Enhancing Channel Coordination Scheme ... - Semantic Scholar
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Enhancing Channel Coordination Scheme Caused by Corrupted Nakagami Signal and Mobility Models on the IEEE 1609.4 Standard Doan Perdana and Riri Fitri Sari Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Depok, Indonesia Kampus Baru UI, Email : {doan.perdana, riri}@ui.ac.id
Abstract—Ensuring high saturated throughput of SCHs and reducing the transmission delay of service packets on CCHs are one of the most challenging issues in multichannel operations IEEE 1609.4 standard. Moreover, multiple issues due to its highly dynamic topology, the high mobility, the change trajectory, and Nakagami propagation channel caused by Additive White Gaussian Noise (AWGN) are challenges in assuring the Quality of Service (QoS) in multichannel operations IEEE 1609.4 standard. In this paper, we propose a coordination scheme based on the multichannel operations IEEE 1609.4 standard, in terms to enhance the Quality of Service (QoS) caused by different dynamic topology, high mobility, change trajectory, and Nakagami propagation channel caused by Additive White Gaussian Noise (AWGN). The propose scheme is enhancement of the previous scheme, i.e. Variable CCH Interval (VCI). We use ns 3.18 and Matlab tools for the simulation and evaluation of the performance. From the simulation, we found that the proposed scheme enhance the saturated throughput and reduces the transmission delay of service packets compared the previous scheme. This paper also evaluates the proposed scheme caused by different mobility models. We also analysis probability of the signal error Nakagami-m distribution parameters caused by the existence of the Additive White Gaussian Noise (AWGN). Index Terms—IEEE 1069.4, Nakagami Propogation, Additive White Gaussian Noise (AWGN), Coordination Scheme, ns 3.18 simulator
I.
INTRODUCTION
Vehicular Ad hoc Network (VANET) has recently become one of the most research topics in the area of Intelligent Transportation System (ITS) and wireless networking [1]. A VANET is mainly characterized by high mobility and the restricted movement patterns governed by roads and traffic rules [2]. These characteristics lead to
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many challenges in designing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols [3]. Due to its decentralized nature, a VANET is highly preferred by a variety of safety applications which cannot easily obtain help from central nodes, such as cooperative collision avoidance, blind sport warning, and approaching emergency warning [4]. It introduces a modification to IEEE 802.11a at the PHY layer in order to cope with the fast-fading propagation environment [3]. At the MAC layer, IEEE 802.11p relies on the prioritized channel access of IEEE 802.11e MAC [3]. The standard of the IEEE 1609 protocols family, which supports multichannel operation and enhances IEEE 802.11p MAC layer, is IEEE 1609.4 [1]. This standard describes seven different channels with different features and usage [1]. Specifically, IEEE 1609.4 defines with seven 10 Mhz-wide channels are available in the frequency band of 5.85–5.925 GHz. i.e. one control channel (CCH) and six service channels (SCHs) [3]. The channel access time is divided into synchronization intervals with a fixed length of 100 ms [3]. A synchronization interval is further divided into CCH and SCH intervals. Each interval lasts 50 ms long [3]. According to the channel switching scheme, all devices must stay tuned to CCH during the CCH interval for exchanging safety and control messages [3]. A device can actively be switched from the CCH to a specific SCH for its desired non-safety application services. IEEE 1609.4 defines a guard interval (GI) at the beginning of both the CCH and SCH [3]. Channel coordination is designed to support data exchanges involving one or more switching devices with concurrent alternating operation on the CCH and an SCH [5]. This allows, for example, a single-PHY device access to high-priority data and the management traffic
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during the CCH interval, as well as general higher layer traffic during the SCH interval [5]. Figure 1 illustrates two examples of channel access: continuous access, which requires no channel coordination, and alternating access, which does require channel coordination [5].
Figure 1. Channel access examples: (a) continuous and (b) alternating [5]
IEEE 1609.4 makes the CCH and SCH intervals to be synchronized to an external time reference, the Coordinated Universal Time (UTC) which is provided by the Global Positioning System (GPS) [3]. However, if a device fails to get the UTC from its local GPS, it should get time information from other nodes over the air [3]. This becomes possible by using wave time advertisement (WTA) frames, which is available in the IEEE 802.11p specification [3]. Given the UTC, a node aligns the start of the CCH interval with the UTC or a multiple of 100 ms after the UTC [3]. A sync interval and its CCH interval and SCH interval components are shown in Figure 2 [5]. The duration of the CCH and SCH intervals are stored in the MIB attributes CchInterval and SchInterval, respectively, and the values of these attributes sum to the length of the sync interval [5]. There shall be an integer number of sync intervals in 1 s [5]. Coordinated Universal Time (UTC) defines the common time base for WAVE channel coordination [5]. At each UTC second, the beginning of a sync interval shall align with the beginning of the UTC second, as shown in Figure 2 [5]. The first part of each channel interval (CCH interval or SCH interval) is a guard interval as shown in Figure 2, used to calculate for radio switching and timing inaccuracies among different devices [5].
Figure 2. Syn interval, guard interval, CCH interval, and SCH interval [5]
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The problem addressed in this paper is to evaluate the multiple issues that reduce the Quality of Service (QoS) based on the multichannel operations IEEE 1609.4 standard. It also analyzed the Nakagami distribution parameter caused by Additive White Gaussian Noise (AWGN). The contribution of this paper is to propose a coordination scheme to enhance the saturated throughput of the SCHs and to reduce transmission delay IEEE 1609.4 standard. It also evaluate the propose scheme due to its highly dynamic topology, the high mobility, change trajectory. This paper also analyzed the probability of signal error the Nakagami-m distribution parameters caused by Additive White Gaussian Noise (AWGN). This paper is organized as follows. In section II, we provide a number of related work and motivation. In Section III, we provide a scenario and simulation for the coordination schemes based on the IEEE 1609.4 standard. In section IV, we evaluate the performance of proposed coordination caused by mobility models. We also evaluate the performance probability of the signal error Nakagamim distribution parameters caused by Additive White Gaussian Noise (AWGN) based on IEEE 1609.4 standard. Finally, we conclude the paper and suggest the future work in Section V. II.
RELATED WORK AND MOTIVATION
Wang, Q. et al. [6-8] proposed a Variable CCH Interval (VCI) multichannel MAC protocol to enhance the saturation throughput of SCHs and ensuring the transmissions of safety messages while maintaining the prioritized transmission of critical safety information on CCH. The CCH interval (safety and WSA intervals) is calculated by the roadside unit (RSU). The RSU broadcasts a packet (VCI packet) containing the length of the CCH interval to the nodes under its transmission range. VCI calculates the optimal ratio between WSA and SCH intervals. We use [6-8] to simulate and evaluate the proposed scheme caused by mobility models based on Multi-channel operations of the IEEE 1609.4.
Figure 3. Operations of VCI [3,6]
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Figure 4. Operations of DID-MMAC [3]
L. Liu et al. [9] introduced the Dynamic Interval Division Multichannel MAC (DID-MMAC) to perform the dynamic adjustment SCH / CCH interval. This scheme divides the CCH interval into 3 phases based on the type of different messages : Service Announce Phase (SAP), Beacon Phase (BP), and Peer-to-Peer Reservation Phase (PRP). For dynamically adjusting the intervals of SAP and BP in a distributed manner, DID-MMAC assigns different channel access priorities to different messages by differentiating the contention window (CW) and the interframe space (IFS) [3]. N. Lu et al. [10] defined the Dedicated Multichannel MAC (DMMAC) to perform an adaptive broadcasting mechanism to reduce the collision rate and a transmission delay. The research aimed to improve the safety performance of packet delivery ratio. The research did not consider the dynamic adjustment of the CCH interval and analytical studies performed on the model of the proposed scheme. H. Yoo and D. Kim [11] proposed a Dynamic Safety Interval (DSI) for calculating the optimal safety interval under dynamic traffic conditions in a distributed manner [7]. In particular, DSI calculates the safety interval considering the presence of hidden nodes [3]. We use [11] to simulate and evaluate the propose scheme caused by hideen node based on Multi-channel operations of the IEEE 1609.4. Nasuf H. et al. [12] estimates the Nakagami-m distribution parameters which uses samples corrupted by multipath and shadow fading, phase fluctuations and noise [12]. Expressions for the probability density function and n-th order moment of noisy channel samples are presented. Moment-based estimator for operation in noisy environments is developed based on the presented probability density function. The sample mean and variance of the estimator are determined [12]. We use [12] to evaluate and analysis the probability of signal error the
