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Publikasjoner (10 av 24) Visa alla publikasjoner
Enqvist, A., Demir, O. T., Cavdar, C. & Björnson, E. (2024). Fundamentals of Energy-Efficienct Wireless Links: Optimal Ratios and Scaling Behaviours. Paper presented at Vehicular Technology Conference (VTC) 2024 Singapore.
Åpne denne publikasjonen i ny fane eller vindu >>Fundamentals of Energy-Efficienct Wireless Links: Optimal Ratios and Scaling Behaviours
2024 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

In this paper, we examine the energy efficiency (EE) of a base station with multiple antennas. We use a state-of-theart power consumption model, taking into account the passive and active parts of the transceiver circuitry, including the effects of radiated power, signal processing, and passive consumption. The paper treats the transmit power, bandwidth, and number ofantennas as the optimization variables. We provide novel closed form solutions for the optimal ratios of power per unit bandwidth and power per transmit antenna. We present a novel algorithm that jointly optimizes these variables to achieve maximum EE, while fulfilling constraints on the variable ranges. We also discover a new relationship between the radiated power and the passive transceiver power consumption. We provide analytical insight into whether using maximum power or bandwidth is optimal.

Emneord
Energy Efficiency, optimization, 6G, multiple antenna communications
HSV kategori
Forskningsprogram
Informations- och kommunikationsteknik
Identifikatorer
urn:nbn:se:kth:diva-343377 (URN)
Konferanse
Vehicular Technology Conference (VTC) 2024 Singapore
Forskningsfinansiär
Swedish Foundation for Strategic Research, FFL18-0227
Merknad

QC 20240214

Tilgjengelig fra: 2024-02-12 Laget: 2024-02-12 Sist oppdatert: 2024-02-14bibliografisk kontrollert
Behdad, Z., Demir, O. T., Sung, K. W. & Cavdar, C. (2024). Interplay Between Sensing and Communication in Cell-Free Massive MIMO with URLLC Users. 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)
Åpne denne publikasjonen i ny fane eller vindu >>Interplay Between Sensing and Communication in Cell-Free Massive MIMO with URLLC Users
2024 (engelsk)Inngår i: 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2024Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper studies integrated sensing and communication (ISAC) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with multi-static sensing and ultra-reliable low-latency communication (URLLC) users. We propose a successive convex approximation-based power allocation algorithm that maximizes energy efficiency while satisfying the sensing and URLLC requirements. In addition, we provide a new definition for network availability, which accounts for both sensing and URLLC requirements. The impact of blocklength, sensing requirement, and required reliability as a function of decoding error probability on network availability and energy ef-ficiency is investigated. The proposed power allocation algorithm is compared to a communication-centric approach where only the URLLC requirement is considered. It is shown that the URLLC-only approach is incapable of meeting sensing requirements, while the proposed ISAC algorithm fulfills both sensing and URLLC requirements, albeit with an associated increase in energy consumption. This increment can be reduced up to 75% by utilizing additional symbols for sensing. It is also demonstrated that larger blocklengths enhance network availability and offer greater robustness against stringent reliability requirements.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024
Emneord
C-RAN, cell-free massive MIMO, Integrated sensing and communication, power allocation, URLLC
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-350995 (URN)10.1109/WCNC57260.2024.10571226 (DOI)001268569304053 ()2-s2.0-85198856616 (Scopus ID)
Konferanse
25th IEEE Wireless Communications and Networking Conference, WCNC 2024, Dubai, United Arab Emirates, Apr 21 2024 - Apr 24 2024
Merknad

Part of ISBN 9798350303582

QC 20240724

Tilgjengelig fra: 2024-07-24 Laget: 2024-07-24 Sist oppdatert: 2024-10-07bibliografisk kontrollert
Behdad, Z., Demir, Ö. T., Subg, K. W. & Cavdar, C. (2024). Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC.
Åpne denne publikasjonen i ny fane eller vindu >>Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC
2024 (engelsk)Annet (Annet vitenskapelig)
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-345788 (URN)10.13140/RG.2.2.35029.81125 (DOI)
Merknad

