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Johansson, Mikael
Publications (10 of 26) Show all publications
Della Penda, D., Wichman, R., Charalambous, T., Fodor, G. & Johansson, M. (2019). A Distributed Mode Selection Scheme for Full-Duplex Device-to-Device Communication. IEEE Transactions on Vehicular Technology, 68(10), 10267-10271
Open this publication in new window or tab >>A Distributed Mode Selection Scheme for Full-Duplex Device-to-Device Communication
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2019 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 10, p. 10267-10271Article in journal (Refereed) Published
Abstract [en]

Networks with device-to-device(D2D) technology allow for two possible communication modes: traditional communication via the base station, and direct communication between the users. Recent studies show that in-band full-duplex(IBFD) operations can be advantageously combined with D2D communication to improve the spectral efficiency. However, no algorithms for selecting the communication mode of mobile users in IBFD networks have yet appeared in the literature. In this paper, we design a distributed mode selection scheme for users in D2D-enabled IBFD networks. The proposed scheme maximizes the users prob-ability of successful communication by leveraging only existing signaling mechanisms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Device-to-device communication, Base stations, Interference, Geometry, Energy measurement, Throughput, Complexity theory, mobile communication, decision theory, full-duplex, sequential test
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-266169 (URN)10.1109/TVT.2019.2932046 (DOI)000501349900075 ()2-s2.0-85073880431 (Scopus ID)
Note

QC 20200113

Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-01-13Bibliographically approved
Khirirat, S., Magnusson, S. & Johansson, M. (2019). CONVERGENCE BOUNDS FOR COMPRESSED GRADIENT METHODS WITH MEMORY BASED ERROR COMPENSATION. In: RTSEKAS D. P., 2011, Optimization for Machine Learning, V2010, P1 ngni J., 2017, arXiv preprint arXiv: 1710. 09854, u Shengyu, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGSIEEE International Conference on Acoustics, Speech, and Signal Processing, MAR 20-25, 2016, Shanghai, PEOPLES R CHINA, P4134 (Ed.), 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. 2857-2861). IEEE
Open this publication in new window or tab >>CONVERGENCE BOUNDS FOR COMPRESSED GRADIENT METHODS WITH MEMORY BASED ERROR COMPENSATION
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) / [ed] RTSEKAS D. P., 2011, Optimization for Machine Learning, V2010, P1 ngni J., 2017, arXiv preprint arXiv: 1710. 09854, u Shengyu, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGSIEEE International Conference on Acoustics, Speech, and Signal Processing, MAR 20-25, 2016, Shanghai, PEOPLES R CHINA, P4134, IEEE , 2019, p. 2857-2861Conference paper, Published paper (Refereed)
Abstract [en]

The veritable scale of modern data necessitates information compression in parallel/distributed big-data optimization. Compression schemes using memory-based error compensation have displayed superior performance in practice, however, to date there are no theoretical explanations for these observed advantages. This paper provides the first theoretical support for why such compression schemes yields higher accuracy solutions in optimization. Our results cover both gradient and incremental gradient algorithms for quadratic optimization. Unlike previous works, our theoretical results explicitly quantify the accuracy gains from error compensation, especially for ill-conditioned problems. Finally, the numerical results on linear least-squares problems validate the benefit of error compensation and demonstrate tightness of our convergence guarantees.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Quadratic optimization, quantization, gradient descent, incremental gradient methods
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-261056 (URN)10.1109/ICASSP.2019.8682931 (DOI)000482554003018 ()2-s2.0-85068974918 (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
Della Penda, D., Abrardo, A., Moretti, M. & Johansson, M. (2019). Distributed Channel Allocation for D2D-Enabled 5G Networks Using Potential Games. IEEE Access, 7, 11195-11208
Open this publication in new window or tab >>Distributed Channel Allocation for D2D-Enabled 5G Networks Using Potential Games
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 11195-11208Article in journal (Refereed) Published
Abstract [en]

Frequency channel allocation is a key technique for improving the performance of cellular networks. In this paper, we address the channel allocation problem for a 5G multi-cell system. We consider a heterogeneous network in which cellular users, micro-cell users, and device-to-device (D2D) communications coexist within the radio footprint of the macro cell. We maximize the aggregate transmission rate, exploiting channel diversity and managing both the inter-cell interference, typical of cellular networks and the intra-cell interference generated by the nonorthogonal transmissions of the small-cell and D2D users. By modeling the allocation problem as a potential game, whose Nash equilibria correspond to the local optima of the objective function, we propose a new decentralized solution. The convergence of our scheme is enforced by using a better response dynamic based on a message passing approach. The simulation results assess the validity of the proposed scheme in terms of convergence time and achievable rate under different settings.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Distributed allocation, 5G system, optimization, OFDMA, device-to-device, potential games, message passing
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-244553 (URN)10.1109/ACCESS.2019.2891823 (DOI)000457752900001 ()2-s2.0-85061138135 (Scopus ID)
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved
Aytekin, A. & Johansson, M. (2019). Exploiting serverless runtimes for large-scale optimization. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD): . Paper presented at 12th IEEE International Conference on Cloud Computing, CLOUD 2019; Milan; Italy; 8 July 2019 through 13 July 2019 (pp. 499-501). IEEE Computer Society, Article ID 8814497.
Open this publication in new window or tab >>Exploiting serverless runtimes for large-scale optimization
2019 (English)In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2019, p. 499-501, article id 8814497Conference paper, Published paper (Refereed)
Abstract [en]

Serverless runtimes provide efficient and cost-effective environments for scalable computations, thanks to their event-driven and elastic nature. So far, they have mostly been used for stateless, data parallel and sporadic computations. In this work, we propose exploiting serverless runtimes to solve generic, large-scale optimization problems. To this end, we implement a parallel optimization algorithm for solving a regularized logistic regression problem, and use AWS Lambda for the compute-intensive work. We show that relative speedups up to 256 workers and efficiencies above 70% up to 64 workers can be expected.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Series
IEEE International Conference on Cloud Computing, CLOUD, ISSN 2159-6182
Keywords
Alternating direction method of multipliers, Distributed optimization, Optimization, Serverless
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-262586 (URN)10.1109/CLOUD.2019.00090 (DOI)2-s2.0-85072323908 (Scopus ID)9781728127057 (ISBN)
Conference
12th IEEE International Conference on Cloud Computing, CLOUD 2019; Milan; Italy; 8 July 2019 through 13 July 2019
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved
Mai, V. V. & Johansson, M. (2019). Noisy Accelerated Power Method for Eigenproblems With Applications. IEEE Transactions on Signal Processing, 67(12), 3287-3299
Open this publication in new window or tab >>Noisy Accelerated Power Method for Eigenproblems With Applications
2019 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 12, p. 3287-3299Article in journal (Refereed) Published
Abstract [en]

This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex optimization, we develop a simple scalable algorithm with strong theoretical performance guarantees. More precisely, the algorithm retains the simplicity of the well-knownpower method but enjoys the asymptotic iteration complexity of the powerful Lanczos method. Unlike these classic techniques, our algorithm is designed to decompose the overall problem into a series of subproblems that only need to be solved approximately. The combination of good initializations, fast iterative solvers, and appropriate error control in solving the subproblems lead to a linear running time in the input sizes compared to the superlinear time for the traditional methods. The improved running time immediately offers acceleration for several applications. As an example, we demonstrate how the proposed algorithm can be used to accelerate canonical correlation analysis, which is a fundamental statistical tool for learning of a low-dimensional representation of high-dimensional objects. Numerical experiments on real-world datasets confirm that our approach yields significant improvements over the current state of the art.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Approximation theory, canonical correlation analysis (CCA), Chebyshev polynomial, generalized eigenvalue (GEV) problems, power method, stochastic convex optimization
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-254003 (URN)10.1109/TSP.2019.2908126 (DOI)000469369900004 ()2-s2.0-85066733192 (Scopus ID)
Note

