Alireza Attar

Bio: Alireza Attar is an academic researcher from University of British Columbia. The author has contributed to research in topics: Cognitive radio & Resource allocation. The author has an hindex of 13, co-authored 34 publications receiving 971 citations. Previous affiliations of Alireza Attar include Pennsylvania State University.

A Survey of Security Challenges in Cognitive Radio Networks: Solutions and Future Research Directions

Abstract: In this survey, we present a comprehensive list of major known security threats within a cognitive radio network (CRN) framework. We classify attack techniques based on the type of attacker, namely exogenous (external) attackers, intruding malicious nodes and greedy cognitive radios (CRs). We further discuss threats related to infrastructure-based CRNs as well as infrastructure-less networks. Besides the short-term effects of attacks over CRN performance, we also discuss the often ignored longer term behavioral changes that are enforced by such attacks via the learning capability of CRN. After elaborating on various attack strategies, we discuss potential solutions to combat those attacks. An overview of robust CR communications is also presented. We finally elaborate on future research directions pertinent to CRN security. We hope this survey paper can provide the insight and the roadmap for future research efforts in the emerging field of CRN security.

Interference management using cognitive base-stations for UMTS LTE

Abstract: In this article we demonstrate the benefits of developing cognitive base-stations in a UMTS Long Term Evolution (LTE) network. Two types of cognitive base-stations are considered: the macro-cell evolved-NodeB (eNB) and the femtocell Home evolved NodeBs (HeNB). In the context of an isolated cell or a multi-cell LTE network, the insufficiency of traditional interference management schemes is shown. Implementation of cognitive tasks such as radio scene analysis and dynamic resource access are then introduced. We argue that such cognitive basestations can exploit their knowledge of the radio scene to intelligently allocate resources and to mitigate prohibitive Co-Channel Interference (CCI). Given the distributed architecture of LTE networks, we will elaborate on cognitive interference mitigation solutions and further propose two different Game Theoretical mechanisms to achieve CCI mitigation in a distributed manner.

Optimal Distributed Vertical Handoff Strategies in Vehicular Heterogeneous Networks

Abstract: This paper addresses the problem of optimal vertical handoff (VHO) in a vehicular network setting The VHO objective can be minimizing the data transfer time or alternatively minimizing the cost of transmitting traffic As a framework for performance evaluations, we first analyze a heterogeneous network consisting of a wide-area cellular network interworking with wireless local area networks (WLAN) with fixed inter-distance between access points (APs) placed along roadsides We further analyze a scenario with random inter-distance between WLAN APs In both aforementioned cases, only Vehicle-to-Infrastructure (V2I) capability is assumed We show that in order to minimize the cost of transmission or alternatively transmission time, performing VHOs is an appropriate choice at lower speeds, whereas it would be better to avoid VHO and stay in the cellular network at higher speeds We further generalize our study, to investigate the VHO strategies in a random inter-distance scenario with both V2I and Vehicle-to-Vehicle (V2V) communication capabilities We demonstrate that the combination of WLAN plus cellular plus ad hoc networking outperforms any other networking strategies considered in this work in terms of transmission times and transmission costs The presented results provide insightful guidelines for optimal VHO decision making based on the characteristics of the network as well as the user mobility profile

Collaborative Sub-Channel Allocation in Cognitive LTE Femto-Cells: A Cooperative Game-Theoretic Approach

Abstract: In this paper, formation of stable coalitions of users, each exploiting resources in a femto-cell, and the resource allocation in each femto-cell is investigated in a UMTS long term evolution (LTE) network. We study a downlink scenario where users collaborate to increase network throughput and, simultaneously, attempt to increase their own payoffs. Payoffs to the users are defined as the monetary equivalent of the individual users' achievable throughput in the specified coalition structure. A distributed game-theoretic resource allocation mechanism is developed whereby users autonomously decide which sub-channel in which coalition to join. If each user operates according to the proposed algorithm, the sum throughput of all links converges with probability one to its maximum feasible value.

Efficient HetNet implementation using broadband wireless access with fiber-connected massively distributed antennas architecture

Abstract: We introduce a novel HetNet architecture employing fiber-connected distributed antenna systems, named broadband wireless access with fiber-connected massively distributed antennas (BWA-FMDA), which facilitates coordination of resource allocation and interference management. Among various opportunities realized by the proposed approach, our focus in this article is on CoMP for UMTS LTE femto- and picocells, to which we refer as femto-CoMP. Through extensive simulation results we demonstrate the superior performance gain of a femto-CoMP HetNet over independent femto- and picocell operation in traditional HetNet scenarios. We analyze both link- and system-level feasible throughput as well as scheduling delay for round-robin and proportional fair schedulers.

Cognitive radio networking and communications: an overview

Abstract: Cognitive radio (CR) is the enabling technology for supporting dynamic spectrum access: the policy that addresses the spectrum scarcity problem that is encountered in many countries. Thus, CR is widely regarded as one of the most promising technologies for future wireless communications. To make radios and wireless networks truly cognitive, however, is by no means a simple task, and it requires collaborative effort from various research communities, including communications theory, networking engineering, signal processing, game theory, software-hardware joint design, and reconfigurable antenna and radio-frequency design. In this paper, we provide a systematic overview on CR networking and communications by looking at the key functions of the physical (PHY), medium access control (MAC), and network layers involved in a CR design and how these layers are crossly related. In particular, for the PHY layer, we will address signal processing techniques for spectrum sensing, cooperative spectrum sensing, and transceiver design for cognitive spectrum access. For the MAC layer, we review sensing scheduling schemes, sensing-access tradeoff design, spectrum-aware access MAC, and CR MAC protocols. In the network layer, cognitive radio network (CRN) tomography, spectrum-aware routing, and quality-of-service (QoS) control will be addressed. Emerging CRNs that are actively developed by various standardization committees and spectrum-sharing economics will also be reviewed. Finally, we point out several open questions and challenges that are related to the CRN design.

Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

Abstract: Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

Energy Efficiency in Wireless Networks via Fractional Programming Theory

Abstract: This monograph presents a unified framework for energy efficiency maximization in wireless networks via fractional programming theory. The definition of energy efficiency is introduced, with reference to single-user and multi-user wireless networks, and it is observed how the problem of resource allocation for energy efficiency optimization is naturally cast as a fractional program. An extensive review of the state-of-the-art in energy efficiency optimization by fractional programming is provided, with reference to centralized and distributed resource allocation schemes. A solid background on fractional programming theory is provided. The key-notion of generalized concavity is presented and its strong connection with fractional functions described. A taxonomy of fractional problems is introduced, and for each class of fractional problem, general solution algorithms are described, discussing their complexity and convergence properties. The described theoretical and algorithmic framework is applied to solve energy efficiency maximization problems in practical wireless networks. A general system and signal model is developed which encompasses many relevant special cases, such as one-hop and two-hop heterogeneous networks, multi-cell networks, small-cell networks, device-to-device systems, cognitive radio systems, and hardware-impaired networks, wherein multiple-antennas and multiple subcarriers are possibly employed. Energy-efficient resource allocation algorithms are developed, considering both centralized, cooperative schemes, as well as distributed approaches for self-organizing networks. Finally, some remarks on future lines of research are given, stating some open problems that remain to be studied. It is shown how the described framework is general enough to be extended in these directions, proving useful in tackling future challenges that may arise in the design of energy-efficient future wireless networks.

Internet of Vehicles: Architecture, Protocols, and Security

Abstract: Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad-hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV). We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Abstract: Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.