Joint signal detection and channel estimation in rank-deficient MIMO systems

L'évolution de la prospère famille des standards 802.11 a encouragé le développement des technologies appliquées aux réseaux locaux sans fil (WLANs). Pour faire face à la toujours croissante nécessité de rendre possible les communications à très haut débit, les systèmes à antennes multiples (MIMO) sont une solution viable. Ils ont l'avantage d'accroître le débit de transmission sans avoir recours à plus de puissance ou de largeur de bande. Cependant, l'industrie hésite encore à augmenter le nombre d'antennes des portables et des accésoires sans fil. De plus, à l'intérieur des bâtiments, la déficience de rang de la matrice de canal peut se produire dû à la nature de la dispersion des parcours de propagation, ce phénomène est aussi occasionné à l'extérieur par de longues distances de transmission. Ce projet est motivé par les raisons décrites antérieurement, il se veut un étude sur la viabilité des transcepteurs sans fil à large bande capables de régulariser la déficience de rang du canal sans fil. On vise le développement des techniques capables de séparer M signaux co-canal, même avec une seule antenne et à faire une estimation précise du canal. Les solutions décrites dans ce document cherchent à surmonter les difficultés posées par le medium aux transcepteurs sans fil à large bande. Le résultat de cette étude est un algorithme transcepteur approprié aux systèmes MIMO à rang déficient

Techniques in secure chaos communication

In today's climate of increased criminal attacks on the privacy of personal or confidential data over digital communication systems, a more secure physical communication link is required. Chaotic signals which have bifurcation behavior (depending on some initial condition) can readily be exploited to enhance the security of communication systems. A chaotic generator produces disordered sequences that provide very good auto- and cross- correlation properties similar to those of random white noise. This would be an important feature in multiple access environments. These sequences are used to scramble data in spread spectrum systems as they can produce low co-channel interference, hence improve the system capacity and performance. The chaotic signal can be created from only a single mathematical relationship and is neither restricted in length nor is repetitive/ cyclic. On the other hand, with the progress in digital signal processing and digital hardware, there has been an increased interest in using adaptive algorithms to improve the performance of digital systems. Adaptive algorithms provide the system with the ability to self-adjust its coefficients according to the signal condition, and can be used with linear or non-linear systems; hence, they might find application in chaos communication. There has been a lot of literature that proposed the use of LMS adaptive algorithm in the communication arena for a variety of applications such as (but not limited to): channel estimation, channel equalization, demodulation, de-noising, and beamforming. In this thesis, we conducted a study on the application of chaos theory in communication systems as well as the application of adaptive algorithms in chaos communication. The First Part of the thesis tackled the application of chaos theory in com- munication. We examined different types of communication techniques utilizing chaos theory. In particular, we considered chaos shift keying (CSK) and mod- ified kind of logistic map. Then, we applied space-time processing and eigen- beamforming technique to enhance the performance of chaos communication. Following on, we conducted a study on CSK and Chaos-CDMA in conjunction with multi-carrier modulation (MCM) techniques such as OFDM (FFT/ IFFT) and wavelet-OFDM. In the Second Part of the thesis, we tried to apply adaptivity to chaos com- munication. Initially, we presented a study of multi-user detection utilizing an adaptive algorithm in a chaotic CDMA multi-user environment, followed by a study of adaptive beamforming and modified weight-vector adaptive beam- forming over CSK communication. At last, a study of modified time-varying adaptive filtering is presented and a conventional adaptive filtering technique is applied in chaotic signal environment. Twelve papers have been published during the PhD candidature, include two journal papers and ten refereed conference papers

Design And Implementation Of An Autonomous Wireless Sensor-Based Smart Home

The Smart home has gained widespread attentions due to its flexible integration into everyday life. This next generation of green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this thesis, a smart home design is put forward that can classify and predict the state of the home utilizing historical data of the home. A wireless sensor network was setup in a home to gather and send data to a sink node. The collected data was utilized to train and test a classification model achieving high accuracy with Support Vector Machine (SVM). SVM was further utilized as a predictor of future home states. Based on the data collection, classification and prediction models, a system was designed that can learn, run with minimal human supervision and detect anomalies in a home. The aforementioned attributes make the system an asset for senior care scenarios

Artificial intelligence enhances the performance of chaotic baseband wireless communication

Funding Information: This work was supported in part by Shaanxi Provincial Special Support Program for Science and Technology Innovation Leader. Dr Bai was supported in part by China Postdoctoral Science Foundation Funded Project (2020M673349), and Open Research Fund from Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing (2020CP02).Peer reviewedPublisher PD

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose