On Green Energy Powered Cognitive Radio Networks

Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters

A Low-Overhead Energy Detection Based Cooperative Sensing Protocol for Cognitive Radio Systems

Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can participate in distributive sensing. In this paper, we shall propose a novel, low overhead and low complexity energy detection based cooperative sensing framework for the cognitive radio systems which addresses the above two issues. The energy detection based cooperative sensing scheme greatly reduces the quiet period overhead (for sensing measurement) as well as sensing reporting overhead of the secondary systems and the power scheduling algorithm dynamically allocate the transmission power of the cooperative sensor nodes based on the channel statistics of the links to the BS as well as the quality of the sensing measurement. In order to obtain design insights, we also derive the asymptotic sensing performance of the proposed cooperative sensing framework based on the mobility model. We show that the false alarm and mis-detection performance of the proposed cooperative sensing framework improve as we increase the number of cooperative sensor nodes.Comment: 11 pages, 8 figures, journal. To appear in IEEE Transactions on Wireless Communication

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

Dynamic Profit Maximization of Cognitive Mobile Virtual Network Operator

We study the profit maximization problem of a cognitive virtual network operator in a dynamic network environment. We consider a downlink OFDM communication system with various network dynamics, including dynamic user demands, uncertain sensing spectrum resources, dynamic spectrum prices, and time-varying channel conditions. In addition, heterogenous users and imperfect sensing technology are incorporated to make the network model more realistic. By exploring the special structural of the problem, we develop a low-complexity on-line control policies that determine pricing and resource scheduling without knowing the statistics of dynamic network parameters. We show that the proposed algorithms can achieve arbitrarily close to the optimal profit with a proper trade-off with the queuing delay

Probability Density Function Estimation in OFDM Transmitter and Receiver in Radio Cognitive Networks based on Recurrent Neural Network

The most important problem in telecommunication is bandwidth limitation due to the uncontrolled growth of wireless technology. Deploying dynamic spectrum access techniques is one of the procedures provided for efficient use of bandwidth. In recent years, cognitive radio network introduced as a tool for efficient use of spectrum. These radios are able to use radio resources by recognizing surroundings via sensors and signal operations that means use these resources only when authorized users do not use their spectrum. Secondary users are unauthorized ones that must avoid from interferences with primary users transmission. Secondary users must leave channel due to preventing damages to primary users whenever these users discretion. In this article, spectrum opportunities prediction based on Recurrent Neural Network for bandwidth optimization and reducing the amount of energy by predicting spectrum holes discovery for quality of services optimization proposed in OFDM-based cognitive radio network based on probability density function. The result of the simulation represent acceptable value of SNR and bandwidth optimization in these networks that allows secondary users to taking spectrum and sending data without collision and overlapping with primary users.Comment: OFDM, Cognitive Radio Networks, Recurrent Neural Network, Probability Density Functio

Green Sensing and Access: Energy-Throughput Tradeoffs in Cognitive Networking

Limited spectrum resources and dramatic growth of high data rate applications have motivated opportunistic spectrum access utilizing the promising concept of cognitive networks. Although this concept has emerged primarily to enhance spectrum utilization and to allow the coexistence of heterogeneous network technologies, the importance of energy consumption imposes additional challenges, because energy consumption and communication performance can be at odds. In this paper, the approaches for energy efficient spectrum sensing and spectrum handoff, fundamental building blocks of cognitive networks is investigated. The tradeoff between energy consumption and throughput, under local as well as under cooperative sensing are characterized, and what further aspects need to be investigated to achieve energy efficient cognitive operation under various application requirements are discussed.Comment: to be published in IEEE Communications Magazine, 8 pages, 1 table, 6 figures. arXiv admin note: substantial text overlap with arXiv:1312.004

FreeNet: Spectrum and Energy Harvesting Wireless Networks

The dramatic mobile data traffic growth is not only resulting in the spectrum crunch but is also leading to exorbitant energy consumption. It is thus desirable to liberate mobile and wireless networks from the constraint of the spectrum scarcity and to rein in the growing energy consumption. This article introduces FreeNet, figuratively synonymous to "Free Network", which engineers the spectrum and energy harvesting techniques to alleviate the spectrum and energy constraints by sensing and harvesting spare spectrum for data communications and utilizing renewable energy as power supplies, respectively. Hence, FreeNet increases the spectrum and energy efficiency of wireless networks and enhances the network availability. As a result, FreeNet can be deployed to alleviate network congestion in urban areas, provision broadband services in rural areas, and upgrade emergency communication capacity. This article provides a brief analysis of the design of FreeNet that accommodates the dynamics of the spare spectrum and employs renewable energy

On Scalable Video Streaming over Cognitive Radio Cellular and Ad Hoc Networks

Video content delivery over wireless networks is expected to grow drastically in the coming years. In this paper, we investigate the challenging problem of video over cognitive radio (CR) networks. Although having high potential, this problem brings about a new level of technical challenges. After reviewing related work, we first address the problem of video over infrastructure-based CR networks, and then extend the problem to video over non-infrastructure-based ad hoc CR networks. We present formulations of cross-layer optimization problems as well as effective algorithms to solving the problems. The proposed algorithms are analyzed with respect to their optimality and validate with simulations

Sensing-Throughput Tradeoff for Superior Selective Reporting-based Spectrum Sensing in Energy Harvesting HCRNs

In this paper, we investigate the performance of conventional cooperative sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing in an energy harvesting-enabled heterogeneous cognitive radio network (HCRN). In particular, we derive expressions for the achievable throughput of both schemes and formulate nonlinear integer programming problems, in order to find the throughput-optimal set of spectrum sensors scheduled to sense a particular channel, given primary user (PU) interference and energy harvesting constraints. Furthermore, we present novel solutions for the underlying optimization problems based on the cross-entropy (CE) method, and compare the performance with exhaustive search and greedy algorithms. Finally, we discuss the tradeoff between the average achievable throughput of the SSR and CCS schemes, and highlight the regime where the SSR scheme outperforms the CCS scheme. Notably, we show that there is an inherent tradeoff between the channel available time and the detection accuracy. Our numerical results show that, as the number of spectrum sensors increases, the channel available time gains a higher priority in an HCRN, as opposed to detection accuracy

Routing Protocols for Cognitive Radio Networks: A Survey

This article has been withdrawn by arXiv administrators because it plagiarises http://www2.ece.ohio-state.edu/~ekici/papers/crnroutingsurvey.pdfComment: This article has been withdrawn by arXiv administrators because it plagiarises http://www2.ece.ohio-state.edu/~ekici/papers/crnroutingsurvey.pd