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Publications (10 of 11) Show all publications
Ghauch, H., Kim, T., Fischione, C. & Skoglund, M. (2019). Compressive Sensing with Applications to Millimeter-wave Architectures. In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND (pp. 7834-7838). IEEE
Open this publication in new window or tab >>Compressive Sensing with Applications to Millimeter-wave Architectures
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 7834-7838Conference paper, Published paper (Refereed)
Abstract [en]

To make the system available at low-cost, millimeter-ave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be " turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-261067 (URN)10.1109/ICASSP.2019.8683604 (DOI)000482554008014 ()2-s2.0-85069003459 (Scopus ID)978-1-4799-8131-1 (ISBN)
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND
Note

QC 20191001

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved
Tolli, A., Ghauch, H., Kaleva, J., Komulainen, P., Bengtsson, M., Skoglund, M., . . . Pajukoski, K. (2019). Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access. IEEE Communications Magazine, 57(1), 58-64
Open this publication in new window or tab >>Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access
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2019 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 57, no 1, p. 58-64Article in journal (Refereed) Published
Abstract [en]

CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-244555 (URN)10.1109/MCOM.2018.1700199 (DOI)000457640200011 ()2-s2.0-85060522227 (Scopus ID)
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved
Shokri-Ghadikolaei, H., Ghauch, H., Fischione, C. & Skoglund, M. (2019). Learning and Data Selection in Big Datasets. In: Proceedings of the 36th International Conference on MachineLearning, Long Beach, California, PMLR 97, 2019.: . Paper presented at 36th International Conference on MachineLearning, Long Beach, California, PMLR 97, 2019..
Open this publication in new window or tab >>Learning and Data Selection in Big Datasets
2019 (English)In: Proceedings of the 36th International Conference on MachineLearning, Long Beach, California, PMLR 97, 2019., 2019Conference paper, Published paper (Refereed)
Abstract [en]

Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.

Keywords
machine learning, optimization, non-convex, data compression
National Category
Computer Sciences
Research subject
Applied and Computational Mathematics, Optimization and Systems Theory; Information and Communication Technology; Computer Science
Identifiers
urn:nbn:se:kth:diva-260389 (URN)
Conference
36th International Conference on MachineLearning, Long Beach, California, PMLR 97, 2019.
Funder
Swedish Research Council
Note

QC 20191008

Available from: 2019-09-29 Created: 2019-09-29 Last updated: 2019-10-08Bibliographically approved
Barros da Silva Jr., J. M., Ghauch, H., Fodor, G. & Fischione, C. (2018). How to Split UL/DL Antennas in Full-Duplex Cellular Networks. In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS): . Paper presented at IEEE International Conference on Communications (ICC), MAY 20-24, 2018, Kansas City, MO. IEEE
Open this publication in new window or tab >>How to Split UL/DL Antennas in Full-Duplex Cellular Networks
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

