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1.4.2.2 Out-of-band Sensing Control

  This cognitive radio will learn from the envi- ronment and adapt its internal states to statistical variations in theexisting 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 6C 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. 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.

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. 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 possiblyspectrum bands that are used in the path, the number ofPU-induced spectrum change events, and the consideration of periodic spectrum sensing functions, among others.

1.3 Spectrum Management Framework for Crahn

  It is important to char-acterize the spectrum band in terms of both the radio envi- ronment and the statistical behaviors of the PUs. 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 anothervacant portion of the spectrum.

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. 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 usersmonitor the spectrum band during transmission and detect the pres- ence of primary networks so as to avoid interference (in-band sensing).

1.4.1 PU Detection

  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. Tothis end, the sensing operations of CR users are controlled and coor- dinated by a sensing controller, which considers two main issues: 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 17Sensing control Interference avoidance Fast discovery (in-band sensing) (out-band sensing) Sensing time Transmission Sensing order Stopping ruletime How long to How long to Which spectrum When to stopsense transmit data?

1.4.2.1 In-Band Sensing Control

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

1.4.2.2 Out-of-Band Sensing Control

  In [15],an n-step serial search scheme is presented to mainly focus on cor- related occupancy channel models, where the spectrum availabilityfrom all spectrum bands is assumed to be dependent on that of its adjacent spectrum bands. In [18], an optimal stopping time is determined to maximize the expected capacity of CR users subject to the maximum number ofspectrum bands a CR user can use simultaneously.

1.4.3 Cooperative Sensing

  Furthermore, if the CR user receives a weak signal with a low signal-to-noise ratio (SNR) due to multipath fading,or it is located in a shadowing area, it cannot detect the signal of thePUs. Such a scheme for wireless mesh net-works is presented in [20], where the mesh router and the mesh clients supported by it form a cluster.

1.4.4 Open Research Issues in Spectrum Sensing

  21E 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 Crns require capabilities to decide on the best spectrum band among the available bands according to the QoS requirements of the applica-tions. As depicted in Figure  1.10, through RF observation,CR users characterize the available spectrum bands by considering the received signal strength, the interference, and the number of userscurrently residing in the spectrum, which are also used for resource allocation in classical ad hoc networks.

1.5.1.1 Radio Environment

  Hence, it is essential to define parameters thatcan represent a particular spectrum band as follows: Interference: From the amount of the interference at the primary receiver, the permissible power of a CR user can be derived, which is used for the estimation of the channel capacity. Wireless link errors: Depending on the modulation scheme and the interference level of the spectrum band, the error rate of the channel changes.

1.5.1.2 PU Activity

  Since there is no guaranteethat a spectrum band will be available during the entire communica- tion of a CR user, the estimation of the PU activity is a very crucialissue in spectrum decision. C O G N I T I V E N E T W O R K S2 4 in [26], a statistical traffic model of wireless lans based on a semi-Markov model is presented to describe the temporal behavior ofwireless lans.

1.5.2 Spectrum Selection

  based on the userQoS requirements and the spectrum characteristics, the data rate, acceptable error rate, the delay bound, the transmission mode, andthe bandwidth of the transmission can be determined. In order to determine the best route and spectrum more efficiently, spectrum decision necessitates the dynamic decisionframework to adapt to the QoS requirements of the user and chan- nel conditions.

1.6 Spectrum Sharing for Crahns

  In this respect, spec-trum sharing provides the capability to maintain the QoS of CR users without causing interference to the PUs by coordinating the multipleaccesses of CR users as well as allocating communication resources adaptively to the changes of radio environment. Thus, spectrum shar-ing is performed in the middle of a communication session and within the spectrum band, and includes much functionality of a MAC proto-col and resource allocation in classical ad hoc networks.

