DC-Informative Joint Color-Frequency Modulation for Visible Light Communications

In this paper, we consider the problem of constellation design for a visible light communication (VLC) system using red/green/blue light-emitting diodes (RGB LED), and propose a method termed DC-informative joint color-frequency modulation (DCI-JCFM). This method jointly utilizes available diversity resources including different optical wavelengths, multiple baseband subcarriers, and adaptive DC-bias. Constellation is designed in a high dimensional space, where the compact sphere packing advantage over lower dimensional counterparts is utilized. Taking into account multiple practical illumination constraints, a non-convex optimization problem is formulated, seeking the least error rate with a fixed spectral efficiency. The proposed scheme is compared with a decoupled scheme, where constellation is designed separately for each LED. Notable gains for DCI-JCFM are observed through simulations where balanced, unbalanced and very unbalanced color illuminations are considered.Comment: submitted to Journal of Lightwave Technology, Aug. 5th 201

Design guidelines for spatial modulation

A new class of low-complexity, yet energyefficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO) has emerged. These systems are capable of exploiting the spatial dimensions (i.e. the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication whilst relying on a single-Radio Frequency (RF) chain. Moreover, SM may also be viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/ cooperative protocol design issues, and on their meritorious variants

Communications protocols for wireless sensor networks in perturbed environment

This thesis is mainly in the Smart Grid (SG) domain. SGs improve the safety of electrical networks and allow a more adapted use of electricity storage, available in a limited way. SGs also increase overall energy efficiency by reducing peak consumption. The use of this technology is the most appropriate solution because it allows more efficient energy management. In this context, manufacturers such as Hydro-Quebec deploy sensor networks in the nerve centers to control major equipment. To reduce deployment costs and cabling complexity, the option of a wireless sensor network seems the most obvious solution. However, deploying a sensor network requires in-depth knowledge of the environment. High voltages substations are strategic points in the power grid and generate impulse noise that can degrade the performance of wireless communications. The works in this thesis are focused on the development of high performance communication protocols for the profoundly disturbed environments. For this purpose, we have proposed an approach based on the concatenation of rank metric and convolutional coding with orthogonal frequency division multiplexing. This technique is very efficient in reducing the bursty nature of impulsive noise while having a quite low level of complexity. Another solution based on a multi-antenna system is also designed. We have proposed a cooperative closed-loop coded MIMO system based on rank metric code and max−dmin precoder. The second technique is also an optimal solution for both improving the reliability of the system and energy saving in wireless sensor networks

Clustering techniques for base station coordination in a wireless cellular system

A lo largo de este Proyecto Fin de Carrera, propondremos mejoras para futuros sistemas de comunicaciones móviles mediante un estudio detallado de la coordinación entre estaciones base en sistemas celulares basados en MIMO. Este proyecto se compone de dos partes fundamentales. Por un lado, nos centraremos en técnicas de procesado de señal para MIMO como filtrado y precodificación lineales en el dominio espacial. Partiendo de los últimos desarrollos en dicho ámbito, se han desarrollado precodificadores de mínimo error cuadrático medio que incluyen restricciones de máxima potencia transmitida por celda. Además, se ha propuesto un concepto novedoso consistente en la introducción de una nueva formulación que, además de minimizar el error cuadrático medio en el interior de cada agrupación de celdas (cluster ), trata de mantener la interferencia entre clusters en niveles suficientemente bajos. Durante la segunda parte, analizaremos el impacto que la agrupación de celdas en clusters, que define qué estaciones base pueden ser coordinadas entre sí , tiene en el rendimiento global del sistema. Se ha estudiado la aplicabilidad de técnicas de agrupamiento dentro del aprendizaje máquina, dando como resultado un conjunto de nuevos algoritmos que han sido desarrollados adaptando algoritmos de agrupamiento de propósito general ya existentes al problema de crear una partición del conjunto de celdas de acuerdo a las condiciones de propagación de señal existentes en el sistema en un determinado instante. Todas nuestras contribuciones se han verificado mediante la simulación de un sistema de comunicaciones móviles basado en modelos de propagación de señal del 3GPP para LTE. De acuerdo a los resultados obtenidos, las técnicas propuestas a lo largo de este proyecto proporcionan un aumento considerable de la media y la mediana de las tasas por usuario respecto a soluciones ya existentes. La idea de introducir la reducción de interferencia entre clusters en la formulación de los precodifiadores MMSE mejora dramáticamente el rendimiento en sistemas celulares MIMO al ser comparados con precodifiadores de Wiener tradicionales. Por otro lado, nuestros algoritmos de agrupamiento dinámico de estaciones base exhiben un notable aumento de las tasas por usuario a la vez que emplean clusters de menor tamaño con respecto a soluciones existentes basadas en particiones estáticas del conjunto de celdas en el sistema. _______________________________________________________________________________________________________________________________In this project, we attempt to provide enhancements for future mobile communications systems by carrying out a throughout study of base-station coordination in cellular MIMO systems. Our work can be divided in two main blocks. During the first part, we focus our attention on linear MIMO signal processing techniques such as linear spatial precoding and linear spatial ltering. Starting from the state-of-the-art in that area of knowledge, we have developed novel MMSE precoders which include per-cell power constraints and a new formulation which, apart from minimizing the intra-cluster MSE, tries to keep inter-cluster interference at low levels. In the second part, we focus on the study of the impact the particular mapping of cells to clusters in the cellular system has on the overall performance of the mobile communication radio access network. The applicability of existing clustering algorithms in the fi eld of machine learning has been studied, resulting in a set of novel algorithms that we developed by adapting existing general-purpose clustering solutions for the problem of dynamically partitioning a set of cells according to the instantaneous signal propagation conditions. All our contributions have been exhaustively tested by simulation of a cellular mobile communication system based on 3GPP signal propagation models for LTE. According to the results obtained, the techniques proposed along this project provide a remarkable increase of both the average and median user rates in the system with respect to previous existing solutions. The inter-cluster interference-awareness we introduced in the formulation of MMSE precoders dramatically increases the performance in cellular coordinated MIMO when comparing it with traditional Wiener precoders. On the other hand, our dynamic base-station clustering has been shown to signi catively enhance the user rates while using smaller clusters that existing solutions based on static partitions of the base-station deployment.Ingeniería de Telecomunicació

Low-complexity antenna selection techniques for massive MIMO systems

PhD ThesisMassive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology in wireless communications, where hundreds of antennas are exploited at the base station (BS) to serve a much smaller number of users. Employing large antenna arrays can improve the performance dramatically in terms of the achievable rates and radiated energy, however, it comes at the price of increased cost, complexity, and power consumption. To reduce the hardware complexity and cost, while maintaining the advantages of M-MIMO, antenna selection (AS) techniques can be applied where only a subset of the available antennas at the BS are selected. Optimal AS can be obtained through exhaustive search, which is suitable for conventional MIMO systems, but is prohibited for systems with hundreds of antennas due to its enormous computational complexity. Therefore, this thesis address the problem of designing low complexity AS algorithms for multi-user (MU) M-MIMO systems. In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired, and heuristic methods are applied for AS in uplink MU M-MIMO systems. It was demonstrated that quantum-inspired and heuristic methods outperform the bio-inspired techniques in terms of both complexity and performance. In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter (MF) precoding. Two novel AS algorithms are proposed where the antennas are selected without any vector multiplications, which resulted in a dramatic complexity reduction. The proposed algorithms outperform the case where all antennas are activated, in terms of both energy and spectral efficiencies. In chapter 5, three AS algorithms are designed and utilized to enhance the performance of cell-edge users, alongside Max-Min power allocation control. The algorithms aim to either maximize the channel gain, or minimize the interference for the worst-case user only. The proposed methods in this thesis are compared with other low complexity AS schemes and showed a great performance-complexity trade-off

Two–Way Relaying Communications with OFDM and BICM/BICM-ID

Relay-aided communication methods have gained strong interests in academic community and been applied in various wireless communication scenarios. Among different techniques in relay-aided communication system, two-way relaying communication (TWRC) achieves the highest spectral efficiency due to its bi-directional transmission capability. Nevertheless, different from the conventional point-to-point communication system, TWRC suffers from detection quality degradation caused by the multiple-access interference (MAI). In addition, because of the propagation characteristics of wireless channels, fading and multipath dispersion also contribute strongly to detection errors. Therefore, this thesis is mainly concerned with designing transmission and detection schemes to provide good detection quality of TWRC while taking into account the negative impacts of fading, multipath dispersion and multiple-access interference. First, a TWRC system operating over multipath fading channels is considered and orthogonal frequency-division multiplexing (OFDM) is adopted to handle the inter-symbol interference (ISI) caused by the multipath dispersion. In particular, adaptive physical-layer network coding (PNC) is employed to address the MAI issue. By analyzing the detection error probability, various adaptive PNC schemes are discussed for using with OFDM and the scheme achieving the best trade-off among performance, overhead and complexity is suggested. In the second part of the thesis, the design of distributed precoding in TWRC using OFDM under multipath fading channels is studied. The objective is to design a distributed precoding scheme which can alleviate MAI and achieve multipath diversity to combat fading. Specifically, three types of errors are introduced when analyzing the error probability in the multiple access (MA) phase. Through analysis and simulation, the scheme that performs precoding in both time and frequency domains is demonstrated to achieve the maximum diversity gains under all types of errors. Finally, the last part of the thesis examines a communication system incorporating forward error correction (FEC) codes. Specifically, bit-interleaved code modulation (BICM) without and with iterative decoding (BICM-ID) are investigated in a TWRC system. Distributed linear constellation precoding (DLCP) is applied to handle MAI and the design of DLCP in a TWRC system using BICM/BICM-ID is discussed. Taking into account the multiple access channel from the terminal nodes to the relay node, decoding based on the quaternary code representation is introduced. Several error probability bounds are derived to aid in the design of DLCP. Based on these bounds, optimal parameters of DLCP are obtained through analysis and computer search. It is also found that, by combining XORbased network coding with successful iterative decoding, the MAI is eliminated and thus DLCP is not required in a BICM-ID system

A framework for low-complexity iterative interference cancellation in communication systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 211-215).Communication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter preceding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterated-decision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol(cont.) decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved preceding, iterated-decision detectors asyrmptotically achieve the performance of ML detection (and thus the interference-free lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with mode-interleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information rates.by Albert M. Chan.Ph.D

A Framework for Low-Complexity Iterative Interference Cancellation in Communication Systems

Thesis Supervisor: Gregory W. Wornell Title: ProfessorCommunication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter precoding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterateddecision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved precoding, iterated-decision detectors asymptotically achieve the performance ofML detection (and thus the interferencefree lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with modeinterleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information ratesHewlett-Packard under the MIT/HPAlliance, the National Science Foundation, the Semiconductor Research Corporation, Texas Instruments through the Leadership Universities Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship Program