Jean-Baptiste Dore

Indium phosphide (InP) semiconductor technology is being explored for radiofrequency (RF) applications, targeting frequencies exceeding 100 GHz, to support the next generation of 6G communication systems. When taking into account sustainability in designing this future generation, growing concerns are emerging regarding the environmental impact of communication networks and the reliance on raw materials for the production of Information and Communication Technologies (ICTs). The extraction, processing, and manufacturing of such materials and semiconductor technologies result in environmental impacts, but these impacts remain insufficiently documented. Firstly, this study evaluates the environmental impacts of manufacturing indium phosphide (InP) wafers based on industrial data and those of InP-based heterojunction bipolar transistors (HBTs) based on early-stage research data. Secondly, this study attempts to highlight the challenges posed by the increasing demand for high-tech solutions, involving raw materials, by evaluating the potential demand for indium for RF 6G applications, with a deployment scenario.

The future generations of communication technologies envision the transmission of signals across the millimeter wave and sub-THz spectrums. However, the characteristics of the propagation channel at such high frequencies differ from what is observed in the conventional low-frequency spectrum with for instance, the apparition of stronger phase noise (PN) induced by the Radio Frequency (RF) transceivers and more especially by the oscillators. That is why there is growing interest in evaluating and adapting the 5G new radio (5G-NR) physical layer to the presence of PN. This article is dedicated to the study of discrete Fourier transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) under uncorrelated Gaussian PN (GPN) impairments. We show that the presence of GPN induces two distortions: (i) a frequency-dependent random rotation of data, namely the subcarrier phase error (SPE) and (ii) a frequency-dependent intercarrier interference (ICI) that are analytically expressed. Then, we investigate the design of the adapted and optimal detection criterion according to the baseband model we derived in this paper. We demonstrate that (i) the proposed polar detector outperforms the conventional Euclidean detector and (ii) contrary to legacy OFDM, DFT-s-OFDM is a promising solution when strong GPN is involved.

Orthogonal frequency-division multiplexing (OFDM) based Cell-free massive multiple-input multiple-output (CF-mMIMO) is the most promising technology that, compared to cellular technology, provide uniformly better quality of service for users in sub-6GHz bands. To make OFDM based CF-mMIMO more cost-effective and power-efficient, the access points (APs) should be equipped with low-cost and low-quality transceiver hardware. However, transmitting OFDM signals with high peak-to-average power ratio (PAPR) via such hardware causes severe hardware impairments (HWI), damaging the system performance. In this paper, we present an energy-efficient downlink transmission scheme adequate to the emerging CF-mMIMO-OFDM technology. Specifically, we introduce a smart PAPR-aware precoding scheme, referred to as localPAPRfree, adequate for cell-free architecture. Interestingly, the proposed technique can be implemented in a distributed and scalable manner, demonstrating benefits of CF-mMIMO-OFDM. The PAPR-aware precoding scheme is formulated as a simple convex optimization problem solved via an efficient and steepest iterative algorithm. In addition, we present a theoretical study to analyze the downlink (DL) spectral-efficiency (SE) and energy-efficiency (EE) performance of CF-mMIMO-OFDM under power amplifier (PA) non-linearity, providing important insights into the practical impact of nonlinear PA. Numerical results show that the proposed localPAPRfree algorithm offers excellent PAPR reduction performance, guaranteeing high EE.

In this work, we study hybrid precoding techniques applied to multi-user Transmitting Reconfigurable Intelligent Surface (T-RIS) systems. The T-RIS considered here is a large array of electronically reconfigurable antenna elements illuminated by a small set of active sources. When it comes to digital signal-processing techniques applied to T-RIS systems, it is necessary to consider realistic models to bridge the gap with theoretical results. For this reason, we propose a multi-beam T-RIS propagation model with strong phase quantization constraints and limited beam codebooks. First, the proposed model is validated by characterizing a Ka-band T-RIS. Then, we optimize the quad-beam T-RIS structure by tuning the focal distance between the lens and the focal sources according to two metrics: (i) the per-user antenna gain (analog-only approach), and (ii) the per-user average rate (hybrid digital/analog approach). For both indicators, the system performance is evaluated in a multi-user scenario by assuming imperfect channel state information. We show that considering only the analog precoder is sufficient to optimize the T-RIS. However, the fully hybrid precoding scheme is required to deal with inter-user interference. We propose a codebook-aware optimization that improves the aperture efficiency of the T-RIS system.

