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He, K., Vu, T. X., Fan, L., Chatzinotas, S. & Ottersten, B. (2025). Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications. IEEE Transactions on Machine Learning in Communications and Networking, 3, 479-490
Open this publication in new window or tab >>Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications
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2025 (English)In: IEEE Transactions on Machine Learning in Communications and Networking, E-ISSN 2831-316X, Vol. 3, p. 479-490Article in journal (Refereed) Published
Abstract [en]

This paper investigates the spatio-temporal predictive learning problem, which is crucial in diverse applications such as MIMO channel prediction, mobile traffic analysis, and network slicing. To address this problem, the attention mechanism has been adopted by many existing models to predict future outputs. However, most of these models use a single-domain attention which captures input dependency structures only in the temporal domain. This limitation reduces their prediction accuracy in spatio-temporal predictive learning, where understanding both spatial and temporal dependencies is essential. To tackle this issue and enhance the prediction performance, we propose a novel crossover attention mechanism in this paper. The crossover attention can be understood as a learnable regression kernel which prioritizes the input sequence with both spatial and temporal similarities and extracts relevant information for generating the output of future time slots. Simulation results and ablation studies based on synthetic and realistic datasets show that the proposed crossover attention achieves considerable prediction accuracy improvement compared to the conventional attention layers.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025
Keywords
Predictive models, Attention mechanisms, Time series analysis, Correlation, Transformers, Accuracy, Convolutional neural networks, Kernel, Data models, Computational modeling, Spatio-temporal, multivariate time series, traffic prediction, crossover attention, transformer model, deep learning
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-364728 (URN)10.1109/TMLCN.2025.3555975 (DOI)001487810300001 ()
Note

QC 20250617

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-17Bibliographically approved
Mishra, K. V., Shankar, M. R., Ottersten, B. & Swindlehurst, A. L. (2024). A Signal Processing Outlook Toward Joint Radar-Communications. In: Signal Processing for Joint Radar Communications: (pp. 3-36). Wiley
Open this publication in new window or tab >>A Signal Processing Outlook Toward Joint Radar-Communications
2024 (English)In: Signal Processing for Joint Radar Communications, Wiley , 2024, p. 3-36Chapter in book (Other academic)
Abstract [en]

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency spectrum. Such a joint radar-communications (JRC) model has advantages of low-cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical in implementation of mmWave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade-off between communications and radar functionalities. Novel multiple-input-multiple-output (MIMO) signal processing techniques are required because mmWave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mmWave JRC systems with an emphasis on waveform design.

Place, publisher, year, edition, pages
Wiley, 2024
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-347140 (URN)10.1002/9781119795568.ch1 (DOI)2-s2.0-85193884948 (Scopus ID)
Note

QC 20240605

Part of ISBN 978-111979556-8, 978-111979553-7

Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-05Bibliographically approved
Marrero, L. M., Duncan, J. M., González, J. L., Krivochiza, J., Chatzinotas, S., Ottersten, B. & Camps, A. (2024). Accurate Phase Synchronization for Precoding-Enabled GEO Multibeam Satellite Systems. IEEE Open Journal of the Communications Society, 5, 712-729
Open this publication in new window or tab >>Accurate Phase Synchronization for Precoding-Enabled GEO Multibeam Satellite Systems
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2024 (English)In: IEEE Open Journal of the Communications Society, Vol. 5, p. 712-729Article in journal (Refereed) Published
Keywords
Satellite broadcasting;Synchronization;Precoding;Phase noise;MIMO communication;Frequency modulation;Symbols;phase noise;phase synchronization;precoding;software-defined radio
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-364751 (URN)10.1109/OJCOMS.2023.3341621 (DOI)
Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-16
Mishra, K. V., Shankar, M. R., González-Prelcic, N., Valkama, M., Yu, W. & Ottersten, B. (2024). Editorial Introduction to the Special Issue on Learning-Based Signal Processing for Integrated Sensing and Communications. IEEE Journal on Selected Topics in Signal Processing, 18(5), 731-736
Open this publication in new window or tab >>Editorial Introduction to the Special Issue on Learning-Based Signal Processing for Integrated Sensing and Communications
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2024 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 18, no 5, p. 731-736Article, review/survey (Refereed) Published
Abstract [en]

Signal processing techniques have played a pivotal role in the early development of joint sensing and communication systems [1]. These efforts were driven by the need to address spectrum scarcity and to reduce hardware size and cost. Initially focused on dual-function radar-communication systems, this field has since evolved into the broader paradigm of Integrated Sensing and Communication (ISAC). ISAC encompasses a wide range of interactions between sensing and communication, incorporating not just radar but also other sensors, and leveraging their capabilities for applications such as autonomous driving, drone-based services, radio-frequency identification, and weather monitoring. With wireless networks now operating at higher frequencies, their dual role as communication networks and environmental sensors has become increasingly significant, providing critical information for both user needs and network operations [2].

