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Proposition de thèse au CNAM Paris : precoding for cell-free massive MIMO systems

18 Juillet 2022


Catégorie : Doctorant


Une proposition de thèse est ouverte au CNAM Paris. Le sujet de la thèse est le suivant : precoding for cell-free massive MIMO systems.

 

Context :

Cell-free massive MIMO (CF-mMIMO) is a key candidate technology expected to address numerous challenges of 6G wireless communications [1]. In CF-mMIMO, the cell boundaries disappear and a virtual antenna array is built via many access points (AP). This results in a network architecture which uses smaller radio modules with few antennas per AP. CF-mMIMO offers many advantages compared to traditional massive MIMO by enabling a good coverage probability, rapid deployment and reduced transmit power levels. A scalable version using a user-centric approach instead of the classical network centric approach has been proposed in [2]. This concept has its roots in the intersection between Massive MIMO, coordinated multipoint processing and ultra-dense networks. The main challenge is to achieve the benefits of cell-free operation with computational complexity and fronthaul requirements that are scalable to enable massively large networks [3][4].

While the concept of CF-mMIMO is promising, it is a recent research topic and several issues need to be addressed considering realistic assumptions before rolling out it into practice. Among those issues, we can cite the channel estimation in Time Division Multiplex (TDD) and Frequency Division Multiplex (FDD) [5], the design of decentralized/local/scalable digital signal processing algorithms for multi-user (MU) precoding techniques in CF-mMIMO systems [6][7], the study of the impact and mitigation of hardware imperfections [8]. The contribution of machine learning (ML) tools for the optimization of the CF-mMIMO physical layer to conventional optimization methods is another open question.

Goals of the thesis :

The first contribution of this thesis is related to channel estimation techniques and the design of limited feedback schemes exploiting partial or full channel reciprocity of FDD/TDD modes in the context of scalable CF-mMIMO schemes.

Then, coordinated and locally computed SU/MU MIMO precoding solutions for CF-mMIMO will be investigated. Efficient SU/MU precoding and detection schemes working with partial CSI obtained from the UE feedbacks will be proposed. The impact of hardware imperfections (non-linearities, limited quantization, …) on the proposed solutions will be also studied.

 

Another goal will be to develop new efficient cooperative or federated machine learning structures [9] to enhance the performance of the proposed solutions.

Bibliography :

[1] E. Bjornson and L. Sanguinetti, “Scalable cell-free massive MIMO systems”, in IEEE Trans. Comm., July 2020.

[2] S. Buzzi & C. D’Andrea. (2017). Cell-free massive MIMO: User-centric approach. IEEE Wireless Communications Letters, 6(6), 706-709.

[3] Ö. Demir, E. Björnson and L. Sanguinetti (2021). Foundations of user-centric cell-free massive MIMO. Foundations and Trends in Signal Processing, 14(3-4), 162-472.

[4] Chen S. et al., “A survey on user-centric cell-free massive MIMO systems”. Digital Communications and Networks, 2021.

[5] Han, T., & Zhao, D., “Downlink channel estimation in FDD cell-free massive MIMO”, Physical Communication, 52, 2022.

[6] G. Interdonato, M. Karlsson, E. Björnson and E. G. Larsson, "Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO", in IEEE Trans. on Wireless Communications, July 2020.

[7] V. M. Palhares, A. R. Flores and R. C. de Lamare (2021). Robust MMSE precoding and power allocation for cell-free massive MIMO systems. IEEE Transactions on Vehicular Technology, 70(5), 5115-5120.

[8] A. Papazafeiropoulos, E. Bjornson, P. Kourtessis, S. Chatzinotas and J. M. Senior, "Scalable Cell-Free Massive MIMO Systems: Impact of Hardware Impairments," in IEEE Transactions on Vehicular Technology, 2021.

[9] T. Gafni, N. Shlezinger, K. Cohen, Y. Eldar and V. Poor. (2022). Federated learning: A signal processing perspective. IEEE Signal Processing Magazine, 39(3), 14-41.

 

 

Contact :

Prof. Didier Le Ruyet leruyet@cnam.fr

Ass. Prof. Hmaied Shaiek hmaied.shaiek@lecnam.net

 

Applications should be sent before sept 2, 2022by e-mail to leruyet@cnam.fr and hmaied.shaiek@lecnam.net with the following documents: a full curriculum vitae, recommendation letters, a cover letter stating your motivation and fit for this thesis proposal as well as grades obtained in MSc or engineering school.

The candidate should have earned an MSc degree or equivalent, in one of the following fields: telecommunications, applied mathematics, signal processing.