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Cognitive Networks

Applications and Deployments

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Cognitive Networks

Applications and Deployments

  

Edited by

Jaime Lloret Mauri • Kayhan Zrar Ghafoor

Danda B. Rawat • Javier Manuel Aguiar Perez

  

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  Contents

  

  

  

  

  

  

C O N T E N T S

  V I

  

  

  

  

  

  

  

  

  

  

  

  

  

C O N T E N T S

  V I I

  

  

  

  

  

  

  

  

  

  ®

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  3 apple hill drive natick, Ma 01760-2098, USa Tel: +1 508 647 7000 Fax: +1 508 647 7001 e-mail web: www.mathworks.com

   Sherine M. Abdel-Kader Athar Ali Khan

  Computers and Systems department of electrical department engineering electronics research institute COMSaTS institute Cairo, egypt of information

  Technology

  Salah M. Abdel-Mageid

  wah Cantt, pakistan Computers and Systems department

  Mariette Awad

  al-azhar University department of electrical Cairo, egypt and Computer engineering

  Kamalrulnizam Abu Bakar

  american University Faculty of Computing of beirut Universiti Teknologi Malaysia beirut, lebanon Johor, Malaysia

  Javier Aguiar Pérez Leire Azpilicueta

  departamento de Teoria de department of electrical la Señal y Comunicaciones and electronics e ingenieria engineering

  Telemáatica Universidad de Universidad pública de navarra Valladolid navarra, Spain

  

C O N T R I B U T O R S

  X Ashish Bagwari Gianluigi Ferrari

  department of electronics department of information and Communication engineering engineering University of parma

  Uttarakhand Technical parma, italy University dehradun, india Norshelia Binti Fisal

  Faculty of electrical

  Chandra Bajracharya

  engineering department of electrical Universiti Teknologi Malaysia and Computer engineering Johor, Malaysia

  Old dominion University norfolk, Virginia

  Miguel Garcia

  department of Computer Science

  Diana Bri

  Universitat politècnica de València research institute for integrated València, Spain

  Management of Coastal areas Universitat politècnica de València

  T. R. Gopalakrishnan Nair

  València, Spain Saudi araMCO endowed

  Chair of Technology

  Stefano Busanelli

  and information r&d department Management

  Guglielmo Srl prince Mohammad bin Fahd reggio emilia, italy

  University al khobar, Saudi arabia

  Alessandro Colazzo

  azcom Technology Srl and Milan, italy research and industry

  Jean-Michel Dricot

  incubation Center Opera department dayananda Sagar institutions Université libre de bruxelles bangalore, india brussels, belgium

  Francisco Falcone Ling Hou

  department of electrical and department of electronic electronics engineering engineering

  Universidad pública de navarra City University of hong kong navarra, Spain kowloon, hong kong

  

C O N T R I B U T O R S

  X I Shengchun Huang Salma Malik Tabassum

  College of electronic Science department of electrical and engineering engineering national University of defense COMSaTS institute

  Technology of information Technology Changsha, people’s republic wah Cantt, pakistan of China

  Youssef Nasser

  department of electrical and

  Mubashir Husain Rehmani

  Computer engineering department of electrical american University of beirut engineering beirut, lebanon

  COMSaTS institute of information Technology

  Francisco Ramos

  wah Cantt, pakistan València nanophotonics

  Technology Center

  Youssef A. Jaffal

  Universitat politècnica de València department of electrical València, Spain and Computer engineering Danda B. Rawat american University of beirut department of electrical beirut, lebanon engineering

  Georgia Southern University Statesboro, Georgia

  José Javier Astráin

  department of Mathematics

  Ali Safa Sadiq

  and Computer Faculty of Computing engineering Universiti Teknologi Malaysia

  Universidad pública de navarra Johor, Malaysia navarra, Spain

  Yasir Saleem Jaime Lloret Mauri

  Sunway University research institute for integrated Selangor, Malaysia

  Management of Coastal areas Tarek M. Salem Universitat politècnica de València Computers and Systems València, Spain department electronics research institute

  Cairo, egypt

  

C O N T R I B U T O R S

  X I I Geetam Singh Tomar Jesús Villadangos

  Machine intelligence research department of Mathematics and labs Computer Science engineering Gwalior, india Universidad pública de navarra navarra, Spain and

  Shan Wang

  department of electrical College of electronic Science engineering and engineering

  University of west indies national University of defense St. augustine, Trinidad and Tobago

  Technology Changsha, people’s republic

  Kavitha Sooda

  of China advanced networking research Group

  Angus K. Y. Wong

  dayananda Sagar institutions School of Science and Technology bangalore, india The Open University of hong kong and hong kong department of Computer

  Xianzhong Xie

  Science and engineering Chongqing key lab on Mobile nitte Meenakshi institute

  Communication of Technology institute of personal bangalore, india

  Communications Chongqing University of posts

  Athanasios V. Vasilakos

  and Telecommunications department of Computer Chongqing, people’s republic and Telecommunications of China engineering

  University of western Macedonia

  Helin Yang

  kozani, Greece Chongqing key lab on Mobile

  Communication and institute of personal department of electrical and Communication

  Computer engineering Chongqing University of posts national Technical University and Telecommunications of athens Chongqing, people’s republic athens, Greece of China

  

C O N T R I B U T O R S

  X I I I Ali Yassin Haitao Zhao

  department of electrical and College of electronic Science Computer engineering and engineering american University of beirut national University of defense beirut, lebanon Technology

  Changsha, people’s republic

  K. H. Yeung of China

  department of electronic engineering Kayhan Zrar Ghafoor City University of hong kong Faculty of engineering kowloon, hong kong koya University koya, iraq

  Mohamed Zaki

  Computers and Systems Muhammad Zubair Farooqi department department of electrical al-azhar University engineering Cairo, egypt COMSaTS institute of information Technology

  Shaojie Zhang wah Cantt, pakistan

  College of electronic Science and engineering national University of defense

  Technology Changsha, people’s republic of China

  

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   Tarek M. SaleM, Sherine M. abd el-kader, Salah M. abdel- MaGeid, and MOhaMed Zaki Contents

  1.1 introduction

  4

  1.2 Classical ad hoc networks versus Crahns

  8

  1.3 Spectrum Management Framework for Crahn

  10

  1.4 Spectrum Sensing for Cr networks

  13 1.4.1 pU detection

  14

1.4.2 Sensing Control

  16 1.4.2.1 in-band Sensing Control

  17

1.4.2.2 Out-of-band Sensing Control

  18

  1.4.3 Cooperative Sensing

  19

  1.4.4 Open research issues in Spectrum Sensing

  20

1.5 Spectrum decision for Cr networks

  21

  1.5.1 Spectrum Characterization

  22 1.5.1.1 radio environment

  22 1.5.1.2 pU activity

  23

  1.5.2 Spectrum Selection

  24

  25 1.5.3 reconfiguration

  1.5.4 Open Research Issues in Spectrum Decision

  25

  1.6 Spectrum Sharing for CRAHNs

  26

  1.6.1 Resource Allocation

  27

  1.6.2 Spectrum Access

  28

  1.6.3 Open Research Issues in Spectrum Sharing

  29

  

C O G N I T I V E N E T W O R K S

  4

  1.7 Spectrum Mobility for CRAHNs

  31

  1.7.1 Spectrum Handoff

  32

  1.7.2 Connection Management

  34

  1.7.3 Open Research Issues in Spectrum Mobility

  34

  1.8 Common Control Channel

  35

  1.9 Conclusion

  36 References

  37 Biographical Sketches

  40

  1.1 introduction

  The usage of radio spectrum resources and the regulation of radio emis- sions are coordinated by national regulatory bodies such as the Federal Communications Commission (FCC). The FCC assigns spectrum to licensed users, also known as primary users (PUs), on a long-term basis for large geographical regions. However, a large portion of the assigned spectrum remains underutilized as illustrated in Figure 1.1. The inef- ficient usage of the limited spectrum necessitates the development of dynamic spectrum access techniques [1], where users who have no spec- trum licenses, also known as secondary users, are allowed to use the temporarily unused licensed spectrum. In recent years, the FCC has been considering more flexible and comprehensive uses of the available spectrum through the use of cognitive radio (CR) technology [2].