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Signal Processing Communication Systems
Identifiers
urn:nbn:se:kth:diva-359242 (URN)10.1109/JSTSP.2024.3522437 (DOI)001394750300013 ()2-s2.0-85215423970 (Scopus ID)
Note

QC 20250203

Available from: 2025-01-29 Created: 2025-01-29 Last updated: 2025-02-03Bibliographically approved
Zivuku, P., Kisseleff, S., Nguyen, V.-D., Martins, W. A., Ntontin, K., Chatzinotas, S. & Ottersten, B. (2024). Joint RIS-aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities. IEEE Transactions on Communications, 72(1), 418-433
Open this publication in new window or tab >>Joint RIS-aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities
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2024 (English)In: IEEE Transactions on Communications, Vol. 72, no 1, p. 418-433Article in journal (Refereed) Published
Abstract [en]

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-Aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Signal Processing
Research subject
Electrical Engineering; Telecommunication
Identifiers
urn:nbn:se:kth:diva-364752 (URN)10.1109/TCOMM.2023.3321731 (DOI)001166809900043 ()2-s2.0-85174834557 (Scopus ID)
Note

QC 20250617

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-17Bibliographically approved
Mishra, K. V., Shankar, M. R., Ottersten, B. & Swindlehurst, A. L. (2024). Preface. In: Signal Processing for Joint Radar Communications: . wiley
Open this publication in new window or tab >>Preface
2024 (English)In: Signal Processing for Joint Radar Communications, wiley , 2024Chapter in book (Other academic)
Place, publisher, year, edition, pages
wiley, 2024
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-347141 (URN)2-s2.0-85193873435 (Scopus ID)
Note

Part of ISBN 9781119795568 9781119795537

QC 20240611

Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-11Bibliographically approved
Khan, W. U., Mahmood, A., Sheemar, C. K., Lagunas, E., Chatzinotas, S. & Ottersten, B. (2024). Reconfigurable Intelligent Surfaces for 6G Non-Terrestrial Networks: Assisting Connectivity from the Sky. IEEE Internet of Things Magazine, 7(1), 34-39
Open this publication in new window or tab >>Reconfigurable Intelligent Surfaces for 6G Non-Terrestrial Networks: Assisting Connectivity from the Sky
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2024 (English)In: IEEE Internet of Things Magazine, E-ISSN 2576-3199, Vol. 7, no 1, p. 34-39Article in journal (Refereed) Published
Abstract [en]

Sixth-generation (6G) non-terrestrial networks (NTNs) are advanced wireless communication systems that operate beyond traditional terrestrial networks. These networks utilize various technologies and platforms to provide flexible, enhanced connectivity and coverage. When operating at high frequency, ground user terminals require low-directional antennas, which experience poor link budgets from satellites and thus drive the quest for novel solutions. Reconfigurable Intelligent Surfaces (RISs) have recently emerged as a promising technology for 6G and beyond cellular systems. This article studies the potential of RIS-in-tegrated NTNs to revolutionize next-generation connectivity. First, it discusses the fundamentals of RIS technology. Secondly, it delves into reporting the recent advances in RIS-integrated NTNs. Subsequently, it presents a novel framework based on the current state-of-the-art for IRS-integrated NTNs with classical single connected diagonal RIS and fully connected beyond diagonal RIS architectures. Finally, the article highlights open challenges and future research directions to revolutionize the realm of RIS-integrated NTNs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-364748 (URN)10.1109/IOTM.001.2300208 (DOI)2-s2.0-85192261745 (Scopus ID)
Note

QC 20250618

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-18Bibliographically approved
Khan, W. U., Lagunas, E., Mahmood, A., Chatzinotas, S. & Ottersten, B. (2024). RIS-Assisted Energy-Efficient LEO Satellite Communications With NOMA. IEEE Transactions on Green Communications and Networking, 8(2), 780-790
Open this publication in new window or tab >>RIS-Assisted Energy-Efficient LEO Satellite Communications With NOMA
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2024 (English)In: IEEE Transactions on Green Communications and Networking, Vol. 8, no 2, p. 780-790Article in journal (Refereed) Published
Abstract [en]

Low Earth Orbit (LEO) satellite networks are expected to play a crucial role in providing high-speed Internet access and low-latency communication worldwide. However, some challenges can affect the performance of LEO satellite networks. For example, they can face energy and spectral efficiency challenges, such as high power consumption and spectral congestion, due to the increasing number of satellites. Furthermore, mobile ground users tend to operate with low directive antennas, which pose significant challenges in closing the LEO-to-ground communication link, especially when operating at a high-frequency range. To overcome these challenges, energy-efficient technologies like reconfigurable intelligent surfaces (RIS) and advanced spectrum management techniques like non-orthogonal multiple access (NOMA) can be employed. RIS can improve signal quality and reduce power consumption, while NOMA can enhance spectral efficiency by sharing the same resources among multiple users. This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks while ensuring the quality of services. The proposed framework simultaneously optimizes the NOMA transmit power of the LEO satellite and the passive beamforming of RIS, considering the assumption of imperfect successive interference cancellation. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47% and 54.9% higher energy efficiency compared to the benchmark and conventional frameworks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Satellites, Low earth orbit satellites, NOMA, Optimization, Array signal processing, Satellite broadcasting, Real-time systems, Reconfigurable intelligent surfaces, LEO satellite, non-orthogonal multiple access, energy efficiency, imp-SIC
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-364750 (URN)10.1109/TGCN.2023.3344102 (DOI)001230177900001 ()2-s2.0-85181800820 (Scopus ID)
Note