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Nakagami-m distribution parameters caused by Additive White Gaussian Noise (AWGN). M.Kostiü et al. [13] proposed analytical approach and performance analysis of the gamma-shadowed Nakagamim fading channels using the moment matching method. In a study conducted analyzed the outage probability and average bit-error. George K. K. et al. [14] propose a model estimation Moments Generating Function (MGF), the Probability Density Function, Cumulative Distribution Function (CDF), and the moments of the distribution of N * Nakagami. This study also conducted to investigate the suitability of the distribution of N * Nakagami fading using lognormal distribution. We use [14] to analyzed the Nakagami-m distribution parameters using Moments Generating Function (MGF), the Probability Density Function, and Cumulative Distribution Function (CDF). A. Coordination scheme based on IEEE 1609.4 standard Channel coordination mechanism is an important scheme for increasing efficiency of transmission at the MAC layer of the IEEE 1609.4 standard. Channel coordination scheme could ensure the saturation throughput of SCHs and reduce the delay transmission of the service packets. The channel coordination scheme is a coordination mechanism between CCHs (safety packets) and SCHs (non-safety packets) standard synchronized by CchInterval and SchInterval in the IEEE 1069.4 standard. B. Nakagami-m Distribution Parameter The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. It has two parameters : a shape parameter m and a second parameter controlling spread, Ω. The Nakagami-m distribution having the pdf [12]: ሺݎሻ ൌ
ଶ ଶ൫ൗΩ൯ ݎଶିଵ ቀെ݉ ݎൗΩቁ ǡ ݎ Γሺሻ
where ݉ ൌ
Ωమ ாሼሺ మ ିΩሻమ ሽ ଶ ሽis
Ͳ
(1)
is the so-called m parameter with
the second moment of distribution ݉ ͲǤͷ, Ω ൌ ܧሼݎ and ΓሺǤ ሻ is the gamma function [18, p. 255]. Ω also represents shadow fading, slow signal power fluctuations usually described with log-normal pdf, which can be very well modeled with gamma pdf as in [13] : ሺΩሻ ൌ
ೞ ቀ ೞൗΩ ቁ ೞ Γሺೞ ሻ
Ωೞ ିଵ ቀെ݉௦
Ω Ωೞ
ቁ ǡ Ω Ͳ(2)
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where ݉௦ Ͳ is shadow severity parameter and Ω > 0 is real, modified received signal power. Algorithm Procedure in selecting CCH interval And SCH Interval for each N T (CCH, SCH) do Detect the type of service packet N for each of service packet N if N S (SendIpPackets and SendWsmpPackets) Setting for N T (Nakagami Propagation Loss Model) if N T (Distance > 150m, Same Channel Number) Hidden node interference else Node not interferer // Executed by nodes at the beginning of the CCH interval if CIprev not equal zero then CIcurr = CIprev else CIcurr = 50 ms repeat Randomize SIcurr Update (CIvci_f2, CIwsa_f2, CIrfs_f2, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f) until CurrentDelay < = PreviousDelay and CurrentThroughput > PreviousThroughput if receive a VCI frame if it is the first time receiving a VCI frame Randomize SIcurr; Update (CIvci_f2, CIwsa_f2, CIrfs_f2, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f) else if CIcurr < CIvci_f then CIcurr = CIvci_f if receive a WSA/RFS/ACK frame and have not yet received a VCI frame Randomize SIcurr; Update(CIvci_f2, CIwsa_f2, CIrfs_f, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f); else if the WSA/RFS/ACK frame is from the node will connect to then if CIcurr < CIwsa_f then // Under different RSUs CIcurr = CIwsa_f end if end if end for end for
III.
