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Architectures of Dynamic Spectrum Access Networks
A number of architectures has been proposed recently for future DSA systems, ranging from fully autonomous and distributed to fully centralized architectures in which dynamic access to spectrum is managed centrally. A classification of these architectures can be described as follows:
• Distributed and autonomous DSA: These types of architectures are mainly proposed for cases where the construction of an infrastructure is not preferable. Here, each MS is responsible for the spectrum allocation and access. In this approach, a MS first senses the spectrum it wishes to use and characterizes the presence, if any, of PUs. Based on that information, and regulatory policies applicable to that spectrum, the MS identifies spectrum opportunities and transmits in a manner that avoids interfering with PUs.
• Centralized DSA: In this approach, a centralized entity controls the spectrum allocation and access procedures. With aid to these procedures, generally, a distributed sensing procedure is proposed such that each MS in the DSA network sense the spectrum it wishes to use and forward their measurements about the spectrum allocation to the central entity and this entity constructs a spectrum allocation map. Finally, the central entity indicates which portions of the spectrum can be utilized.
• Coordinated DSA: This approach is the less ambitious form of DSA. Nevertheless, as currently wireless networks are still regulated by the FSA model, this solution represents a more realistic approach to be implemented in near future. Here, dynamic access to spectrum takes place exclusively within spectrum portions reserved by regulatory authorities or spectrum owners for secondary use [45]. The access to this piece of spectrum is then managed via a central entity which permanently owns the spectrum and only grants a timebound lease to the requesters. Within the spectrum available to the central entity, certain fixed frequencies are for spectrum information channels. These channels can be used to receive request for spectrum usage and to transmit instructions on available channels.
Architecture Components of Dynamic Spectrum Access Networks
The architecture components of DSA networks can be classified into two groups as the primary system and the secondary system. The basic elements of these two systems are defined as follows:
The primary system or primary network can be a primary system in licensed band or a primary system in unlicensed band according to their band of operation. The primary system in licensed band is an existing network (e.g. 3G/LTE cellular, digital TV broadcast, WiMAX, etc) that has an exclusive right to use a certain spectrum band. Therefore, unlicensed networks can neither interfere with the primary network in an intolerable way nor occupy the license band. The components of the primary system in licensed bands are as follows [3]:
• The Primary base-station or licensed base-station is a fixed infrastructure network component which has a spectrum license such as base-station transceiver system (BTS) in a cellular system. In principle, the primary base-station does not have any capability for sharing spectrum with SUs. Nevertheless, in future next generation networks the primary base-station may have both legacy and secondary protocols for the access of SUs in primary networks.
• The PU or licensed user has a license to operate in a certain spectrum band. This access can only be controlled by the primary base-station and should not be affected by the operations of any other unlicensed users. Primary users do not need any modification or additional functions for coexistence with secondary base-stations and SUs.
DSA Techniques in Next Generation Wireless Networks
In literature, there exist two different approaches of secondary use of spectrum in cognitive radio context. One is in the form of Overlay, which is the opportunistic usage of idle bands in the PU spectrum by cognitive radios and another in the form of Underlay, on which a MS begins transmission such that its transmit power, at a certain portion of the spectrum, is regarded as noise by PUs so avoiding interference. To achieve this, the Underlay approach imposes severe restrictions on transmitted power levels. Therefore, it requires operating over ultra wide bandwidths to achieve good performances in terms of throughput. In this section, we present the principal characteristics of these two approaches.
Table of contents :
Acknowledgments
1 Résumé en Français
1.1 Introduction
1.2 Contexte et Motivations
1.3 Organisation de la Thèse
1.4 Contributions
1.5 Conclusions et Perspectives
1.5.1 Perspectives
Abstract
List of Figures
List of Tables
Acronyms
2 Introduction
2.1 Background and Motivation
2.2 Thesis Contributions