QC 20250618

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-18Bibliographically approved
He, K., Vu, T. X., Hoang, D. T., Nguyen, D. N., Chatzinotas, S. & Ottersten, B. (2024). Risk-Aware Antenna Selection for Multiuser Massive MIMO under Incomplete CSI. IEEE Transactions on Wireless Communications, 23(9), 11001-11014
Open this publication in new window or tab >>Risk-Aware Antenna Selection for Multiuser Massive MIMO under Incomplete CSI
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2024 (English)In: IEEE Transactions on Wireless Communications, Vol. 23, no 9, p. 11001-11014Article in journal (Refereed) Published
Abstract [en]

This paper investigates the antenna selection problem in massive multiple-input multiple-out (MIMO) systems under incomplete channel state information (CSI), with a particular interest on risk-aware planning subjected to practical constraints such as transmit power budgets and quality of services (QoS). Due to a very large number of antennas, obtaining complete channel measurements becomes a cost-prohibitive, energy-inefficient and spectral-inefficient task. To reduce pilot overhead, incomplete CSI and antenna selection (AS) are expected in practical massive MIMO systems. However, most existing AS algorithms heavily rely on the complete CSI, which imposes a high probability of violating the practical constraints in the scenarios of our interests. Motivated by this, we propose a joint channel prediction and antenna selection framework (JCPAS) which efficiently performs AS and is robust against the incomplete CSI and practical constraints. The proposed framework comprises i) a channel tracker which estimates the channel dynamics based on historical incomplete observations, and ii) a risk-aware Monte Carlo tree search (RA-MCTS) algorithm which utilizes the estimated channel dynamics to select antennas in a risk-aware manner. Simulation results show that the proposed RA-MCTS not only achieves much lower energy consumption compared to the existing typical algorithms, but also significantly reduces the probability of violating the practical constraints.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Antennas, Massive MIMO, Channel estimation, Radio frequency, Antenna arrays, Antenna measurements, Transmitting antennas, Massive MIMO, antenna selection, incomplete CSI, machine learning, risk-aware planning, Monte Carlo tree search
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-364745 (URN)10.1109/TWC.2024.3377733 (DOI)001312963400079 ()2-s2.0-85188949551 (Scopus ID)
Note

QC 20250617

Available from: 2025-06-16 Created: 2025-06-16 Last updated: 2025-06-17Bibliographically approved
Mishra, K. V., Shankar, M. R., Ottersten, B. & Swindlehurst, A. L. (2024). Signal Processing for Joint Radar Communications. wiley
Open this publication in new window or tab >>Signal Processing for Joint Radar Communications
2024 (English)Book (Other academic)
Abstract [en]

Signal Processing for Joint Radar Communications A one-stop, comprehensive source for the latest research in joint radar communications In Signal Processing for Joint Radar Communications, four eminent electrical engineers deliver a practical and informative contribution to the diffusion of newly developed joint radar communications (JRC) tools into the sensing and communications communities. This book illustrates recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithms and applications of JRC from diverse perspectives, including waveform design, physical layer processing, privacy, security, hardware prototyping, resource allocation, and sampling theory. The distinguished editors bring together contributions from more than 40 leading JRC researchers working on remote sensing, electromagnetics, optimization, signal processing, and beyond 5G wireless networks. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowing readers to take full advantage of JRC systems. Readers will also find: Thorough introductions to fundamental limits and background on JRC theory and applications, including dual-function radar communications, cooperative JRC, distributed JRC, and passive JRC Comprehensive explorations of JRC processing via waveform analyses, interference mitigation, and modeling with jamming and clutter Practical discussions of information-theoretic, optimization, and networking aspects of JRC In-depth examinations of JRC applications in cutting-edge scenarios including automotive systems, intelligent reflecting surfaces, and secure parameter estimation Perfect for researchers and professionals in the fields of radar, signal processing, communications, information theory, networking, and electronic warfare, Signal Processing for Joint Radar Communications will also earn a place in the libraries of engineers working in the defense, aerospace, wireless communications, and automotive industries.

Place, publisher, year, edition, pages
wiley, 2024
Series
Signal Processing for Joint Radar Communications
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-347139 (URN)10.1002/9781119795568 (DOI)2-s2.0-85193879790 (Scopus ID)9781119795568 (ISBN)9781119795537 (ISBN)
Note

QC 20240611

Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-11Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-2298-6774

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