Cognitive Communications in White Space: Opportunistic Scheduling, Spectrum Shaping and Delay Analysis

abstract: A unique feature, yet a challenge, in cognitive radio (CR) networks is the user hierarchy: secondary users (SU) wishing for data transmission must defer in the presence of active primary users (PUs), whose priority to channel access is strictly higher.Under a common thread of characterizing and improving Quality of Service (QoS) for the SUs, this dissertation is progressively organized under two main thrusts: the first thrust focuses on SU's throughput by exploiting the underlying properties of the PU spectrum to perform effective scheduling algorithms; and the second thrust aims at another important QoS performance of the SUs, namely delay, subject to the impact of PUs' activities, and proposes enhancement and control mechanisms. More specifically, in the first thrust, opportunistic spectrum scheduling for SU is first considered by jointly exploiting the memory in PU's occupancy and channel fading. In particular, the underexplored scenario where PU occupancy presents a {long} temporal memory is taken into consideration. By casting the problem as a partially observable Markov decision process, a set of {multi-tier} tradeoffs are quantified and illustrated. Next, a spectrum shaping framework is proposed by leveraging network coding as a {spectrum shaper} on the PU's traffic. Such shaping effect brings in predictability of the primary spectrum, which is utilized by the SUs to carry out adaptive channel sensing by prioritizing channel access order, and hence significantly improve their throughput. On the other hand, such predictability can make wireless channels more susceptible to jamming attacks. As a result, caution must be taken in designing wireless systems to balance the throughput and the jamming-resistant capability. The second thrust turns attention to an equally important performance metric, i.e., delay performance. Specifically, queueing delay analysis is conducted for SUs employing random access over the PU channels. Fluid approximation is taken and Poisson driven stochastic differential equations are applied to characterize the moments of the SUs' steady-state queueing delay. Then, dynamic packet generation control mechanisms are developed to meet the given delay requirements for SUs.Dissertation/ThesisPh.D. Electrical Engineering 201

Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks

Vehicular Dynamic Spectrum Access (VDSA) combines the advantages of dynamic spectrum access to achieve higher spectrum efficiency and the special mobility pattern of vehicle fleets. This dissertation presents several noval contributions with respect to vehicular communications, especially vehicle-to-vehicle communications. Starting from a system engineering aspect, this dissertation will present several promising future directions for vehicle communications, taking into consideration both the theoretical and practical aspects of wireless communication deployment. This dissertation starts with presenting a feasibility analysis using queueing theory to model and estimate the performance of VDSA within a TV whitespace environment. The analytical tool uses spectrum measurement data and vehicle density to find upper bounds of several performance metrics for a VDSA scenario in TVWS. Then, a framework for optimizing VDSA via artificial intelligence and learning, as well as simulation testbeds that reflect realistic spectrum sharing scenarios between vehicle networks and heterogeneous wireless networks including wireless local area networks and wireless regional area networks. Detailed experimental results justify the testbed for emulating a mobile dynamic spectrum access environment composed of heterogeneous networks with four dimensional mutual interference. Vehicular cooperative communication is the other proposed technique that combines the cooperative communication technology and vehicle platooning, an emerging concept that is expected to both increase highway utilization and enhance both driver experience and safety. This dissertation will focus on the coexistence of multiple vehicle groups in shared spectrum, where intra-group cooperation and inter-group competition are investigated in the aspect of channel access. Finally, a testbed implementation VDSA is presented and a few applications are developed within a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Energy Efficient and Cooperative Solutions for Next-Generation Wireless Networks

Energy efficiency is increasingly important for next-generation wireless systems due to the limited battery resources of mobile clients. While fourth generation cellular standards emphasize low client battery consumption, existing techniques do not explicitly focus on reducing power that is consumed when a client is actively communicating with the network. Based on high data rate demands of modern multimedia applications, active mode power consumption is expected to become a critical consideration for the development and deployment of future wireless technologies. Another reason for focusing more attention on energy efficient studies is given by the relatively slow progress in battery technology and the growing quality of service requirements of multimedia applications. The disproportion between demanded and available battery capacity is becoming especially significant for small-scale mobile client devices, where wireless power consumption dominates within the total device power budget. To compensate for this growing gap, aggressive improvements in all aspects of wireless system design are necessary. Recent work in this area indicates that joint link adaptation and resource allocation techniques optimizing energy efficient metrics can provide a considerable gain in client power consumption. Consequently, it is crucial to adapt state-of-the-art energy efficient approaches for practical use, as well as to illustrate the pros and cons associated with applying power-bandwidth optimization to improve client energy efficiency and develop insights for future research in this area. This constitutes the first objective of the present research. Together with energy efficiency, next-generation cellular technologies are emphasizing stronger support for heterogeneous multimedia applications. Since the integration of diverse services within a single radio platform is expected to result in higher operator profits and, at the same time, reduce network management expenses, intensive research efforts have been invested into design principles of such networks. However, as wireless resources are limited and shared by clients, service integration may become challenging. A key element in such systems is the packet scheduler, which typically helps ensure that the individual quality of service requirements of wireless clients are satisfied. In contrastingly different distributed wireless environments, random multiple access protocols are beginning to provide mechanisms for statistical quality of service assurance. However, there is currently a lack of comprehensive analytical frameworks which allow reliable control of the quality of service parameters for both cellular and local area networks. Providing such frameworks is therefore the second objective of this thesis. Additionally, the study addresses the simultaneous operation of a cellular and a local area network in spectrally intense metropolitan deployments and solves some related problems. Further improving the performance of battery-driven mobile clients, cooperative communications are sought as a promising and practical concept. In particular, they are capable of mitigating the negative effects of fading in a wireless channel and are thus expected to enhance next-generation cellular networks in terms of client spectral and energy efficiencies. At the cell edges or in areas missing any supportive relaying infrastructure, client-based cooperative techniques are becoming even more important. As such, a mobile client with poor channel quality may take advantage of neighboring clients which would relay data on its behalf. The key idea behind the concept of client relay is to provide flexible and distributed control over cooperative communications by the wireless clients themselves. By contrast to fully centralized control, this is expected to minimize overhead protocol signaling and hence ensure simpler implementation. Compared to infrastructure relay, client relay will also be cheaper to deploy. Developing the novel concept of client relay, proposing simple and feasible cooperation protocols, and analyzing the basic trade-offs behind client relay functionality become the third objective of this research. Envisioning the evolution of cellular technologies beyond their fourth generation, it appears important to study a wireless network capable of supporting machine-to-machine applications. Recent standardization documents cover a plethora of machine-to-machine use cases, as they also outline the respective technical requirements and features according to the application or network environment. As follows from this activity, a smart grid is one of the primary machine-to-machine use cases that involves meters autonomously reporting usage and alarm information to the grid infrastructure to help reduce operational cost, as well as regulate a customer's utility usage. The preliminary analysis of the reference smart grid scenario indicates weak system architecture components. For instance, the large population of machine-to-machine devices may connect nearly simultaneously to the wireless infrastructure and, consequently, suffer from excessive network entry delays. Another concern is the performance of cell-edge machine-to-machine devices with weak wireless links. Therefore, mitigating the above architecture vulnerabilities and improving the performance of future smart grid deployments is the fourth objective of this thesis. Summarizing, this thesis is generally aimed at the improvement of energy efficient properties of mobile devices in next-generation wireless networks. The related research also embraces a novel cooperation technique where clients may assist each other to increase per-client and network-wide performance. Applying the proposed solutions, the operation time of mobile clients without recharging may be increased dramatically. Our approach incorporates both analytical and simulation components to evaluate complex interactions between the studied objectives. It brings important conclusions about energy efficient and cooperative client behaviors, which is crucial for further development of wireless communications technologies

Prediction-based Dynamic Capacity Alloction for Traffic Cost Minimization

Department of Computer Science and EngineeringRecent advances in network virtualization techniques have shed light on dynamic resource allocation according to traffic usage. In particular, the minimum total network usage cost is achievable by using on-the-fly capacity allocation with accurate traffic estimation. In practice, there is an unavoidable delay for system reconfiguration, and thus a precise prediction on the traffic usage is required, which is, however, challenging due to unexpected system dynamics such as mobility and time-varying wireless channels. In this work, we address the prediction-based capacity allocation to minimize traffic cost by exploiting deep learning techniques. We develop an MLP model for accurate prediction of traffic usage, which is trained with real-world system logs obtained in a firewall. Taking into account the prediction errors and asymmetric structure of capacity pricing, we develop an efficient online capacity allocation scheme that achieves low traffic cost. We also evaluate the performance of our solution using the real-world data.clos