SCENARIO AND SIMULATION
By using ns 3.18 simulator, we evaluate the performance propose coordination scheme caused by mobility on IEEE 1609.4 standard. We also analysis Nakagami distribution parameter caused by Additive White Gaussian Noise (AWGN) using Matlab tools. We simulate the scenario with the number of cars range from 10 to 100 nodes and using Manhattan, Traffic sign, and IDM_IM model mobility. The channel configuration using variable values for control and service channel intervals, and the guard intervals value is 4 ms. Table I. presents all
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parameters used in our simulation.While some parameters stay fixed, others are varied in order for us to observe the changing behavior of the network. TABLEI.SIMULATION PARAMETERS Parameters MAC Protocol Number of vehicles Number of CCH Number of SCHs Service packet length Data rate of each channel PHY header MAC header WSA/RFS ACK Slot time SIFS DIFS
Values IEEE 1609.4 10-100 nodes 1 4 2000 bytes 3 Mbps 192 bits 256 bits 160 bits + PHY header 112 bits + PHY header 20 μs 10 μs 50 μs
Based on [12], the transmitted signal is assumed known in the estimation of the fading distribution parameters. This is the case for received signal samples taken during the transmission of training sequences (which may be for channel estimation, synchronizer training or equalize training) [12]. This is also the case when the receiver makes correct decisions which occur with high probability in a well designed [12]. The fading signal is corrupted by additive white Gaussian Noise (AWGN), which is independent of the fading. The received signal in the i-th symbol period can be expressed as
i (t) = ri
ି୨୧ si(t) + wi (t)
(3)
where si(t) is the transmitted signal in the i-th symbol period, ri is the fading amplitude in the i-th symbol period having a Nakagami-m distribution. ߠ݅ is the fading phase (phase fluctuation) in the i-th symbol period, whereߠ݅ א [െߨǡ ߨሿ. In this paper, the propose coordination scheme also considered the hidden node. Based on [11], hidden node refer to the nodes located within ri of the intended destination and out of rc of the sender. When a receiver is receiving a packet, if a hidden node tries to start a concurrent transmission, collisions can happen at the receiver [11]. For example as shown in Figure 5, node B is located within interference ranges of both nodes A and D so that node D’s transmission interferers with the transmission
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from node A to node B [11]. On the other hand, node A’s rt is not overlapped with node E’s ri, since node A and E are separated by a distance denoted by dsr [11]. Therefore, both nodes can concurrently transmit their packets without interfering with each other. dsr is called spatial reuse distance and should be larger than the sum of rt and ri [11].
Fig. 6. Average delay of Enhanced Variable CCH Interval
Figure 5. Hidden Node Problem [11]
IV.
RESULT AND EVALUATION
Base on the scenario above, in Figure 6, 7, and 8 the simulation was performed to obtain data according to three aspects to be measured, i.e the average delay, number of delivered packet, and throughput. 1) Performance of Enhanced Variable CCH Interval for Different Mobility on the IEEE 1609.4 standard a) Average Delay Fig. 5 shows the average of delay enhanced Variable CCH Interval (VCI) for different mobility models on the IEEE 1609.4 standard, by varying the number of nodes. We focus on the average access delay which calculate the MAC layer. Delay and access will be used interchangeably on this work by varying the number of nodes. This can be seen in Fig. 6. The following is the equation for the average delay (>G@ derived as [16,17] : (>G@ (>F@(>T@
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Fig. 6, we found that the average delay of Enhanced Variable CCH Interval lower compared with Variable CCH Interval caused by mobility models based on IEEE 1609.4 standard. We can evaluate from the performance simulation that the new propose coordination schemes, basically the variable CCH interval (safety and and WSA intervals) is calculated by roadside unit (RSU) and neighbor nodes. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be handle for heavy traffic, caused by hidden node, and also caused by high mobility on the IEEE 1609.4 standard. b) Throughput Fig. 7 shows the throughput of Enhanced Variable CCH Interval on the IEEE 1609.4 standard, by varying the number of nodes. Throughput Ti (t) is the rate of successful packet delivery through a network connection per unit time. We focus on the throughput calculated at the MAC layer, then Ti (t) derived as [16,17] : Throughput Ti (t) = x* (1-p)*d*data rate G WhereGd = DATA/(DIFS+PACKET+SIFS+ACK) GG x is the number of nodes Ti (t) is the throughputG a is the distance of nodes p is the collision probability for a transmission
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Fig.9 Probability Density Function Corrupted Nakagami Signal for m = 0.6, 1.1, 1.5, 2, 5
Using eqn. (6), we evaluate probability of signal error caused by corrupted Nakagami [15] : Fig. 7 System throughput of Enhanced Variable CCH Interval
Fig. 7 shows that the throughput of Enhanced Variable CCH Interval higher compared with Variable CCH Interval caused by mobility models based on IEEE 1609.4 standard. We can evaluate from the performance simulation that the new propose coordination schemes, calculates the optimal safety interval and WSA interval based on network traffic conditions for service packet in the CCH interval. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be enhance the saturated throughput under heavy traffic or congested traffic.
ר
ܲ ൌ
ଵ ಶ್ ൘ಿ ൛భశబ ൫మഏವ ೄ൯ൟ רబ శభ శభ ାଵ ಶ್ ൘ಿ ൛భషబ ൫మഏವ ೄ൯ൟ רబ శభ శభ
ר
2) Probability of signal error caused by corrupted Nakagami signal Based on [12], the m-parameter estimator over Nakagami-m multipath / gamma-shadowed noisy channel is obtained as : ר
݉
ͳ
ר
ൌ Ǥ σ݆ܭൌͳ ݆݉ ܭ
(6)
Where K represents total number of realizations of the shadow fading as a random process.
