Yongkang Liu

Spectrum-Aware Opportunistic Routing in Multi-Hop Cognitive Radio Networks

In this paper, cognitive routing coupled with spectrum sensing and sharing in a multi-channel multi-hop cognitive radio network (CRN) is investigated. Recognizing the spectrum dynamics in CRN, we propose an opportunistic cognitive routing (OCR) protocol that allows users to exploit the geographic location information and discover the local spectrum access opportunities to improve the transmission performance over each hop. Specifically, based on location information and channel usage statistics, a secondary user (SU) distributedly selects the next hop relay and adapts its transmission to the dynamic spectrum access opportunities in its neighborhood. In addition, we introduce a novel metric, namely, cognitive transport throughput (CTT), to capture the unique properties of CRN and evaluate the potential relay gain of each relay candidate. A heuristic algorithm is proposed to reduce the searching complexity of the optimal selection of channel and relay. Simulation results are given to demonstrate that our proposed OCR well adapts to the spectrum dynamics and outperforms existing routing protocols in CRN.

Dimensioning network deployment and resource management in green mesh networks

In this article, network deployment and resource management issues are revisited in the context of green radio communication networks with sustainable energy supply. It is argued that under the green network paradigm powered by renewable energy, the fundamental design criterion and main performance metric have shifted from energy efficiency to energy sustainability. As an effort to this end, in this article, new network solutions are proposed with an objective of improving network sustainability; the proposed solutions ensure that dynamically harvested energy can sustain the traffic demands in the network. Specifically, the placement issue of green access points (i.e., APs powered by sustainable energy sources) is investigated to meet the energy and QoS demands of mobile users; and an adaptive resource management scheme is proposed to address the unreliability of renewable energy in QoS provisioning. It is shown that by mitigating the energy depletion probability of green APs, sustainable network performance can be significantly improved.

Sustainability Analysis and Resource Management for Wireless Mesh Networks with Renewable Energy Supplies

There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

Deploying cognitive cellular networks under dynamic resource management

Smartphone fever along with roaring mobile traffic pose great challenges for cellular networks to provide seamless wireless access to end users. Operators and vendors realize that new techniques are required to improve spectrum efficiency to meet the ever increasing user demand. In this article, we exploit the great opportunities provided by cognitive radio technology in conventional cellular networks. Specifically, we first present challenging issues including interference management, network coordination, and interworking between access networks in a tiered cognitive cellular network with both macrocells and small cells. Taking into consideration the different network characteristics of macrocells and small cells, we then propose an adaptive resource management framework to improve spectrum utilization efficiency and mitigate the co-channel interference between macrocell and small cell users. A game-theory-based approach to efficient power control has also been provided.

Adaptive Resource Management in Sustainable Energy Powered Wireless Mesh Networks

Next generation communication networks are anticipated to make use of renewable energy sources, e.g., solar and wind power, to reduce carbon footprints and achieve an environmentally sustainable system. However, renewable energy sources have the limitation of unstable availability and capacity, which introduces new challenges for network planning and resource management. In this paper, adaptive resource management is introduced for wireless mesh networks that are powered by sustainable energy sources. The objective is to address the unreliability of the energy supply and to maximize the energy sustainability of the network, or equivalently, minimize the probability that mesh access points (APs) deplete their energy and go out of service. Specifically, the energy buffer of a mesh AP is modeled as a

Distributed QoS-Aware MAC for Multimedia over Cognitive Radio Networks

G/G/1

Exploiting Heterogeneity Wireless Channels for Opportunistic Routing in Dynamic Spectrum Access Networks

queue and a diffusion approximation is applied to analyze the transient evolution of the queue length and energy depletion duration. Based on the analysis, a resource management scheme is proposed to adaptively distribute traffic over various relay paths across the network and a distributed admission control strategy is applied to further guarantee high resource utilization under the energy sustainability constraint. By considering the first and second order statistics of the energy charging and discharging processes, it is demonstrated that the proposed scheme outperforms some existing state-of- the-art solutions.

POSE: Design of Hardware-Friendly Particle-Based Observation Selection PHD Filter

We propose a distributed quality of service (QoS)-aware MAC protocol for multi-channel cognitive radio networks supporting multimedia applications. Specifically, based on the channel usage patterns of primary users (PUs), secondary users (SUs) determine a set of channels for channel sensing and data transmissions to satisfy their QoS requirements. We further enhance the QoS provisioning of the proposed cognitive MAC by applying differentiated arbitrary sensing periods for various types of traffic. An analytical model is developed to study the performance of the proposed MAC, taking the activities of both PUs and SUs into consideration. Extensive simulations validate our analysis and demonstrate that our proposed MAC can achieve multiple levels of QoS provisioning for various types of multimedia applications in cognitive radio networks.

Joint Channel Selection and Opportunistic Forwarding in Multi-Hop Cognitive Radio Networks

In this paper, we exploit the heterogeneity of wireless channels and propose an efficient opportunistic cognitive routing (OCR) scheme for dynamic spectrum access (DSA) networks. We first introduce a novel routing metric by jointly considering physical characteristics of spectrum bands and diverse activities of primary users (PU) in each band. To effectively explore the spectrum opportunities, a proper channel sensing sequence for fast and reliable message delivery is determined by secondary users (SU) in a distributed way. We then develop a greedy forwarding scheme that SUs can select the next hop relay based on the geometry information and channel access opportunity of their one hop neighbors. For the proposed OCR, as routing control messages are locally exchanged, SUs can efficiently make the routing decision and opportunistically access the available channels. We further evaluate the performance of OCR via extensive simulations. It is shown that our proposed scheme outperforms existing opportunistic routing schemes in DSA networks by exploiting the heterogeneity of spectrum bands for opportunistic channel access.

Spectrum sharing strategy using bipartite matching for cooperative cognitive radio networks

Particle probability hypothesis density (PHD) filtering is a promising technology for the multitarget-tracking problem. Traditional particle PHD filter solutions usually have high computational complexity, and the lack of dedicated hardware has seriously limited their usages in real-time industrial applications. The hardware implementation difficulty of the particle PHD filtering in field-programmable gate array (FPGA) platforms lies in that the number of observations for filtering is time varying while the number of parallel processing units in circuit is fixed. To overcome this challenge, we propose a novel particle-based observation selection (POSE) PHD filter algorithm and its hardware implementation in this paper. Specifically, we opportunistically select a fixed number of observations out of a varying number of observations for filtering, where the approximation error is proved to be negligible by adapting the circuit budget to the environment accordingly. To implement the proposed POSE PHD filter, the hardware design issues are addressed in depth. Extensive simulations demonstrate that the POSE PHD filter has a comparable performance with the traditional one while its hardware implementation challenge is overcome. The hardware experiment results of the POSE PHD filter on a Xilinx Virtex-II Pro FPGA platform match the simulation ones well. Furthermore, the execution time of the implemented hardware circuit is evaluated, and the results show that it can achieve a processing rate of 6.892 kHz with a 50-MHz system clock.