SpringerBriefs in Electrical and Computer Engineering More information about this series at http://www.springer.com/series/10059 Zhiyong Feng • Qixun Zhang • Ping Zhang Cognitive Wireless Networks 1 3 Zhiyong Feng Ping Zhang Key Laboratory of Universal Wireless State Key Laboratory of Networking and Communications, Ministry of Education Switching School of Information and Beijing University of Posts and Communication Engineering Telecommunications Beijing University of Posts and Beijing Telecommunications China Beijing China Qixun Zhang Key Laboratory of Universal Wireless Communications, Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISBN 978-3-319-15767-2 ISBN 978-3-319-15768-9 (eBook) DOI 10.1007/978-3-319-15768-9 Library of Congress Control Number: 2015935656 Springer Cham Heidelberg New York Dordrecht London © The Author(s) 2015 This work is subject to copyright. 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Printed on acid-free paper Springer is a brand of Springer International Publishing Switzerland Springer is part of Springer Science+Business Media (www.springer.com) Recommended by Xuemin (Sherman) Shen Contents 1 Introduction ................................................................................................ 1 2 Theoretical Study in CWNs ...................................................................... 11 3 Novel Architecture Model in CWNs ......................................................... 25 4 Cognitive Information Awareness and Delivery ..................................... 47 5 Intelligent Resource Management ............................................................ 85 6 TD-LTE Based CWN Testbed ................................................................... 107 7 Standardization Progress .......................................................................... 123 8 Conclusion and Future Research Directions ........................................... 139 vii Chapter 1 Introduction With the explosive surge of various applications and high data rate services, differ- ent wireless networks and communication technologies are proposed and developed in recent years. Considering the limited radio spectrum resources in various wire- less networks, the scarcity of radio spectrum is becoming a bottleneck in face of the exponential surge of service demands. At the same time, spectrum measurement results depict that the average spectrum utilization over a period of time at different locations is quite low, leading to a waste of the valuable spectrum resources. There- fore, how to improve the efficiency of spectrum utilization is the first challenge to be solved for different wireless networks deployment. Besides, the heterogeneity and coexistence of different wireless networks will cause the low radio resource usage and mutual interference among heterogeneous networks due to the lack of cooperative control and information sharing among various networks in practice. Therefore, another big challenge is how to realize the seamless and efficient conver- gence of different heterogeneous networks to improve the end-to-end performance of users in wireless networks. To solve these challenges, the heterogeneous wireless networks should be much more intelligent with adaptively reconfigurable parameters and working modes, in order to be aware of the changing wireless network environment. Therefore, the in- telligent environment awareness and radio resource utilization technologies should be applied to obtain and analyze the network information learnt from the knowledge representing the dynamics and changing characteristics of radio environment, net- work traffic and various user demands. Thus, a cognitive wireless network (CWN) is proposed as a novel wireless network enabled by cognitive information aware- ness, analysis and management technologies, which can improve the efficiency of spectrum utilization and heterogeneous networks convergence. © The Author(s) 2015 1 Z. Feng et al., Cognitive Wireless Networks, SpringerBriefs in Electrical and Computer Engineering, DOI 10.1007/978-3-319-15768-9_1 2 1 Introduction 1.1 Challenges in Cognitive Wireless Networks This section introduces three critical challenges faced by wireless communication in detail, which can promote the development of cognitive wireless networks. 1.1.1 Spectrum Scarcity and Spectrum Waste As one of the most precious non-renewable resources, spectrum resources are licensed and managed by the government, which are facing a big challenge of spec- trum scarcity for ubiquitous wireless applications. The feature of spectrum manage- ment policy is that a certain part of the spectrum is allocated to dedicated service, and meanwhile, other services are prohibited to utilize this part of spectrum. That is to say, the spectrum is assigned to license holders for a long term basis using a fixed spectrum assignment policy [5]. Figure 1.1 shows the situation of frequency alloca- tion in the United States (U.S.). In Fig. 1.1, the spectrum from 3 KHz to 300 GHz has been allocated completely, which means that there is a small part of the spec- trum which can be licensed to new wireless applications [2]. In the U.S., between 2004 and 2005, the Federal Communication Commission (FCC) and Shared Spectrum Company (SSC) had made a survey [3] about the spectrum utilization of 30 MHz ~ 3 GHz in Chicago and New York City. As shown in Fig. 1.2, the survey illustrated that in 2005 long-term spectrum utilization rate was only 5.2 % in Chicago and 13.1 % in New York. Some spectrum bands were Fig. 1.1 Frequency allocations in the United States [6] 1.1 Challenges in Cognitive Wireless Networks 3 Measured Spectrum Occupancy in Chicago and New York City PLM, Amateur, others:… Air traffic Control, Aero… TV 7-13: 174-216 MHz Fixed Mobile, Aero,… TV 14-20: 470-512 MHz TV 37-51: 608-698 MHz Cell phone and SMR:… Paging, SMS, Fixed, BX… Chicago Amateur: 1240-1300… New York City Space/Satellite, Fixed… Fixed, Fixed Mobile:… TV Aux: 1990-2110 MHz Space Opera‘on, Fixed:… Telemetry: 2360-2390… ITFS, MMDS: 2500-… 0.0% 25.0% 50.0% 75.0%100.0% Spectrum Occupancy Fig. 1.2 Average spectrum occupancy by band—Chicago vs. New York [3] overloaded while others were in the state of a low utilization, such as the spectrum band that assigned to radio astronomy. In Europe, the radio spectrum utilization measurements have been carried out in three different locations, namely, in the suburb of the city of Brno, in the Czech Republic and in the suburb and the city of Paris in France, respectively [7]. The result of the measurement, shown in Fig. 1.3, analyzed the radio spectrum from 400 MHz to 3 GHz. In China, the measurement results unveiled by [8] are shown in Fig. 1.4, which indicate a low spectrum utilization in Beijing over one month. The results in Beijing are similar to the results released by FCC. The measurement results above show that some spectrum resources are heavily used by licensed systems in a specific location at a particular time. However, there are many spectrum bands which are only partly occupied or largely unoccupied. Besides, new services and applications need new spectrum resources which is an urgent problem and the bottleneck for the future wireless network. Therefore, how to improve the vacant spectrum utilization and solve the spectrum scarcity problem are key challenges to be addressed. Therefore, new technologies should be utilized in order to detect the vacant spectrum resources efficiently in different locations and