Vincent Savaux

Bio: Vincent Savaux is an academic researcher from CentraleSupélec. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Minimum mean square error. The author has an hindex of 10, co-authored 45 publications receiving 240 citations. Previous affiliations of Vincent Savaux include Supélec.

A Tutorial on NB-IoT Physical Layer Design

Abstract: The Internet of Things (IoT) is transforming the whole of society. It represents the next evolution of the Internet and will significantly improve the ability to gather and analyze data, as well as the ability to control devices remotely. In this respect, the usage of connected devices is continuously growing with the expansion of the applications being offered to individuals and industries. To address IoT market needs, many low-power wide-area (LPWA) technologies have been developed, some operating on licensed frequencies (e.g., narrowband-IoT [NB-IoT] and Long-Term Evolution-M [LTE-M]), and others on unlicensed frequencies (e.g., LoRa, Sigfox, etc.). In this article, we address the Release 13 of the NB-IoT 3rd generation partnership project (3GPP) standardized LPWA technology and provide a tutorial on its physical layer (PHY) design. Specifically, we focus on the characteristics and the scheduling of downlink and uplink physical channels at the NB-IoT base station side and the user equipment (UE) side. The goal is to help readers easily understand the NB-IoT system without having to read all the 3GPP specifications or the state-of-the-art papers that generally describe the system. To this end, each presented concept is followed by examples and concrete use-cases to further aid in the reader’s comprehension. Finally, we briefly describe and highlight the new features added to the NB-IoT system in Releases 14 and 15.

LMMSE channel estimation in OFDM context: a review

Abstract: Linear minimum mean square error (LMMSE) is by definition the optimal channel estimator in the sense of mean square error criterion, but its practical application is limited by its high complexity. Furthermore, the LMMSE estimation method requires the knowledge of both the channel and the noise statistics, which are a priori unknown at the receiver. A wide range of techniques are proposed in the literature in order to overcome these two drawbacks. In this study, the authors give an overview of the LMMSE-based channel estimation in an orthogonal frequency division multiplexing (OFDM) context. A didactic reminder concerning the basics of LMMSE estimation and its performance is provided, and a survey of techniques of the literature, which enable the practical application of LMMSE and the reduction of its complexity, is presented in both single-input single-output and multiple-input multiple-output contexts. Finally, some perspectives are provided, in particular the application of the LMMSE estimator to flexible waveforms beyond OFDM.

A Joint MMSE Channel and Noise Variance Estimation for OFDM/OQAM Modulation

Abstract: This paper deals with the minimum mean square error (MMSE)-based multipath channel and noise variance estimation in the case of a pilot-aided orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) system. The theoretical expression of the LMMSE channel estimation is formulated and a simpler closed-form is derived. The MSE analysis shows that LMMSE inevitably reaches an error floor due to the interference inherent to OFDM/OQAM modulation. Furthermore, an algorithm for the joint MMSE estimation of both the channel and the noise variance (J-MCNE) is proposed and features a reduced complexity thanks to the low-rank approximation method. The noise level estimation represents a challenge as it is concealed in the intrinsic interferences generated by the OFDM/OQAM. Simulations reveal the capability of the proposed J-MCNE method to estimate the noise variance and show that the achieved bit error rate (BER) is close to that of the perfect estimation.

Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Abstract: This article presents an iterative minimum mean square error- (MMSE-) based method for the joint estimation of signal-to-noise ratio (SNR) and frequency-selective channel in an orthogonal frequency division multiplexing (OFDM) context. We estimate the SNR thanks to the MMSE criterion and the channel frequency response by means of the linear MMSE (LMMSE). As each estimation requires the other one to be performed, the proposed algorithm is iterative. In this article, a realistic case is considered; i.e., the channel covariance matrix used in LMMSE is supposed to be totally unknown at the receiver and must be estimated. We will theoretically prove that the algorithm converges for a relevantly chosen initialization value. Furthermore simulations show that the algorithm quickly converges to a solution that is close to the one in which the covariance matrix is perfectly known. Compared to existing SNR estimation methods, the algorithm improves the trade-off between the number of required pilots and the SNR estimation quality.

Mean square error analysis and linear minimum mean square error application for preamble-based channel estimation in orthogonal frequency division multiplexing/offset quadrature amplitude modulation systems

Abstract: In this paper the performance of different preamble-based channel estimation techniques is analyzed for orthogonal frequency division multiplexing/offset QAM (OFDM/OQAM) modulation systems. Three pilot allocation methods, the pair of pilot (POP), the interference cancellation method (ICM), and the interference approximation method (IAM) are considered. A theoretical expression of the estimation mean square error (MSE) is performed for each of them, which considers the selectivity of the channel and the interferences from all the neighboring time-frequency positions. Moreover, an application of the linear minimum mean square error (LMMSE) estimator in OFDM/OQAM is investigated and an analysis of its MSE performance is provided as well. It is shown that, due to the intrinsic interference, the MSE of the channel estimation inevitably reaches an error floor whatever the applied method is. Numerical results show that the theoretical MSE assessments match those obtained by simulation. In addition to the MSE, the bit error rate (BER) performance for the different channel estimators is depicted.

Advanced Digital Signal Processing And Noise Reduction

Abstract: Thank you very much for reading advanced digital signal processing and noise reduction. Maybe you have knowledge that, people have search hundreds times for their chosen books like this advanced digital signal processing and noise reduction, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their laptop.

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

Abstract: We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Successive Localization and Beamforming in 5G mmWave MIMO Communication Systems

Abstract: Beamforming is an attractive technique to improve the system performance for multi-input multi-output (MIMO) communications. Previous works mainly focus on improving the data transmission quality. However, the potential of beamforming for improving the localization quality is not yet fully studied. In this paper, we focus on active beamforming to reduce the user equipment (UE) localization error for millimeter-wave MIMO systems. Such beamforming for localization is of challenge because its optimization cost function (e.g., the localization error bound) also depends on the actual UE location and instantaneous channel states, which are unknown in advance. To address this challenge, a novel successive localization and beamforming (SLAB) scheme is proposed, where the long-term UE location and the instantaneous channel state will be jointly estimated and then the beamforming vector will be successively optimized as per the obtained estimation results. The proposed SLAB scheme will yield a sequence of beamforming weights and UE location estimates, which will converge to the stationary point of the associated optimization problem. Simulation results show that the proposed SLAB scheme achieves a huge performance gain for UE localization compared with state-of-the-art baselines.

Interference-Alignment and Soft-Space-Reuse Based Cooperative Transmission for Multi-cell Massive MIMO Networks

Abstract: As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of cell-edge users. However, the acquisition of the global channel state information for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR)-based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms. Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.