  The limitations in spectrum access due to the static spectrum licensing scheme can be summarized as follows (Figure 1.1):

  

Fixed type of spectrum usage: In the current spectrum licensing

  scheme, the type of spectrum use cannot be changed. For _{Heavy use Heavy use Incise new users} example, a TV band in Egypt cannot be used by digital TV _{...Most spectrum is unused! Less than 6% occupancy Maximum amplitudes Maximum amplitudes Amplitude ( Amplitude (} dBm) dBm) _{Sparse use Medium use}

_{Frequency (MHz) Frequency (MHz)}

  (a) (b)

Figure 1.1 Spectrum is wasted. Opportunistic spectrum access can provide improvements in

  

spectrum utilization. (a) Spectrum usage by traditional spectrum management, (b) spectrum usage

by utilizing spectrum holes.

  

E F F I C I E N T S P E C T R U M M A N A G E M E N T

  5

  broadcast or broadband wireless access technologies. however, this TV band could remain largely unused due to cable TV systems.

  

Licensed for a large region: when a spectrum is licensed, it is usu-

  ally allocated to a particular user or wireless service provider in a large region (e.g., an entire city or state). however, the wireless service provider may use the spectrum only in areas with a good number of subscribers to gain the highest return on investment. Consequently, the allocated frequency spec- trum remains unused in other areas, and other users or service providers are prohibited from accessing this spectrum.

  

Large chunk of licensed spectrum: a wireless service provider is

  generally licensed with a large chunk of radio spectrum (e.g., 50  Mhz). For a service provider, it may not be possible to obtain license for a small spectrum band to use in a certain area for a short period of time to meet a temporary peak traffic load. For example, a CdMa2000 cellular service pro- vider may require a spectrum with a bandwidth of 1.25 or 3.75 Mhz to provide temporary wireless access service in a hotspot area.

  

Prohibit spectrum access by unlicensed users: in the current spec-

  trum licensing scheme, only a licensed user can access the corresponding radio spectrum and unlicensed users are pro- hibited from accessing the spectrum, even though it is unoc- cupied by the licensed users. For example, in a cellular system, there could be areas in a cell without any users. in such a case, unlicensed users with short-range wireless communications would not be able to access the spectrum, even though their transmission would not interfere with cellular users.

  The term cognitive radio was defined in [3] as follows: “Cognitive radio is an intelligent wireless communication system that is aware of its ambient environment. This cognitive radio will learn from the envi- ronment and adapt its internal states to statistical variations in the existing RF environment by adjusting the transmission parameters (e.g. frequency band, modulation mode, and transmit power) in real- time.” A CR network enables us to establish communications among CR nodes or users. The communication parameters can be adjusted

  6 ^{C O G N I T I V E N E T W O R K S}

  according to the change in the environment, topology, operating con- ditions, or user requirements. From this definition, two main charac- teristics of the CR can be defined as follows:

• Cognitive capability: It refers to the ability of the radio tech- nology to capture or sense the information from its radio environment. This capability cannot simply be realized by monitoring the power in some frequency bands of interest, but more sophisticated techniques, such as autonomous learn- ing and action decision, are required in order to capture the temporal and spatial variations in the radio environment and avoid interference with other users.
• Reconfigurability: The cognitive capability provides spectrum awareness, whereas reconfigurability enables the radio to be dynam ically programmed according to the radio environment [36]. More specifically, the CR can be programmed to transmit and receive signals at various frequencies and to use different trans- mission access technologies supported by its hardware design.

  The ultimate objective of the CR is to obtain the best available spec- trum through cognitive capability and reconfigurability as described earlier. Since most of the spectrum is already assigned, the most important challenge is to share the licensed spectrum without inter- fering with the transmission of other licensed users as illustrated in

Figure 1.2. The CR enables the usage of a temporarily unused spec- trum, which is referred to as a spectrum hole or a white space [3]. If this

  band is further utilized by a licensed user, the CR moves to another

  Spectrum holes Time Power

  Frequency Spectrum in use

Figure 1.2 Spectrum holes concept.