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ܲ ൌ
ଵ ಶ್ ൘ಿ ଶቌ רబ ାଵቍ శభ
ಶ್ ൘ಿ బ
ר
൮െ ಶ್ శభ ൲ (7) ൘ಿ బ ାଵ ר శభ
ಶ್ ൘ಿ బ
ר
൮െ ಶ್ శభ ൲ ൘ಿ బ ାଵ ר శభ
(8)
Fig.9 shows that the probability density function (pdf) on IEEE 1609.4 standard with different mparameter estimator over Nakagami-m (m= 0.6, 1.1, 1.5, 2, 5). We can evaluate from the performance simulation that m parameter characterise fading and shadowing on the desired signal disturbed by Additive White Gaussian Noise (AWGN). We also can evaluate corrupted signal at m=5 has the highest fluctuative probability function (pdf).
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Fig.10 Probability of signal error modeled versus simulation result (m=0.6)
Fig.12 Probability of signal error modeled Vs simulation result (m=1.5)
Fig.10 shows that the probability of signal error and Eb/No caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=0.6).
Fig.12 shows that the probability of signal error caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease with Eb/No. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=1.1).
V.
Fig.11 Probability of Signal Error modeled versus simulation result (m=1.1)
Fig.11 shows that the probability of signal error caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease with Eb/No. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=1.1).
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CONCLUSION
Our performance simulation for the new propose coordination schemes show the enhancement of the saturation throughput and reduction of the delay because it basically the variable CCH interval (safety and and WSA intervals) is calculated by roadside unit (RSU) and neighbor nodes. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be handled by heavy traffic, impact of hidden node, and also high mobility on IEEE 1609.4 standard. Finally, we evaluate the probability of signal error for m = 0.6, 1.1, and 2 using modeled by Nasuf H. et al. compared simulation (ns-3). We also evaluate the Probability Density Function (PDF) corrupted signal Nakagami caused by Additive White Noise Gaussian (AWGN) for m = 0.6, 1.1, 1.5, 2, and 5 using modeled compared simulation (ns-3).
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Jafari, A., Performance Evaluation of IEEE 802.11p for Vehicular Communication Networks. PhD Dissertation, Faculty of Arts, Computing, Engineering, and Sciences, Postgraduate Program, Sheffield Hallam University, South Yorkshire, England, UK, 2011. J. Harri, F. Filali, and C. Bonnet, “Mobility models for vehicular Ad Hoc networks: a survey and taxonomy,” IEEE Communications Surveys and Tutorials, vol. 11, no. 4, pp. 19–41, 2009. H. Yoo and D. Kim, “A dynamic safety interval protocol for VANETs,” in Proceedings of the ACM Research in Applied Computation Symposium (RACS ’12), pp. 209–214, October 2012.
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IEEE Std 1609.4-2010, IEEE Standard for Wireless Access in Vehicular Environments (WAVE) Multichannel Operation, IEEE, 2011. Q. Wang, S. Leng, H. Fu, and Y. Zhang, “An IEEE 802. 11pbased multichannel MAC scheme with channel coordination for vehicular Ad Hoc networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 2013, no. 2, pp. 449– 458, 2012. Q. Wang, S. Leng, H. Fu, Y. Zhang, and H. Weerasinghe, “An enhanced multi-channel MAC for the IEEE 1609.4 based vehicular Ad Hoc networks,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM ’10), San Diego, Calif, USA, March 2010. Q. Wang, S. Leng, Y. Zhang, and H. Fu, “A QoS supported multichannel MAC for vehicular Ad Hoc networks,” in Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC ’11), Budapest, Hungary, May 2011. L. Liu, W. Xia, and L. Shen, “An adaptive multi-channel MAC protocol with dynamic interval division in vehicular environment,” in Proceedings of the 1st International Conference on Information Science and Engineering (ICISE ’09), pp. 2534–2537, Nanjing, China, December 2009. N. Lu, Y. S. Ji, F. Q. Liu, and X. H. Wang, “A dedicated multichannel MAC protocol design for VANET with adaptive broadcasting,” in Proc. WCNC, 2010, pp. 1–6. H. Yoo and D. Kim, “A dynamic safety interval protocol for VANETs,” in Proceedings of the ACM Research in Applied Computation Symposium (RACS ’12), pp. 209–214, October 2012. Nasuf H., Mirza M., Melita A.C., and Mesud H., “Estimation of Nakagami Distribution Parameters Based on Signal Samples Corrupted with Multiplicative and Additive Disturbances,” 49th International Symposium ELMAR-2007, 12-14 September 2007, Zadar, Croatia. M.Kostiü, “An analytical approach to performance analysis for channel subject to shadowing and fading”, IEEE Proc. On Communications, vol. 152, No. 6, 2005, pp. 821-827 George K. Karagiannidis, Niko C.S and P. Takis Mathiopoulos “The N * Nakagami Fading Channel Model,” IEEE Communication Letters 2005. Sasamori, F., Yamano, A. ; Handa, S. ; Maehara, F. ; Takahata, F. ; Oshita, S., “Approximate Equation of Bit Error Rate in OFDM Systems over Specular Multipath Fading Channels”, Wireless Communications and Networking, 2003. WCNC 2003. 2003 IEEE (Volume:3), 20 March 2003, pp. 2096 - 2101 vol.3, ISSN : 1525-3511.
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[16] Doan Perdana and Riri Fitri Sari, “Mobility Models Performance Analysis using Random Dijkstra Algorithm and Doppler Effect for IEEE 1609.4 Standard”, International Journal of Simulation, Systems, Science, and Technology, United Kingdom Simulation Society. [17] Ali J. Ghandour, Marco Di Felice, Hassan Artail and Luciano Bononi, “Modeling and Simulation of WAVE 1609.4-based Multi-channel Vehicular Ad Hoc Networks”, 5th ACM International Conference on Simulations Tools and Techniques (SIMUTools 2012), March 19-23, 2012, Sirmione-Desenzano, Italy. [18] M. Abramowitz and I.A. Stegun, Eds. Handbook of Mathematical Functions.New York: Dover, 1972.
Doan Perdana received his BSc and MSc degrees in Telecommunication Engineering, from the Institute of Technology Telkom in 2004 and 2012, respectively. He is currently undergoing doctoral study in Electrical Engineering Department, University of Indonesia. His interests include Telecommunication Systems and Computer Engineering. Riri Fitri Sari, PhD is a Professor of computer engineering at Electrical Engineering Department of Universitas Indonesia. She received her BSc degree in Electrical Engineering from Universitas Indonesia. She completed her MSc in Computer Science and Parallel Processing from the University of Sheffeld, UK. She has awarded a PhD in Computer Science received from the University of Leeds, UK. Riri Fitri Sari is a senior member of the Institute of Electrical and Electronic Engineers (IEEE).
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Enhancing Channel Coordination Scheme Caused by Corrupted Nakagami Signal and Mobility Models on the IEEE 1609.4 Standard Doan Perdana and Riri Fitri Sari Department of Electrical Engineering, Faculty of Engineering, University of Indonesia, Depok, Indonesia Kampus Baru UI, Email : {doan.perdana, riri}@ui.ac.id
Abstract—Ensuring high saturated throughput of SCHs and reducing the transmission delay of service packets on CCHs are one of the most challenging issues in multichannel operations IEEE 1609.4 standard. Moreover, multiple issues due to its highly dynamic topology, the high mobility, the change trajectory, and Nakagami propagation channel caused by Additive White Gaussian Noise (AWGN) are challenges in assuring the Quality of Service (QoS) in multichannel operations IEEE 1609.4 standard. In this paper, we propose a coordination scheme based on the multichannel operations IEEE 1609.4 standard, in terms to enhance the Quality of Service (QoS) caused by different dynamic topology, high mobility, change trajectory, and Nakagami propagation channel caused by Additive White Gaussian Noise (AWGN). The propose scheme is enhancement of the previous scheme, i.e. Variable CCH Interval (VCI). We use ns 3.18 and Matlab tools for the simulation and evaluation of the performance. From the simulation, we found that the proposed scheme enhance the saturated throughput and reduces the transmission delay of service packets compared the previous scheme. This paper also evaluates the proposed scheme caused by different mobility models. We also analysis probability of the signal error Nakagami-m distribution parameters caused by the existence of the Additive White Gaussian Noise (AWGN). Index Terms—IEEE 1069.4, Nakagami Propogation, Additive White Gaussian Noise (AWGN), Coordination Scheme, ns 3.18 simulator
I.
INTRODUCTION
Vehicular Ad hoc Network (VANET) has recently become one of the most research topics in the area of Intelligent Transportation System (ITS) and wireless networking [1]. A VANET is mainly characterized by high mobility and the restricted movement patterns governed by roads and traffic rules [2]. These characteristics lead to
© 2014 ACADEMY PUBLISHER doi:10.4304/jnw.9.12.3454-3461
many challenges in designing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols [3]. Due to its decentralized nature, a VANET is highly preferred by a variety of safety applications which cannot easily obtain help from central nodes, such as cooperative collision avoidance, blind sport warning, and approaching emergency warning [4]. It introduces a modification to IEEE 802.11a at the PHY layer in order to cope with the fast-fading propagation environment [3]. At the MAC layer, IEEE 802.11p relies on the prioritized channel access of IEEE 802.11e MAC [3]. The standard of the IEEE 1609 protocols family, which supports multichannel operation and enhances IEEE 802.11p MAC layer, is IEEE 1609.4 [1]. This standard describes seven different channels with different features and usage [1]. Specifically, IEEE 1609.4 defines with seven 10 Mhz-wide channels are available in the frequency band of 5.85–5.925 GHz. i.e. one control channel (CCH) and six service channels (SCHs) [3]. The channel access time is divided into synchronization intervals with a fixed length of 100 ms [3]. A synchronization interval is further divided into CCH and SCH intervals. Each interval lasts 50 ms long [3]. According to the channel switching scheme, all devices must stay tuned to CCH during the CCH interval for exchanging safety and control messages [3]. A device can actively be switched from the CCH to a specific SCH for its desired non-safety application services. IEEE 1609.4 defines a guard interval (GI) at the beginning of both the CCH and SCH [3]. Channel coordination is designed to support data exchanges involving one or more switching devices with concurrent alternating operation on the CCH and an SCH [5]. This allows, for example, a single-PHY device access to high-priority data and the management traffic
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during the CCH interval, as well as general higher layer traffic during the SCH interval [5]. Figure 1 illustrates two examples of channel access: continuous access, which requires no channel coordination, and alternating access, which does require channel coordination [5].
Figure 1. Channel access examples: (a) continuous and (b) alternating [5]
IEEE 1609.4 makes the CCH and SCH intervals to be synchronized to an external time reference, the Coordinated Universal Time (UTC) which is provided by the Global Positioning System (GPS) [3]. However, if a device fails to get the UTC from its local GPS, it should get time information from other nodes over the air [3]. This becomes possible by using wave time advertisement (WTA) frames, which is available in the IEEE 802.11p specification [3]. Given the UTC, a node aligns the start of the CCH interval with the UTC or a multiple of 100 ms after the UTC [3]. A sync interval and its CCH interval and SCH interval components are shown in Figure 2 [5]. The duration of the CCH and SCH intervals are stored in the MIB attributes CchInterval and SchInterval, respectively, and the values of these attributes sum to the length of the sync interval [5]. There shall be an integer number of sync intervals in 1 s [5]. Coordinated Universal Time (UTC) defines the common time base for WAVE channel coordination [5]. At each UTC second, the beginning of a sync interval shall align with the beginning of the UTC second, as shown in Figure 2 [5]. The first part of each channel interval (CCH interval or SCH interval) is a guard interval as shown in Figure 2, used to calculate for radio switching and timing inaccuracies among different devices [5].
Figure 2. Syn interval, guard interval, CCH interval, and SCH interval [5]
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The problem addressed in this paper is to evaluate the multiple issues that reduce the Quality of Service (QoS) based on the multichannel operations IEEE 1609.4 standard. It also analyzed the Nakagami distribution parameter caused by Additive White Gaussian Noise (AWGN). The contribution of this paper is to propose a coordination scheme to enhance the saturated throughput of the SCHs and to reduce transmission delay IEEE 1609.4 standard. It also evaluate the propose scheme due to its highly dynamic topology, the high mobility, change trajectory. This paper also analyzed the probability of signal error the Nakagami-m distribution parameters caused by Additive White Gaussian Noise (AWGN). This paper is organized as follows. In section II, we provide a number of related work and motivation. In Section III, we provide a scenario and simulation for the coordination schemes based on the IEEE 1609.4 standard. In section IV, we evaluate the performance of proposed coordination caused by mobility models. We also evaluate the performance probability of the signal error Nakagamim distribution parameters caused by Additive White Gaussian Noise (AWGN) based on IEEE 1609.4 standard. Finally, we conclude the paper and suggest the future work in Section V. II.