To further improve the potential of full-duplex communications, networks may employ multiple antennas at the base station or user equipment. To this end, networks that employ current radios usually deal with self-interference and multi-user interference by beamforming techniques. Although previous works investigated beamforming design to improve spectral efficiency, the fundamental question of how to split the antennas at a base station between uplink and downlink in full-duplex networks has not been investigated rigorously. This paper addresses this question by posing antenna splitting as a binary nonlinear optimization problem to minimize the sum mean squared error of the received data symbols. It is shown that this is an NP-hard problem. This combinatorial problem is dealt with by equivalent formulations, iterative convex approximations, and a binary relaxation. The proposed algorithm is guaranteed to converge to a stationary solution of the relaxed problem with much smaller complexity than exhaustive search. Numerical results indicate that the proposed solution is close to the optimal in both high and low self-interference capable scenarios, while the usually assumed antenna splitting is far from optimal. For large number of antennas, a simple antenna splitting is close to the proposed solution. This reveals that the importance of antenna splitting diminishes with the number of antennas.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Communications Workshops, ISSN 2164-7038
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-237174 (URN)10.1109/ICCW.2018.8403645 (DOI)000445022200159 ()2-s2.0-85050259231 (Scopus ID)978-1-5386-4328-0 (ISBN)
Conference
IEEE International Conference on Communications (ICC), MAY 20-24, 2018, Kansas City, MO
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-30Bibliographically approved
Shokri-Ghadikolaei, H., Ghauch, H. & Fischione, C. (2018). Learning-based tracking of AoAs and AoDs in mmWave networks. In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM: . Paper presented at 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems, mmNets 2018, , Co-located with MobiCom 2018, 29 October 2018 (pp. 45-50). Association for Computing Machinery
Open this publication in new window or tab >>Learning-based tracking of AoAs and AoDs in mmWave networks
2018 (English)In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Association for Computing Machinery , 2018, p. 45-50Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers a millimeter-wave communication system and proposes an efficient channel estimation scheme with a minimum number of pilots. We model the dynamics of the channel’s second-order statistics by a Markov process and develop a learning framework to obtain these dynamics from an unlabeled set of measured angles of arrival and departure. We then find the optimal precoding and combining vectors for pilot signals. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the channel gains.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2018
Keywords
Channel estimation, Machine learning, Markov decision process, Millimeter-wave, Tracking, Learning algorithms, Learning systems, Markov processes, Surface discharges, Angles of arrival, Channel gains, Learning frameworks, Markov Decision Processes, Millimeter-wave communication, Pilot signals, Second order statistics, Transmitter and receiver, Millimeter waves
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247151 (URN)10.1145/3264492.3264500 (DOI)000480467200009 ()2-s2.0-85061507133 (Scopus ID)9781450359283 (ISBN)
Conference
2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems, mmNets 2018, , Co-located with MobiCom 2018, 29 October 2018
Note

QC 20190507

Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-09-03Bibliographically approved
Ghauch, H., Kim, T., Skoglund, M. & Fischione, C. (2018). Low-Overhead Coordination in Sub-28 Millimeter-Wave Networks. In: 2018 IEEE International Conference on Communications (ICC): . Paper presented at 2018 IEEE International Conference on Communications, ICC 2018, 20 May 2018 through 24 May 2018.
Open this publication in new window or tab >>Low-Overhead Coordination in Sub-28 Millimeter-Wave Networks
2018 (English)In: 2018 IEEE International Conference on Communications (ICC), 2018Conference paper, Published paper (Refereed)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:kth:diva-223703 (URN)10.1109/ICC.2018.8422456 (DOI)2-s2.0-85051444915 (Scopus ID)9781538631805 (ISBN)
Conference
2018 IEEE International Conference on Communications, ICC 2018, 20 May 2018 through 24 May 2018
Note

QC 20180327

Available from: 2018-02-28 Created: 2018-02-28 Last updated: 2019-10-09Bibliographically approved
Imtiaz, S., Koudouridis, G. P., Ghauch, H. & Gross, J. (2018). Random forests for resource allocation in 5G cloud radio access networks based on position information. EURASIP Journal on Wireless Communications and Networking, 2018(1), Article ID 142.
Open this publication in new window or tab >>Random forests for resource allocation in 5G cloud radio access networks based on position information
2018 (English)In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499, Vol. 2018, no 1, article id 142Article in journal (Refereed) Published
Abstract [en]

Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal.

Place, publisher, year, edition, pages
Springer, 2018
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-238855 (URN)10.1186/s13638-018-1149-7 (DOI)000447851900004 ()2-s2.0-85048290841 (Scopus ID)
Note

QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2019-09-19Bibliographically approved
Ghauch, H., Rahman, M. M., Imtiaz, S., Qvarfordt, C., Skoglund, M. & Gross, J. (2018). User Assignment in C-RAN Systems: Algorithms and Bounds. IEEE Transactions on Wireless Communications, 17(6), 3889-3902
Open this publication in new window or tab >>User Assignment in C-RAN Systems: Algorithms and Bounds
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2018 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 6, p. 3889-3902Article in journal (Refereed) Published
Abstract [en]

In this paper, we investigate the problem of mitigating interference between so-called antenna domains of a cloud radio access network (C-RAN). In contrast to previous work, we turn to an approach utilizing primarily the optimal assignment of users to central processors in a C-RAN deployment. We formulate this user assignment problem as an integer optimization problem and propose an iterative algorithm for obtaining a solution. Motivated by the lack of optimality guarantees on such solutions, we opt to find lower bounds on the problem and the resulting interference leakage in the network. We thus derive the corresponding Dantzig-Wolfe decomposition, formulate the dual problem, and show that the former offers a tighter bound than the latter. We highlight the fact that the bounds in question consist of linear problems with an exponential number of variables and adapt the column generation method for solving them. In addition to shedding light on the tightness of the bounds in question, our numerical results show significant sum-rate gains over several comparison schemes. Moreover, the proposed scheme delivers similar performance as weighted minimum mean squared-error (MMSE) with a significantly lower complexity (around 10 times less).

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
Cloud radio access networks, user assignment, interference coupling coefficients, block-coordinate descent, Dantzig-Wolfe decomposition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-231722 (URN)10.1109/TWC.2018.2817223 (DOI)000435196200028 ()2-s2.0-85044848161 (Scopus ID)
Note

QC 20180814

Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2019-08-20Bibliographically approved
Ghauch, H., Kim, T., Bengtsson, M. & Skoglund, M. (2017). Sum-Rate Maximization in Sub-28-GHz Millimeter-Wave MIMO Interfering Networks. IEEE Journal on Selected Areas in Communications, 35(7), 1649-1662
Open this publication in new window or tab >>Sum-Rate Maximization in Sub-28-GHz Millimeter-Wave MIMO Interfering Networks
2017 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 35, no 7, p. 1649-1662Article in journal (Refereed) Published
Abstract [en]

MIMO systems in the lower part of the millimetre-wave (mmWave) spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as compared to their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this paper, we propose algorithms for tackling the sum-rate maximization problem that are designed to address the above-mentioned limitations. We derive a lower bound on the sum rate, a so-called difference of log and trace (DLT) bound, shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-OFF streams exhibiting low SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the mmWave spectrum. Moreover, our results suggest that interference management still brings about significant performance gains, especially in dense deployments.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Sub-28 GHz millimeter-wave, interference management, fast-converging algorithms, distributed optimization, difference of log and trace (DLT), non-homogeneous waterfilling, max-DLT, alternating iterative maximal separation (AIMS)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-210997 (URN)10.1109/JSAC.2017.2698779 (DOI)000404242600018 ()2-s2.0-85021337121 (Scopus ID)
Note

QC 20170807

Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-08-07Bibliographically approved
Ghauch, H., Kim, T., Bengtsson, M. & Skoglund, M. (2013). Interference Alignment via Controlled Perturbations. In: 2013 IEEE Global Communications Conference (GLOBECOM): . Paper presented at 2013 IEEE Global Communications Conference, GLOBECOM 2013; Atlanta, GA; United States; 9 December 2013 through 13 December 2013 (pp. 3996-4001). IEEE
Open this publication in new window or tab >>Interference Alignment via Controlled Perturbations
2013 (English)In: 2013 IEEE Global Communications Conference (GLOBECOM), IEEE , 2013, p. 3996-4001Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value.

Place, publisher, year, edition, pages
IEEE, 2013
Keywords
Controlled perturbation, Convergence speed, Interference alignment, Leakage minimization, Show through, Stochastic optimizations, Sum-rate capacity, Worst-case performance
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-140212 (URN)10.1109/GLOCOM.2013.6831698 (DOI)2-s2.0-84904098551 (Scopus ID)978-147991353-4 (ISBN)
Conference
2013 IEEE Global Communications Conference, GLOBECOM 2013; Atlanta, GA; United States; 9 December 2013 through 13 December 2013
Projects
METIS
Note

QC 20140602

Available from: 2014-01-17 Created: 2014-01-17 Last updated: 2014-09-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9442-671X

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