Design and optimisation of a low cost Cognitive Mesh Network

Wireless Mesh Networks (WMNs) have been touted as the most promising wireless technology in providing high-bandwidth Internet access to rural, remote and under-served areas, with relatively lower investment cost as compared to traditional access networks. WMNs structurally comprise of mesh routers and mesh clients. Furthermore, WMNs have an envisaged ability to provide a heterogeneous network system that integrates wireless technologies such as IEEE 802.22 WRAN, IEEE 802.16 WiMAX, IEEE 802.11 Wi-Fi, Blue-tooth etc. The recent proliferation of new devices on the market such as smart phones and, tablets, and the growing number of resource hungry applications has placed a serious strain on spectrum availability which gives rise to the spectrum scarcity problem. The spectrum scarcity problem essentially results in increased spectrum prices that hamper the growth and efficient performance of WMNs as well as subsequent transformation of WMN into the envisaged next generation networks. Recent developments in TV white space communications technology and the emergence of Cognitive radio devices that facilitate Dynamic Spectrum Access (DSA) have provided an opportunity to mitigate the spectrum scarcity problem. To solve the scarcity problem, this thesis reconsiders the classical Network Engineering (NE) and Traffic Engineering (TE) problems to objectively design a low cost Cognitive Mesh network that promotes efficient resources utilization and thereby achieve better Quality of Service (QoS) levels

Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error. The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics. In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information. In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding

Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic

Wireless mesh networking has emerged as a promising technology for future broadband wireless access, providing a viable and economical solution for both peer-to-peer applications and Internet access. The success of wireless mesh networks (WMNs) is highly contingent on effective radio resource management. In conventional wireless networks, system throughput is usually a common performance metric. However, next-generation broadband wireless access networks including WMNs are anticipated to support multimedia traffic (e.g., voice, video, and data traffic). With heterogeneous traffic, quality-of-service (QoS) provisioning and fairness support are also imperative. Recently, wireless mesh networking for suburban/rural residential areas has been attracting a plethora of attentions from industry and academia. With austere suburban and rural networking environments, multi-hop communications with decentralized resource allocation are preferred. In WMNs without powerful centralized control, simple yet effective resource allocation approaches are desired for the sake of system performance melioration. In this dissertation, we conduct a comprehensive research study on the topic of radio resource management for WMNs supporting multimedia traffic. In specific, this dissertation is intended to shed light on how to effectively and efficiently manage a WMN for suburban/rural residential areas, provide users with high-speed wireless access, support the QoS of multimedia applications, and improve spectrum utilization by means of novel radio resource allocation. As such, five important resource allocation problems for WMNs are addressed, and our research accomplishments are briefly outlined as follows: Firstly, we propose a novel node clustering algorithm with effective subcarrier allocation for WMNs. The proposed node clustering algorithm is QoS-aware, and the subcarrier allocation is optimality-driven and can be performed in a decentralized manner. Simulation results show that, compared to a conventional conflict-graph approach, our proposed approach effectively fosters frequency reuse, thereby improving system performance; Secondly, we propose three approaches for joint power-frequency-time resource allocation. Simulation results show that all of the proposed approaches are effective in provisioning packet-level QoS over their conventional resource allocation counterparts. Our proposed approaches are of low complexity, leading to preferred candidates for practical implementation; Thirdly, to further enhance system performance, we propose two low-complexity node cooperative resource allocation approaches for WMNs with partner selection/allocation. Simulation results show that, with beneficial node cooperation, both proposed approaches are promising in supporting QoS and elevating system throughput over their non-cooperative counterparts; Fourthly, to further utilize the temporarily available radio spectrum, we propose a simple channel sensing order for unlicensed secondary users. By sensing the channels according to the descending order of their achievable rates, we prove that a secondary user should stop at the first sensed free channel for the sake of optimality; and Lastly, we derive a unified optimization framework to effectively attain different degrees of performance tradeoff between throughput and fairness with QoS support. By introducing a bargaining floor, the optimal tradeoff curve between system throughput and fairness can be obtained by solving the proposed optimization problem iteratively