilot assignment, spectral efficiency, Wireless communication
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-350292 (URN)10.1109/TWC.2023.3330199 (DOI)001247163400028 ()2-s2.0-85177080883 (Scopus ID)
Note

QC 20240711

Available from: 2024-07-11 Created: 2024-07-11 Last updated: 2025-04-15Bibliographically approved
Zaher, M., Björnson, E. & Petrova, M. (2024). Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks. In: 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings: . Paper presented at 25th IEEE Wireless Communications and Networking Conference, WCNC 2024, Dubai, United Arab Emirates, Apr 21 2024 - Apr 24 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks
2024 (English)In: 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
cell-free massive MIMO, Outage probability, spectral efficiency, unknown interference, uplink
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-350993 (URN)10.1109/WCNC57260.2024.10570598 (DOI)001268569300095 ()2-s2.0-85198828692 (Scopus ID)
Conference
25th IEEE Wireless Communications and Networking Conference, WCNC 2024, Dubai, United Arab Emirates, Apr 21 2024 - Apr 24 2024
Note

Part of ISBN 9798350303582

QC 20240725

Available from: 2024-07-24 Created: 2024-07-24 Last updated: 2025-04-15Bibliographically approved
Zaher, M., Björnson, E. & Petrova, M. (2023). A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks. IEEE Wireless Communications Letters, 12(8), 1374-1378
Open this publication in new window or tab >>A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks
2023 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 12, no 8, p. 1374-1378Article in journal (Refereed) Published
Abstract [en]

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Index Terms-Outage probability, MU-MIMO, unknown interference, multiple access, spectral efficiency, uplink (UL)
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-335139 (URN)10.1109/LWC.2023.3275174 (DOI)001045291900016 ()2-s2.0-85159793647 (Scopus ID)
Note

QC 20230901

Available from: 2023-09-01 Created: 2023-09-01 Last updated: 2025-04-15Bibliographically approved
Khosravi, S., Shokri Ghadikolaei, H., Zander, J. & Petrova, M. (2023). Beam Alignment Using Trajectory Information in Mobile Millimeter-wave Networks. In: ICC 2023 - IEEE International Conference on Communications: . Paper presented at 2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28 May - 1 June 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Beam Alignment Using Trajectory Information in Mobile Millimeter-wave Networks
2023 (English)In: ICC 2023 - IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Millimeter-wave and terahertz systems rely on beam-forming/combining codebooks to determine the best beam directions during the initial access and data transmission. Existing approaches suffer from large codebook sizes and high beam searching overhead in the presence of mobile devices. To address this issue, we utilize the similarity of the channel in adjacent locations to divide the user trajectory into a set of separate regions and maintain a set of candidate beams for each region in a database. Due to the tradeoff between the number of regions and the signalling overhead, i.e., the greater number of regions results in a higher signal-to-noise ratio (SNR) but also a larger signalling overhead for the database, we propose an optimization framework to find the minimum number of regions based on the trajectory of a mobile device. Using a ray tracing tool, we demonstrate that the proposed method provides high SNR while being more robust to the location information accuracy in comparison to the lookup table baseline and fixed size region baseline.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
beam alignment, beamforming codebook, Millimeter-wave systems, terahertz systems
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-326538 (URN)10.1109/ICC45041.2023.10279741 (DOI)001094862601155 ()2-s2.0-85178292178 (Scopus ID)
Conference
2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28 May - 1 June 2023
Note

Part of ISBN 978-153867462-8

QC 20231214

Available from: 2023-05-04 Created: 2023-05-04 Last updated: 2024-03-15Bibliographically approved
Khosravi, S. (2023). Beam Selection Using Trajectory Information in Mobile Millimeter-wave Networks.
Open this publication in new window or tab >>Beam Selection Using Trajectory Information in Mobile Millimeter-wave Networks
2023 (English)In: Article in journal, Editorial material (Refereed) Submitted
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-326540 (URN)
Note

