Cognitive Radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than existing multi-channel multi-radio (MC-MR) technology. In this thesis, we investigate the important problem of multi-hop networking with CR nodes. In a CR network, each node has a set of frequency bands (not necessarily of equal size) that may not be the same as those at other nodes. The uneven size of frequency bands prompts the need of further division into sub-bands for optimal spectrum sharing. We characterize behaviors and constraints for such multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We give a formal mathematical formulation with the objective of maximizing the network throughput for a set of user communication sessions. Since such problem formulation falls into mixed integer non-linear programming (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and linearization. Subsequently, we develop a nearoptimal algorithm to this MINLP problem. This algorithm is based on a novel sequential fixing (SF) procedure, where the integer variables are determined iteratively via a sequence of linear program (LP). In order to implement and evaluate these algorithms in a controlled laboratory setting, we design and implement an emulation testbed. The highlights of our experimental research include: • Emulation of a multi-hop CR network with arbitrary topology; • An implementation of the proposed SF algorithm at the application layer; • A source routing implementation that can easily support comparative study between SF algorithm and other schemes; • Experiments comparing the SF algorithm with another algorithm called Layered Greedy Algorithm (LGA); • Experimental results show that the proposed SF significantly outperforms LGA. In summary, the experimental research in this thesis has demonstrated that SF algorithm is a viable algorithm for optimal spectrum sharing in multi-hop CR networks. / Master of Science

Mobile Ad hoc Networks (MANETs) can be deployed instantaneously and adaptively, making them highly suitable to military, medical and disaster-response scenarios. Using real-time applications for provision of instantaneous and dependable communications, media streaming, and device control in these scenarios is a growing research field. Realising timing requirements in packet delivery is essential to safety-critical real-time applications that are both delay- and loss-sensitive. Safety of these applications is compromised by packet loss, both on the network and by the applications themselves that will drop packets exceeding delay bounds. However, the provision of this required Quality of Service (QoS) must overcome issues relating to the lack of reliable existing infrastructure, conservation of safety-certified functionality. It must also overcome issues relating to the layer-2 dynamics with causal factors including hidden transmitters and fading channels. This thesis proposes that bounded maximum delay and safety-critical application support can be achieved by using cross-layer middleware. Such an approach benefits from the use of established protocols without requiring modifications to safety-certified ones. This research proposes ROAM: a novel, adaptive and scalable cross-layer Real-time Optimising Ad hoc Middleware framework for the provision and maintenance of performance guarantees in self-configuring MANETs. The ROAM framework is designed to be scalable to new optimisers and MANET protocols and requires no modifications of protocol functionality. Four original contributions are proposed: (1) ROAM, a middleware entity abstracts information from the protocol stack using application programming interfaces (APIs) and that implements optimisers to monitor and autonomously tune conditions at protocol layers in response to dynamic network conditions. The cross-layer approach is MANET protocol generic, using minimal imposition on the protocol stack, without protocol modification requirements. (2) A horizontal handoff optimiser that responds to time-varying link quality to ensure optimal and most robust channel usage. (3) A distributed contention reduction optimiser that reduces channel contention and related delay, in response to detection of the presence of a hidden transmitter. (4) A feasibility evaluation of the ROAM architecture to bound maximum delay and jitter in a comprehensive range of ns2-MIRACLE simulation scenarios that demonstrate independence from the key causes of network dynamics: application setting and MANET configuration; including mobility or topology. Experimental results show that ROAM can constrain end-to-end delay, jitter and packet loss, to support real-time applications with critical timing requirements.

Device-to-Device communications have the great potential to bring significant performance boost to the conventional heterogeneous network by reusing cellular resources. In cellular networks, Device-to-Device communication is defined as two user equipments in a close range communicating directly with each other without going through the base station, thus offloading cellular traffic from cellular networks. In addition to improve network spectral efficiency, D2D communication can also improve energy efficiency and user experience. However, the co-existence of D2D communication on the same spectrum with cellular users can cause severe interference to the primary cellular users. Thus the performance of cellular users must be assured when supporting underlay D2D users. In this work, we have investigated cross-layer optimization, resource allocation and interference management schemes to improve user experience, system spectral efficiency and energy efficiency for D2D communication underlaying heterogeneous networks. By exploiting frequency reuse and multi-user diversity, this research work aims to design wireless system level algorithms to utilize the spectrum and energy resources efficiently in the next generation wireless heterogeneous network.