1.6.1 Resource Allocation

  based on the QoS monitoring results, Cr users select the proper chan- nels (channel allocation) and adjust their transmission power (powercontrol) so as to achieve QoS requirements as well as resource fairness.especially, in power control, sensing results need to be considered so as not to violate the interference constraints. While this method considers the channel and power allocation at the same time, it does not address the heterogeneous spectrum availabilityover time and space, which is a unique characteristic of CRAHNs.

1.6.2 Spectrum Access

  A key difference of this proto- col as against the previous work is that the sensing at either ends of the CR MAC protocol classification Random access Time slotted Hybrid(CSMA/CA-like random (time-synchronized control and (partially time access for control packets data slots) slotted and partiallyand data) C-MAC [36] random access) HC-MAC [18] OS-MAC [11] Figure 1.12 Classification of CR MAC protocols. However, this work does not completely address the means to detect the presence of a jammer and how theongoing data transmission is switched immediately to one of the possible backup channels when the user is suddenly interrupted.

1.6.3 Open Research Issues in Spectrum Sharing

  Since spectrum sharing and sensing share some of the functionalities, most of the issues are similar to those of spectrum sensing, which areexplained as follows: Distributed power allocation: The CRAHN user determines the transmission power in a distributed manner without sup- port of the central entity, which may cause interference dueto the limitation of sensing area even if it does not detect any transmission in its observation range. For thisreason, open challenges include how the theoretical research and network operation are combined, so that the gains arisingfrom the choice of the spectrum at the link layer are appropri- ately weighted in each decision round, and the computationaltime for considering the past history is minimized.

1.7 Spectrum Mobility for Crahns

  Hence, if the specific portion of the spectrum in use is required by a PU, the commu-nication needs to be continued in another vacant portion of the spectrum. Furthermore, in spectrum mobility, the protocols for different layers of the network stack should be transpar-ent to the spectrum handoff and the associated latency and adapt to the channel parameters of the operating frequency.

1.7.1 Spectrum Handoff

  However,in the proactive spectrum handoff, CR users predict future activity in the current link and determine a new spectrum while maintaining 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 T3 3 current transmission, and then perform spectrum switching before the link failure happens. depending on the events that trigger the spectrum mobility, different handoff strategies are needed.while the reactive spectrum handoff is generally used in the event of a pU appearance, the proactive spectrum handoff is suitable forthe events of user mobility or spectrum quality degradation.

1.8 Common Control Channel

  The operation of the CCC is different from the data transmission over the licensed band in the following aspects:CR users may optimize their channel use over a number of con- straints, such as channel quality, access time, observed PUactivity, and network load, among others during CR data transmission. However, these parameters are not known tothe CR users in advance at the start of the network operation, and, thus, it is a challenge to choose the CCC with a mini-mum or no exchange of network information.

5. M. Nekovee, “A survey of cognitive radio access on TV white spaces,”

  Abdel-Kader (sherine@eri.sci.eg) obtained her MSc and phd degrees from the electronics and communications department and computers department, Faculty of engineering, Cairo University,Giza, egypt in 1998 and 2003, respectively. This page intentionally left blank This page intentionally left blank 2a S u rv E y o n J o I n t r o u t I n g a n d d y n a m I c S p Ec t ru m a c c E S S I n c o g n I t I v E r a d I o n E t wo rkSX i a nZ hOnG X i e, h e l i n Ya nG, an d aT h a n a SiO S V.

2.4.1.2 Centralized pOMdp Framework

572.4.1.3 hidden Markov Model 58 59 2.4.2.1 Repeated Game 60 2.4.2.2 Potential Games 62 2.4.3 Graph Theory 63 2.4.4 Artificial Intelligence 66 C O G N I T I V E N E T W O R K S4 4 2.5 routing Schemes of Crns 692.5.1 routing in Multihop Crns 702.5.2 routing in Crahns 74 2.5.2.1 Optimization Modeling approach 752.5.2.2 probabilistic Modeling approach 76 2.5.2.3 Graph Modeling approach 772.5.3 adaptive routing in Cognitive radio networks 792.6 routing Technology research for dSa Crns 80 80

2.6.1 Spectrum-aware Opportunistic routing Protocols

  A routing and dynamic spectrum allocation (ROSA) algorithm C O G N I T I V E N E T W O R K S4 6 was proposed in [7], which is a joint spectrum allocation schedul- ing and transmission power control scheme to maximize the networkthroughput and limit the physical interference to the users.in addition, the dynamic nature of the radio spectrum calls for the development of novel spectrum-aware routing algorithms, withmultihop communication requirements in Crns. Based on the presentation of the SA and routing, we break through the existing routing classification criteria such as multipath routingalgorithm, quality-of-service (QoS) routing algorithm, routing algo- rithm based on geographical information, and power-aware routingalgorithm, and summarize the latest research of routing technology research for DSA.

2.2 Cr Technology

  The key enabling technologies of CRNs are the CR techniques that provide the capability to share the spectrum in an opportunistic man-ner. For instance, when a pU reuses its frequency band, the SUs using the licensedband can direct their transmission to other available frequen- cies, according to the channel capacity determined by thenoise and interference levels, path loss, channel error rate, and holding time.

2.3 Crn architecture

2.3.1 Network Components

  C O G N I T I V E N E T W O R K S5 0 a pn is composed of a set of pUs and one or more primary base stations, where pUs are authorized to use certain licensed spectrumbands under the coordination of primary base stations. Therefore, if an SN shares a licensed spectrum band with a PN, besides detecting the spectrum white space andutilizing the best spectrum band, it immediately detects the presence of a PU and directs the secondary transmission to another availableband.

2.3.2 Spectrum Heterogeneity

  SUs are capable of accessing both the licensed portions of the spec- trum used by PUs and the unlicensed portions of the spectrum. Consequently, the operation types of CRNs can be classified into licensed band operation and unlicensed band operation.

2.3.4.1 Spectrum Sharing Architecture Both centralized and distrib-

uted spectrum sharing techniques are enabled in CRNs. They are described as follows: The available spectrum can be allo-

2.3.4.2 Spectrum Allocation

  The sensing–access coordinator controlsthe operations of these two functions on a time basis by taking care of the trade-off between the sensing requirement and the SA opportu-nity that the SUs may achieve. Solution is found through Nash equilibrium (Ne) Reflects the behaviors of decision makers easily; considers the fairnessamong users; can be used for both cooperative and noncooperativedecisions between Sus Ne point is not always guaranteed; theutility function and the game formulation are not always structuredactually due to other factors, such as cheating users Artificial intelligence algorithmsThese are stochastic search methods that mimic natural evolution andsocial behavior.

2.4.1.1 Decentralized Partially Observable MDP Framework an SU can

  obtain a better estimate about the primary channel occupancy based on the observations of the channels not only on the current but alsoon the past observations of the channels, if the primary traffic exhibits some temporal correlation. Considering the complexity of the POMDP scheme[19], a modified scheme based on POMDP framework reduces the complexity of the POMDP formulation by decoupling the designof sensing strategy from the design of the access strategy.

2.4.1.2 Centralized POMDP Framework

  Thus, at the output of CAQ there is a random splitting with probabil- ity p if the serving request is a feedback to SAQ and with probability q if it is a feedback to CAQ , where, for both p and q, 0 < p, q < 1.q λ 1 − p − q1 SAQ CAQ λ2 (a) p SAQ CAQ (b) Figure 2.6 queuing models for SCDqMAC (a) and MCDqMAC (b). Thus, while distributing a num-ber of frequencies to the PU and SU, the arrival rates of both the users are summed to access the frequencies with the Head.

2.4.1.3 Hidden Markov Model The hidden Markov model (HMM)

  Khosla, IACSIT International Journal of Engineering and Technology, 3(1), 1–4, 2011.) Tranter [27] developed an hMM-based dSa algorithm where the hMM models and predicts the spectrum occupancy of the licensedradio bands for Crns. The economic models include principles such as setting the price of the spectrum available in orderto maximize the revenue of the PUs, choosing the best seller for the spectrum in order to maximize the satisfaction from the usage ofthe spectrum for SUs, modeling the market competition, and so on.

2.4.2.1 Repeated Game

  They considered a punishment scheme and showed that a more efficient equilibrium can be reached when autono-mous radios interact repeatedly, as opposed to when they interact in a single-stage game (in general, a well-known result in game theory, anexample of which is the repeated prisoner’s dilemma). use repeated games to model the evo- lution of reputation among SUs, when one of them is chosen tomanage the spectrum made available by the PU.

2.4.2.2 Potential Games

  This approach does not con- sider the dynamic nature of CRNs, which results from the mobilityof the users, the dynamic service requirements of the users, and the existence of primary transmissions. In [52], the auction framework that allows SUs share the avail-able spectrum of PUs acts as a resource provider announcing a price and a reserve bid to allocate the received power as a function of thebids.

2.4.3 Graph Theory

  Graph theory algorithms: A cognitive network can be modeled as a graph G = ( , U E L C , B ) , where U is the set of users sharing the spec- B trum, LC represents the channel availability list at each vertex, and Eis the set of edges modeling the interference constraints. Interference graphs are commonly used in centralized approaches where the spectrum server constructs the graph and assigns the chan-nels.

A, B, C

  Future work in graphcoloring-based cognitive SA should incorporate another layer in the color graph corresponding to the ACI and prevent the connected ver-tices from using either the same or adjacent spectrum bands. Nguyen and Lee [61] proposed a suboptimal heuris-tic greedy algorithm with a much lower complexity and reasonable performance, which is based on the coloring interference graph con-structed for unnerved SUs under the negative influence of all servicedSUs and active PUs.

2.4.4 Artificial Intelligence

  In order to decrease the search space, a mapping process is proposed between the channel assignment matrix and the position of the beesof ABC based on the characteristics of the channel availability and the interference constraints. The input to the FLS canbe the arrival rate of the PUs or SUs, the channel availability, the distance between users (either PUs or SUs), the velocity (if SUs aremoving), the conflict graph (or any other model that captures the interference relationship between users), and so on.

2.5.1 Routing in Multihop CRNs

  Therefore, Linand Sasase [90] proposed the primary traffic-based multihop routing with the cooperative transmission that reduces the average number ofcognitive receivers on the route from the CS to the CD by using the cooperative transmission and has better performance in terms of thee2e reliability, the e2e throughput, and the average required transmis- sion power of transmitting the data from the CS to the CD. However, if CR implementation will play more flexibly when the available spec-trum bands and their usage happen, the perfect routing scheme should be judiciously chosen first for every situation, depending on the traffichappening and the availability of the primary bands, even the history in the considered wireless environment.

2.5.2 Routing in CRAHNs

  Several classification methods have been proposed to categorize A S U R V E Y O N J O I N T R O U T I N G7 5 present a new classification of CRAHN routing schemes based on the approaches used for establishing the e2e routes. The CRAHNrouting schemes are further classified into three subclasses: optimi- zation modeling, probabilistic modeling, and graph modeling (seeFigure 2.2).

2.5.2.1 Optimization Modeling Approach

  The spectrum selection scheme also considers a signal interference model; this method allows at mostone SU to use a sub-band in the area caused by the interference ranges of the transmitter and the receiver (see Figure 2.14). C O G N I T I V E N E T W O R K S7 6 [93] proposed an optimized Crahn routing scheme by setting the objective of maximizing the network throughput.in addition, recently, researchers have defined a new routing metric: an efficient shortest path algorithm by discovering the lowest weightroute between any pair of nodes in a CRN.

2.5.2.2 Probabilistic Modeling Approach Recently, researchers have

  However,the proposed routing metric is able to capture effectively through A S U R V E Y O N J O I N T R O U T I N G7 7 probabilistic calculation, the specific constraints imposed on a cog- nitive node by the activity, and the density of the primary nodes. Based on this argument, the weight of a channel is defined as a function of the history of tem-poral usage of the channel, and the probabilistic routing schemes are proposed.

2.5.2.3 Graph Modeling Approach

  Furthermore, considering that the Gymkhana routing on a number of topologies with different PUs’ activities plays an impor-tant role in CRAHNs, they [100] used the Gymkhana model to C O G N I T I V E N E T W O R K S7 8 derive a utility function for comparing routing paths and provided a distributed protocol [106] for efficiently routing data in a CRAHN. Destination SU Update list of nodesFormation of the Receive RREQvirtual graph V Update list of Compare the influence vectorslaplacian of V Compute the utility Compute the utility Spectrum function U Compute the utilityfunction U sensing function U Path 1 Path 2 Path 3 Send RREPCompare the different paths Figure 2.15 gymkhana operations performed at the destination.

2.5.3 Adaptive Routing in Cognitive Radio Networks

  Therefore, various important factors, such as the time-variant channel attenua-tion, dynamic changes of spectrum, and the frequency–space domain characteristics of the radio environment should beconsidered jointly in constituting multihop CR routes. An adaptive traffic route control scheme was presented in [111] in order to offer a stable and flexible wireless communication, which pro-vides high QoS for CR technology and examines the performance of the proposed scheme.

2.6.1 Spectrum-Aware Opportunistic Routing Protocols

  For wireless networks, new routing algorithms are necessary due to the dynamic nature of the physical layer (in both time and fre- A S U R V E Y O N J O I N T R O U T I N G 81 a less-explored area. Moreover, the ROSA algorithm is one of the advanced proposals to maximize the throughput by minimizing the interferenceand maximizing the weighted sum of differential backlogs in [97].

2.6.2 Considering Control Channel for Routing Algorithms

  In [84,94,119], it is assumed that the global shared control channel that is used by an interface of stationary cognitive nodes to completecommunication coordination, exchange of spectrum information, neighbor discovery, and some other operations is configured in DSAnetworks; however, how to select the global shared control channel has not been explained in these references. The distribution of the available spectrum of cognitive nodes is asymmetrical and discrete; therefore, cognitive nodes transmit neigh-bor discovery messages in all available channels and get a collection of the available spectra of neighbor nodes and current working channel,thus discovering the network topology in [85,120–125].

2.6.3 Joint Routing and Spectrum Selection Algorithm

  Wang and Zheng [121] analyzed and compared the hierarchical design of spectrum and routing selection; as a result, routing selectionis independent of the spectrum selection that can simplify the pro- tocol design in a hierarchical scheme, but the performance of its e2eis not as good as joint design in the DSA dynamic spectrum change networks. According to the allocated spectrum information on the path, cog- nitive nodes calculate the delay and other indicators; however, the largecomputation of the nodes increases the delay of route establishing, which does not apply to high real-time requirements of the network.

2.6.4 Considering Channel Allocation for Routing Algorithms

  According to the allocation granularity, multichannel distribution strategy can be divided into three kinds of packet-based groups in theexisting wireless network based on the package, the link, and the data flow. The advan-tages of both channel allocation based on comprehensive data streams and link were synthesized in [122]; therefore, according to the distri-bution characteristics of spectrum, segmentation-based channel allo- cation utilizing the flexibility of link and small delay based on dataflow to allocate channels was proposed.

2.6.5 PU Interference Avoidance for Routing Algorithm

  Cognitive nodes can take the fol-lowing measures to deal with when PU appears in the bands occupied by cognitive nodes: (1) bypass the interference regions and create anew route, (2) switch the band and find idle bands for communica- tion, and (3) change the transmit power or modulation to avoid inter-ference to the communication of PU. Fujii and Suzuki [125] proposed a distributed automatic retrans- mission request technology using space–time block code (STBC)-distributed Automatic Repeat-reQuest (AQR), which identifies the interference proactively and selects the route adaptively by bypassingthe interference region, but cannot be applied into energy-constrained networks due to the large energy consumption of the nodes caused byomnidirectional broadcast of the package.