2.3 Thesis Overview
3 DSA in Next Generation Wireless Networks
3.1 Dynamic Spectrum Access Networks
3.1.1 Architectures of Dynamic Spectrum Access Networks
3.1.2 Architecture Components of Dynamic Spectrum Access Networks
3.1.3 DSA Techniques in Next Generation Wireless Networks
3.1.3.1 Overlay
3.1.3.2 Underlay
3.2 Standards allowing Dynamic Spectrum Access
3.2.1 DSA and Cognitive Radio Standardization inside the IEEE
3.2.1.1 IEEE SCC41 formerly (IEEE P1900)
3.2.1.2 IEEE 802.22
3.2.1.3 Other IEEE 802 standards with CR capabilities
3.2.2 DSA and Cognitive Radio Standardization outside the IEEE
3.2.2.1 ITU-R Activities Related to CR
3.2.2.2 ETSI Standards Related to CR
3.2.2.3 Software Defined Radio Forum Activities
3.2.2.4 3GPP Activities Related to CR
3.2.2.5 Object Management Group Activities Related to CR
3.3 Conclusions
4 DSA via Multi-Channel MAC Protocols
4.1 DSA Issues in Distributed Ad-hoc Networks
4.1.1 Rendezvous in Multi-Channel Protocols
4.1.2 Hidden Terminal Problem
4.1.2.1 Hidden Terminal Problem in a Single Channel Environment
4.1.2.2 Virtual Carrier Sensing using RTS/CTS Exchange
4.1.2.3 Multi-Channel Hidden Terminal Problem
4.2 An Overview of DSA via Multi-Channel MAC Protocols
4.2.1 “McMAC: A Parallel Rendezvous Multi-Channel MAC Protocol”
4.2.2 “Multi-ChannelMAC for Ad Hoc Networks: HandlingMulti-Channel Hidden Terminals Using A Single Transceiver”
4.2.3 “A Distributed Multichannel MAC Protocol for Cognitive Radio Networks with Primary User Recognition”
4.2.4 “Hardware-constrained Multi-Channel Cognitive MAC”
4.2.5 “Distributed Coordinated Spectrum Sharing MAC Protocol for Cognitive Radio”
4.2.6 “Os-MAC: An Efficient MAC Protocol for Spectrum-Agile Wireless Networks”
4.2.7 “Performance Evaluation of a Medium Access Control Protocol for IEEE 802.11s Mesh Networks”
4.2.8 “TMMAC: An Energy Efficient Multi-Channel MAC Protocol for Ad Hoc Networks”
4.2.9 “Single-Radio Adaptive Channel Algorithm for Spectrum AgileWireless Ad Hoc Networks”
4.2.10 “A Full Duplex Multi channel MAC Protocol for Multi-hop Cognitive Radio Networks”
4.2.11 “SSCH: Slotted Seeded Channel Hopping for Capacity Improvement in IEEE 802.11 Ad-Hoc Wireless Networks”
4.2.12 “CREAM-MAC: An efficient Cognitive Radio-EnAbledMulti-Channel MAC Protocol for Wireless Networks”
4.2.13 “Distributed Coordination in Dynamic Spectrum Allocation Networks”
4.2.14 “Primary Channel Assignment BasedMAC (PCAM) AMulti-Channel MAC Protocol for Multi-Hop Wireless Networks”
4.2.15 “Performance of Multi Channel MAC Incorporating Opportunistic Cooperative Diversity”
4.2.16 “A Multi channel MAC for Opportunistic Spectrum Sharing in Cognitive Networks”
4.2.17 “Adaptive MAC Protocol for Throughput Enhancement in Cognitive Radio Networks”
4.2.18 “Spectrum Sharing Radios”
4.2.19 “Cognitive Radio System using IEEE 802.11a over UHF TVWS”
4.3 Comparison of Key Features
4.4 Conclusions
5 DSA via Cognitive Control Channels
5.1 CPC Issues
5.1.1 Regulatory Issues
5.1.2 Secondary Use and DSA via the CPC
5.1.3 Proposed Architectures for the CPC
5.1.4 CPC Capturing Procedure at the Terminal Side
5.1.5 CPC Delivery Strategies
5.2 Other Cognitive Control Channels
5.3 Cognitive Beacon Channel via GSM and UMTS
5.4 Evaluation Model
5.4.1 Analysis Broadcast Approaches
5.4.2 Analysis On-demand Approaches
5.5 Analysis Results: Broadcast and On-demand CPC, GSM and UMTS Proposals
5.6 Conclusions
6 Poisson Point Process and Interference Temperature Model in Cognitive Radio Networks
6.1 Preliminaries
6.1.1 Interference Temperature Model as Underlay technique for DSA
6.1.2 Poisson Point Process
6.1.3 Physical features of the system
6.2 Secondary User Mean Capacity
6.2.1 Calculations of the mean capacity
6.2.2 Numerical Analysis of the Mean Capacity of the Secondary Network
6.2.2.1 Mean Capacity of the Secondary Network
6.2.2.2 Mean Capacity of the Secondary Network as a Function of the Path Loss Exponent
6.2.2.3 Mean Capacity of the Secondary Network as a Function of the SUs Intensity
6.3 Upper bound of PU Outage Probability
6.3.1 Calculations Upper bound of PU Outage Probability
6.3.2 Results Upper bound of PU Outage Probability
6.4 Mean Interference, Mean SUs Transmission Power and Mean SUs Capacity Taking into Account the Reference Distance
6.4.1 Mean Interference Power Taking into Account the Reference Distance112
6.4.2 Mean SUs Transmission Power Taking into Account the Reference Distance
6.4.3 Mean SUs Capacity Taking into Account the Reference Distance
6.4.4 Expressions of the Upper Bound of PU Outage Probability Taking into Account the Reference Distance
6.4.4.1 Results of theMean SUs Transmission Power and theMean Per-Link Capacity Taking into Account the Reference Distance
6.5 Conclusion
7 Conclusion and Perspectives
7.1 Thesis Conclusions
7.2 Perspectives and Future Work
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