A Study Of Cooperative Spectrum Sharing Schemes For Internet Of Things Systems

The Internet of Things (IoT) has gained much attention in recent years with the massive increase in the number of connected devices. Cognitive Machine-to-Machine (CM2M) communications is a hot research topic in which a cognitive dimension allows M2M networks to overcome the challenges of spectrum scarcity, interference, and green requirements. In this paper, we propose a Generalized Cooperative Spectrum Sharing (GCSS) scheme for M2M communication. Cooperation extends the coverage of wireless networks as well as increasing their throughput while reducing the energy consumption of the connected low power devices. We study the outage performance of the proposed GCSS scheme for M2M system and derive exact expressions for the outage probability. We also analyze the effect of varying transmission powers on the performance of the system

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

Joint Energy and Spectrum Cooperation for Cellular Communication Systems

Powered by renewable energy sources, cellular communication systems usually have different wireless traffic loads and available resources over time. To match their traffics, it is beneficial for two neighboring systems to cooperate in resource sharing when one is excessive in one resource (e.g., spectrum), while the other is sufficient in another (e.g., energy). In this paper, we propose a joint energy and spectrum cooperation scheme between different cellular systems to reduce their operational costs. When the two systems are fully cooperative in nature (e.g., belonging to the same entity), we formulate the cooperation problem as a convex optimization problem to minimize their weighted sum cost and obtain the optimal solution in closed form. We also study another partially cooperative scenario where the two systems have their own interests. We show that the two systems seek for partial cooperation as long as they find inter-system complementarity between the energy and spectrum resources. Under the partial cooperation conditions, we propose a distributed algorithm for the two systems to gradually and simultaneously reduce their costs from the non-cooperative benchmark to the Pareto optimum. This distributed algorithm also has proportional fair cost reduction by reducing each system's cost proportionally over iterations. Finally, we provide numerical results to validate the convergence of the distributed algorithm to the Pareto optimality and compare the centralized and distributed cost reduction approaches for fully and partially cooperative scenarios.Comment: This is the longer version of a paper to appear in IEEE Transactions on Communication

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

Cooperation in 5G HetNets: Advanced Spectrum Access and D2D Assisted Communications

The evolution of conventional wireless communication networks to the fifth generation (5G) is driven by an explosive increase in the number of wireless mobile devices and services, as well as their demand for all-time and everywhere connectivity, high data rates, low latency, high energy-efficiency and improved quality of service. To address these challenges, 5G relies on key technologies, such as full duplex (FD), device-to-device (D2D) communications, and network densification. In this article, a heterogeneous networking architecture is envisioned, where cells of different sizes and radio access technologies coexist. Specifically, collaboration for spectrum access is explored for both FD- and cognitive-based approaches, and cooperation among devices is discussed in the context of the state-of-the-art D2D assisted communication paradigm. The presented cooperative framework is expected to advance the understandings of the critical technical issues towards dynamic spectrum management for 5G heterogeneous networks.Comment: to appear in IEEE Wireless Communication

Outage Analysis of Spectrum Sharing Energy Harvesting Cognitive Relays in Nakagami-mm Channels

Energy harvesting (EH) cognitive relays are an exciting solution to the problem of inefficient use of spectrum while achieving green communications and spatial diversity. In a spectrum sharing scenario, we investigate the performance of a cognitive relay network, where a secondary source communicates with its destination over Nakagami-$m$ channels via decode-and-forward EH relays while maintaining the outage probability of the primary user below a predefined threshold. Specifically, we derive a closed-form expression for the secondary outage probability and show that it is a function of the probability of an EH relay having sufficient energy for relaying, which in turn, depends on the energy harvesting and consumption rates of the EH relay and the primary outage probability threshold. We also show that relaxing the primary outage constraint may not always benefit the cognitive EH relay network due to the limitations imposed on the relay's transmit power by the energy constraint.Comment: To be presented at IEEE GLOBECOM 201

Dynamic Sleep Control in Green Relay-Assisted Networks for Energy Saving and QoS Improving

We study the relay station (RS) sleep control mechanism targeting on reducing energy consumption while improving users' quality of service (QoS) in green relay-assisted cellular networks, where the base station (BS) is powered by grid power and the RSs are powered by renewable energy. By adopting green RSs, the grid power consumption of the BS is greatly reduced. But due to the uncertainty and stochastic characteristics of the renewable energy, power supply for RSs is not always sufficient. Thus the harvested energy needs to be scheduled appropriately to cater to the dynamic traffic so as to minimize the energy saving in the long term. An optimization problem is formulated to find the optimal sleep ratio of RSs to match the time variation of energy harvesting and traffic arrival. To fully use the renewable energy, green-RS-first principle is adopted in the user association process. The optimal RS sleeping policy is obtained through dynamic programming (DP) approach, which divides the original optimization problem into per-stage subproblems. A reduced DP algorithm and a greedy algorithm are further proposed to greatly reduce the computation complexity. By simulations, the reduced DP algorithm outperforms the greedy algorithm in achieving satisfactory energy saving and QoS performance.Comment: 7 papers, 4 figure

D2D User Selection For Simultaneous Spectrum Sharing And Energy Harvesting

This paper presents a device-to-device (D2D) user selection protocol wherein multiple D2D pairs coexist with a cellular network. In the developed framework, certain D2D users harvest energy and share the spectrum of the cellular users by adopting a hybrid time switching and power splitting protocol. The D2D user which harvests the maximum energy and achieves the desired target rate for the cellular communication is selected to serve as a decode-and-forward (DF) relay for the cellular user. The proposed work analyzes the impact of increase in the number of D2D users on the performance of cellular user as well as derives an upper bound on the time duration of energy harvesting within which best possible rate for cellular user can be obtained. The performance of the proposed protocol has been quantified by obtaining the closed form expressions of outage probability

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

Aqua Computing: Coupling Computing and Communications

The authors introduce a new vision for providing computing services for connected devices. It is based on the key concept that future computing resources will be coupled with communication resources, for enhancing user experience of the connected users, and also for optimising resources in the providers' infrastructures. Such coupling is achieved by Joint/Cooperative resource allocation algorithms, by integrating computing and communication services and by integrating hardware in networks. Such type of computing, by which computing services are not delivered independently but dependent of networking services, is named Aqua Computing. The authors see Aqua Computing as a novel approach for delivering computing resources to end devices, where computing power of the devices are enhanced automatically once they are connected to an Aqua Computing enabled network. The process of resource coupling is named computation dissolving. Then, an Aqua Computing architecture is proposed for mobile edge networks, in which computing and wireless networking resources are allocated jointly or cooperatively by a Mobile Cloud Controller, for the benefit of the end-users and/or for the benefit of the service providers. Finally, a working prototype of the system is shown and the gathered results show the performance of the Aqua Computing prototype.Comment: A shorter version of this paper will be submitted to an IEEE magazin