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Sadeghi, M. & Larsson, E. G. (2019). Adversarial Attacks on Deep-Learning Based Radio Signal Classification. IEEE Wireless Communications Letters, 8(1), 213-216
Open this publication in new window or tab >>Adversarial Attacks on Deep-Learning Based Radio Signal Classification
2019 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 8, no 1, p. 213-216Article in journal (Refereed) Published
Abstract [en]

Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Adversarial attacks, Deep learning, Wireless security, Modulation classification, Neural networks.
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-150945 (URN)10.1109/LWC.2018.2867459 (DOI)000459510200053 ()2-s2.0-85052663750 (Scopus ID)
Note

Funding agencies: ELLIIT, Security-Link; SURPRISE project - Swedish Foundation for Strategic Research (SSF)

Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-03-08Bibliographically approved
Interdonato, G., Ngo, H. Q., Frenger, P. & Larsson, E. G. (2019). Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise. IEEE Transactions on Wireless Communications, 18(11), 5153-5169
Open this publication in new window or tab >>Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
2019 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 18, no 11, p. 5153-5169Article in journal (Refereed) Published
Abstract [en]

Cell-free Massive MIMO (multiple-input multipleoutput) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works 1, 2 demonstrated that, unlike co-located Massive MIMO, the channel hardening is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the analysis in 1 to non-orthogonal uplink and downlink pilots. Assuming singleantenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the peruser achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacitybounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Cell-Free Massive MIMO;downlink training;conjugate beamforming;max-min fairness power control;capacity lower bound;achievable downlink rate;channel hardening.
National Category
Telecommunications Signal Processing Communication Systems Computer Engineering
Identifiers
urn:nbn:se:liu:diva-161332 (URN)10.1109/TWC.2019.2933831 (DOI)000496947800010 ()
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; Swedish Research Council (VR)Swedish Research Council; U.K. Research and Innovation Future Leaders Fellowships [MR/S017666/1]

Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-12-18Bibliographically approved
Özdogan, Ö., Björnson, E. & Larsson, E. G. (2019). Massive MIMO With Spatially Correlated Rician Fading Channels. Paper presented at 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE Transactions on Communications, 67(5), 3234-3250
Open this publication in new window or tab >>Massive MIMO With Spatially Correlated Rician Fading Channels
2019 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 5, p. 3234-3250Article in journal (Refereed) Published
Abstract [en]

This paper considers multi-cell massive multiple-input multiple-output systems, where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight path and a stochastic non-line-of-sight component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model. Using these estimates for maximum ratio combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) achievable spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior, when using the different channel estimators, are also analyzed. The numerical results show that the SE is higher when using the MMSE estimator than that of the other estimators, and the performance gap increases with the number of antennas.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Massive MIMO; spatially correlated Rician fading; channel estimation; spectral efficiency
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-158364 (URN)10.1109/TCOMM.2019.2893221 (DOI)000468228900011 ()2-s2.0-85059952523 (Scopus ID)
Conference
19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Note

Funding Agencies|ELLIIT; Swedish Research Council

Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2020-02-18Bibliographically approved
Interdonato, G., Frenger, P. & Larsson, E. G. (2019). Scalability Aspects of Cell-Free Massive MIMO. In: 2019 IEEE International Conference on Communications (ICC), Proceedings Shanghai, China 20–24 May 2019: . Paper presented at 2019 IEEE International Conference on Communications (ICC), Shanghai, China 20–24 May 2019 (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Scalability Aspects of Cell-Free Massive MIMO
2019 (English)In: 2019 IEEE International Conference on Communications (ICC), Proceedings Shanghai, China 20–24 May 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the “whole world” could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE International Conference on Communications (ICC), ISSN 1550-3607, E-ISSN 1938-1883 ; 2019
Keywords
MIMO communication; power control; telecommunication network topology; ubiquitous cell-free massive MIMO; data processing; network topology; power control;time-frequency resource; system scalability aspects; user-centric transmission; massive MIMO technology; Mathematical model; Electrodes; Analytical models; Photovoltaic systems; Voltage measurement; Current measurement
National Category
Telecommunications Communication Systems Signal Processing Computer Engineering
Identifiers
urn:nbn:se:liu:diva-161334 (URN)10.1109/ICC.2019.8761828 (DOI)000492038804138 ()9781538680889 (ISBN)978-1-5386-8089-6 (ISBN)
Conference
2019 IEEE International Conference on Communications (ICC), Shanghai, China 20–24 May 2019
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; ELLIIT

Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-11-11Bibliographically approved
Van Chien, T., Björnson, E. & Larsson, E. G. (2019). Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning. In: ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC): . Paper presented at IEEE International Conference on Communications (ICC). IEEE Communications Society
Open this publication in new window or tab >>Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
2019 (English)In: ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE Communications Society, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the joint data and pilot power optimization for maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which is a non-convex problem. We first propose a new optimization algorithm, inspired by the weighted minimum mean square error (MMSE) approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime, making it suitable for online optimization in real multi-cell Massive MIMO systems. The numerical result demonstrates that the solution obtained by the neural network is 1% less than the stationary point for four-cell systems, while the sum SE loss is 2% in a nine-cell system.