QC 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved
Mai, V. V. & Johansson, M. (2019). NONLINEAR ACCELERATION OF CONSTRAINED OPTIMIZATION ALGORITHMS. 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. 4903-4907). IEEE
Open this publication in new window or tab >>NONLINEAR ACCELERATION OF CONSTRAINED OPTIMIZATION ALGORITHMS
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 4903-4907Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a novel technique for nonlinear acceleration of first-order methods for constrained convex optimization. Previous studies of nonlinear acceleration have only been able to provide convergence guarantees for unconstrained convex optimization. In contrast, our method is able to avoid infeasibility of the accelerated iterates and retains the theoretical performance guarantees of the unconstrained case. We focus on Anderson acceleration of the classical projected gradient descent (PGD) method, but our techniques can easily be extended to more sophisticated algorithms, such as mirror descent. Due to the presence of a constraint set, the relevant fixed-point mapping for PGD is not differentiable. However, we show that the convergence results for Anderson acceleration of smooth fixed-point iterations can be extended to the non-smooth case under certain technical conditions.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Anderson acceleration, constrained optimization, projected gradient descent, semi-smoothness
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-261054 (URN)10.1109/ICASSP.2019.8682962 (DOI)000482554005028 ()2-s2.0-85068981869 (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 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved
Biel, M., Aytekin, A. & Johansson, M. (2019). POLO.Jl: Policy-based optimization algorithms in Julia. Advances in Engineering Software, 136, Article ID 102695.
Open this publication in new window or tab >>POLO.Jl: Policy-based optimization algorithms in Julia
2019 (English)In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 136, article id 102695Article in journal (Refereed) Published
Abstract [en]

We present POLO. j1- a Julia package that helps algorithm developers and machine-learning practitioners design and use state-of-the-art parallel optimization algorithms in a flexible and efficient way. POLO. j1 extends our C+ + library POLO, which has been designed and implemented with the same intentions. POLO. j1 not only wraps selected algorithms in POLO and provides an easy mechanism to use data manipulation facilities and loss function definitions in Julia together with the underlying compiled C+ + library, but it also uses the policy-based design technique in a Julian way to help users prototype optimization algorithms from their own building blocks. In our experiments, we observe that there is little overhead when using the compiled C+ + code directly within Julia. We also notice that the performance of algorithms implemented in pure Julia is comparable with that of their C+ + counterparts. Both libraries are hosted on GitHub(1)under the free MIT license, and can be used easily by pulling the pre-built 64-bit architecture Docker images.(2)

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-260996 (URN)10.1016/j.advengsoft.2019.102695 (DOI)000484437800004 ()2-s2.0-85073652658 (Scopus ID)
Note

QC 20191004

Available from: 2019-10-04 Created: 2019-10-04 Last updated: 2019-11-15Bibliographically approved
Charalambous, T., Kim, S. M., Nomikos, N., Bengtsson, M. & Johansson, M. (2019). Relay-pair selection in buffer-aided successive opportunistic relaying using a multi-antenna source. Ad hoc networks, 84, 29-41
Open this publication in new window or tab >>Relay-pair selection in buffer-aided successive opportunistic relaying using a multi-antenna source
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2019 (English)In: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 84, p. 29-41Article in journal (Refereed) Published
Abstract [en]