This research investigates cross-layer design in multi-hop wireless networks with random access. Due to the complexity of the problem, we study cross-layer design with a simple slotted ALOHA medium access control (MAC) protocol without considering any network dynamics. Firstly, we study the optimal joint configuration of routing and MAC parameters in slotted ALOHA based wireless networks under a signal to interference plus noise ratio based physical interference model. We formulate a joint routing and MAC (JRM) optimization problem under a saturation assumption to determine the optimal max-min throughput of the flows and the optimal configuration of routing and MAC parameters. The JRM optimization problem is a complex non-convex problem. We solve it by an iterated optimal search (IOS) technique and validate our model via simulation. Via numerical and simulation results, we show that JRM design provides a significant throughput gain over a default configuration in a slotted ALOHA based wireless network. Next, we study the optimal joint configuration of routing, MAC, and network coding in wireless mesh networks using an XOR-like network coding without opportunistic listening. We reformulate the JRM optimization problem to include the simple network coding and obtain a more complex non-convex problem. Similar to the JRM problem, we solve it by the IOS technique and validate our model via simulation. Numerical and simulation results for different networks illustrate that (i) the jointly optimized configuration provides a remarkable throughput gain with respect to a default configuration in a slotted ALOHA system with network coding and (ii) the throughput gain obtained by the simple network coding is significant, especially at low transmission power, i.e., the gain obtained by jointly optimizing routing, MAC, and network coding is significant even when compared to an optimized network without network coding. We then show that, in a mesh network, a significant fraction of the throughput gain for network coding can be obtained by limiting network coding to nodes directly adjacent to the gateway. Next, we propose simple heuristics to configure slotted ALOHA based wireless networks without and with network coding. These heuristics are extensively evaluated via simulation and found to be very efficient. We also formulate problems to jointly configure not only the routing and MAC parameters but also the transmission rate parameters in multi-rate slotted ALOHA systems without and with network coding. We compare the performance of multi-rate and single rate systems via numerical results. We model the energy consumption in terms of slotted ALOHA system parameters. We found out that the energy consumption for various cross-layer systems, i.e., single rate and multi-rate slotted ALOHA systems without and with network coding, are very close.

In order to meet the increasing demand for higher data rates, next generation wireless networks must incorporate additional functionalities to enhance network throughput. Multihop networks are considered as a promising alternative due to their ability to exploit spatial reuse and to extend coverage. Recently, industry has shown increased interest in multihop networks as they do not require additional infrastructure and have relatively low deployment costs. Many advances in physical and network layer techniques have been proposed in the recent past and they have been studied mostly in single-hop networks. Very few studies, if any, have tried to quantify the gains that these techniques could provide in multihop networks. We investigate the impact of simple network coding, advanced physical layer and cooperative techniques on the maximum achievable throughput of wireless multihop networks of practical size. We consider the following advanced physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding, and some of their combinations. We achieve this by formulating several cross-layer frameworks when these techniques are jointly optimized with routing and scheduling. We also formulate power allocation subproblems for the cases of continuous power control and superposition coding. We also provide numerous engineering insights by solving these problems to optimality.

This research investigates cross-layer design in multi-hop wireless networks with random access. Due to the complexity of the problem, we study cross-layer design with a simple slotted ALOHA medium access control (MAC) protocol without considering any network dynamics. Firstly, we study the optimal joint configuration of routing and MAC parameters in slotted ALOHA based wireless networks under a signal to interference plus noise ratio based physical interference model. We formulate a joint routing and MAC (JRM) optimization problem under a saturation assumption to determine the optimal max-min throughput of the flows and the optimal configuration of routing and MAC parameters. The JRM optimization problem is a complex non-convex problem. We solve it by an iterated optimal search (IOS) technique and validate our model via simulation. Via numerical and simulation results, we show that JRM design provides a significant throughput gain over a default configuration in a slotted ALOHA based wireless network. Next, we study the optimal joint configuration of routing, MAC, and network coding in wireless mesh networks using an XOR-like network coding without opportunistic listening. We reformulate the JRM optimization problem to include the simple network coding and obtain a more complex non-convex problem. Similar to the JRM problem, we solve it by the IOS technique and validate our model via simulation. Numerical and simulation results for different networks illustrate that (i) the jointly optimized configuration provides a remarkable throughput gain with respect to a default configuration in a slotted ALOHA system with network coding and (ii) the throughput gain obtained by the simple network coding is significant, especially at low transmission power, i.e., the gain obtained by jointly optimizing routing, MAC, and network coding is significant even when compared to an optimized network without network coding. We then show that, in a mesh network, a significant fraction of the throughput gain for network coding can be obtained by limiting network coding to nodes directly adjacent to the gateway. Next, we propose simple heuristics to configure slotted ALOHA based wireless networks without and with network coding. These heuristics are extensively evaluated via simulation and found to be very efficient. We also formulate problems to jointly configure not only the routing and MAC parameters but also the transmission rate parameters in multi-rate slotted ALOHA systems without and with network coding. We compare the performance of multi-rate and single rate systems via numerical results. We model the energy consumption in terms of slotted ALOHA system parameters. We found out that the energy consumption for various cross-layer systems, i.e., single rate and multi-rate slotted ALOHA systems without and with network coding, are very close.

