End to End Delay Analysis of Dynamic Cooperative Spectrum Sensing Mechanism in IEEE 802.22 WRAN

doi:10.21203/rs.3.rs-2305453/v1

IEEE 802.22 Wireless Regional Area Networks (WRAN) utilizes the Cognitive Radio dynamic spectrum access technique in TV bands. When allocating available spectrum to Secondary Users (SUs), in the absence of Primary Users (PUs), it is essential to ensure the Quality of Service (QoS) of SUs. The motivation behind this research is to devise a mechanism to ensure the QoS of SUs. The End to End delay (EED) experienced by SU through the devised mechanism need to be kept within the threshold suggested by WRAN standard. To achieve the above requirement, Dynamic Cooperative Spectrum Sensing (DCSS) is developed in this research. Two entities namely Spectrum Manager (SM) in Base Station (BS) and Spectrum Automation (SA) in SUs are engaged to realize them. They does the sensing of channel occupancy by the PUs. Further, a concept of Urgent Coexistence Situation (UCS) notification is issued by BS to indicate the re-arrival of PUs to SUs. Buffer mechanism is provisioned to restore the ongoing traffic of SUs until further resource allocation is accomplished. This strategy assures guaranteed data delivery of SUs traffic. However, this may introduce EED. Hence, this research investigates the performance of the developed DCSS mechanism in Netsim version 12 Simulator. The scenario with 1 Base Station, 3 PUs and 1 pair of SUs is constructed. Three different SUs traffic namely CBR, Voice and Video is considered. The impact of modulation schemes, occurrence of PUs traffic on average EED is analysed. From the simulation results, the modulation scheme that provides average EED within the threshold as per IEEE 802.22 is identified, thereby ensuring the QoS of the SUs.

IEEE 802.22

Wireless Regional Area Network

Cognitive Radio

DCSS

EED and Netsim Simulator

The demand for spectrum usage is increasing tremendously due to the continuous proliferation of wireless devices. The available spectrum is found to be underutilized. Hence an efficient resource management approach is required to address this problem. Cognitive Radio (CR) technology is identified to provide promising solution to the above problem [1]. The CR users can be classified into two types. They are Primary (PU) and Secondary (SU) [2]. The PUs are the traditional user that has the license to operate certain spectrum. While SUs are the unlicensed user who can utilize the spectrum of PU when found unused. The above task can be accomplished by the process of spectrum sensing and sharing. The procedures involved in them need to be standardised.

IEEE 802.22 [3] is the first wireless standard devised for CR network. The working group of this standard was established in November 2004, to develop operational standard for fixed wireless broadband connectivity in both rural and remote locations. Wireless Regional Area Networks (WRAN) is the new wireless air interface standardized by IEEE 802.22 for Television (TV) bands incorporated with CR technology. The major services supported by WRAN are TV broadcast, wireless microphone, terrestrial mobile radio service and medical telemetry. IEEE 802.22 provides lower frequency TV channels for SUs. This standard ensures to provide enhanced QoS for various services namely data, voice, audio, and video.

IEEE 802.22 utilizes the available white space that spans from 54 to 862 MHz for the allocation of resource to VHF/UHF TV broadcasting [4]. The PUs utilizes this TV white space for analog, digital TV channels and wireless microphones applications [5]. Digital TV channel requires only 1/6th of the bandwidth when compared to analog TV channels [6]. Thus, the unused spectrum of analog TV Technology has a potential availability of white space spectrum that can be assigned to SUs.

The SUs of IEEE 802.22 WRAN can be of various service types. Some of them are Constant Bit Rate (CBR), Voice and Video. In general, as mentioned earlier the SUs utilize the resource of PU in its absentia. However, are bound to return the same upon their arrival aborting their ongoing traffic. Hence, the motivation behind this research is to devise certain mechanism to alleviate this issue and ensure the QoS of SUs.

The organization of the report is as follows. Section 2 discusses the state of art related to improvement of QoS for various services through the concept of CRN. Section 3 details the concept of developed work with system model and configuration used in analysing the performance. Section 4 elaborates the results along with inference. The future work and the conclusion are highlighted in Section 5.

This section details the state of art that are similar to the carried out investigation. A reliable channel estimation scheme for IEEE 802.22 to enhance communication quality is developed in [7]. It utilizes the pilot symbols in adjacent subcarriers to extend interpolation range and shorten interpolation interval. The Bit Error Rate (BER) analysis for both Down Stream (DS) and Up Stream (US) sub frames are performed. The developed technique achieves the BER of 2 x 10^− 4.

In [8], to encourage the partial SUs in participation of cooperative spectrum sensing, a “contract theory” based approach is developed. This strategy promotes cooperation of SUs that has a partial understanding about their wireless environment. To limit the fading impact, the SUs that exhibit excellent Signal to Interference plus Noise ratio (SINR) with respect to the PU alone are selected for the contract. The simulation findings demonstrate that the contract successfully incentivizes collaboration and outperforms other benchmark systems in terms of PUs detection probability. The developed work has used 40 SUs simulation scenario to achieve above results.

A distributed non-cooperative technique based on a game theory framework is given in [9] to ensure self-coexistence over several IEEE 802.22 networks. The Base Stations (BS) is assumed as players in a game whose goal is to maximize spectrum usage to reach the Nash equilibrium point. The experimental findings reveal that when the PU increases, the interference increases and the data rate decreases. Furthermore, the recommended approach delivers a greater data rate by boosting the spectrum consumption of PU and SU. The suggested technique, on the other hand, does not include a thorough examination of other operational factors of QoS and uplink resource allocation.

In [10], the authors have analysed the impact of the throughput and spectral efficiency for different congestion techniques namely Old Tahoe, Tahoe, Reno, and New Reno. For the designed network, all modulation approaches for different congestion strategies were compared. The investigation on delay for modulation systems was studied. It has been observed that 64QAM modulation technique with centralised cooperative sensing method outperforms the decentralised one in terms of throughput and packet loss for a given number of nodes. The authors envision to expand the presented analysis to other QoS metrics namely spectrum efficiency and throughput

In [11], a sub-nyquist sensing method is applied for cluster based CRN. For Nyquist and sub-nyquist based sampling, the performance is assessed and evaluated in terms of throughput. Clustered CRN networks provide more throughput than non-clustered CRN networks, while sub-nyquist based clustered CRN networks also perform better in terms of throughput as compared to Nyquist based clustered CRN networks.

In [12], a two-tier CR Network that uses game theory to improve the QoS of SUs is developed. Because of the variation in priority, Secondary real-time users are assigned to the first tier, whereas Secondary non-real Time users are assigned to the second tier. In the absence of PUs, an auction game is performed for selecting an optimum pair of real-time and non-real Time SUs for transmission across the available channels. The selection is done on the basis of utility function. It estimates gain of each user with respect to the utilization of silent gaps of VoIP transmission. In comparison to one-tier CRN the proposed two-tier CRN has enhanced bandwidth utilization efficiency, spectral efficiency, and an increased number of Secondary Users served.

In [13], an analytical framework is provided to investigate the effect of traffic characteristics of Opportunistic Spectrum – Orthogonal Frequency Division Multiple Access (OS-OFDMA) in an IEEE 802.22 standardized network. The interactions between the Medium Access Control (MAC) and spectrum sensing layers along with cross-layer Markov-chain are explored. The effect of changing the number of PUs and PU activity on neighbouring channel interference were analysed for CBR and VBR traffics. Further, the impact of co-channel interference on system throughput is also investigated under the condition of minimal false alarm likelihood. The two-stage spectrum sensing technology employed in OS-OFDMA has found to improve average network throughput. The developed work takes into account QoS classes, PU priority, and capacity calculations. However, it does not include any investigation about the various modulation used in IEEE 802.22.

The performance of IEEE 802.22 on energy detection sensing method and spectrum sensing interval are investigated in [14]. The proposed algorithm performs sensing in k consecutive sensing time slots starting from the current slot and continues with k + 1 and k-1 adjacent slots. Depending on the traffic model, the probability of false alarm and detection probability is computed for any value of k. The sensing performance increases with increase in number of time slots. However the developed methodology is restricted to only to non-cooperative sensing.

In [15], a comparison of traditional sensing methods namely Energy Detector (ED), Cyclostationery with two stage Energy Detection method is developed. In the first stage, to determine the presence of the PU, its Signal to Noise ratio (SNR) value is compared with a predetermined threshold value. In the second phase, whenever the SNR value falls below the threshold, another comparative analysis is made with Energy Detection with Two Adaptive Threshold (ED-TAT). The comparison using three thresholds proves the merit of the developed method in determining the presence of the PU and thereby minimise the sensing failure problem.

In summary, a reliable channel estimation approach is presented to improvise the communication quality related to QoS in [7]. Contract-based incentive scheme to entice malicious SUs to participate in spectrum sensing designed in [8]. The effects of PU as well as false sensing resulted due to malicious Sus is ignored in this work. Game theory-based spectrum utilisation model is detailed in [9], the developed game has not concentrated uplink resource allocations. The performance analysis of IEEE 802.22 with various congestion techniques and modulation are studied in [10]. However, it fails to explore QoS parameters.

As mentioned earlier the re-arrival of the PUs, leads to termination of ongoing traffic of SUs. This can tremendously affect their QoS. In this research, a new mechanism namely Dynamic Cooperative Spectrum Sensing (DCSS) is developed to address the above issue. It does cooperative sensing through the association of Spectrum Manager (SM) present in the Base Station with Spectrum Automation (SA) present in Secondary User. A concept of Urgent Coexistence Situation (UCS) notification is issued by Base Station to indicate the re-arrival of PUs to SUs. Further provisioning of buffer mechanism to restore the ongoing traffic of SUs until the re-allocation of resource is also introduced. This can ensure lossless transmission of traffic of SUs. However this strategy may introduce end to end delay of SUs data transmission. Hence the objective of this research is to investigate the end-to end delay experienced by the SUs under above circumstances. Three types of traffics namely CBR, Voice and Video are considered in the analysis. Further, the impacts of number of re-arrival of PUs and modulation schemes are also examined.

3.1 System Model

The system model of the developed DCSS is illustrated in Fig. 1. Let α_i represent the BS of the considered WRAN scenario. Here, i represent the number of BS, which can vary from1 to N. In this investigation, it is restricted to1.

Let β_j represent the number of PUs and ℽ_k represent number of SUs served by α_i. Let j varies from 1 to 3 while k from 1 to 2.The maximum coverage distance offered by α_i be represented as d_i. Let Tt_m be the different traffics transferred between pair of SUs (ℽ_k) through α_i at t_m instances. Here Tt_m varies from 1 to 3 (t_m ϵ Tt_1, Tt_2, Tt₃). Let ℽ₁ and ℽ₂ be the pair of SUs, then the CBR data traffic transmitted between γ₁ and γ₂ be denoted as Tt_1. Similarly the voice be denoted as Tt₂ and the video traffic be denoted as Tt₃ (Tt_{1 =} CBR, Tt_{2 =} Voice and Tt₃ = Video). Every α_i possess a functional entity known as the SM while every γ_k has a lighter version of the SM known as a SA to assist α_i in sensing the availability status of the spectrum.

Table 1

Notations Used
Notations Used	Description
α_i	No of Base Station (BS)
β_j	No of Primary User (PU)
γ_k	No of Secondary User (SU)
T_tm	Types of Traffics
τ_si	Departure time at source node
τ_di	Arrival Time at destination node
d_i	Coverage Distance offered by BS
Δσ	Delay when operation channel is allocated
Δ_K	Delay when backup channel is allocated
Δ_C	Delay when candidate channel is allocated
\({\sigma }_{N}\)	Set of operational channels
\({K}_{N}\)	Set of backup channels
\({C}_{N}\)	Set of candidate channels

The SM has the responsibility to provide connectivity and resource allocation to γ_k that are within the coverage of corresponding α_i. Every γ_k, that are present within the coverage of α_i, execute the process of initialization to get associated with α_i The SA module present in the γ_k does continuous spectrum sensing mechanism to obtain the availability status of the channel and report this information periodically to the SM module of α_i.

This functionality is executed by the SA module mainly to avoid the γ_k experiencing interference with the other channel. The detail of developed DCSS is discussed in the subsequent section.

Channel Classification

In this research, DCSS mechanism is executed to realize efficient spectrum management. It is accomplished by exploring the association between SM of αi and SA of γ_k. Upon initialization of γ_k with α_i, the authorized γ_k served by αi are instructed to execute spectrum sensing. Based on the duration of spectrum sensing, the available channels served by αi are classified into operational channel, backup channel and candidate channel.

The primary function of αi is to assign the available channels to the βj. As long as the βj are effectively utilizing the available channels, spectrum efficiency can be ensured. However in real time scenarios, the presence of βj is stochastic which leads to underutilization of the available spectrum. Hence the concept of DCSS is introduced in this research to improvise the spectral utilization. The working mechanism of DCSS is illustrated in the Fig. 2.

Let C be the total number of channels that are available in the database of α_i. The SM of α_i instructs SA of γ_k to observe the ideal duration of the available channels. Based on the ideal duration, the channels are classified into operational, backup and candidate. The available channels can be either idle or busy. Whenever it is not occupied by β_j, it is idle. Then, α_i can allocate the idle channel to γ_k. Thus the available channel is utilized effectively.

The available operational channels are used for the communication between the α_i and γ_k. The SA of γ_k senses the operational channel at least once every two seconds in order to safeguard from β_j interference. When the β_j detection is done for every 6 sec, then it is labelled as backup channel. If the case is up to 30secs, it is categorized as candidate channel. When the channel is idle for more than 60 seconds, it is reported as protected channel as indicated in Fig. 3. The SA of the γ_k executes the above operation and provides the detail periodically to the SM of α_i. The channel classification mechanism detailed above is dynamic in nature, since the idle status of the channel depends on the traffic flow of β_j. Hence the reassessment of the above channels is performed by γ_k and α_i periodically [15].

Channel allocation to SU (γ _k )

From the previous section, it is found that the available channels maintained by the database are categorized as operating, backup, candidate channels and protected respectively. Whenever the γ_k request α_i for resource allocation, the operating channels are initially allocated to the authorized γ_k. However, when the β_j resumes its transmission with this operating channel, immediately α_i issues UCS notification to the γ_k. This notification indicates that α_i indicate the process of relocation of resource assigned to γ_k from operating channel to backup channel, as the available operating channels are re-assigned to the licensed β_j. Thus, the γ_k will experience certain level of latency due to this switching operation. However the packet delivery is ensured by the above operation. Based on the resource availability and circumstances, this switching mechanism is repeated with respect to backup and candidate channels. Thus the spectral efficiency and throughout is enhanced by the developed DCSS mechanism.

From the perspective of DCSS, the capability of γ_k in spectrum sensing is limited within its coverage distance (d₁). The developed DCSS mechanism utilizes the inputs obtained from both SA and SM. Energy detection technique [16] is adopted in spectrum sensing.

Two Way Handshaking

Let σ_N represent set of operating channels as expressed in Eq. 1. Let K_N denote the set of backup channels represented in Eq. 2 and\({C}_{N}\) be the set of candidate channels represented in Eq. 3.

\({\sigma }_{N}\) = [\({\sigma }_{1},{\sigma }_{2},{\sigma }_{3\dots }{\sigma }_{N}\) ] (1)

\({K}_{N}\) = [\({K}_{1},{K}_{2}\dots {K}_{N}]\)(2)

\({C}_{N}\) = [\({C}_{1},{C}_{2},{C}_{3\dots }{C}_{N}\) ] (3)

In execution of conventional Binary Hypothesis Test (BHT) over σ_N,, whenever σ₁ is measured below threshold TH, it is declared as idle (available) and allotted to γ_k through Two Way RTS / CTS handshake procedure. However, with the advent of UCS notification, γ_k abrupt its ongoing transmission with the corresponding channel and begin the process of searching for other free channels.

On the other hand, in the developed DCSS, when UCS notification is witnessed, the ongoing transmission of γ_k is buffered, and the energy level next operational channel σ₂ is measured and the process is repeated for all other channels in σ_N. If all the channels are being utilized, then the above mentioned process is extended with back-up channels (K_N) and resource reallocation is ensured. All the above activities are executed by γ_k in coordination with SM mechanism of αi.

In order to acquire the channel availability at any given instant of time, the γ_k has to register itself with the α_i and then depending on the presence / absence of the β_j activity, the channel is allocated to the γ_k by the α_i. The Two Way handshaking procedure involved in this process is represented in Fig. 3. The commands REG_REQ indicates the request made by γ_k to register with α_i. The acknowledgement to this request is provided by REG_RSP respectively.

After successful registration, γ_k contends for the channel by sending CHA_REQ packet to the α_i which cross-checks with the β_j for its activity. If β_j is idle, then α_i acknowledges the request with CHA_RSP packet, after which the data transmission is carried out. In case of appearance of β_j, the UCS packet is realised making the γ_k to switch to next available channel. But if β_j is active, α_i sends a busy signal to γ_k, SM in association with SA searches the availability of channels among the back-up channels. Upon release of UCS packet indicating the return of β_j, the γ_k is switched to the next available channels. This process is repeated until resource reallocation is accomplished.

Traffic and Modulation Schemes

Upon allocation of channels from either σ_N / K_N / C_N, γ_k begins transmission. CBR, voice and video are three different traffic applications considered in this research. CBR is a basic application with ON/OFF traffic pattern, while voice traffic follows constant and exponential inter arrival time. Video traffic consumes more bandwidth than voice. However is dependent on the codec and kind of video being streamed. Thus, the data rates of each application differ, demanding the usage of different modulation schemes in order to meet out their required QoS. QPSK, 16QAM and 64QAM are the three modulation schemes specified by IEEE 802.22 to achieve the above requirement [17]. In this research, three types of modulation schemes are analysed to determine the suitable scheme to provide application specific end to end delay.

The outcome of the simulated result is detailed in this section. The EED achieved by the developed DCSS mechanism for three different traffic namely CBR, Video and Voice made between pair of SUs is presented.

4.1 Average End to End Delay

The End to End Delay (EED) experienced by a packet is computed as the time taken by the packet to travel from source node and destination node [18]. Let τ_si represents the time at which the packet is transmitted from the source node. τ_di represents the time of packet arrival at the destination node. Δ_σ represent the delay experienced by the above transmitted packets when allocated with channels σ_N. Similarly Δ_K represent delay when K_N is allocated while Δ_C when C_N is allocated. Thus the EED can be mathematically expressed as:

EED = \(\frac{1}{N}\sum _{i=1}^{N}({\tau }_{si}- {\tau }_{di)}\) (4)

Where N denotes the number of packets on - flow. EED is also the sum of queueing delay, transmission delay and propagation delay. This research considers only the transmission delay. For N packet transmission, the average EED is calculated using Eq. 4.

4.2 Simulation Parameters

Table 4.1 present the simulation parameters used in the developed work. Netsim version 12 is the simulator used in this research. The operating frequency and bandwidth used are as per the IEEE 802.22 WRAN. The probability of False alarm (P_Fa) and Missed detection (P_Md) are kept minimal to increase the accuracy of detection and overall detection probability (P_d) [19].

Table 3

EED experienced by three applications under three modulation schemes
Application	Modulation	EED (µs)	Acceptable Level (µs)
Voice	QPSK	143612.69	100000
Voice	16QAM	35796.63	100000
Voice	64QAM	5798.09	100000
Video	QPSK	137.06	250000
Video	16QAM	131.31	250000
Video	64QAM	127.05	250000
CBR	QPSK	10004.80	10000
CBR	16QAM	1024.79	10000
CBR	64QAM	223.31	10000

Table 4.1

Simulation Parameters
S.No	Parameter	Value
1.	Operating Frequency	54–72 MHz
2.	Channel Bandwidth	6 MHz
3.	P_Fa, P_Md	0.1, 0.1
4.	P_d	0.9
5.	Modulation schemes	QPSK, 16QAM, 64 QAM
6.	Coding rate	½
7.	Traffic (Tt_m)	CBR, Voice, Video
8.	Access Technique	OFDMA
9.	Channel Model	Log Normal path loss
10.	Controller	SDN

The developed scenario comprises of 3 PUs with the frequency ranges: 54–60 MHz, 60–66 MHz and 66–72 MHz. The channel bandwidth allocated for each PU is 6 MHz as mentioned in standard. Pfa is assumed to be 0.1 to get maximum detection probability. The modulation schemes used are QPSK, 16 QAM and 64 QAM as per the IEEE 802.22 standard. The log normal path loss model is taken into account since it is the model used for TV white space and OFDMA multiple access is employed [20].

Here only one BS is considered. Hence α_i = 1. It controls, β = 1 to 3 and γ = 1 & 2. γ₁ act as transmitter and γ₂ act as receiver as shown in Fig. 4. Each traffic packet is split into 15 equal segments. 500 packets are transmitted between γ₁ and γ₂ as shown in Fig. 5. Thus, total of 75,000 segments are transmitted from γ₁ to α₁ (uplink) and then from α₁ to γ₂ (downlink). The sequence of these segments transmitted will not be in order.

Average End to End delay for Voice Traffic

The average EED for voice traffic with respect to number of packets transmitted when QPSK, 16QAM, 64 QAM modulation are used is shown in Fig. 4 to 6. The size of a voice packet is 1480 bytes.

The average EED for voice traffic with respect to number of packets transmitted when QPSK, 16 QAM and 64QAM shown in Figs. 6, 7 & 8.

The average EED for voice traffic when we use QPSK, 16QAM and 64 QAM are 143612.69 35796.6 and 5798.09 microseconds respectively. From the results obtained, it is inferred that 16QAM modulation has a reduced delay of 4% when compared to QPSK. Similarly 64QAM modulation result with 24% reduction when compared to QPSK and 6% when compared to 16QAM.

It is observed that γ₁ experiences 1 UCS notification during the transmission of (i) first 100, (ii) 101–200, (iii) 201–300 and 3 UCS notifications during the transmission of packets 301–400. There is no UCS notification during the 401–500 packet transmission. The time delay created by 1 UCS notification is 5.29 µs as per IEEE 802.22. Thus, overall UCS notification received during the transmission of these 500 packets is 6. Further, it is also observed that whenever β₁ enters into the channel used by γ_1, an UCS notification is released by the α₁ and additional 5.29 µs delay is added with the existing delay.

Average End to End delay for CBR Traffic

Figure 9, 10 and 11 illustrates the average EED for CBR traffic with respect to number of packets transmitted when QPSK, 16QAM, and 64QAM modulation are used. The size of a CBR packet is 1460 bytes.

According to the results obtained, an extra delay in packet transmission is found to be caused by the UCS notification.The 16QAM modulation reduces the averaeg EED by 15% when compared to QPSK. When compared to 16QAM, the 64QAM modulation decreases the avearge EED by 7.5%. In all three scenarios (Figs. 9, 10 and 11), it is seen that there is 1 UCS notification while transmitting packets 1–100, 101–200, 201–300, 301–400 and 401–500. However the delay created during the transmission of last 100 packets is reduced in 64QAM modulation. This is due to the availability of channels at that instant of time. It is also to be agreed that whenever higher modulations are used reduction in average EED can be observed. From the results, it is inferred that, depending on the application QoS, it is essential to employ the modulation.

Average End to End delay for Video Traffic

The average EED for video traffic with respect to the number of packets transmitted when QPSK, 16QAM and 64QAM are used are shown in Figs. 12, 13 and 14.

Unlike CBR and Voice traffics, Video traffic has variable packet sizes since it has audio and image files. Each packet is divided into 8 to 13 segments based on the size. The packet size varies from 56 to 1240 bytes. From the results, it is found that reduction of 100 µs is observed when 16QAM is used while comparing to QPSK. It is observed that γ₁ experiences 1 UCS notification during the transmission of first 100 packets. Similarly it experiences 3 UCS during the transmission of packets 101–200, no UCS notification from 201–300 and 301–400 and 1 UCS notification during the 401–500 packet transmission. Overall UCS notification received during the transmission of 500 packets is 5. The UCS notification creates an additional 5.29 µs delay with the existing one.

Impact of PU Count on Average EED

The impact of average EED with respect to β count is shown in Fig. 15. As per the simulation, the count of β can be increased from 1 to 3. When the β count increases, the UCS notification is issued by α₁ and an additional delay is created for allocating channels to γ₁.

The simulation results have been taken for voice traffic with 500 packets transmitted from γ₁ to γ₂. From Fig. 15, it is inferred that the delay has been increased thrice when β = 2 and 25 times when β = 3. Thus it is concluded that increase in β will definitely influence the QoS parameters of γ. To substantiate this Table 3 present average EED experienced by three applications under three modulation schemes. When compared with the acceptable level of average EED as mentioned by the IEEE 802.22, from the investigation it is concluded that (i) 64QAM is suitable for Voice, (ii) all three modulation scheme for Video and (iii) 16QAM & 64 QAM for CBR traffic.

In this research, DCSS mechanism is developed to guarantee the throughput of SUs in a IEEE 802.22 WRAN. Cooperative spectrum sensing is realized by the association of two modules namely SM and SA. The concept of UCS notification is introduced to indicate the re-entry of PUs to the SUs. Buffering mechanism is also introduced to provide lossless communication. The average EED experienced by packets of CBR, Video and Voice under QPSK, 16QAM and 64QAM modulation schemes are analysed in a Netsim based simulated environment. From the results obtained the modulation schemes suitable for each of the above traffic is determined by comparing the average EED with the EED suggested by the IEEE 802.22 standard. As mentioned earlier, propagation delay also influence EED, Hence, the above investigation can be extended to various channel conditions which is considered as the future work.

Ethical Approval

Not Applicable

Competing interests

Not Applicable

Authors' contributions

Bino deals with analysing the results and framing the manuscript and prepared the figures in accordance with the supervision of Dr. P.T.V.Bhuvaneswari

Funding

Not Applicable

Availability of data and materials

Not Applicable

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No competing interests reported.

End to End Delay Analysis of Dynamic Cooperative Spectrum Sensing Mechanism in IEEE 802.22 WRAN

Status:

Version 1

Abstract

Figures

1. Introduction

2 Literature Survey

3. Dynamic Cooperative Spectrum Sensing

3.1 System Model

4. Results And Discussion

4.1 Average End to End Delay

4.2 Simulation Parameters

Conclusion & Future Work

Declarations

References

Additional Declarations

Status:

Version 1