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Power-efficient transceivers for 6G cell free massive MIMO networks

Technological challenge: Communication networks, IOT, radiofrequencies and antennas (learn more)

Department: Département Systèmes (LETI)

Laboratory: Laboratoire Sans fils Haut Débit

Start Date: 01-09-2022

Location: Grenoble

CEA Code: SL-DRT-22-0173

Contact: david.demmer@cea.fr

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a key technology of the forthcoming 6th generation of wireless networks (6G) representing an alternative network infrastructure and being the most ultimate enabler of energy-efficiency (EE) and spectral-efficiency (SE). In CF-mMIMO, the cell boundaries disappear and many access points (APs) share the different antennas at the (virtual) base station. Consequently, it results in smaller and lighter radio modules with only few antennas per AP. Most importantly, CF-mMIMO harvests all benefits of classical/co-located massive MIMO, and offers many advantages compared to traditional wireless systems, by enabling huge coverage probability, rapid deployment and reduced power requirement in the installation site with less heat generated. However, CF-mMIMO can be economically attractive only if its implementation is based on low-quality/power-efficient hardware at a time where communications are about to reach up to 14% of global CO2 emissions by 2040. Such hardware generates severe Radio Frequency (RF) impairments limiting the system performance and is considered as the major bottleneck in CF-mMIMO systems in practice. This PhD aims at exploring the potentiality of distributed optimization methods and Machine Learning (ML) algorithms to achieve, besides the performance requirements, considerable power consumption reduction, by mitigating RF impairments in power-efficient transceivers. Specifically, we aim to: 1. Study new local precoding schemes for joint multi-user (MU) interference avoidance and peak-to-average power ratio (PAPR) reduction in CF-mMIMO: the precoder generates low-PAPR signals that are less sensitive to RF impairments. 2. Study local joint MU precoding and HWI mitigation: HWIs are dominated by the power amplifier and DACs. 3. Investigate new ML architectures and training methods that are adapted to the problem of tackling the RF impairments in scalable CF-mMIMO systems, while considering the computational and hardware complexity. The candidate should have diverse skills including signal processing dedicated to digital communications, optimization and machine learning.

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