Cognitive Radio Networks: Efficient Resource Allocation in Cooperative Sensing, Cellular Communications, High-Speed Vehicles, and Smart Grid, 1st Edition (Hardback) book cover

Cognitive Radio Networks

Efficient Resource Allocation in Cooperative Sensing, Cellular Communications, High-Speed Vehicles, and Smart Grid, 1st Edition

By Tao Jiang, Zhiqiang Wang, Yang Cao

CRC Press

148 pages | 71 B/W Illus.

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Hardback: 9781498721134
pub: 2015-04-08
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pub: 2015-04-08
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Resource allocation is an important issue in wireless communication networks. In recent decades, cognitive radio-based networks have garnered increased attention and have been well studied to overcome the problem of spectrum scarcity in future wireless communications systems. Many new challenges in resource allocation appear in cognitive radio-based networks. This book focuses on effective resource allocation solutions in several important cognitive radio-based networks, including opportunistic spectrum access networks, cooperative sensing networks, cellular networks, high-speed vehicle networks, and smart grids.

Cognitive radio networks are composed of cognitive, spectrum-agile devices capable of changing their configuration on the fly based on the spectral environment. This capability makes it possible to design flexible and dynamic spectrum access strategies with the purpose of opportunistically reusing portions of the spectrum temporarily vacated by licensed primary users. Different cognitive radio-based networks focus on different network resources, such as transmission slots, sensing nodes, transmission power, white space, and sensing channels.

This book introduces several innovative resource allocation schemes for different cognitive radio-based networks according to their network characteristics:

  • Opportunistic spectrum access networks – Introduces a probabilistic slot allocation scheme to effectively allocate the transmission slots to secondary users to maximize throughput
  • Cooperative sensing networks – Introduces a new adaptive collaboration sensing scheme in which the resources of secondary users are effectively utilized to sense the channels for efficient acquisition of spectrum opportunities
  • Cellular networks – Introduces a framework of cognitive radio-assisted cooperation for downlink transmissions to allocate transmission modes, relay stations, and transmission power/sub-channels to secondary users to maximize throughput
  • High-speed vehicle networks – Introduces schemes to maximize the utilized TV white space through effective allocation of white space resources to secondary users
  • Smart grids – Introduces effective sensing channel allocation strategies for acquiring enough available spectrum channels for communications between utility and electricity consumers

Table of Contents



About the Authors


Cognitive Radio-Based Networks

Opportunistic Spectrum Access Networks

Cognitive Radio Networks with Cooperative Sensing

Cognitive Radio Networks for Cellular Communications

Cognitive Radio Networks for High-Speed Vehicles

Cognitive Radio Networks for a Smart Grid

Content and Organization

Transmission Slot Allocation in an Opportunistic Spectrum Access Network

Single-User Single-Channel System Model

Probabilistic Slot Allocation Scheme

Optimal Probabilistic Slot Allocation

Baseline Performance

Exponential Distribution

Hyper-Erlang Distribution

Performance Analysis and Evaluation

Impact of Sensing Errors

Impact of Unknown Primary User Idle Period Distribution

Performance Comparisons


Sensing Node Allocation in a Cognitive Radio Network with Cooperative Sensing

Multi-User Multi-Channel System Model

Adaptive Collaboration Sensing Scheme

Basic Idea

Sequential Probability Ratio Test

Optimal Sensing Node Allocation

Performance Evaluation and Analysis


Transmission Power Allocation in a Cognitive Radio Network

Cognitive Radio-Assisted Cooperation Framework

Optimal Transmission Power Allocation

Cross-Layer Optimization

Power Constraint Elimination

Throughput Maximization

Performance Analysis and Evaluation

Simulation Scenario

Performance Comparisons

Impact of the Cell Population

Impact of the Primary User Traffic Load


White Space Allocation in a Cognitive Radio-Based High-Speed Vehicle Network

A Cognitive Radio-Based High-Speed Vehicle Network

System Model

Poss Loss Model

Available Channel List

Spectrum Sharing List

Maximization of Utilized White Space

Separation Computing

Branch and Bound Search Method

Single-Channel Method with Low Complexity

Linear Programming Method with Low Complexity

Performance Analysis and Evaluation


Sensing Channel Allocation in a Cognitive Radio Network for a Smart Grid

Electricity Load Shaping Framework

Smart Grid Model

The Cognitive Radio Network Model

Sensing Channel Allocation and Load Shaping Strategies

Sensing Channel Allocation Strategies

Load Shaping Strategy

Performance Analysis and Evaluation

Performance of Sensing Channel Allocation

Performance of Electricity Load Shaping




About the Authors

Tao Jiang is currently a chair professor in the School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, P. R. China (PRC). He received the Ph.D. degree in information and communication engineering from Huazhong University of Science and Technology, Wuhan, PRC in April 2004. He has authored or co-authored over 200 technical papers in major journals and conferences and six books/chapters in the areas of communications and networks. He served or is serving as associate editor of some technical journals in communications, including IEEE Transactions on Signal Processing, IEEE Communications Surveys and Tutorials, IEEE Transactions on Vehicular Technology, and IEEE Internet of Things Journal. He is a recipient of the NSFC for Distinguished Young Scholars Award in PRC.

Zhiqiang Wang currently works at State Grid Shaanxi Electric Power Company Telematics. He received a B.S. from Xian Jiaotong University, Xian, PRC in 2006, and M.S. and Ph.D. degrees from Huazhong University of Science and Technology, Wuhan, PRC in 2009 and 2012, respectively. Wang’s current research interests include the areas of energy management and smart grid communications.

Yang Cao is currently an assistant professor in School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, PRC. He received Ph.D. and B.S. degrees in information and communications engineering at Huazhong University of Science and Technology, Wuhan, PRC in 2014 and 2009, respectively. His research interests include resource allocation for cellular device-to-device communications and smart grids.

Subject Categories

BISAC Subject Codes/Headings:
TECHNOLOGY & ENGINEERING / Telecommunications
TECHNOLOGY & ENGINEERING / Mobile & Wireless Communications