QC 20230508

Available from: 2023-05-04 Created: 2023-05-04 Last updated: 2023-05-08Bibliographically approved
Shi, W., Ganjalizadeh, M., Ghadikolaei, H. S. & Petrova, M. (2023). Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning. In: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications: 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023: . Paper presented at 34th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2023, Toronto, Canada, Sep 5 2023 - Sep 8 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning
2023 (English)In: 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications: 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Ultra-reliable low latency communications (URLLC) service is envisioned to enable use cases with strict reliability and latency requirements in 5G. One approach for enabling URLLC services is to leverage Reinforcement Learning (RL) to efficiently allocate wireless resources. However, with conventional RL methods, the decision variables (though being deployed at various network layers) are typically optimized in the same control loop, leading to significant practical limitations on the control loop's delay as well as excessive signaling and energy consumption. In this paper, we propose a multi-agent Hierarchical RL (HRL) framework that enables the implementation of multi-level policies with different control loop timescales. Agents with faster control loops are deployed closer to the base station, while the ones with slower control loops are at the edge or closer to the core network providing high-level guidelines for low-level actions. On a use case from the prior art, with our HRL framework, we optimized the maximum number of retransmissions and transmission power of industrial devices. Our extensive simulation results on the factory automation scenario show that the HRL framework achieves better performance as the baseline single-agent RL method, with significantly less overhead of signal transmissions and delay compared to the one-agent RL methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
6G, availability, factory automation, hierarchical reinforcement learning (HRL), reliability, URLLC
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:kth:diva-341467 (URN)10.1109/PIMRC56721.2023.10293856 (DOI)001103214700109 ()2-s2.0-85178252780 (Scopus ID)
Conference
34th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2023, Toronto, Canada, Sep 5 2023 - Sep 8 2023
Note

QC 20231213

Part of ISBN 978-1-6654-6483-3

Available from: 2024-01-10 Created: 2024-01-10 Last updated: 2024-02-26Bibliographically approved
Ganjalizadeh, M. (2023). Device Selection for the Coexistence of URLLC and Distributed Learning Services.
Open this publication in new window or tab >>Device Selection for the Coexistence of URLLC and Distributed Learning Services
2023 (English)In: Article in journal (Refereed) Submitted
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-328764 (URN)
Note

QC 20230613

Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-06-13Bibliographically approved
Zaher, M., Demir, O. T., Björnson, E. & Petrova, M. (2023). Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems. IEEE Transactions on Wireless Communications, 22(1), 174-188
Open this publication in new window or tab >>Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems
2023 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 22, no 1, p. 174-188Article in journal (Refereed) Published
Abstract [en]

This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Cell-free massive MIMO, power allocation, sum-SE maximization, proportional fairness, deep learning, spectral efficiency, downlink
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-324707 (URN)10.1109/TWC.2022.3192203 (DOI)000925620400012 ()2-s2.0-85135748426 (Scopus ID)
Note

QC 20230509

Available from: 2023-03-15 Created: 2023-03-15 Last updated: 2025-04-15Bibliographically approved
Zaher, M., Björnson, E. & Petrova, M. (2023). Mobility Management in mmWave Cell-Free Massive MIMO Networks. In: ICC 2023 - IEEE International Conference on Communications: . Paper presented at 2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28 May - 1 June 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Mobility Management in mmWave Cell-Free Massive MIMO Networks
2023 (English)In: ICC 2023 - IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses mobility management in the downlink of a mmWave cell-free massive MIMO (multiple-input multiple-output) network with imperfect channel knowledge obtained from pilot training. The network consists of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. The objective is to extend traditional handover concepts to the challenging AP-UE association strategies of cell-free networks. To this end, we propose a distributed algorithm for joint pilot assignment and cluster formation in a dynamic environment considering UE mobility. The primary goal is to limit the necessary number of AP and pilot changes, with reasonable computational complexity. We evaluate the performance in terms of the spectral efficiency with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithm effectively identifies the essential AP-UE association refinements. Moreover, it provides a significantly lower average number of pilot changes compared to an ultra-dense network.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Communications, E-ISSN 1938-1883
Keywords
cell-free massive MIMO, handover, mobility management
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-326360 (URN)10.1109/ICC45041.2023.10279192 (DOI)001094862606101 ()2-s2.0-85178297950 (Scopus ID)
Conference
2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28 May - 1 June 2023
Funder
Swedish Foundation for Strategic Research
Note

Part of ISBN 978-153867462-8

QC 20231214

Available from: 2023-04-30 Created: 2023-12-14 Last updated: 2024-03-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3876-2214

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