In order to meet the increasing demand for higher data rates, next generation wireless networks must incorporate additional functionalities to enhance network throughput. Multihop networks are considered as a promising alternative due to their ability to exploit spatial reuse and to extend coverage. Recently, industry has shown increased interest in multihop networks as they do not require additional infrastructure and have relatively low deployment costs. Many advances in physical and network layer techniques have been proposed in the recent past and they have been studied mostly in single-hop networks. Very few studies, if any, have tried to quantify the gains that these techniques could provide in multihop networks. We investigate the impact of simple network coding, advanced physical layer and cooperative techniques on the maximum achievable throughput of wireless multihop networks of practical size. We consider the following advanced physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding, and some of their combinations. We achieve this by formulating several cross-layer frameworks when these techniques are jointly optimized with routing and scheduling. We also formulate power allocation subproblems for the cases of continuous power control and superposition coding. We also provide numerous engineering insights by solving these problems to optimality.

The novelty of this dissertation is the optimization and acceleration of deep neural networks aimed at real-time predictions with minimal energy consumption. It consists of cross-layer optimization, output directed dynamic quantization, and opportunistic near-data computation for deep neural network acceleration. On two datasets (CIFAR-10 and CIFAR-100), the proposed deep neural network optimization and acceleration frameworks are tested using a variety of Convolutional neural networks (e.g., LeNet-5, VGG-16, GoogLeNet, DenseNet, ResNet). Experimental results are promising when compared to other state-of-the-art deep neural network acceleration efforts in the literature.

For complex computer networks with many tunable parameters and network performance objectives, the task of selecting the ideal network operating state is difficult. To improve the performance of these kinds of networks, this research proposes the idea of the cognitive network. A cognitive network is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. In a cognitive network, decisions are made to meet the requirements of the network as a whole, rather than the individual network components. We examine the cognitive network concept by first providing a definition and then outlining the difference between it and other cognitive and cross-layer technologies. From this definition, we develop a general, three-layer cognitive network framework, based loosely on the framework used for cognitive radio. In this framework, we consider the possibility of a cognitive process consisting of one or more cognitive elements, software agents that operate somewhere between autonomy and cooperation. To understand how to design a cognitive network within this framework we identify three critical design decisions that affect the performance of the cognitive network: the selfishness of the cognitive elements, their degree of ignorance, and the amount of control they have over the network. To evaluate the impact of these decisions, we created a metric called the price of a feature, defined as the ratio of the network performance with a certain design decision to the performance without the feature. To further aid in the design of cognitive networks, we identify classes of cognitive networks that are structurally similar to one another. We examined two of these classes: the potential class and the quasi-concave class. Both classes of networks will converge to Nash Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desirable properties, reacting well to the absence of certain kinds of information and degrading gracefully under reduced network control. In addition to these analytical, high level contributions, we develop cognitive networks for two open problems in resource management for self-organizing networks, validating and illustrating the cognitive network approach. For the first problem, a cognitive network is shown to increase the lifetime of a wireless multicast route by up to 125\%. For this problem, we show that the price of selfishness and control are more significant than the price of ignorance. For the second problem, a cognitive network minimizes the transmission power and spectral impact of a wireless network topology under static and dynamic conditions. The cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12\% of the minimum channel assignment. For this problem, we investigate the price of ignorance under dynamic networks and the cost of maintaining knowledge in the network. Today's computer networking technology will not be able to solve the complex problems that arise from increasingly bandwidth-intensive applications competing for scarce resources. Cognitive networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future investigation and implementation. / Ph. D.

Wireless ad-hoc networks are easily deployed, untethered to infrastructure, and have virtually an unlimited number of applications. However, this flexibility comes at the cost of finite and often unreplenishable power supplies. Once a node has consumed all of its power, it can no longer receive, transmit, gather information, or otherwise participate in the network. Therefore, reducing the amount of energy necessary for node communication has been an area of intense research. Previous work has investigated the use of directional antennas as a method to reduce inter-node power requirements. However, most proposed methods ignore inter-session interference, propose heuristic solution methods, and ignore the use of directional antennas for signal reception. We develop a flexible mixed-integer linear program (MILP) designed to optimize max-min multicast path lifetime for directional antenna equipped networks in the presence of interference. The MILP is utilized to perform a comparison directional antenna use for signal transmission and reception. Results indicate that directional reception is slightly superior to transmission for the defined max-min lifetime metric, and vastly superior when considering cumulative power use. We further analyze the performance of interference-ignorant link-based heuristics designed for both directional transmission and directional reception as they perform in our more realistic model. Our results show that interference-ignorant methods cannot find feasible solutions unless all nodes are equipped with high gain, high efficiency directional antennas. Even in these cases, directional reception outperforms directional transmission. Because of the superiority of directional reception, we focus our attention on this method. A heterogeneity study is performed, and two heuristic methods for approximating the MILP optima are developed. We find that even under heterogeneous conditions, directional reception can increase network lifetime. Finally, a genetic algorithm (GA) and semi-distributed heuristic method are developed as alternatives to the MILP. Results show that the GA often can find solutions with lifetimes 85% as long as the optimal. Our semi-distributed heuristic, designed to be even more computationally simple than the GA, and to serve as a basis for a distributed protocol, is almost as effective as the GA as approximating optimal solutions. We conclude that directional reception is the superior method of antenna use for extending max-min multicast tree lifetime, that it works well in heterogeneous conditions, and lends itself well to heuristic design. / Ph. D.