Research training at INSA Rennes - Resource allocation techniques based on machine learning for optimizing the performance of massive cell-free MIMO in 6G and beyond networks

4 Octobre 2023

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Catégorie : Stagiaire

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Context

Wireless communication systems are currently under pressure due to increasing data consumption by individuals and widespread teleworking. To address this issue, a number of disruptive technologies need to be implemented in future 6G and beyond standards. One of these major technologies is the cell-free paradigm, based on the elimination of the concept of subdividing coverage areas into cells. In the cell-free context, a large number of access points (APs) are distributed over a large geographical area to optimally serve all users present in the zone. These APs are connected to a central processing unit (CPU) via wired or wireless links. Cell-free Massive MIMO (CF-mMIMO) is a generalization of 5G Massive MIMO (Mutiple Input Multiple Output), enabling more uniform coverage and increased connectivity for users thanks to the proximity and diversity of APs. However, these systems require the implementation of new resource management techniques that are appropriate to the context of distributed deployment, and that take into account several constraints like limited power consumption, low latency and high Quality of Service (QoS).

 

Objectives

The aim of this internship is to propose new techniques for optimizing B5G and 6G communications to enable power-efficient and spectrum-efficient massive access. Adequate interference management is essential to achieve this goal. Novel optimization strategies will be introduced so as to reduce the energy consumption of communications while guaranteeing a required QoS and fairness level to users. Non-orthogonal multiple access (NOMA), recently introduced in 5G, will also be considered. This technique consists of allocating two or more users to the same spectral/temporal resource through appropriate power multiplexing. By properly optimizing the assignment of users and their power in the cell-free context, it will be possible to achieve important trade-offs between the spectral and energy efficiencies of the systems on the one hand, and between the capacities achieved and the level of fairness of the services offered on the other.

The studied CF-mMIMO system will be assisted by Intelligent Reflecting Surfaces (IRS) which constitute a very promising solution for improving the link quality and enhance system performance. Additionally, machine learning tools will be leveraged for building efficient solutions to overcome the problems associated with high complexity and latency.

Keywords: Cell-free networks, massive MIMO systems, Non orthogonal multiple access, resource allocation, optimization, machine learning.

 

References

[1]O. T. Demir, E. Bjornson, and L. Sanguinetti, “Foundations of User-Centric Cell-Free Massive MIMO,” Foundations and Trends® in Signal Processing, vol. 14, no. 3-4, pp. 162–472, 2021.

[2]N. T. Nguyen, V. -D. Nguyen, H. V. Nguyen, H. Q. Ngo, S. Chatzinotas and M. Juntti, "Spectral Efficiency Analysis of Hybrid Relay-Reflecting Intelligent Surface-Assisted Cell-Free Massive MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3397-3416, May 2023.

[3]J. Farah, C. Ghanem, E. P. Simon, "Uncoordinated Transmissions in Uplink IoT Cell-Free Massive MIMO Systems based on NOMA", the 31st European Signal Processing Conference (EUSIPCO 2023), September 4-8 2023, Helsinki, Finland.

[4]J. Farah, E. P. Simon, P. Laly and G. Delbarre, "Efficient Combinations of NOMA With Distributed Antenna Systems Based on Channel Measurements for Mitigating Jamming Attacks," in IEEE Systems Journal, vol. 15, no. 2, pp. 2212-2221, June 2021.

[5]E. P. Simon, J. Farah, P. Laly, and G. Delbarre, “A gradual resource allocation technique for massive mimo-noma,” IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 3, pp. 476–480, 2021.

[6]T. K. Nguyen, H. Nguyen and H. D. Tuan, "Max-Min QoS Power Control in Generalized Cell-Free Massive MIMO-NOMA With Optimal Backhaul Combining," in IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 10949-10964, Oct. 2020.

[7]C. A. Schmidt, J. F. Schmidt, J. L. Figueroa and M. Crussière, "Achievable Energy Efficiency in Massive MIMO: Impact of DAC Resolution and PAPR Reduction for Practical Network Topologies at mm-Waves," in IEEE Communications Letters, vol. 26, no. 11, pp. 2784-2788, Nov. 2022.

 

Duration:4 to 6 month, preferably starting during February 2024.

Internship allowance:around 600 Euros / month

Internship Location:Institut National des Sciences Appliquées (INSA)

20 Av. des Buttes de Coësmes, 35700 Rennes, France

 

Candidate profile

The candidate must be in the final stages of obtaining an engineering and/or Master's degree in Telecommunications, Electricity-Electronics or other related fields.

Knowledge requirements include Mobile Communications, Signal Processing, Machine Learning, and Matlab programming.

 

To apply

Please send a CV, a motivation letter, copies of all academic records and grades (preferably with rankings), and (optionally) a recommendation letter to:

Joumana Farah: joumana.farah@insa-rennes.fr

Matthieu Crussière: Matthieu.Crussiere@insa-rennes.fr

 

Only complete applications will be considered.

A remote interview will be scheduled for shortlisted candidates after examination of their application.

 

Possibility of pursuing a doctoral thesis

Funding is available to pursue the internship subject as a doctoral thesis. This will depend on the quality of the work carried out during the internship.