QC 20240430

Tilgjengelig fra: 2024-04-19 Laget: 2024-04-19 Sist oppdatert: 2024-04-30bibliografisk kontrollert
Rivetti, S., Demir, Ö. T., Björnson, E. & Skoglund, M. (2024). Malicious Reconfigurable Intelligent Surfaces: How Impactful Can Destructive Beamforming be?. IEEE Wireless Communications Letters, 13(7), 1918-1922
Åpne denne publikasjonen i ny fane eller vindu >>Malicious Reconfigurable Intelligent Surfaces: How Impactful Can Destructive Beamforming be?
2024 (engelsk)Inngår i: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 13, nr 7, s. 1918-1922Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Reconfigurable intelligent surfaces (RISs) have demonstrated significant potential for enhancing communication system performance if properly configured. However, a RIS might also pose a risk to the network security. In this letter, we explore the impact of a malicious RIS on a multi-user multiple-input single-output (MISO) system when the system is unaware of the RIS's malicious intentions. The objective of the malicious RIS is to degrade the signal-to-noise ratio (SNR) of a specific user equipment (UE), with the option of preserving the SNR of the other UEs, making the attack harder to detect. To achieve this goal, we derive the optimal RIS phase-shift pattern, assuming perfect channel state information (CSI) at the hacker. We then relax this assumption by introducing CSI uncertainties and subsequently determine the RIS's phase-shift pattern using a robust optimization approach. Our simulations reveal a direct proportionality between the performance degradation caused by the malicious RIS and the number of reflective elements, along with resilience toward CSI uncertainties.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024
Emneord
Reconfigurable intelligent surface (RIS), malicious RIS, imperfect CSI, SNR degradation
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-350836 (URN)10.1109/LWC.2024.3395831 (DOI)001266360800012 ()2-s2.0-85192201063 (Scopus ID)
Merknad

QC 20240722

Tilgjengelig fra: 2024-07-22 Laget: 2024-07-22 Sist oppdatert: 2024-08-20bibliografisk kontrollert
Topal, O. A., He, Q., Demir, O. T., Masoudi, M. & Cavdar, C. (2023). DRL-Based Joint AP Deployment and Network-Centric Cluster Formation for Maximizing Long-Term Energy Efficiency in Cell-free Massive MIMO. In: Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023: . Paper presented at 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, October 29 - November 1 , 2023, Pacific Grove, United States of America (pp. 993-999). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>DRL-Based Joint AP Deployment and Network-Centric Cluster Formation for Maximizing Long-Term Energy Efficiency in Cell-free Massive MIMO
Vise andre…
2023 (engelsk)Inngår i: Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, s. 993-999Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In cell-free massive MIMO networks, scalability is one of the fundamental problems since a significant number of access points (APs) are widely distributed throughout the network area to cater to the needs of multiple user equipments (UEs). One approach to addressing this issue is through network-centric clustering, which involves dividing the network area into isolated clusters of APs, each connected to its cloud unit (CU). To address these challenges, this paper proposes a deep reinforcement learning (DRL) algorithm that jointly optimizes the network-centric cluster boundaries and decides AP deployment in each cluster to improve long-term energy efficiency. The DRL agent also aims to minimize the average UE drop rate by considering the delay requirements of each UE's requested service. The results show that at least 16% improvement in energy efficiency is obtained compared to the heuristically developed benchmarks.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Emneord
access point deployment, cell-free cluster formation, Cell-free massive MIMO, deep reinforcement learning, energy efficiency, network-centric clustering
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-350000 (URN)10.1109/IEEECONF59524.2023.10477038 (DOI)001207755100179 ()2-s2.0-85190369985 (Scopus ID)
Konferanse
57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, October 29 - November 1 , 2023, Pacific Grove, United States of America
Merknad

Part of ISBN 9798350325744

QC 20241023

Tilgjengelig fra: 2024-07-05 Laget: 2024-07-05 Sist oppdatert: 2024-10-23bibliografisk kontrollert
Chen, S., Zhang, J., Björnson, E., Demir, O. T. & Ai, B. (2023). Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing. IEEE Transactions on Wireless Communications, 22(12), 9374-9389
Åpne denne publikasjonen i ny fane eller vindu >>Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
Vise andre…
2023 (engelsk)Inngår i: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 22, nr 12, s. 9374-9389Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Emneord
Cell-free massive MIMO, energy efficiency, distributed processing, large-scale fading, sparse optimization
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-344482 (URN)10.1109/TWC.2023.3270299 (DOI)001128031700038 ()2-s2.0-85159797448 (Scopus ID)
Merknad

QC 20240318

Tilgjengelig fra: 2024-03-18 Laget: 2024-03-18 Sist oppdatert: 2024-03-18bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems
2023 (engelsk)Inngår i: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 22, nr 1, s. 174-188Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Emneord
Cell-free massive MIMO, power allocation, sum-SE maximization, proportional fairness, deep learning, spectral efficiency, downlink
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-324707 (URN)10.1109/TWC.2022.3192203 (DOI)000925620400012 ()2-s2.0-85135748426 (Scopus ID)
Merknad

QC 20230509

Tilgjengelig fra: 2023-03-15 Laget: 2023-03-15 Sist oppdatert: 2025-04-15bibliografisk kontrollert
Li, Z., Topal, O. A., Demir, O. T., Björnson, E. & Cavdar, C. (2023). mmWave Coverage Extension Using Reconfigurable Intelligent Surfaces in Indoor Dense Spaces. In: ICC 2023 - IEEE International Conference on Communications: . Paper presented at IEEE International Conference on Communications, 28 May 2023 - 01 June 2023, Rome, Italy (pp. 5805-5810). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>mmWave Coverage Extension Using Reconfigurable Intelligent Surfaces in Indoor Dense Spaces
Vise andre…
2023 (engelsk)Inngår i: ICC 2023 - IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers (IEEE) , 2023, s. 5805-5810Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this work, we consider the deployment of reconfigurable intelligent surfaces (RISs) to extend the coverage of a millimeter-wave (mmWave) network in indoor dense spaces. We first integrate RIS into ray-tracing simulations to realistically capture the propagation characteristics, then formulate a non-convex optimization problem that minimizes the number of RISs under rate constraints. We propose a feasible point pursuit and successive convex approximation-based algorithm, which solves the problem by jointly selecting the RIS locations, optimizing the RIS phase-shifts, and allocating time resources to user equipments (UEs). The numerical results demonstrate substantial coverage extension by using at least four RISs, and a data rate of 130 Mbit/s is guaranteed for UEs in the considered area of an airplane cabin.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
IEEE International Conference on Communications, E-ISSN 1938-1883
Emneord
mmWave communication, reconfigurable intelligent surface, ray tracing, indoor dense spaces, aircraft
HSV kategori
Forskningsprogram
Telekommunikation
Identifikatorer
urn:nbn:se:kth:diva-338851 (URN)10.1109/ICC45041.2023.10279515 (DOI)001094862605149 ()2-s2.0-85178279374 (Scopus ID)
Konferanse
IEEE International Conference on Communications, 28 May 2023 - 01 June 2023, Rome, Italy
Merknad

Part of ISBN 978-1-5386-7463-5

QC 20231123

Tilgjengelig fra: 2023-10-29 Laget: 2023-10-29 Sist oppdatert: 2024-03-12bibliografisk kontrollert
Kosasih, A., Demir, Ö. T. & Björnson, E. (2023). Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays. In: Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023: . Paper presented at 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, October 29 - November 1, 2023, Pacific Grove, United States of America (pp. 162-166). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays
2023 (engelsk)Inngår i: Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, s. 162-166Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Accurate channel estimation is critical to fully ex-ploit the beamforming gains when communicating with extremely large aperture arrays. The propagation distances between the user and receiver, which potentially has thousands of anten-nas/elements, are such that they are located in the radiative near-field region of each other when considering the Fraunhofer distance of the entire array. Therefore, it is imperative to consider near-field effects to achieve proper channel estimation. This paper proposes a parametric multi-user near-field channel estimation algorithm based on MUltiple SIgnal Classification (MUSIC) method to obtain the essential parameters describing the users' locations. We derive the estimated channel by incorporating the estimated parameters into the near-field channel model. Additionally, we implement a least-squares-based estimation corrector, resulting in a precise near-field channel estimation. Simulation results demonstrate that our proposed scheme outperforms classical least-squares and minimum mean-square error channel estimation methods in terms of normalized beamforming gain and normalized mean-square error.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Emneord
active arrays, channel estimation, finite-depth beamforming, MUSIC, Radiative near-field
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-349998 (URN)10.1109/IEEECONF59524.2023.10476971 (DOI)001207755100029 ()2-s2.0-85190359261 (Scopus ID)
Konferanse
57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, October 29 - November 1, 2023, Pacific Grove, United States of America
Merknad

Part of ISBN 9798350325744

QC 20241023

Tilgjengelig fra: 2024-07-05 Laget: 2024-07-05 Sist oppdatert: 2024-10-23bibliografisk kontrollert
Demir, O. T., Masoudi, M., Björnson, E. & Cavdar, C. (2022). Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total Network Power?. In: Ieee International Conference On Communications (Icc 2022): . Paper presented at IEEE International Conference on Communications (ICC), MAY 16-20, 2022, Seoul, SOUTH KOREA (pp. 159-164). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total Network Power?
2022 (engelsk)Inngår i: Ieee International Conference On Communications (Icc 2022), Institute of Electrical and Electronics Engineers (IEEE) , 2022, s. 159-164Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2022
Serie
IEEE International Conference on Communications, ISSN 1550-3607
Emneord
Cell-free massive MIMO, virtualized CRAN, network virtualization, fronthaul transport
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-322299 (URN)10.1109/ICC45855.2022.9838846 (DOI)000864709900027 ()2-s2.0-85132176582 (Scopus ID)
Konferanse
IEEE International Conference on Communications (ICC), MAY 16-20, 2022, Seoul, SOUTH KOREA
Merknad

Part of proceedings: ISBN 978-1-5386-8347-7

QC 20221212

Tilgjengelig fra: 2022-12-12 Laget: 2022-12-12 Sist oppdatert: 2022-12-15bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-9059-2799