Robust Precoding with Bayesian Error Modeling for Limited Feedback MU-MISO Systems

The final publication is available http://dx.doi.org/10.1109/TSP.2010.2052046[Abstract] We consider the robust precoder design for multiuser multiple-input single-output (MU-MISO) systems where the channel state information (CSI) is fed back from the single antenna receivers to the centralized transmitter equipped with multiple antennas. We propose to compress the feedback data by projecting the channel estimates onto a vector basis, known at the receivers and the transmitter, and quantizing the resulting coefficients. The channel estimator and the basis for the rank reduction are jointly optimized by minimizing the mean-square error (MSE) between the true and the rank-reduced CSI. Expressions for the conditional mean and the conditional covariance of the channel are derived which are necessary for the robust precoder design. These expressions take into account the following sources of error: channel estimation, truncation for rank reduction, quantization, and feedback channel delay. As an example for the robust problem formulation, vector precoding (VP) is designed based on the expectation of the MSE conditioned on the fed-back CSI. Our results show that robust precoding based on fed-back CSI clearly outperforms conventional precoding designs which do not take into account the errors in the CSI.Galicia, Consellería de Innovación, Industria e Comercio; PGIDT06TIC10501PRMinisterio de Educacion y Ciencia; TEC2007-68020-C04-01Ministerio de Educacion y Ciencia; CSD2008-00010.Ministerio de Educacion y Ciencia; HA2006-0112Alemania. Deutscher Akademischer Austauschdienst; D/06/1280

Design of limited feedback for robust MMSE precoding in multiuser MISO systems

[Resumen] En este trabajo consideramos un sistema multiusuario con múltiples antenas en transmisión y una única antena en cada uno de los usuarios receptores y que se denota por brevedad como MU-MISO, del inglés Multi–User Multiple–Input/Single–Output. Este modelo MU–MISO se ajusta perfectamente al enlace descendente de un sistema de comunicaciones móviles, donde múltiples antenas situadas en la estación base envían información a varios usuarios dentro de su zona de cobertura y cuyos terminales móviles disponen generalmente de una única antena. Este canal descendente se denomina también canal de difusión (BC, del inglés Broadcast Channel). Cuando se considera un canal de difusión, el transmisor centralizado tiene claramente más grados de libertad que cada uno de los receptores descentralizados, por lo que es más apropiado separar las señales aplicando precodificación en transmisión. Para poder realizar el diseño de los parámetros del precodificador, el transmisor necesita conocer la información de canal (CSI, en inglés Channel State Information) correspondiente a los distintos usuarios receptores. En el caso de sistemas FDD (del inglés, Frequency Division Duplex), esta información puede obtenerse (al menos parcialmente) mediante realimentación, siempre tras haber aplicado un proceso de cuantificación de la información enviada con el objetivo de adaptarse a las condiciones de ancho de banda limitado del canal de retorno

Lights and Shadows: A Comprehensive Survey on Cooperative and Precoding Schemes to Overcome LOS Blockage and Interference in Indoor VLC

Visible light communications (VLC) have received significant attention as a way of moving part of the saturated indoor wireless traffic to the wide and unregulated visible optical spectrum. Nowadays, VLC are considered as a suitable technology, for several applications such as high-rate data transmission, supporting internet of things communications or positioning. The signal processing originally derived from radio-frequency (RF) systems such as cooperative or precoding schemes can be applied to VLC. However, its implementation is not straightforward. Furthermore, unlike RF transmission, VLC present a predominant line-of-sight link, although a weak non-LoS component may appear due to the reflection of the light on walls, floor, ceiling and nearby objects. Blocking effects may compromise the performance of the aforementioned transmission schemes. There exist several surveys in the literature focused on VLC and its applications, but the management of the shadowing and interference in VLC requires a comprehensive study. To fill this gap, this work introduces the implementation of cooperative and precoding schemes to VLC, while remarking their benefits and drawbacks for overcoming the shadowing effects. After that, the combination of both cooperative and precoding schemes is analyzed as a way of providing resilient VLC networks. Finally, we propose several open issues that the cooperative and precoding schemes must face in order to provide satisfactory VLC performance in indoor scenarios.This work has been supported partially by Spanish National Project TERESA-ADA(TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE), the research project GEOVEOLUZ-CM-UC3Mfunded by the call “Programa de apoyo a la realización de proyectos interdisciplinares de I+D parajóvenes investigadores de la Universidad Carlos III de Madrid 2019-2020” under the frame ofthe Convenio Plurianual Comunidad de Madrid-Universidad Carlos III de Madrid and projectMadrid Flight on Chip (Innovation Cooperative Projects Comunidad of Madrid - HUBS 2018/MadridFlightOnChip). Additionally, it has been supported partially by the Juan de la CiervaIncorporación grant IJC2019-040317-I and Juan de la Cierva Formación grant (FJC2019-039541-I/AEI/10.13039/501100011033)

Deep Learning Designs for Physical Layer Communications

Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

Recent Advances in Acquiring Channel State Information in Cellular MIMO Systems

In cellular multi-user multiple input multiple output (MU-MIMO) systems the quality of the available channel state information (CSI) has a large impact on the system performance. Specifically, reliable CSI at the transmitter is required to determine the appropriate modulation and coding scheme, transmit power and the precoder vector, while CSI at the receiver is needed to decode the received data symbols. Therefore, cellular MUMIMO systems employ predefined pilot sequences and configure associated time, frequency, code and power resources to facilitate the acquisition of high quality CSI for data transmission and reception. Although the trade-off between the resources used user data transmission has been known for long, the near-optimal configuration of the vailable system resources for pilot and data transmission is a topic of current research efforts. Indeed, since the fifth generation of cellular systems utilizes heterogeneous networks in which base stations are equipped with a large number of transmit and receive antennas, the appropriate configuration of pilot-data resources becomes a critical design aspect. In this article, we review recent advances in system design approaches that are designed for the acquisition of CSI and discuss some of the recent results that help to dimension the pilot and data resources specifically in cellular MU-MIMO systems