  

E F F I C I E N T S P E C T R U M M A N A G E M E N T

  7

  spectrum hole or stays in the same band, altering its transmission power level or modulation scheme to avoid interference. according to the network architecture, Cr networks can be clas- sified as infrastructure-based CR networks and CR ad hoc networks

  (CRAHNs) [3]. An infrastructure-based CR network has a central network entity such as a base station in cellular networks or an access point in wireless local area networks (LANs), whereas a CRAHN does not have any infrastructure backbone. Thus, a CR user can com- municate with other CR users through ad hoc connection on both licensed and unlicensed spectrum bands.

  In infrastructure-based CR networks, the observations and analy- sis performed by each CR user feed the central CR base station, so that it can make decisions on how to avoid interfering with primary networks. According to this decision, each CR user reconfigures its communication parameters, as shown in Figure 1.3a. On the con- trary, in CRAHNs, each user needs to have all CR capabilities and is responsible for determining its actions based on the local observation, as shown in Figure  1.3b. Because the CR user cannot predict the influence of its actions on the entire network with its local observa- tion, cooperation schemes are essential, where the observed informa- tion can be exchanged among devices to broaden the knowledge on the network.

  In this chapter, an up-to-date survey of the key researches on spec- trum management in CRAHNs is provided. We also identify and dis- cuss some of the key open research challenges related to each aspect of spectrum management. The remainder of this chapter is arranged as follows: The differences between CRAHNs and classical ad hoc net- works are introduced in Section 1.2. A brief overview of the spectrum management framework for CRAHNs is provided in Section 1.3. The challenges associated with spectrum sensing are given and enabling spectrum sensing methods are explained in Section 1.4. An over- view of the spectrum decision for CR networks with open research issues is presented in Section 1.5. Spectrum sharing for CRAHNs is introduced in Section 1.6. Spectrum mobility and proposed tool to solve spectrum management research challenges for CRAHNs are explained in Sections 1.7 and 1.8, respectively. Common control channels (CCCs) are declared in Section 1.8. Finally, Section 1.9 con- cludes the chapter.

  

C O G N I T I V E N E T W O R K S

  8 (3) Learning and (1) Local observation action decision

  (4) Reconfiguration (2) Cooperation (a) (1) Local observation

  (2) Learning and action decision (3) Reconfiguration (b)

Figure 1.3 Comparison of CR capabilities between infrastructure-based CR networks (a) and CRAHNs (b).

1.2 Classical ad hoc networks versus Crahns

  The changing spectrum environment and the importance of protecting the transmission of the licensed users of the spectrum mainly differenti- ate classical ad hoc networks from CRAHNs. We describe these unique features of CRAHNs compared to classical ad hoc networks as follows:

  

E F F I C I E N T S P E C T R U M M A N A G E M E N T

  9

• Choice of transmission spectrum: in Crahns, the available spectrum bands are distributed over a wide frequency range, which vary over time and space. Thus, each user shows dif- ferent spectrum availability according to the PU activity. As opposed to this, classical ad hoc networks generally oper- ate on a pre-decided channel that remains unchanged with time. For the ad hoc networks with multichannel support, all the channels are continuously available for transmission, although nodes may select few of the latter from this set based on self-interference constraints. A key distinguishing factor is the primary consideration of protecting the PU transmission, which is entirely missing in classical ad hoc networks.
• Topology control: Ad hoc networks lack centralized support, and hence must rely on local coordination to gather topol- ogy information. In classical ad hoc networks, this is easily accomplished by periodic beacon messages on the channel. However, in CRAHNs, as the licensed spectrum opportu- nity exists over a large range of frequencies, sending beacons over all the possible channels is not feasible. Thus, CRAHNs are highly probable to have incomplete topology information, which leads to an increase in collisions among CR users as well as interference with the PUs.
• Multihop/multispectrum transmission: The end-to-end route in

  CRAHNs consists of multiple hops having different chan- nels according to the spectrum availability. Thus, CRAHNs require collaboration between routing and spectrum alloca- tion in establishing these routes. Moreover, the spectrum switches on the links are frequent based on PU arrivals. As opposed to classical ad hoc networks, maintaining an end- to-end quality of service (QoS) involves not only the traffic load but also the number of different channels and possibly spectrum bands that are used in the path, the number of PU-induced spectrum change events, and the consideration of periodic spectrum sensing functions, among others.