Place, publisher, year, edition, pages
IEEE Communications Society, 2019
Series
IEEE International Conference on Communications (ICC), ISSN 1550-3607, E-ISSN 1938-1883
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-156972 (URN)10.1109/ICC.2019.8761234 (DOI)000492038801037 ()978-1-5386-8088-9 (ISBN)978-1-5386-8089-6 (ISBN)
Conference
IEEE International Conference on Communications (ICC)
Projects
5GWirelessCENIIT
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; ELLIIT; CENIIT

Available from: 2019-05-18 Created: 2019-05-18 Last updated: 2019-11-11
Van Chien, T., Björnson, E., Larsson, E. G. & Le, T. A. (2018). Distributed Power Control in Downlink Cellular Massive MIMO Systems. In: WSA 2018: 22nd International ITG Workshop on Smart Antennas. Paper presented at IEEE 22nd International ITG Workshop on Smart Antennas (WSA2018) (pp. 1-7). VDE Verlag GmbH
Open this publication in new window or tab >>Distributed Power Control in Downlink Cellular Massive MIMO Systems
2018 (English)In: WSA 2018: 22nd International ITG Workshop on Smart Antennas, VDE Verlag GmbH, 2018, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares centralized and distributed methods to solve the power minimization problem with quality-of-service (QoS) constraints in the downlink (DL) of multi-cell Massive multiple-input multiple-output (MIMO) systems. In particular, we study the computational complexity, number of parameters that need to be exchanged between base stations (BSs), and the convergence of iterative implementations. Although a distributed implementation based on dual decomposition (which only requires statistical channel knowledge at each BS) typically converges to the global optimum after a few iterations, many parameters need to be exchanged to reach convergence.

Place, publisher, year, edition, pages
VDE Verlag GmbH, 2018
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-148936 (URN)978-3-8007-4541-8 (ISBN)
Conference
IEEE 22nd International ITG Workshop on Smart Antennas (WSA2018)
Projects
5GwirelessELLIITCENIIT
Available from: 2018-06-22 Created: 2018-06-22 Last updated: 2019-06-28
Interdonato, G., Karlsson, M., Björnson, E. & Larsson, E. G. (2018). Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP): . Paper presented at 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA (pp. 1003-1007).
Open this publication in new window or tab >>Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing
2018 (English)In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018, p. 1003-1007Conference paper, Published paper (Refereed)
Abstract [en]

Cell-free Massive multiple-input multiple-output (MIMO) ensures ubiquitous communication at high spectral efficiency (SE) thanks to increased macro-diversity as compared cellular communications. However, system scalability and performance are limited by fronthauling traffic and interference. Unlike conventional precoding schemes that only suppress intra-cell interference, full-pilot zero-forcing (fpZF), introduced in [1], actively suppresses also inter-cell interference, without sharing channel state information (CSI) among the access points (APs). In this study, we derive a new closed-form expression for the downlink (DL) SE of a cell-free Massive MIMO system with multi-antenna APs and fpZF precoding, under imperfect CSI and pilot contamination. The analysis also includes max-min fairness DL power optimization. Numerical results show that fpZF significantly outperforms maximum ratio transmission scheme, without increasing the fronthauling overhead, as long as the system is sufficiently distributed.

