Pengbo Si

Optimal Cooperative Internetwork Spectrum Sharing for Cognitive Radio Systems With Spectrum Pooling

Spectrum pooling in cognitive radio systems is an approach to manage the available spectrum bands from different licensed networks. Most previous work on spectrum pooling concentrates on the system architecture and the design of flexible-access algorithms and schemes. In this paper, we present a cooperative scheme for internetwork spectrum sharing among multiple secondary systems, which takes into account the price and spectrum efficiency as the design criteria. Specifically, the spectrum-sharing problem is formulated as a stochastic bandit system; thus, the optimal spectrum-sharing scheme is simply allocating the new available band to the secondary network with the lowest index. Extensive simulation examples illustrate that the proposed scheme significantly improves the performance compared with the existing scheme that ignores optimal spectrum sharing.

Optimal Network Selection in Heterogeneous Wireless Multimedia Networks

The complementary characteristics of different wireless networks make it attractive to integrate a wide range of radio access technologies. Most of previous work on integrating heterogeneous wireless networks concentrates on network layer quality of service (QoS), such as blocking probability and utilization, as design criteria. However, from a user’s point of view, application layer QoS, such as multimedia distortion, is an important issue. In this paper, we propose an optimal distributed network selection scheme in heterogeneous wireless networks considering multimedia application layer QoS. Specifically, we formulate the integrated network as a restless bandit system. With this stochastic optimization formulation, the optimal network selection policy is indexable, meaning that the network with the lowest index should be selected. The proposed scheme can be applicable to both tight coupling and loose coupling scenarios in the integration of heterogeneous wireless networks. Simulation results are presented to illustrate the performance of the proposed scheme.

Distributed sender scheduling for multimedia transmission in wireless mobile peer-to-peer networks

Multi-source multimedia transmission is a popular architecture in wireless mobile peer-to-peer (P2P) networks. Most of previous work on wireless mobile P2P networks concentrates on the protocols and network structures, and consequently ignores the multiple senders scheduling problem. In this paper, we present a distributed algorithm for scheduling the multiple senders for multi-source transmission in wireless mobile P2P networks, which can maximize the data rate and minimize the power consumption. Specifically, we formulate the wireless mobile P2P network as a multi-armed bandit system. The optimal distributed sender scheduling policy can be found according to the Gittins indices of the senders. Extensive simulation examples illustrate the effectiveness of the proposed scheme. It is shown that the data rate and power consumption in the proposed scheme can be improved significantly compared to existing schemes.

Dynamic Spectrum Access with QoS Provisioning in Cognitive Radio Networks

Dynamic spectrum access is one of the most important premises of spectrum reuse based on cognitive radio technologies, which are considered to be the best way to alleviate the controversy on spectrum scarcity and low efficiency. However, most of previous work focuses on the increasing of system throughput, ignoring the QoS requirement of secondary users. In this paper, dynamic spectrum access with QoS provisioning is studied. We adopt discrete-time Markov chain to analyze and model the spectrum usage in time-slotted cognitive radio networks. Furthermore, three admission control schemes are proposed to minimize the forced termination probability of secondary users. Simulation results show that our proposed schemes can significantly improve the forced termination probability of secondary users, though slightly increase the blocking probability.

Spectrum Management for Proactive Video Caching in Information-Centric Cognitive Radio Networks

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.

Dynamic Channel and Power Allocation in Cognitive Radio Networks Supporting Heterogeneous Services

Resource allocation problem in cognitive radio networks (CRN) is one of the key issues to improve the efficiency of spectrum utilization. Most of previous work on resource allocation mainly concentrates on the secondary users (SUs) with only one type of service requirement, without considering the scenario with heterogenous services requirement. In this paper, we study the dynamic channel and power allocation for SUs supporting heterogenous services in CRN. Firstly we classify the SUs by service requirement, i.e., SUs with minimum rate guarantee and SUs with best-effort services. Then we introduce the minimum rate constraints and proportional fairness constraints for SUs respectively. Under this setup, we formulate the problem of dynamic channel and power allocation for SUs as a mixed integer programming problem. And the heuristic optimal algorithm and suboptimal algorithm are proposed to realize the dynamic channel and power allocation. Extensive simulation results are presented to demonstrate the performance of the proposed scheme.

QoS-aware dynamic resource management in heterogeneous mobile cloud computing networks

In mobile cloud computing (MCC) systems, both the mobile access network and the cloud computing network are heterogeneous, implying the diverse configurations of hardware, software, architecture, resource, etc. In such heterogeneous mobile cloud (HMC) networks, both radio and cloud resources could become the system bottleneck, thus designing the schemes that separately and independently manage the resources may severely hinder the system performance. In this paper, we aim to design the network as the integration of the mobile access part and the cloud computing part, utilizing the inherent heterogeneity to meet the diverse quality of service (QoS) requirements of tenants. Furthermore, we propose a novel cross-network radio and cloud resource management scheme for HMC networks, which is QoS-aware, with the objective of maximizing the tenant revenue while satisfying the QoS requirements. The proposed scheme is formulated as a restless bandits problem, whose “indexability” feature guarantees the low complexity with scalable and distributed characteristics. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing ones.

Cooperative and distributed spectrum sharing in dynamic spectrum pooling networks

In dynamic spectrum access systems, such as cognitive radio networks, spectrum pooling is one of the approaches to manage the available spectrum bands from different licensed networks. Based on the concept of spectrum pooling, most previous work focuses on the system architecture and the design of flexible access algorithms and schemes. In this paper, a cooperative and distributed scheme for dynamic internetwork spectrum sharing among multiple networks is proposed, taking into account the spectrum access price and the spectrum efficiency. Specifically, the spectrum sharing problem is formulated as a restless bandits model-based optimization system, which dramatically reduces the complexity of the scheme by allowing the spectrum allocation scheme to be simply select the network with the lowest index. Extensive simulation results illustrate that the proposed scheme improves the performance significantly compared to the existing scheme that ignores the distributed and cooperative spectrum sharing.

Distributed Multisource Transmission in Wireless Mobile Peer-to-Peer Networks: A Restless-Bandit Approach

In wireless mobile peer-to-peer (P2P) networks, multiple sources can provide multimedia file sharing at the same time. The selection of multiple sources is one of the key issues in the design of a multisource-transmission system in wireless mobile P2P networks. In this paper, we propose a distributed multisource sender-selection scheme to maximize the receiving data rate and minimize the energy consumption. Our scheme is based on recent advances in restless-bandit algorithms. The proposed sender-selection scheme has an indexability property that dramatically simplifies the computation and implementation of the policy. In addition, there is no need for a centralized control point, and senders can freely join and leave from the wireless mobile P2P network. Simulation results show that the proposed scheme significantly improves the receiving data rate and energy-consumption performance compared with the existing scheme.

DaVe: Offloading Delay-Tolerant Data Traffic to Connected Vehicle Networks

The promising connected vehicle technologies will enable a huge network of roadside units (RSUs) and vehicles equipped with communication, computing, storage, and positioning devices. Current research on connected vehicle networks focuses on delivering the data generated from or required by the vehicle networks themselves, of which the data traffic is light; thus, the vehicle-network resource utilization efficiency is low. On the other hand, a large amount of delay-tolerant traffic in other data networks consumes significant communication resources. In this paper, we introduce a new architecture of DaVe to utilize efficiently the potential resource from connected vehicles and to mitigate the congestion problem in other data networks. Delay-tolerant data traffic is offloaded from the data networks to the connected vehicle networks without extra infrastructure/hardware deployment. An optimal distributed data hopping mechanism is also proposed to enable delay-tolerant data routing over connected vehicle networks. We formulate the next-hop decision optimization problem as a partially observable Markov decision process (POMDP) and propose a heuristic algorithm to reduce computational complexity. Extensive simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme.