CogNS: A Simulation Framework for Cognitive Radio Networks

CogNS: A Simulation Framework for Cognitive Radio Networks Vahid Esmaeelzadeh · Reza Berangi · Seyyed Mohammad Sebt · Elahe Sadat Hosseini · Moein Parsinia

the date of receipt and acceptance should be inserted later

Abstract Cognitive radio technology has been used to efficiently utilize the spectrum in wireless networks. Although many research have been done recently in the area of cognitive radio networks (CRNs), little effort has been made to propose a simulation framework for CRNs. In this paper, a simulation framework based on NS2 (CogNS) for cognitive radio networks is proposed. This framework can be used to investigate and evaluate the impact of lower layers, i.e., MAC and physical layer, on the transport and network layers protocols. Due to the importance of packet drop probability, end-to-end delay and throughput as QoS requirements in real-time reliable applications, these metrics are evaluated over CRNs through CogNS framework. Our simulations demonstrate that the design of new network and transport layer protocols over CRNs should be considered based on CR-related parameters such as activity model of primary users, sensing time and frequency. Keywords Cognitive Radio Networks (CRN) · Simulation framework · Network Simulator 2 (NS2) · Performance evaluation 1 Introduction With the growth of wireless spectrum demands and significant inefficient wireless channel utilization, Dynamic Spectrum Access (DSA) methods were conVahid Esmaeelzadeh ( ) · Reza Berangi · Seyyed Mohammad Sebt · Elahe Sadat Hosseini · Moein Parsinia Wireless Networks Laboratory, Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran V. Esmaeelzadeh (E-mail: [email protected]; [email protected]) R. Bernagi (E-mail: [email protected]) S. M. Sebt (E-mail: [email protected]) E. S. Hosseini (E-mail: [email protected]) M. Parsinia (E-mail: [email protected])

The final publication is available at link.springer.com: http://link.springer.com/article/10.1007%2Fs11277-013-1184-y

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sidered to address the spectrum scarcity and spectrum inefficiency challenges [1]. Cognitive Radio (CR) [2] technology is used as an efficient tool for dynamic spectrum access in wireless networks. CR technology makes the selfmanaging ability to sense, find, select and use of vacant licensed spectrum bands which belong to licensed users (primary users) [3]. In cognitive radio networks (CRNs) each CR user has a CR-equipped transceiver to dynamically access the spectrum bands opportunistically. Since CR users are considered lower priority, a requirement is to avoid the interference to primary users in their neighborhood [4]. In the use of spectrum band, there are two kinds of CRNs, overlay and underlay. In underlay CRN, CR users use the licensed spectrum band in spite of presence of primary users with an interference threshold imposed to primary users. The CR users of overlay CRN can use the licensed spectrum bands in the absence of primary users [9]. In this paper, the overlay CRN is considered. In the literature, there are many studies in cognitive radio networks. In [59], some of important challenges and studies in the different layers of CRNs are presented and discussed. However, there are limited simulation frameworks for multi-radio multi-channel in wireless networks [10-13]. These frameworks do not support dynamic spectrum access; hence, those cannot be directly used for simulation of CRNs. In [14], a CRAHN simulator based on NS2 is presented. However, this simulator uses two radio transceivers for each CR node and the considered spectrum management imposes communication overhead because of negotiation phase between CR nodes. Despite some simulation frameworks for multi-radio multi-channel networks and cognitive radio networks, it is essential to develop a simulation framework which accelerates the simulation and evaluation of scenarios and protocols in cognitive radio networks. In this paper, a simulation framework for cognitive radio networks (CogNS) is introduced. The CogNS is developed based on the open source network simulator (NS2) [15]. This framework is useful to yield a better understanding of CRNs and simplify the development of new protocols, validation of theoretical results and performance evaluation of CRNs in deferent layers. TCP performance analysis can be used to verify this framework; however, there is no analytical model for TCP performance over CRNs in the literature and there are some simulation-based studies about performance of the existing transport protocols in CRNs. In [16], the efficiency and throughput of TCP over cognitive radio networks has been studied. In this research, the impact of sensing time on TCP throughput and primary users activities on TCP efficiency has been investigated. The effects of primary users traffic, number of wireless channels and sensing time on TCP throughput have been studied in [17]. In [14], authors have been focused on TCP performance in the term of throughput, RTT and congestion window. In this research, behavior of mentioned parameters based on sensing frequency, primary users traffic and channels heterogeneity has been investigated. In [18], impact of sensing time on TCP throughput has been considered. Also, impact of variation of accessible bandwidth for CR users on congestion control mechanism of TCP is studied. It was shown that TCP changes its

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congestion windows regardless of the changes of accessible bandwidth. In [19], TCP performance degradation in cognitive radio networks has been studied through considering the congestion window, RTT behavior and RTO event in TCP. The performance of different congestion control mechanisms which is proposed for wireless sensor networks are studied over cognitive radio sensor networks in [20]; impact of sensing time, primary users activity model and CR users coordination on the performance of different transport protocols is studied in details. In [9], throughput of TCP is studied based on primary users traffic model and number of available channels. Based on [9], there is an optimal value for the number of available channels to maximize the TCP throughput. In most studies, the impact of physical layer parameters are only considered such as sensing time, primary users activity model and channel bandwidth. However, it can be more useful to consider the physical layer parameters jointly with upper layer parameters to evaluate the performance of transport layer protocols. Performance of TCP has been considered in the term of TCP throughput, efficiency, behavior of round trip time (RTT) and congestion window over CRNs in the most previous studies. However, other performance metrics such as TCP end-to-end delay, packet drop probability are not investigated in cognitive radio networks. In this paper, the TCP end-to-end delay, throughput and packet drop probability based on packet size, sensing time, sensing accuracy and activity model of primary users are investigated through proposed CogNS. Furthermore, acceptable range of packet size is determined to satisfy QoS of cognitive radio users. In summary, the main contributions of this paper are: – Propose a simulation platform for cognitive radio networks (CogNS) – Evaluation of TCP performance in the terms of end-to-end delay, throughput and packet drop probability over cognitive radio networks and calculating the range of packet size to provide QoS of CR users The rest of this paper is structured as follows. The following section studies some basic models of cognitive radio. In section 3, the proposed simulation framework, CogNS, is explained in detail. Performance evaluation of cognitive radio networks through CogNS is provided in section 4. Finally, section 5 presents the conclusions and discusses future works.

2 Cognitive radio basic models 2.1 Activity model of primary users Performance of cognitive radio networks is strongly affected by primary users activities; thus the modeling of primary users (PUs) activities is a crucial task in CRNs. Since arrivals of PUs are assumed to be independent, inter-arrivals can be modeled with exponential distribution. Therefore, primary user activity is modeled as two-state (ON and OFF) birth/death Markovian process with

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birth rate re and death rate rd [21]. In CR context, the ON state means channel is busy by a primary user and OFF state means the channel is free of PUs; birth rate and death rate are equivalent with entrance and departure rate of PUs in/of the channel respectively. Durations of ON and OFF periods are exponentially distributed and based on the assumed model for activities of PUs, steady state probabilities of ON and OFF states is calculated as follows [22]: rd re , POF F = (1) PON = re + rd re + rd where PON and POF F is the probability of presence and absence of PUs respectively. re is the PU entrance rate in the channel and rd means departure rate of PU from the channel. This model has been used for simulating the primary users activities in the CogNS.

2.2 Sensing accuracy Each CR user can operate in a channel that is free of primary users activity. It is important to find the free channels of the spectrum that is done through spectrum sensing in CRNs. Thus, spectrum sensing is the first and most important step in cognitive radio networks and the accuracy of sensing has significant impact on CRNs performance. Spectrum sensing accuracy is calculated by two parameters: probability of detection (Pd ) and probability of false alarm (Pf ) [23]. Probability of detection is the probability that the channel is occupied by primary users and the spectrum sensing has detected that the channel is busy. Probability of false alarm is the probability that CR user senses the channel is busy but the spectrum is not used by primary users. The higher probability of detection results the lower interference with the primary users. From the CR users perspective, the lower false alarm probability leads to more use of spectrum, thus performance of CR network will be increased. Hence, the performance of CRNs is closely related to spectrum sensing and its accuracy parameters (Pd and Pf ). There are different sensing techniques which are used in CRNs. Energy detection-based sensing techniques are the most popular. Maximum A Posteriori (MAP) Energy Detection for spectrum sensing is known as an optimal sensing scheme [24]. In the next subsection, the models of PU detection and false alarm probabilities for MAP energy detection are described [21]. These models have been used for simulating the sensing accuracy in the CogNS. 2.2.1 Models of PU detection and false alarm probabilities Probability of detection and false alarm in MAP energy detection are modeled in terms of Q function as follows [21]: λd − 2ts W σn 2 Pf (ts , POF F , W ) = POF F . Q( √ ) 4ts W σn 4

(2)

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Fig. 1 Structure of the CR node object in CogNS. The MAC layer, physical layer and the channels have been modified based on the structure of mobile node object in NS2.

λd − 2ts W (σs 2 + σn 2 ) ) Pd (ts , PON , W ) = PON . Q( ? 4ts W (σs 2 + σn 2 )2

(3)

where POF F and PON are calculated by Equ. 1, W is the bandwidth of the sensed channel and ts is the duration of sensing; the σs 2 and σn 2 are the variances of the received signal and the noise respectively; the λd is the decision threshold of MAP energy detection.

3 Proposed simulation framework (CogNS) 3.1 Structure of the CR node object in CogNS CR users are equipped with CR transceiver which senses frequency channels and decides about selecting a free channel from the list of free channels and utilize it. These abilities have been simulated in CogNS. All of the nodes have same abilities and configuration. The structure of each CR node object of CogNS, modules, timers and relation of them are illustrated in Fig. 1. As can be seen in this figure, The MAC layer, physical layer and the channels are modified based on the structure of mobile node object in NS2. The arrows between different layers represent the transmitting of the control information and data packets. Main modification has been done on MAC layer of the Mobile node. In MAC layer of CogNS, a cognitive CSMA/CA-based MAC protocol is implemented which is named as CogMAC. This protocol and its state diagram are explained in Subsection 3.2.

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Channels and WirelessPHY layers: A non-cognitive wireless node accesses to a channel through its physical layer in NS2; also other wireless nodes can access to the same channel. Hence, it is needed to provide a multi-channel environment to simulate the behavior of a CR node. As can be seen in Fig. 1, the physical layer of a CR node connects to an array of channels. The number of the channels can be determined through a network configuration TCL script. The physical layer (WirelessPHY) is responsible for channels control and management. In this layer, a new module Analyze Channels Information() has been added to the NS2 wireless physical layer; also some modifications have been done in some modules such as SendUp() and SendDown() to support the multi-channel structure. The WirelessPHY has two phases in CogNS: sensing phase and operating phase. In the operating phase, in order to receive a packet, the WirelessPHY layer of a CR node listens the channels based on Round Robin algorithm. If there is a packet in a channel, receiving process (Rx ) is started immediately and does not switch to a new channel until receiving the last bit of packet. After receiving the packet, it is transmitted to CogMAC layer through SendUp() module (PktRx ). In order to transmit a packet, the WirelessPHY layer receives a packet from CogMAC layer (PktTx ) and sends it to the selected channel which is already chosen in the previous sensing period (Tx ). Sending is done through SendDown() module. In sensing phase, normal functions of WirelessPHY layer, i.e., sending and receiving the packets, are paused and spectrum sensing is done to detect the channels which are free of primary users. Due to prior knowledge of PU activities, the absence/presence of PUs are determined for all of the channels; thus CR users can recognize the vacant channels in each sensing phase. After sensing, Analyze Channels Information() module sends the status of channels (being busy or free from primary users) to CogMAC layer based on each channel information such as noise and signal level, interference. Status of channels and each channel information are illustrated by SensingData and Each Ch Inf respectively in Fig. 1. CogMAC layer: The basic modules of CogMAC layer are shown in Fig. 1. In this layer, a CSMA/CA-based MAC protocol (CogMAC) has been implemented. The periods of sensing and operating phases are simulated by SensingTimer and OperatingTimer and the channel hand-off is simulated through a Hand-offTimer. Back-offTimer and ContentionTimer are used to simulate the back-off and contention operations. After sensing phase, ChannelSelection() module selects the appropriate channel for sending a packet based on the received information from WirelessPHY layer (SensingData arrow) and the considered selection policy. Two policies are considered for channel selection mechanism: (1) random policy and (2) QoS-based policy. In random policy, a channel is randomly selected from free channels. In QoS-based policy, a channel is selected based on desired QoS metric (for example the channel with minimum interference). Then, the selected channel number is sent to WirelessPHY layer (SelectedChannel arrow). In the operating phase, the CarrierSense module decides about status of the considered channel based on the

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Fig. 2 State diagram of the CR user physical interface and the events notation table.

information (CarrierData) about other cognitive users signal levels in each channel which has been already sent from WirelessPHY layer. Module Send() is responsible for sending a packet to WirelessPHY (PktTx ). The functions and state diagram of CogMAC layer is explained in the following subsection in detail. 3.2 State diagram of the CR user physical interface In this research, the set of physical layer and MAC layer is referred to as physical interface. CR node is a node with spectrum aware physical interface. The cognitive capability of a CR node enables immediate interaction with environment to determine proper communication parameters and adjust to the dynamic radio environment. The physical interface operation of CR users is described in this section in detail. The operations required for adaptive operation in the spectrum is referred to as the cognitive cycle [3,5]. This cognitive

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cycle usually consists of spectrum sensing, analysis and adaptation steps. In this section, the state diagram of CR user operation based on cognitive cycle is described and depicted in Fig. 2. The CR user first enters the sensing phase to sense the spectrum and create a list of the free channels of PUs. Then the CR user selects one of the free channels to operate based on the channel selection policy. The last used channel has high priority to select because of the hand-off time overhead. If last used channel is free, then CR user enters operating phase immediately; else another free channel is selected based on the selection policy and CR user enters the hand-off phase for switching to new selected channel; after hand-off, CR user enters operating phase. In operating phase, CR user performs carrier sensing for selected channel. If the considered channel is free, the contention is started in order to compete for access the channel. After the contention operation, the carrier sensing is done for considered channel again and if the considered channel is free again, the sending operation is done. In the carrier sensing steps, if channel is not free, the back-off operation is started. In backoff operation, CR user waits for back-off window; and at the end of operating phase, CR user starts the sensing phase again. As illustrated in the state diagram of Fig. 2, this CR user model consists of three main states: sensing, operating and hand-off. The sensing state is consists of two sub-states: spectrum sensing and spectrum decision. Also the operating state is consists of four sub-states which are the states of basic CSMA/CA MAC protocol: carrier sensing, contention, back-off and sending. In spectrum sensing state, CR user senses the spectrum and finds the free channels. In spectrum decision state, CR user decides about the channel selection that can be done based on various QoS metrics (it depends to application type of CRN). In hand-off state, CR user switches to the new selected channel. The transition among these states is done through some events. The notations of events are mentioned in Fig. 2. For each event e, the notations of e¯ means that e has not been occurred. CR user remains in sensing, hand-off, operating, and contention and backoff states until the completion of the sensing, hand-off, operating, contention and back-off periods respectively. CR user is transitioned from: – Operating state (each sub-state of operating state) to spectrum sensing state at the end of operational period (EOP has been occurred) – Hand-off state to carrier sensing state at the end of hand-off period (EOH has been occurred) – Spectrum sensing state to spectrum decision state when there are some free channels (Evt1 has been occurred) – Spectrum decision state to carrier sensing state (sub-state of operating state) when the spectrum sensing period is ended (EOS has been occurred) and the last used channel by this CR user is still free thus there is no need to do hand-off (Evt2 has been occurred)

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– Spectrum decision state to hand-off state when the spectrum sensing period is ended (EOS has been occurred) and the previous used channel by this CR user is busy then it is needed to do hand-off (Evt2 has been occurred) – Carrier sensing state to contention state when the selected channel is free of CR users (Evt4 has been occurred) and contention has not been done yet (Evt3 has been occurred) – Contention state to carrier sensing state at the end of contention period (EOC has been occurred) – Carrier sensing state to sending state when the selected channel is free (Evt3 has been occurred) and contention operation has been done (Evt4 has been occurred) – Carrier sensing state to back-off state when the selected channel is not free of CR users (Evt4 has been occurred) – Back-off state to carrier sensing state at the end of contention period (EOB has been occurred) 4 Performance evaluation In this section, performance of CRNs is evaluated through the CogNS. In the subsection 4.1, the considered CR-related parameters and performance evaluation metrics are described. Simulation results are presented in subsection 4.2. 4.1 CR-related parameters and performance evaluation metrics Real-timeness and reliability are two important issues which considered for quality of service (QoS) provisioning in wireless networks. Level of reliability and real-timeness depends on the type of applications. Achieving a low endto-end delay is an important QoS requirement of real-time applications in wireless networks. On the other hand, packet drop probability is a significant metric which must be minimized for reliable applications in wireless networks. As the demand for real-time reliable applications increases in cognitive radio networks, studying on the impacts of CR-related parameters on the delay, drop probability and throughput will be useful to provide better reliability and real-timeness in CRNs. In this section of paper, performance of CRNs in the terms of – TCP end-to-end throughput – TCP end-to-end delay – Packet drop probability is investigated through the CogNS simulation which are calculated as follows: T hroughput =

N tlast − tf irst

(4)

where N is total number of bits received by transport layer of destination nodes, tlast is receive time of last packet in network by a destination node and

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Table 1 CR network configuration and simulation setup parameters CR network topology parameters Parameter Value/type Network area 500 × 500 m2 Nodes number 25 Nodes spatial distribution Uniform random distribution Physical layer parameters Wireless channels number 11 Bandwidth of each channel 2Mbps Radio transceivers number per node 1 Propagation model Two-ray ground Radio transceivers number per node 1 Sensing time 0.025 sec Operating time 0.6 sec hand-off time 0.001 sec Protocols and parameters of network, transport and application layers Routing protocol Dynamic Source Routing (DSR) Queue management strategy Droptail Transport protocol TCP Reno TCP connections number 13 Traffic type Constant Bit Rate (CBR) with 256kbps rate Simulation time 100 sec

tf irst is send time of first packet in network. Delay =

? Np

r i=1 (ti

Np

− tsi )

(5)

where Np is total number of received packets by destination nodes, tri is receive time of ith packet by transport layer of the destination node and tsi is send time of ith packet by source node. P acket drop probability =

Ndrop Ndrop + Nrecv

(6)

where Ndrop is total number of dropped packets and Nrecv is total number of successfully received packets by destination nodes. In the simulations, throughput, delay and packet drop probability are evaluated based on the impact of the following parameters: – Packet size – Entrance rate of primary users (re ) – Departure rate of primary users (rd ) – Detection and false alarm probabilities of primary user (Pd , Pf ) 4.2 Simulation results 4.2.1 CR network configuration and simulation setup The simulation setup and CR network configuration parameters are illustrated in Table 1. 25 nodes are placed randomly with uniform distribution in a 500 m × 500 m field. Each CR node has a single radio transceiver and can access to 11 channels. Each wireless channel has 2Mbps bandwidth. Two-ray ground

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Fig. 3 Packet drop probability vs. packet size for different activities of PUs

model is set as propagation model of CR nodes. Sensing, operating and handoff times has been determined 0.025, 0.6 and 0.001 second respectively. Host CR nodes use TCP Reno as transport protocol. The Dynamic Source Routing (DSR) protocol is used by CR nodes as routing protocol. The number of TCP connections in the CR networks is 13 connections. The traffic type of application layer is constant bit rate (CBR) traffic with rate 256kbps. All of our simulations were run 100 second for each simulation. Each simulation configuration is run 10 times and results are averaged and the trends of the results are presented. 4.2.2 End-to-end throughput, delay and packet drop probability evaluation In this section, in order to evaluation of TCP end-to-end throughput, delay and packet drop probability, the value of packet size varies from 200 to 2000 bytes; activities of primary users, rd and re , are considered in five states (rd , re ) = (3,1), (2,1), (1,1), (1,2) and (1,3). The PU detection and false alarm probabilities are calculated through Equ. 2 and Equ. 3 with regard to the PU activities and considered sensing time. PU detection and false alarm probabilities are calculated as (Pd , Pf ) =(0.75,0), (0.67,0), (0.5,0), (0.33,0) and (0.25,0) for the considered states of PU activities (rd , re ) = (3,1), (2,1), (1,1), (1,2) and (1,3) respectively. Packet drop probability increases with increasing packet size as illustrated in Fig. 3. Since it is needed more time to transmitting a large packet from source to destination, the probability of interference and other reasons of packet drop such as TCP retransmission timeout (RTO) increases. When PU entrance rate increases, the opportunity of CR users to access the channel decreases. This imposes additional delay on packets which leads to more TCP timeouts and more packet drop probability. As seen in Fig. 3, packet drop probability curve with (rd , re ) = (1,3) state is the highest one. When PU de-

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Fig. 4 TCP end-to-end delay vs. packet size for different activities of PUs

Fig. 5 TCP end-to-end throughput vs. packet size different activities of PUs

parture rate increases, the drop probability decreases; hence drop probability with (rd , re ) = (3,1) state is the lowest one. End-to-end delay increases with increasing packet size as illustrated in Fig. 4. A larger packet needs more time to reach destination than smaller packets; also due to more packet drops, more packet retransmissions are needed; hence the TCP end-to-end delay increases. With increasing PU entrance rate, the channel access chance of CR users decreases and CR users wait more time to access a free channel; also the competition of CR users to access the selected free channel increases and more time is spent in contention and back-off states of CSMA/CA between competitor CR users; hence the number of TCP retransmissions increases (delay curve with (rd , re ) = (1,3) state is the highest one). Conversely, with increasing PU departure rate, the delay decreases (delay curve with (rd , re ) = (3,1) state is the lowest one).

CogNS: A Simulation Framework for Cognitive Radio Networks

(a) TCP end-to-end throughput vs. packet size

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(b) TCP end-to-end delay vs. packet size

Fig. 6 (a) TCP end-to-end throughput and (b) TCP end-to-end delay vs. packet size varying from 200 to 2000 bytes. Two sensing modes are considered in the PUs activity state (rd , re ) = (1,1): normal sensing (with sensing errors) and ideal sensing (without sensing errors)

With increasing the size of packet, end-to-end throughput increases as illustrated in Fig. 5. The larger packet size leads to send more data bits and hence increases throughput. When the entrance rate of PUs is high, availability of channels is low which causes to send fewer packets and CR network end-to-end throughput decreases. Hence, throughput curve of (rd , re ) = (1,3) state is the lowest one. Conversely, with increasing PU departure rate, the throughput increases. Throughput of CRN with (rd , re ) = (3,1) state is the highest one. 4.2.3 Impact of sensing accuracy parameters In this subsection, the impact of sensing accuracy parameters on the mentioned performance metrics are investigated. In these simulations, packet size varies between 200 to 2000 bytes and the primary users’ activity model parameters are set (rd , re ) = (1,1). Two modes are considered in each simulation: (1) ideal sensing and (2) normal sensing. In first mode, it is assumed that the sensing process is done without any error, i.e., (Pd , Pf ) = (1,0). In second mode, sensing accuracy parameters are considered, i.e., (Pd , Pf ) = (0.5,0) which is calculated based on Equ. 2 and Equ. 3. The curves of TCP end-to-end throughput and delay based on ideal sensing and normal sensing are depicted in figures 6(a) and 6(b) respectively. As mentioned before, there is no sensing error in ideal sensing; therefore there is no collision between CR users and primary users in operating phase and packets can be communicated without any additional delay. However, in normal sensing, there are some collisions between CR users and primary users

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Fig. 7 Ranges of packet size to satisfy (D,DP,TH)=(0.4 sec, 0.06, 300 kbps)

in operating phase because of PU detection error which increases the packet drop probability. Consequently, TCP decreases its congestion window size and the throughput decreases. In Fig. 6(a), it is seen that throughput in normal sensing mode is lower than ideal sensing for all packet sizes and conversely delay in normal sensing mode is higher than ideal sensing as illustrated in Fig. 6(b). 4.2.4 Packet size analysis based on QoS requirements As explained in previous section, increasing the packet size leads to the higher throughput; on the other hand, end-to-end delay increases by increasing the packet size. Hence, packet size is an important parameter which impacts on throughput, delay and packet drop probability and an acceptable range of packet size should be obtained to provide jointly the desired TCP throughput, delay and packet drop probability. We consider TCP throughput, delay and packet drop probability jointly as QoS requirement: – Delay < D – Packet drop probability < DP – Throughput > TH where D and DP are the upper bounds of delay and packet drop probability respectively and TH is the lower bound of throughput. The acceptable range of packet size for different jointly QoS parameters, i.e., (D,DP,TH) and different primary user models are presented in figures 7, 8 and 9. As seen in Fig. 7, there are different packet size ranges to satisfy (D,DP,TH)=(0.4 sec, 0.06, 300 kbps), i.e., Delay < 0.4 sec, Packet Drop Probability < 0.06 and Throughput > 300 kbps for different activities states of PUs. Ranges of packet size to satisfy the (D,DP,TH)=(0.4 sec, 0.06, 300 kbps) are 500-2000, 550-1900 and 750-900 bytes for (rd , re ) =(3,1), (2,1) and (1,1)

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Fig. 8 Ranges of packet size to satisfy (D,DP,TH)=(0.7 sec, 0.07, 250 kbps)

Fig. 9 Ranges of packet size to satisfy (D,DP,TH)=(0.75 sec, 0.09, 100 kbps)

states respectively and also there is no acceptable packet size for (rd , re ) =(1,2) and (1,3) states. With increasing the activity of PUs, the range of packet size becomes smaller. As seen in figures 8 and 9, with increasing the desired upper bound of packet drop probability and end-to-end delay and decreasing the desired lower bound of TCP throughput, the ranges of acceptable packet size increase. Ranges of packet size to satisfy the (D,DP,TH)=(0.7 sec, 0.07, 250 kbps) are 3502000, 400-2000, 550-2000 and 1050-1150 bytes respectively for (rd , re ) =(3,1), (2,1), (1,1) and (1,2) states and also there is no acceptable packet size for (rd , re ) = (1, 3) state. Ranges of packet size to satisfy the (D,DP,TH)=(0.75 sec, 0.09, 100 kbps) are 200-2000, 200-2000, 200-2000, 200-1850 and 400-550 bytes respectively for (rd , re ) =(3,1), (2,1), (1,1), (1,2) and (1,3) states.

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5 Conclusion In this paper, we have proposed a simulation framework (CogNS) for CRNs. We have studied the impact of primary users activity, spectrum management parameters and packet size on TCP performance metrics. Simulation results show that activity model of PUs, sensing time, sensing frequency and sensing accuracy play a critical role in determining the acceptable packet size to provide QoS of CR users. It is needed to calculate an acceptable range of packet size to satisfy QoS, i.e., desired packet drop probability, TCP end-to-end delay and throughput. Future studies in this research area could include: (1) finding optimal packet size to establish a trade off between TCP performance metrics such as throughput and delay; (2) optimization of transport layer performance based on specific features of CRNs; (3) proposing a spectrum-aware transport protocol for CRNs. Acknowledgements We thank Iran Telecommunication Research Center (ITRC) for supporting this research (http://www.itrc.ac.ir).

References 1. I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp. 2127-2159, Sep. 2006. 2. I. Mitola, J. and J. Maguire, G.Q., “Cognitive radio: making software radios more personal,” Personal Communications, IEEE, vol. 6, no. 4, pp. 13-18, Aug. 1999. 3. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” Selected Areas in Communications, IEEE Journal on, vol. 23, no. 2, pp. 201-220, Feb. 2005. 4. J. Marinho and E. Monteiro, “Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions,” Wireless Networks, vol. 18, no. 2, pp. 147164, Oct. 2011. 5. I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury, “CRAHNs: Cognitive radio ad hoc networks,” Ad Hoc Networks, vol. 7, no. 5, pp. 810-836, Jul. 2009. 6. O. B. Akan, O. B. Karli, and O. Ergul, “Cognitive radio sensor networks,” IEEE Network., vol. 23, no. 4, pp. 34-40, Jul. 2009. 7. C. Cormio and K. R. Chowdhury, “A survey on MAC protocols for cognitive radio networks,” Ad Hoc Networks, vol. 7, no. 7, pp. 1315-1329, Sep. 2009. 8. M. Cesana, F. Cuomo, and E. Ekici, “Routing in cognitive radio networks: Challenges and solutions,” Ad Hoc Networks, vol. 9, no. 3, pp. 228-248, May 2011. 9. T. Issariyakul, L. S. Pillutla, and V. Krishnamurthy, “Tuning radio resource in an overlay cognitive radio network for TCP: Greed isnt good,” Communications Magazine, IEEE, vol. 47, no. 7, pp. 57-63, Jul. 2009. 10. R. A. Calvo and J. P. Campo, “Adding Multiple Interface Support in NS-2,” University of Cantabria, Jan. 2007. 11. B. Wang, “NS2 Notebook: Multi-channel Multi-interface Simulation in NS2 (2.29),” http://www.cse.msu.edu/ wangbo1/ns2/nshowto8.html. 12. T. C. Chiueh, A. Raniwala, R. Krishnan and K. Gopalan, “ Hyacinth: An IEEE 802.11-based Multi-channel Wireless Mesh Network,” http://www.ecsl.cs.sunysb.edu/multichannel/. 13. Cognitive radio cognitive network simulator. http://stuweb.ee.mtu.edu/ljialian/. 14. M. Di Felice, K. R. Chowdhury, W. Kim, A. Kassler, and L. Bononi, “End-to-end protocols for Cognitive Radio Ad Hoc Networks: An evaluation study,” Performance Evaluation, vol. 68, no. 9, pp. 859-875, Sep. 2011.

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15. Network simulator version 2. http://www.isi.edu/nsnam/ns/. 16. A. M. R. Slingerland, P. Pawelczak, R. V. Prasad, A. Lo, and R. Hekmat, “Performance of Transport Control Protocol Over Dynamic Spectrum Access Links,” in New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE International Symposium on, 2007, pp. 486 495. 17. Y. R. Kondareddy and P. Agrawal, “Effect of Dynamic Spectrum Access on Transport Control Protocol Performance,” in Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, 2009, pp. 1 6. 18. K. R. Chowdhury, M. Di Felice, and I. F. Akyildiz, “TP-CRAHN: a Transport Protocol for Cognitive Radio Ad-Hoc Networks,” in INFOCOM 2009, IEEE, 2009, pp. 2482-2490. 19. D. Sarkar and H. Narayan, “Transport Layer Protocols for Cognitive Networks,” in INFOCOM IEEE Conference on Computer Communications Workshops , 2010, 2010, pp. 1 6. 20. A. O. Bicen and O. B. Akan, “Reliability and congestion control in cognitive radio sensor networks,” Ad Hoc Networks, vol. 9, no. 7, pp. 1154-1164, Sep. 2011. 21. W.-Y. Lee and I. F. Akyildiz, “Optimal spectrum sensing framework for cognitive radio networks,” Wireless Communications, IEEE Transactions on, vol. 7, no. 10, pp. 38453857, Oct. 2008. 22. G. Bolch, S. Greiner, H. de Meer, K. S. Trivedi, and K. S. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications. Wiley-Interscience, 1998. 23. S. Tang and B. L. Mark, “Modeling and analysis of opportunistic spectrum sharing with unreliable spectrum sensing,” Wireless Communications, IEEE Transactions on, vol. 8, no. 4, pp. 1934 1943, Apr. 2009. 24. J. G. Proakis, Digital communications, fourth ed. McGraw-Hill, 2001.