2.6.6 Joint Dynamic Routing and Cross-Layer Opportunistic Spectrum Access

  The selected relay will send a new RTR packetafter receiving the RTS and CTS packets, and the new RTR packet will announce the accessed spectrum information to neighbors and thestatue of the selected relay to the receiver. In particular, this scheme is affected by both the presence of PUs A S U R V E Y O N J O I N T R O U T I N G8 7 signaling and data transmissions are managed efficiently by the two radios of the CCC scheme; the sensing and signaling exchange pro-cess may be interrupted when only one radio decreases the device costs and energy consumption data transmissions to perform.

2.6.7 Considering Network Routing Model for Routing Algorithms

  Channel allocation is identified by the vertical edge; however, the connection of verti-cal edges is restricted by the number of idle interfaces of nodes. The interference caused by PU, the received signal strength,and the data transmission rate can be achieved are calculated according to probability models as to be the weight of the link,then optimal path is selected by the conventional distance vector algorithm.

2.7 Challenges of Jointing routing and dSa in Crns The research of CR technology utilizes spectrum resources efficiently

  To make cognitive devicestransmit information efficiently in the most optimal way in a lim- ited signal space, cross-layer design of routing algorithms for DSAnetworks must be adopted, which allocates spectrum and selects the route through spectrum sensing information provided by the physicallayer and spectrum scheduling information provided by the link layer synthesized by the network layer. The investigation of incentives for the participation of selfish users into the SA schemes and how they can benefit (in terms of profit orperformance) from the cooperation with other SUs in the SA process is not covered in the literature, which may degrade the performancein the route path selection and MAC protocol too.

3. J. Mitola and G. Q. Maguire, “Cognitive radio: Making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999

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11. S. Haykin, “Cognitive radio: Brain-empowered wireless communica- tions,” IEEE JSAC, vol. 23, no. 2, pp. 201–220, February 2005

  Hossain, “A game-theoretic approach to competi- tive spectrum sharing in cognitive radio networks,” Proceedings of the IEEE  Wireless Communications and Networking Conference, 2007, pp. 16–20. Han, “A two-tier market for decentralized dynamic spectrum access in cognitive radio networks,” Proceedings of the IEEESociety Conference on Sensor Mesh and Ad Hoc Communications and Networks, Boston, MA, June 21–25, 2010, pp.

48. E. Maasland and S. Onderstal, “Auction theory,” Medium Econometrische Toepassingen, vol. 13, no. 4, pp. 4–8, 2005

  Suri, “A general framework for wireless spectrum auctions,” Proceedings of the 2nd IEEEInternational Symposium on New Frontiers in Dynamic Spectrum Access A S U R V E Y O N J O I N T R O U T I N G9 9 50. Xie, “Intelligent cognitive radio: Research on learning and evaluation of CR based on neural network,” Proceedings of the ITI 5thInternational Conference on ICICT, Dhaka, Bangladesh, December 16–18, 2007, pp.

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  he is currently a profes- sor of the School of Computer Science and Technology at ChongqingUniversity of posts and Telecommunications, Chongqing, people’s republic of China, and a director of the institute of broadbandaccess Technologies, Chongqing, people’s republic of China. He is currently a professor at the department of computer and tele- communications engineering, University of Western Macedonia,Kozani, Greece, and a visiting professor of the graduate program of the department of electrical and computer engineering, NationalTechnical University of Athens (NTUA), Athens, Greece.

3.3 Cognitive radio networks 113

  In the distance-bounding approach, the signal round-trip time is measured and multiplied by the propagation speed in order to calcu-late the distance to a potential neighbor. Some of the features of the related work summarized here are the parameters, whether the coordination is required between a trans-mitter and a receiver, whether the algorithm is employed on a single channel or multichannels, the algorithm type, and the initial knowl-edge required.

3.3 Cognitive radio networks

  If the PU returns to the chan- nel, the SU has to vacate the channel and switch to some other avail-able channel for communication [3]. Moreover, no information about the IDs of neighbors, diverse signal propagationcharacteristics, geographical scattering of nodes, collisions, and the unknown size of neighborhood makes the ND a noticeable problemdue to which the termination of discovery process becomes difficult.

3.3.1 ND in CRNs

  ND in CRNs may be based on certain characteristics and operational metrics. A brief overview about ND protocols is given in Tables 3.2 and 3.3.

3.3.1.1 Synchronous Operation

  The information about the size of space used for assigning labels is available to the nodes (k). The information about thenumber of channels over which these nodes can operate (M) is also available, but nodes do not know the actual number ofnodes present in the network (N).

2. There is no relationship between N and k, and M ≥ 1

  Nodes may operate over multiple channels and different chan- nel availability sets are available to different nodes. Moreover,nodes do not have directional antennas and the NDA has strong termination guarantees.

3.3.1.1.1 ND for Collision-Aware Nodes In undirected networks

  For multichannels, the same algorithm is modified when each timeslot is replaced by a frame in NDA_SC to give NDA_MC algorithm, with the same characteristics and a time complexity of N E I G H B O R D I S C O V E R Y F O R C R N E T W O R K S121 change in the channel availability sets of CR nodes. Now if nodes are size aware in 3.3.1.1.2 ND for Size-Aware Nodes undirected networks and they have a polynomial upper bound on the network size, then a fast deterministic gossiping algorithm for a single channel (GA_SC) is described with a strong termination condition 2  2  and a time complexity of (ϕ N log N log k), for some known constant ϕ.

3.3.1.1.5 ND for Multihop Environment

  process in a heterogeneous multihop CRN, a CR node knows only the universal channel set, the size of this set, the channel avail abilityset for this CR node, the ID of CR node, and space size, and every- thing else is unknown. In order to evaluate this study, the development of a reliable simu- lation platform is required for an accurate modeling of all the relevantaspects in the CRN function.

3.3.1.1.7 Quorum-Based ND

  This algorithm works by using random backoff time before each transmission and by employing quorum systems to discover neighbors, so that discovery procedure becomes independent of the number of nodes in the network. For each channel in the universal channel set, the quorum system allocates the activeinterval set without considering the node’s available channel set and the quorum system of same size is used for tuning on each channel.

3.3.1.1.8 Diameter-Aware and Diameter-Unaware NDAs

  have access to synchronized clocks and slotted time, an L2AC enables each participating node to determine a common set of channels, lead-ing to a time-efficient distributed algorithm with its diameter-aware and diameter-unaware versions. Both diameter-aware anddiameter-unaware versions use 2 Mk  +  O(Dk) timeslots to find the globally common channel set, where a range [1, 2,..., k] is used to assign a unique identifier to each node, M is the maximum number of channels available, and N E I G H B O R D I S C O V E R Y F O R C R N E T W O R K S12 5 Every node has the information about M and k but does not know about the neighborhood.

3.3.1.1.9 Rendezvous Problem for CR Nodes Rendezvous problem

  When unbounded, the modular clock algorithms enhance the speed C O G N I T I V E N E T W O R K S12 6 in expected TTR at less pre-coordination requirement and the flex- ibility to operate in both the shared and individual models. p p p p 1 2 1 2 If the observed channels are analyzed for a fixed ratio, the probabil- ity of the different prime numbers being selected increases when the number of observed channels by each radio increases [33].

3.3.1.2 Asynchronous Operation In asynchronous operation, channel

  Asynchronous behavior is simple andbeneficial when CR nodes are initiated at different times and remain connected for a relatively short time interval. Asynchronous modeoperation is difficult to implement as CR nodes have different time intervals; therefore, CR nodes may have a little time to perform ND.

3.3.1.2.1 NDA for Jamming Attacks

  Its dissemination performance depends on the actual number of nodes in the network instead of the label space N E I G H B O R D I S C O V E R Y F O R C R N E T W O R K S12 7 size, and it also operates without the need to know the number of nodes in advance. Considering the codingperformance, the increase in the size of Galois fields (finite number of elements) does not provide improved gains in terms of dissemina-tion delay, but this may not be true for small number of CR nodes where packet diversity is highly beneficial.

3.3.1.2.2 Leader Election ND There are various NDAs. Another

  The main attribute of this algorithm is the ability to find a leader node at the start of the discovery process. Dealing with a situationwhere cognitive nodes are fully connected, an C O G N I T I V E N E T W O R K S12 8 algorithm is proposed for single-hop CRNs, allowing neighbors and channel discovery that can be used to communicate among CR nodes.

3.3.1.2.3 Energy-Efficient ND

  Besides achieving energy efficiency, this research work has the success probability of almost 1 and remainshigher than WSF-(7, 3, 1) and WSF-(73, 9, 1). Despite the fact that all success probabilities attain the value of 1 as B increases, the pro- posed algorithm has more energy consumption than the other two schemes.

3.3.1.2.4 Sequence-Based Rendezvous

  This enables CR nodes to determine theorder in which they attain rendezvous, that is, the ability to meet and establish a link on a common channel. This enables to mark an upper bound to the TTR and to set a priority order for channels.

3.3.1.2.5 Rendezvous with Cooperative Environments A novel

  In CRNs, dynamicity in the network may result in the variation of potentially available channel set, and findinga CCC is an open challenge [24]. As the control channel duration and the number of control channel in a decentralized ad hoc environment without the requirement of any synchronization.

3.3.1.3 Common Channel Set

  C O G N I T I V E N E T W O R K S13 0 achieved by using minimum infrastructure [31]. These problems have been investigated by many researchers as described in Sections3.3.1.3.1 and 3.3.1.3.2 respectively.

3.3.1.3.1 NDAs for Distributed Learning As discussed above, a

  The distributed learn- ing schemes relying at the basis of the context information acquisi-tion in multiple channels are applied on optimal network selection to discuss the relationship between the ND and the distributed learn-ing process. The establishment of a common communication channel is the first and important step in ND and gives the efficiency of the distributedlearning algorithm.

3.3.1.3.2 CCC Allocation through Ultra Wideband Communication It

  The performance of this UWB scheme is found to be better suited in terms of success probability of transmitted single messages onthe UWB CCC and of the complete message handshake required to establish a communication link. The 16-PPM has almost half average delay compared to the 2-PPM and the average delay in single-hop has the duration of a successfulmessage handshake on the order of tens of milliseconds whatever C O G N I T I V E N E T W O R K S13 2 may be the number of nodes.

3.3.1.4 Collision-Free Algorithm In a CRN, a successful communica-

  Besides this, there must be no collisionbetween the transmission of sender and the transmission of another node when both of them are within the communication range of thereceiver. The time complexity of this algorithm is given by   k M O max( M k , ) log r )   + ( r  where: M is the maximum number of channels on which a node can operate k is the size of the space used to assign identifiers to nodes N E I G H B O R D I S C O V E R Y F O R C R N E T W O R K S13 3 Here, the time complexity is related to the size of the space from which identifiers are selected and it does not depend on the actualnumber of nodes.

3.3.2 Classification of ND Protocols in CRNs

  These parameters include the sizeof space, the number of nodes, the number of available channel sets, and the network size [1,4,5,25]. Some of the discoveryschemes have the termination guarantees, that is, each node detects the completion of the discovery process and this process terminatessimultaneously at each node [1,6].

3.4.1 Coordination among Nodes

  It is essential that all the nodes must have the capability to detect the ND completion on onechannel simultaneously and move on to the next channel in a coor- dinated manner. 3.4.5 Termination Detection If the time complexity depends on the actual number of nodes, then the nodes can easily detect the termination of the NDA.

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