For future wireless communication technologies, an increase in capabilities such as throughput is strongly expected. Transmission in the sub-THz bands (>90 GHz) seems to be the potential solution to meet the ever-increasing capacity demands due to the large unexploited bandwidth. Oscillators used at these frequencies generate phase noise that induces critical distortions in the signal that must be addressed. The correlated nature of PN makes it difficult to overcome. Nowadays, there is a growing interest in considering the extension of multicarrier based waveforms of the 5G new radio for transmissions in the sub-THz bands. In this paper, we introduce a new algorithm called the interpolation filter (IF), which efficiently estimates and compensates PN effects on DFT-s-OFDM systems. Specifically, it is based on the use of stochastic properties of the PN and is compatible with the 3GPP phase tracking reference signal scheme. We highlight a performance improvement over known techniques when using high-order modulation.

In this paper, we investigate low-complexity and adaptive digital predistorsion (DPD) techniques in order to enhance wireless communication system latency, complexity and power consumption. Specifically, we introduce efficient meta-learning based solutions for time-varying power amplifier (PA) linearization that allow to reduce hardware complexity. Our first proposed solution consists in a meta-learning based neural network (NN) model that is capable to perform, offline, an optimal initialization of the NN based DPD. This leads to an efficient and fast adaptation of the DPD to the time-varying PA characteristics, i.e., few shots are needed during online calibration. Interestingly, we introduced a different approach, referred to as DPD NN Weights Selector (DPDNNWS), that offers the ability to approximate more accurately the current PA characteristic. These solutions have been compared in terms of complexity and performance w.r.t Error Vector Magnitude (EVM) and Adjacent Channel Leakage Ratio (ACLR). Performance of the proposed approaches are benchmarked with the conventional learning based DPD approach. To offer low-complexity DPD, a dedicated NN structure is designed to derive DPD functions using few neurons. Our numerical simulations demonstrated that our proposed DPDNNWS and meta-learning approaches provide satisfying results when used online for several PA models. Contrary to conventional DPD, these approaches can be used to reduce hardware complexity implementation and data usage during online adaptation. Indeed, through numerical evaluation, it appears that meta-learning can reach satisfying performance using only $\mathbf{3000}$ IQ symbols during online adaptation. Besides, the DPDNNWS approach exhibits performance close to a linear power amplifier. Both solutions allow to achieve excellent performance compared to the state of the art solutions.

The significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated to realize high rate wireless communications for beyond 5G networks. Yet, the performance of radio-frequency sub-TeraHertz systems is severely degraded by strong oscillator phase noise. Therefore, we investigate in this paper the use of multiple-input multiple-output (MIMO) systems with energy detection receivers to achieve high rate communications robust to phase noise. First, the design of the receiver detection algorithm is addressed. Two detectors are proposed for the studied nonlinear MIMO channel, either derived from the maximum likelihood decision rule by using a Gaussian approximation, or based on the use of neural networks. Second, the communication performance is assessed through numerical simulations for uncoded and coded systems. We consider a realistic scenario modeling an indoor wireless link in D-band with directive antennas and strongly correlated line-of-sight channels. Our results demonstrate that spatial multiplexing with non-coherent sub-TeraHertz transceivers can be realized on strongly correlated line-of-sight channels using the proposed detection schemes. Thereby, we highlight that high-rate radio-frequency sub-TeraHertz systems can be implemented with low-complexity and low-power architectures using MIMO systems with energy detection receivers.

To meet the requirements of beyond 5G networks, the significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated for high-rate wireless communications. Yet, the performance of sub-TeraHertz systems is severely degraded by strong oscillator phase noise. We investigate in this paper the design of digital communications robust to phase noise. This problem is addressed in three steps: the characterization of the phase noise channel, the design of the optimum receiver, and the optimization of the modulation scheme. This paper proposes a joint performance and implementation optimization. First, we address the design of the demodulation scheme for phase noise channels and propose the polar metric, a soft-decision rule for symbol detection. It is shown that performance gains are achieved for coded and uncoded systems with valuable complexity reductions of the receiver. Second, we investigate the optimization of the modulation scheme for phase noise. We demonstrate that using a constellation defined upon a lattice in the amplitude-phase domain leads to significant performance gains and a low-complexity implementation. Thereupon, we propose the Polar-QAM scheme with efficient binary labeling and demodulation. Numerical simulation results show that the proposed modulation and demodulation schemes offer valuable solutions to achieve high-rate communications on systems strongly impaired by phase noise.