We study a cooperative network with a buffer-aided multi-antenna source, multiple half-duplex (HD) buffer-aided relays and a single destination. Such a setup could represent a cellular downlink scenario, in which the source can be a more powerful wireless device with a buffer and multiple antennas, while a set of intermediate less powerful devices are used as relays to reach the destination. The main target is to recover the multiplexing loss of the network by having the source and a relay to simultaneously transmit their information to another relay and the destination, respectively. Successive transmissions in such a cooperative network, however, cause inter-relay interference (IRI). First, by assuming global channel state information (CSI), we show that the detrimental effect of IRI can be alleviated by precoding at the source, mitigating or even fully cancelling the interference. A cooperative relaying policy is proposed that employs a joint precoding design and relay-pair selection. Note that both fixed rate and adaptive rate transmissions can be considered. For the case when channel state information is only available at the receiver side (CSIR), we propose a relay selection policy that employs a phase alignment technique to reduce the IRI. The performance of the two proposed relay pair selection policies are evaluated and compared with other state-of-the-art relaying schemes in terms of outage and throughput. The results show that the use of a powerful source can provide considerable performance improvements.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Keywords
Opportunistic relaying, Buffer-aided relays, Precoding, Phase-alignment, Interference cancellation, Interference mitigation
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-243930 (URN)10.1016/j.adhoc.2018.09.002 (DOI)000456751400005 ()2-s2.0-85054687404 (Scopus ID)
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved
Ohnishi, M., Yukawa, M., Johansson, M. & Sugiyama, M. (2018). Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. In: Bengio, S Wallach, H Larochelle, H Grauman, K CesaBianchi, N Garnett, R (Ed.), Advances in Neural Information Processing Systems 31 (NIPS 2018): . Paper presented at 32nd Conference on Neural Information Processing Systems (NIPS), DEC 02-08, 2018, Montreal, CANADA. Neural Information Processing Systems (NIPS), 31
Open this publication in new window or tab >>Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
2018 (English)In: Advances in Neural Information Processing Systems 31 (NIPS 2018) / [ed] Bengio, S Wallach, H Larochelle, H Grauman, K CesaBianchi, N Garnett, R, Neural Information Processing Systems (NIPS) , 2018, Vol. 31Conference paper, Published paper (Refereed)
Abstract [en]

Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf. many challenging tasks in OpenAI Gym and DeepMind Control Suite). Since discretization of time is susceptible to error, it is methodologically more desirable to handle the system dynamics directly in continuous time. However, very few techniques exist for continuous-time RL and they lack flexibility in value function approximation. In this paper, we propose a novel framework for model-based continuous-time value function approximation in reproducing kernel Hilbert spaces. The resulting framework is so flexible that it can accommodate any kind of kernel-based approach, such as Gaussian processes and kernel adaptive filters, and it allows us to handle uncertainties and nonstationarity without prior knowledge about the environment or what basis functions to employ. We demonstrate the validity of the presented framework through experiments.

Place, publisher, year, edition, pages
Neural Information Processing Systems (NIPS), 2018
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258 ; 31
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-249920 (URN)000461823302080 ()
Conference
32nd Conference on Neural Information Processing Systems (NIPS), DEC 02-08, 2018, Montreal, CANADA
Note

QC 20190426

Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved
Åstrand, M., Johansson, M. & Zanarini, A. (2018). Fleet scheduling in underground mines using constraint programming. In: 15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018: . Paper presented at 15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018, Delft, Netherlands, 26 June 2018 through 29 June 2018 (pp. 605-613). Springer, 10848
Open this publication in new window or tab >>Fleet scheduling in underground mines using constraint programming
2018 (English)In: 15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018, Springer, 2018, Vol. 10848, p. 605-613Conference paper, Published paper (Refereed)
Abstract [en]

The profitability of an underground mine is greatly affected by the scheduling of the mobile production fleet. Today, most mine operations are scheduled manually, which is a tedious and error-prone activity. In this contribution, we present and formalize the underground mine scheduling problem, and propose a CP-based model for solving it. The model is evaluated on instances generated from real data. The results are promising and show a potential for further extensions.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10848
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-238429 (URN)10.1007/978-3-319-93031-2_44 (DOI)2-s2.0-85048601171 (Scopus ID)9783319930305 (ISBN)
Conference
15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018, Delft, Netherlands, 26 June 2018 through 29 June 2018
Note

QC 20181105

Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2018-11-05Bibliographically approved
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