Cross-layer Optimization Resource Allocation in Wireless Networks

The rapid increase of the Internet and wireless communication in the past few years has made personal communication easier and less. The use of potable wireless devices for example smart phones and PDAs is on the increase. Video conferencing, and Multimedia streaming are expected to attract an increasing number of users in the nearest possible future. The requirement for such applications to run efficiently and effectively would be availability of higher resources, such as processing power, high battery life and data rate capabilities, just to mention a few. The TCP/IP protocol suite provides the protocols which is the standard for communications in the Internet today. However, they are not suitable for the next generation wireless devices and the mobile systems; this performance degradation is due to the limitations of wireless medium in terms of bandwidth, information loss, and latency which will be looked at in this paper. This paper compares the delay time of real time and non-real time traffic using two algorithms. Maximum Weighted Capacity uses a cross-layer optimization to share resources, while Maximum Capacity allocates resources based only on the users channel capacity. Keywords: Cross-layer optimization, TCP/IP, Maximum Capacity, Maximum Weighted Capacity, channel capacit

Energy Efficient Reduced Complexity Multi-Service, Multi-Channel Scheduling Techniques

The need for energy efficient communications is essential in current and next-generation wireless communications systems. A large component of energy expenditure in mobile devices is in the mobile radio interface. Proper scheduling and resource allocation techniques that exploit instantaneous and long-term average knowledge of the channel, queue state and quality of service parameters can be used to improve the energy efficiency of communication. This thesis focuses on exploiting queue and channel state information as well as quality of service parameters in order to design energy efficient scheduling techniques. The proposed designs are for multi-stream, multi-channel systems and in general have high computational complexity. The large contributions of this thesis are in both the design of optimal/near-optimal scheduling/resource allocation schemes for these systems as well as proposing complexity reduction methods in their design. Methods are proposed for both a MIMO downlink system as well as an LTE uplink system. The effect of power efficiency on quality of service parameters is well studied as well as complexity/efficiency comparisons between optimal/near optimal allocation

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, 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

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

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest

Resource management in future mobile networks: from millimetre-wave backhauls to airborne access networks

The next generation of mobile networks will connect vast numbers of devices and support services with diverse requirements. Enabling technologies such as millimetre-wave (mm-wave) backhauling and network slicing allow for increased wireless capacities and logical partitioning of physical deployments, yet introduce a number of challenges. These include among others the precise and rapid allocation of network resources among applications, elucidating the interactions between new mobile networking technology and widely used protocols, and the agile control of mobile infrastructure, to provide users with reliable wireless connectivity in extreme scenarios. This thesis presents several original contributions that address these challenges. In particular, I will first describe the design and evaluation of an airtime allocation and scheduling mechanism devised specifically for mm-wave backhauls, explicitly addressing inter-flow fairness and capturing the unique characteristics of mm-wave communications. Simulation results will demonstrate 5x throughput gains and a 5-fold improvement in fairness over recent mm-wave scheduling solutions. Second, I will introduce a utility optimisation framework targeting virtually sliced mm-wave backhauls that are shared by a number of applications with distinct requirements. Based on this framework, I will present a deep learning solution that can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms. The proposed solution outperforms a baseline greedy approach by up to 62%, in terms of network utility, while running orders of magnitude faster. Third, the thesis investigates the behaviour of the Transport Control Protocol (TCP) in Long-Term Evolution (LTE) networks and discusses the implications of employing Radio Link Control (RLC) acknowledgements under different link qualities, on the performance of transport protocols. Fourth, I will introduce a reinforcement learning approach to optimising the performance of airborne cellular networks serving users in emergency settings, demonstrating rapid convergence (approx. 2.5 hours on a desktop machine) and a 5dB improvement of the median Signal-to-Noise-plus-Interference-Ratio (SINR) perceived by users, over a heuristic based benchmark solution. Finally, the thesis discusses promising future research directions that follow from the results obtained throughout this PhD project

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks