29/04/2019

PHD proposal Telecom SudParis/Centrale Supelec: Bayesian statistical methods for joint user activity detection, channel estimation, and data decoding in dynamic wireless networks

This job offer has expired


  • ORGANISATION/COMPANY
    Telecom Sudparis
  • RESEARCH FIELD
    EngineeringCommunication engineering
    EngineeringElectronic engineering
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    20/05/2019 00:00 - Europe/Athens
  • LOCATION
    France › EVRY
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/10/2019

Abstract:

This doctoral project explores the benefits of dispensing with protocol-based user identification for multi-access communications for the sake of increasing the spectral efficiency and managing interference. We propose to explore the potential of advanced tools from information theory, coding theory and signal processing, in order to perform joint user activity detection, channel estimation and data decoding in an integrated way with affordable complexity. We plan to develop solutions for challenging use cases, that is under severe channel uncertainty and highly dynamic random-access channels.

Context:

In today’s wireless communications networks, increasing the spectral efficiency and managing interference are two of the fundamental design issues. Relaxing the orthogonality constraint at the transmitter side as advised by network information theory, i.e., considering non-orthogonal multiple access (NOMA) [1], is one of the diverse solutions proposed in 5G (and beyond) to solve the first issue. This goes along with advanced signal processing techniques at the receiver side to cancel interference and hence cope with the second issue. The problem becomes even more complicated in random-access based wireless systems characterized by no or very limited coordination compared to cellular systems, and often imperfect distributed mechanisms to establish communication. In such dynamic environments, the number of active users, their location, as well as the identities and parameters that specify their modulation coding schemes and channel state, vary with time and has considerable impact on receiver’s performance.

Examples of applications in communications theory have to be found in multiuser detection (MUD), spatial multiplexing schemes, or ad hoc networks. The problem of detecting the number of users in a multiuser system was addressed in the context of code-division multiple-access (CDMA) in the 2000’s [22, 23] where it was recognized that the simplifying assumption that all users were active at all time was, in general, a cause of performance degradation and sub-optimality. Some nonlinear receivers based on successive interference cancellation need to know the strongest user to better fight back the so-called near-far effect. Moreover, identifying the active users helps the system to promptly process requests and efficiently allocate resources leading to system capacity improvements. In spatial multiplexing schemes, the total system capacity can be increased by properly selecting a subset of active users to which the power is allocated. In ad hoc networks, optimum transmission strategies require the identification and localization of actives nodes in the neighborhood of the transmitter [2]. Timely applications related to 5G and beyond include grant free access in M2M/IoT [3], cognitive radio [4], cooperative half and/or full-duplex relaying with advanced decode-and-forward protocols (e.g., dynamic and/or selective decode-and-forward without dedicated feedforward control signals and network coding in case of multisource multirelay cooperating clusters), and hybrid half-duplex/full-duplex transmissions [5].

To conclude, it is worth pointing out that the problem of active user identification and data detection has deep connections with multisource-multitarget estimation in radar theory [16].

Objectives:

The scientific objectives of the PHD thesis are twofold: (i) develop new Bayesian inference methods and design new algorithms with manageable complexity to solve these four problems, (ii) advance the state-of-the-art in deriving information-theoretic reference bounds to evaluate the performance of the proposed solutions.

Expected results:

Solve the following problems:

Problem 1: Reliable communications over static synchronous multiaccess channel with an unknown number of active users and perfect channel state information at the receiver (CSIR). The number of active users and their identities do not change during the reference transmission duration. The channel parameters and responses of the active users are known. The number of active users, their identity, and their data must be jointly estimated.

Problem 2: Reliable communications over static synchronous multiaccess channel with an unknown number of active users and no or imperfect CSIR. The number of active users and their identities do not change during the reference transmission duration. The channel parameters and responses of the active users are unknown or partially known, and may vary according to a dynamical model. The number of active users, their identity, their channel, and their data must be jointly estimated.

Problem 3: Reliable communications over dynamic asynchronous multiaccess channel with an unknown number of active users and perfect CSIR. The number of active users and their identities change during the reference transmission duration according to a dynamic model characterizing the users’s activity. The channel parameters and responses of the active users are known. The number of active users, their identity, and their data must be jointly estimated.

Problem 4: Reliable communications over dynamic asynchronous multiaccess channel with an unknown number of active users and no or imperfect CSIR. The number of active users and their identities change during the reference transmission duration according to a dynamic model characterizing the users’s activity. The channel parameters and responses of the active users are unknown or partially known, and may vary according to a dynamical model. The number of active users, their identity, their channel, and their data must be jointly estimated.

Bibliography:

 

[1] L. Dai, B. Wang, Y. Yuan, S. Han, C.-L. I, and Z. Wang, Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future Research trends, IEEE Comm. Mag., vol. 53, no. 9, pp. 74-81, Sept. 2015.

[2] R. Prasad and L. Deneire. From WPANs to Personal Networks: Technologies and Applications (ch. 7). Artech House, Boston, 2006.

[3] F. Wei, W. Chen, Y. Wu, J. Ma and T. A. Tsiftsis, Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems, IEEE Transactions on Wireless Communications, vol. 18, no. 1, pp. 167-181, Jan. 2019.

[4] M. Liu, T. Song and G. Gui, Deep Cognitive Perspective: Resource Allocation for NOMA based Heterogeneous IoT with Imperfect SIC, IEEE Internet of Things Journal, in Press.

[5] G. Liu, X. Chen, Z. Ding, Z. Ma and F. R. Yu, Hybrid Half-Duplex/Full-Duplex Cooperative Non-Orthogonal Multiple Access With Transmit Power Adaptation, IEEE Transactions on Wireless Communications, vol. 17, no. 1, pp. 506-519, Jan. 2018.

[6] D. Alspach and H. Sorenson, Nonlinear Bayesian estimation using Gaussian sum appproximation, IEEE Transactions on Automatic Control, vol. 17, no. 4, pp. 439–448, Aug. 1972.

[7] D. Angelosante, E. Biglieri, and M. Lops, Multiuser detection in a dynamic environment– Part II: Joint user identification and parameter estimation, IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2365–2374, May 2009.

[8] E. Biglieri and M. Lops, Multiuser detection in a dynamic environment– Part I: User identification and data detection, IEEE Transactions on Information Theory, vol. 53, no. 9, pp. 3158–3170, Sep. 2007.

[9] I.R. Goodman, R.P.S. Mahler, and H.T. Nguyen, Mathematics of Data Fusion, Kluwer, 1997.

[10] F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory, vol. 47, no. 2, pp. 498–519, Feb. 2001.

[11] G. Kitagawa, The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother, Ann. Inst. Statist. Math. vol. 46, no. 4, pp. 605–623, 1994.

[12] E.G. Larsson, Multiuser detection with an unknown number of users, IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 724–728, Feb. 2005.

[13] F. Lehmann, A.O. Berthet, A factor graph approach to digital self-interference mitigation in OFDM full-duplex systems, IEEE Signal Processing Letters, vol. 24, no. 3, pp. 344–348, Mar. 2017.

[14] F. Lehmann, Joint user activity detection, channel estimation, and decoding for multiuser/multiantenna OFDM systems, IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8263–8275, Sep. 2018.

[15] H.-A. Loeliger et al., The factor graph approach to model-based signal processing, Proceedings of the IEEE, vol. 95, no. 6, pp. 1295–1322, Jun. 2007.

[16] R.P.S. Mahler, Statistical Multisource-Multitarget Information Fusion, Artech House, 2007.

[17] T.P. Minka, A Family of Algorithms for Approximate Bayesian Inference, Doctoral dissertation, MIT, 2001.

[18] E. Plotnik, On the capacity region of the random-multiple access channel, Proc. 16th Conf. Electrical and Electronics Engineers Israel, Tel Aviv, Israel, Mar. 1989.

[19] P. Stoica and Y. Selén, Model order selection– A review of information criterion rules, IEEE Signal Processing Magazine, vol. 21, no. 4, pp. 36–47, Jul. 2004.

[20] A. Tauste Campo and E. Biglieri, Asymptotic capacity of static multiuser channels with an unknown number of users, Proc. 11th International Symposium on Wireless Personal Multimedia Communications (WPMC’08), Lapland, Finland, Sep. 2008.

[21] B.-N. Vo, S. Singh, and A. Doucet, Sequential Monte Carlo methods for multitarget filtering with random finite sets, IEEE Transactions on Aerospace Electronic Systems, vol. 41, no. 4, pp. 1224–1245, Oct. 2005.

[22] W.-C. Wu and K.C. Chen, Identification of active users in synchronous CDMA multiuser detection, IEEE Journal of Selected Areas in Communications, vol. 16, no. 9, pp. 1723–1735, Dec. 1998.

[23] Z. Xu, Blind identification of co-existing synchronous and asynchronous users for CDMA systems, IEEE Signal Processing Letters, vol. 8, no. 7, pp. 212–214, Jul. 2001.

Selection process

Your application should be sent to the contact below before May 20, 2019, including

• a CV

• a letter of intent

• grades and ranking of your master's thesis (M1/M2) or engineering school

• 2 recommandation letters

• a list of courses related to the research subject.

 

Contact:

Frédéric Lehmann,

Professor Télécom SudParis, Laboratoire SAMOVAR (UMR 5157) -

email : frederic.lehmann@telecom-sudparis.eu

web : http://www-public.imtbs-tsp.eu/~lehmann/

 

Additional comments

 

 

Required Research Experiences

  • RESEARCH FIELD
    Engineering
  • YEARS OF RESEARCH EXPERIENCE
    4 - 10

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Engineering: Master Degree or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Good
    FRENCH: Basic

Skills/Qualifications

The PhD candidate should have:

• a research-oriented MSc. degree in applied mathematics/statistics, electrical engineeering, signal pro-

cessing, or equivalent, with excellent study records

• a strong background in mathematics and statistics, and a deep knowledge in at least one of the following

fields: information theory, communication theory, coding theory, signal processing for communications,

Bayesian inference and learning;

• a demonstrated ability to work autonomously;

• strong programming skills in one of the following languages: MATLAB, C/C++, or python;

• very good communication skills (French or English), both oral and written;

• a strong motivation for research and a taste for analytical and theoretical subjects.

Work location(s)
1 position(s) available at
Telecom Sudparis
France
EVRY
91011
9, rue Charles Fourier

EURAXESS offer ID: 403261

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