123 research outputs found

    Intelligent Approaches for Energy-Efficient Resource Allocation in the Cognitive Radio Network

    Get PDF
    The cognitive radio (CR) is evolved as the promising technology to alleviate the spectrum scarcity issues by allowing the secondary users (SUs) to use the licensed band in an opportunistic manner. Various challenges need to be addressed before the successful deployment of CR technology. This thesis work presents intelligent resource allocation techniques for improving energy efficiency (EE) of low battery powered CR nodes where resources refer to certain important parameters that directly or indirectly affect EE. As far as the primary user (PU) is concerned, the SUs are allowed to transmit on the licensed band until their transmission power would not cause any interference to the primary network. Also, the SUs must use the licensed band efficiently during the PU’s absence. Therefore, the two key factors such as protection to the primary network and throughput above the threshold are important from the PU’s and SUs’ perspective, respectively. In deployment of CR, malicious users may be more active to prevent the CR users from accessing the spectrum or cause unnecessary interference to the both primary and secondary transmission. Considering these aspects, this thesis focuses on developing novel approaches for energy-efficient resource allocation under the constraints of interference to the PR, minimum achievable data rate and maximum transmission power by optimizing the resource parameters such as sensing time and the secondary transmission power with suitably selecting SUs. Two different domains considered in this thesis are the soft decision fusion (SDF)-based cooperative spectrum sensing CR network (CRN) models without and with the primary user emulation attack (PUEA). An efficient iterative algorithm called iterative Dinkelbach method (IDM) is proposed to maximize EE with suitable SUs in the absence of the attacker. In the proposed approaches, different constraints are evaluated considering the negative impact of the PUE attacker on the secondary transmission while maximizing EE with the PUE attacker. The optimization problem associated with the non-convex constraints is solved by our proposed iterative resource allocation algorithms (novel iterative resource allocation (NIRA) and novel adaptive resource allocation (NARA)) with suitable selection of SUs for jointly optimizing the sensing time and power allocation. In the CR enhanced vehicular ad hoc network (CR-VANET), the time varying channel responses with the vehicular movement are considered without and with the attacker. In the absence of the PUE attacker, an interference-aware power allocation scheme based on normalized least mean square (NLMS) algorithm is proposed to maximize EE considering the dynamic constraints. In the presence of the attacker, the optimization problem associated with the non-convex and time-varying constraints is solved by an efficient approach based on genetic algorithm (GA). Further, an investigation is attempted to apply the CR technology in industrial, scientific and medical (ISM) band through spectrum occupancy prediction, sub-band selection and optimal power allocation to the CR users using the real time indoor measurement data. Efficacies of the proposed approaches are verified through extensive simulation studies in the MATLAB environment and by comparing with the existing literature. Further, the impacts of different network parameters on the system performance are analyzed in detail. The proposed approaches will be highly helpful in designing energy-efficient CRN model with low complexity for future CR deployment

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

    Full text link
    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.Comment: 46 pages, 22 fig

    From MANET to people-centric networking: Milestones and open research challenges

    Get PDF
    In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

    A Comprehensive Survey on Networking over TV White Spaces

    Full text link
    The 2008 Federal Communication Commission (FCC) ruling in the United States opened up new opportunities for unlicensed operation in the TV white space spectrum. Networking protocols over the TV white spaces promise to subdue the shortcomings of existing short-range multi-hop wireless architectures and protocols by offering more availability, wider bandwidth, and longer-range communication. The TV white space protocols are the enabling technologies for sensing and monitoring, Internet-of-Things (IoT), wireless broadband access, real-time, smart and connected community, and smart utility applications. In this paper, we perform a retrospective review of the protocols that have been built over the last decade and also the new challenges and the directions for future work. To the best of our knowledge, this is the first comprehensive survey to present and compare existing networking protocols over the TV white spaces.Comment: 19 page

    Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

    Full text link
    © 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements
    corecore