• Distinguishing mobility from PU activity: In classical ad hoc networks, routes formed over multiple hops may periodically experience disconnections caused by node mobility. These cases may be detected when the next hop node in the path

  

C O G N I T I V E N E T W O R K S

  10

  does not reply to messages and the retry limit is exceeded at the link layer. however, in Crahns, a node may not be able to transmit immediately if it detects the presence of a pU on the spectrum, even in the absence of mobility. Thus, correctly inferring mobility conditions and initiating an appropri- ate recovery mechanism in CRAHNs necessitate a different approach from the classical ad hoc networks.

1.3 Spectrum Management Framework for Crahn

  The components of the CRAHN architecture, as shown in Figure 1.4a, can be classified into two groups: the primary network and the CR network components. The primary network is referred to as an exist- ing network, where the PUs have a license to operate in a certain spectrum band. If primary networks have an infrastructure support, the operations of the PUs are controlled through primary base sta- tions. Due to their priority in spectrum access, the PUs should not be affected by unlicensed users. The CR network (or secondary network) does not have a license to operate in a desired band. Hence, addi- tional functionality is required for CR users (or secondary users) to share the licensed spectrum band. Also, CR users are mobile and can communicate with each other in a multihop manner on both licensed and unlicensed spectrum bands. Usually, CR networks are assumed to function as stand-alone networks, which do not have direct com- munication channels with the primary networks. Thus, every action in CR networks depends on their local observations.

  In order to adapt to a dynamic spectrum environment, the CRAHN necessitates the spectrum-aware operations, which form a cognitive cycle [4]. As shown in Figure 1.4b, the steps of the cogni- tive cycle consist of four spectrum management categories: spectrum

  

sensing, spectrum decision, spectrum sharing, and spectrum mobility. To

  implement CR networks, each function needs to be incorporated into the classical layering protocols, as shown in Figure 1.5. The main fea- tures of spectrum management functions are as follows [3]:

  

Spectrum sensing: A CR user can be allocated to only an unused por-

  tion of the spectrum. Therefore, a CR user should monitor the available spectrum bands and then detect the spectrum holes.

  11 ^{E F F I C I E N T S P E C T R U M M A N A G E M E N T}

  Spectrum sensing is a basic functionality in Cr networks, and hence it is closely related to other spectrum management func- tions as well as layering protocols to provide information on

  

Spectrum band

CR user Unlicensed band Licensed band 1 Licensed band 2 Primary user

  CR user

CRAHNs Licensed band 1

(a) Primary base station

  Radio environment Spectrum mobility Spectrum sensing Spectrum sharing

  Channe l capa city

  PU detection Spectrum holes

  Spectrum decision D ec

ision

request

  RF stimuli T ra n sm it te d sig nal

  (b) Figure 1.4 The CRAHN architecture (a) and the CR cycle (b).

  

C O G N I T I V E N E T W O R K S

  12 _{reconfiguration management Application} Connection User application/ _{User QoS} end-to-end QoS manager Connection management _{reconfiguration Transport} Transport protocol Spectrum

  Spectrum decision mobility

Network layer protocol

  Cooperation Cooperation

Link layer protocol Cooperation

MAC (distributed)

  Spectrum reconfiguration _RF Spectrum sharing _{observation Sensing} sensing Physical layer _coordination Physical _Spectrum reconfiguration _switching Sensing results

Figure 1.5 Spectrum management framework for CRNs.

  Spectrum decision: Once the available spectrums are identified, it

  is essential that the CR users select the most appropriate band according to their QoS requirements. It is important to char- acterize the spectrum band in terms of both the radio envi- ronment and the statistical behaviors of the PUs. In order to design a decision algorithm that incorporates dynamic spec- trum characteristics, we need to obtain a priori information regarding the PU activity. Furthermore, in CRAHNs, spec- trum decision involves jointly undertaking spectrum selection and route formation.

  

Spectrum sharing: Since there may be multiple CR users trying

  to access the spectrum, their transmissions should be coor- dinated to prevent collisions in overlapping portions of the spectrum. Spectrum sharing provides the capability to share the spectrum resource opportunistically with multiple CR users, which includes resource allocation to avoid interference caused to the primary network. For this reason, game theo- retical approaches have also been used to analyze the behavior of selfish CR users. Furthermore, this function necessitates a CR medium access control (MAC) protocol, which facilitates the sensing control to distribute the sensing task among the

  

E F F I C I E N T S P E C T R U M M A N A G E M E N T

  13

  coordinating nodes as well as spectrum access to determine the timing for transmission.

  

Spectrum mobility: if a pU is detected in the specific portion of

  the spectrum in use, CR users should vacate the spectrum immediately and continue their communications in another vacant portion of the spectrum. For this reason, either a new spectrum must be chosen or the affected links may be cir- cumvented entirely. Thus, spectrum mobility necessitates a spectrum handoff scheme to detect the link failure and to switch the current transmission to a new route or a new spec- trum band with minimum quality degradation. This requires collaborating with spectrum sensing, neighbor discovery in a link layer, and routing protocols. Furthermore, this function- ality needs a connection management scheme to sustain the performance of upper layer protocols by mitigating the influ- ence of spectrum switching. To overcome the drawback caused by the limited knowledge of the network, all of spectrum management categories are based on coop- erative operations where CR users determine their actions based on the observed information exchanged with their neighbors. In the fol- lowing Sections 1.4 through 1.7, spectrum management categories for CRAHNs are introduced. Then, we investigate how these spec- trum management functions are integrated into the existing layering functionalities in ad hoc networks and address their challenges. Also, open research issues for this spectrum management are declared.

1.4 Spectrum Sensing for Cr networks

  A CR is designed to be aware of and sensitive to the changes in its surrounding, which makes spectrum sensing an important require- ment for the realization of CR networks. Spectrum sensing enables CR users to exploit the unused spectrum portion adaptively to the radio environment. This capability is required in the following cases: (1) CR users find available spectrum holes over a wide frequency range for their transmission (out-of-band sensing) and (2) CR users monitor the spectrum band during transmission and detect the pres- ence of primary networks so as to avoid interference (in-band sensing).

  

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  14 Cooperation (distributed)

  

Sensing Spectrum

sharing control PU

  RF observation detection

Figure 1.6 Spectrum sensing structure for CRAHNs.

  as shown in Figure 1.6, the Crn necessitates the following func- tionalities for spectrum sensing:

• PU detection: The CR user observes and analyzes its local radio environment. Based on these location observations of itself and its neighbors, CR users determine the presence of PU transmissions, and accordingly identify the current spec- trum availability.
• Sensing control: This function enables each CR user to per- form its sensing operations adaptively to the dynamic radio environment.
• Cooperation: The observed information in each CR user is exchanged with its neighbors so as to improve sensing accuracy.

  In order to achieve high spectrum utilization while avoiding inter- ference, spectrum sensing needs to provide high detection accuracy. However, due to the lack of a central network entity, CR ad hoc users perform sensing operations independently of each other, leading to an adverse influence on sensing performance. We investigate these basic functionalities required for spectrum sensing to address this challenge in CRAHNs. In Sections 1.4.1 through 1.4.4, more details about functionalities for spectrum sensing are provided.

1.4.1 PU Detection

  Since CR users are generally assumed not to have any real-time inter- action with the PU transmitters and receivers, they do not know the exact information of the ongoing transmissions within the pri- mary networks. Thus, PU detection depends only on the local radio observations of CR users. Generally, PU detection techniques for

  

E F F I C I E N T S P E C T R U M M A N A G E M E N T

  15 Spectrum sensing Transmitter detection Receiver detection Interference temperature management Energy detection Matched filter Feature detection detection Figure 1.7 Classification of spectrum sensing.

  No interaction between CR user and primary Tx/Rx Primary Primary transmitter transmitter

  Primary receiver Primary CR receiver user

  CR must rely on locally sensed signals from the primary transmitters to infer PU activity Figure 1.8 Spectrum sensing techniques. Tx, Transmitter, Rx, Receiver.

  Crahns can be classified into three groups [3,5]: primary trans-

  

mitter detection, primary receiver detection, and interference temperature

management as declared in Figure  1.7. As shown in Figure  1.8,

  the primary transmitter detection is based on the detection of the weak signal from a primary transmitter through the local observa- tions of CR users. The primary receiver detection aims at finding the PUs that receive data within the communication range of a CR user. Also, the local oscillator leakage power emitted by the radio-frequency (RF) front end of the primary receiver is usually

  

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  exploited, which is typically weak. Thus, although it provides the most effective way to find spectrum holes, this method (i.e., pri- mary receiver detection) is only feasible in the detection of the TV receivers. Interference temperature management accounts for the cumulative RF energy from multiple transmissions and sets a max- imum cap on their aggregate level that the primary receiver could tolerate, called an interference temperature limit [6]. As long as CR users do not exceed this limit by their transmissions, they can use this spectrum band. However, the difficulty of this model lies in accurately measuring the interference temperature since CR users cannot distinguish between actual signals from the PU and noise or interference. For these reasons, most of the current research on spectrum sensing in CRAHNs has mainly focused on the primary transmitter detection.

  Waleed et al. [7] presented a two-stage local spectrum sensing approach. In the first stage, each CR performs the existing spectrum sensing techniques, that is, energy detection, matched filter detection, and feature detection. In the second stage, the output from each tech- nique is combined using fuzzy logic in order to deduce the presence or absence of a primary transmitter. Simulation results verify that the sensing approach technique outperforms the existing local spec- trum sensing techniques. The sensing approach shows a significant improvement in sensing accuracy by exhibiting a higher probability of detection and low false alarms.

  Ghasemi and Sousa [8] presented a scheme for cooperative spec- trum sensing on distributed CR networks. A fuzzy logic rule-based inference system is used to estimate the presence possibility of the licensed user’s signal based on the observed energy at each CR ter- minal. The estimated results are aggregated to make the final sensing decision at the fusion center.

1.4.2 Sensing Control

  The main objective of spectrum sensing is to find more spectrum access opportunities without interfering with primary networks. To this end, the sensing operations of CR users are controlled and coor- dinated by a sensing controller, which considers two main issues:

  

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  17 Sensing control Interference avoidance Fast discovery

  

(in-band sensing) (out-band sensing)

Sensing time Transmission Sensing order Stopping rule

time

How long to How long to Which spectrum When to stop

sense transmit data? to sense first? searching sensing? the spectrum?

Figure 1.9 Configuration parameters coordinated by sensing control.

  (1) how long and how frequently Cr users should sense the spectrum to achieve sufficient sensing accuracy in in-band sensing and (2) how quickly Cr users can find the available spectrum band in out-of-band sensing, which are summarized in Figure 1.9.

  The first issue is related to the maxi-

1.4.2.1 In-Band Sensing Control

  mum spectrum opportunities as well as interference avoidance. The in-band sensing generally adopts the sensing period structure where CR users are allowed to access the spectrum only during the trans- mission period followed by sensing (observation) period. In the peri- odic sensing, longer sensing time leads to higher sensing accuracy, and hence less interference. But as the sensing time becomes longer, the transmission time of CR users will be decreased. Conversely, while longer transmission time increases the access opportunities, it causes higher interference due to the lack of sensing information. Thus, how to select the proper sensing and transmission times is an important issue in spectrum sensing.

  Sensing time optimization is investigated in [9,10]. The sensing time is determined to maximize the channel efficiency while main- taining the required detection probability, which does not consider the influence of a false alarm probability. In [3], the sensing time is optimized for a multiple spectrum environment so as to maximize the throughput of CR users.

  

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  The focus of [11,12] is on determining the optimal transmission time. In [12], for a given sensing time, the transmission time is deter- mined to maximize the throughput of the CR network while the packet collision probability for the primary network is under a certain threshold. This method does not consider a false alarm probability for estimating collision probability and throughput. In [11], a maximum transmission time is determined to protect multiple heterogeneous PUs based on the perfect sensing where no detection error is con- sidered. All efforts stated above mainly focus on determining either optimal sensing time or optimal transmission time.

  However, in [13], a theoretical framework is presented to optimize both sensing and transmission times simultaneously in such a way as to maximize the transmission efficiency subject to interference avoid- ance constraints where both parameters are determined adaptively depending on the time-varying cooperative gain.