Keywords
MIMO communication, Interference, Precoding, Channel estimation, Fading channels, Downlink, Signal to noise ratio, Cell-free Massive MIMO, full-pilot zero-forcing, downlink spectral efficiency, max-min fairness power control
National Category
Telecommunications
Identifiers
urn:nbn:se:liu:diva-154885 (URN)10.1109/GlobalSIP.2018.8646666 (DOI)000462968100205 ()978-1-7281-1295-4 (ISBN)978-1-7281-1294-7 (ISBN)978-1-7281-1296-1 (ISBN)
Conference
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA
Note

Funding agencies: European Union [641985]

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-06-28
Do, T. T., Björnson, E., Larsson, E. G. & Mohammad Razavizadeh, S. (2018). Jamming-Resistant Receivers for the Massive MIMO Uplink. Paper presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE Transactions on Information Forensics and Security, 13(1), 210-223
Open this publication in new window or tab >>Jamming-Resistant Receivers for the Massive MIMO Uplink
2018 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 13, no 1, p. 210-223Article in journal (Refereed) Published
Abstract [en]

We design a jamming-resistant receiver scheme to enhance the robustness of a massive MIMO uplink system against jamming. We assume that a jammer attacks the system both in the pilot and data transmission phases. The key feature of the proposed scheme is that, in the pilot phase, the base station estimates not only the legitimate channel, but also the jamming channel by exploiting a purposely unused pilot sequence. The jamming channel estimate is used to construct linear receiver filters that reject the impact of the jamming signal. The performance of the proposed scheme is analytically evaluated using the asymptotic properties of massive MIMO. The best regularized zero-forcing receiver and the optimal power allocations for the legitimate system and the jammer are also studied. Numerical results are provided to verify our analysis and show that the proposed scheme greatly improves the achievable rates, as compared with conventional receivers. Interestingly, the proposed scheme works particularly well under strong jamming attacks, since the improved estimate of the jamming channel outweighs the extra jamming power.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Massive MIMO; jamming attack; receiver filter; optimal power allocation
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-143986 (URN)10.1109/TIFS.2017.2746007 (DOI)000417725500016 ()2-s2.0-85028560448 (Scopus ID)
Conference
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Note

Funding Agencies|ELLIIT; CENIIT

Available from: 2018-01-02 Created: 2018-01-02 Last updated: 2019-06-28Bibliographically approved
Van Chien, T., Björnson, E. & Larsson, E. G. (2018). Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems. IEEE Transactions on Wireless Communications, 17(3), 2000-2015
Open this publication in new window or tab >>Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems
2018 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 3, p. 2000-2015Article in journal (Refereed) Published
Abstract [en]

This paper considers pilot design to mitigate pilot contamination and provide good service for everyone in multi-cell Massive multiple input multiple output (MIMO) systems. Instead of modeling the pilot design as a combinatorial assignment problem, as in prior works, we express the pilot signals using a pilot basis and treat the associated power coefficients as continuous optimization variables. We compute a lower bound on the uplink capacity for Rayleigh fading channels with maximum ratio detection that applies with arbitrary pilot signals. We further formulate the max-min fairness problem under power budget constraints, with the pilot signals and data powers as optimization variables. Because this optimization problem is non-deterministic polynomial-time hard due to signomial constraints, we then propose an algorithm to obtain a local optimum with polynomial complexity. Our framework serves as a benchmark for pilot design in scenarios with either ideal or non-ideal hardware. Numerical results manifest that the proposed optimization algorithms are close to the optimal solution obtained by exhaustive search for different pilot assignments and the new pilot structure and optimization bring large gains over the state-of-the-art suboptimal pilot design.

Place, publisher, year, edition, pages
IEEE Communications Society, 2018
Keywords
Massive MIMO, Pilot Design, Signomial Programming, Geometric Programming, Hardware Impairments.
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-145713 (URN)10.1109/TWC.2017.2787702 (DOI)000427226500042 ()2-s2.0-85040035548 (Scopus ID)
Projects
CENIIT
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, Horizon 2020, 641985
Note

Funding agencies:This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant 641985 (5Gwireless), in part by ELLIIT, and in part by CENIIT. This paper was presented at the IEEE ICC 2017. The associate editor coordinating the review of this paper and approving it for publication was T. Lok.

Available from: 2018-03-19 Created: 2018-03-19 Last updated: 2019-12-09Bibliographically approved
Sadeghi, M., Björnson, E., Larsson, E. G., Yuen, C. & Marzetta, T. L. (2018). Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems. IEEE Transactions on Wireless Communications, 17(10), 6375-6388
Open this publication in new window or tab >>Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems
Show others...
2018 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 10, p. 6375-6388Article in journal (Refereed) Published
Abstract [en]

We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-150940 (URN)10.1109/TWC.2018.2854554 (DOI)000447047200001 ()2-s2.0-85050020525 (Scopus ID)
Note

Funding agencies: Swedish Research Council (VR); Swedish Foundation for Strategic Research (SSF); ELLIIT; Singapore University of Technology and Design; A*Star SERC Project [142-02-00043]; National Science Foundation of China [61750110529]

Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-06-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7599-4367

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