RELATED WORK AND MOTIVATION
Wang, Q. et al. [6-8] proposed a Variable CCH Interval (VCI) multichannel MAC protocol to enhance the saturation throughput of SCHs and ensuring the transmissions of safety messages while maintaining the prioritized transmission of critical safety information on CCH. The CCH interval (safety and WSA intervals) is calculated by the roadside unit (RSU). The RSU broadcasts a packet (VCI packet) containing the length of the CCH interval to the nodes under its transmission range. VCI calculates the optimal ratio between WSA and SCH intervals. We use [6-8] to simulate and evaluate the proposed scheme caused by mobility models based on Multi-channel operations of the IEEE 1609.4.
Figure 3. Operations of VCI [3,6]
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Figure 4. Operations of DID-MMAC [3]
L. Liu et al. [9] introduced the Dynamic Interval Division Multichannel MAC (DID-MMAC) to perform the dynamic adjustment SCH / CCH interval. This scheme divides the CCH interval into 3 phases based on the type of different messages : Service Announce Phase (SAP), Beacon Phase (BP), and Peer-to-Peer Reservation Phase (PRP). For dynamically adjusting the intervals of SAP and BP in a distributed manner, DID-MMAC assigns different channel access priorities to different messages by differentiating the contention window (CW) and the interframe space (IFS) [3]. N. Lu et al. [10] defined the Dedicated Multichannel MAC (DMMAC) to perform an adaptive broadcasting mechanism to reduce the collision rate and a transmission delay. The research aimed to improve the safety performance of packet delivery ratio. The research did not consider the dynamic adjustment of the CCH interval and analytical studies performed on the model of the proposed scheme. H. Yoo and D. Kim [11] proposed a Dynamic Safety Interval (DSI) for calculating the optimal safety interval under dynamic traffic conditions in a distributed manner [7]. In particular, DSI calculates the safety interval considering the presence of hidden nodes [3]. We use [11] to simulate and evaluate the propose scheme caused by hideen node based on Multi-channel operations of the IEEE 1609.4. Nasuf H. et al. [12] estimates the Nakagami-m distribution parameters which uses samples corrupted by multipath and shadow fading, phase fluctuations and noise [12]. Expressions for the probability density function and n-th order moment of noisy channel samples are presented. Moment-based estimator for operation in noisy environments is developed based on the presented probability density function. The sample mean and variance of the estimator are determined [12]. We use [12] to evaluate and analysis the probability of signal error the
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Nakagami-m distribution parameters caused by Additive White Gaussian Noise (AWGN). M.Kostiü et al. [13] proposed analytical approach and performance analysis of the gamma-shadowed Nakagamim fading channels using the moment matching method. In a study conducted analyzed the outage probability and average bit-error. George K. K. et al. [14] propose a model estimation Moments Generating Function (MGF), the Probability Density Function, Cumulative Distribution Function (CDF), and the moments of the distribution of N * Nakagami. This study also conducted to investigate the suitability of the distribution of N * Nakagami fading using lognormal distribution. We use [14] to analyzed the Nakagami-m distribution parameters using Moments Generating Function (MGF), the Probability Density Function, and Cumulative Distribution Function (CDF). A. Coordination scheme based on IEEE 1609.4 standard Channel coordination mechanism is an important scheme for increasing efficiency of transmission at the MAC layer of the IEEE 1609.4 standard. Channel coordination scheme could ensure the saturation throughput of SCHs and reduce the delay transmission of the service packets. The channel coordination scheme is a coordination mechanism between CCHs (safety packets) and SCHs (non-safety packets) standard synchronized by CchInterval and SchInterval in the IEEE 1069.4 standard. B. Nakagami-m Distribution Parameter The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. It has two parameters : a shape parameter m and a second parameter controlling spread, Ω. The Nakagami-m distribution having the pdf [12]: ሺݎሻ ൌ
ଶ ଶ൫ൗΩ൯ ݎଶିଵ ቀെ݉ ݎൗΩቁ ǡ ݎ Γሺሻ
where ݉ ൌ
Ωమ ாሼሺ మ ିΩሻమ ሽ ଶ ሽis
Ͳ
(1)
is the so-called m parameter with
the second moment of distribution ݉ ͲǤͷ, Ω ൌ ܧሼݎ and ΓሺǤ ሻ is the gamma function [18, p. 255]. Ω also represents shadow fading, slow signal power fluctuations usually described with log-normal pdf, which can be very well modeled with gamma pdf as in [13] : ሺΩሻ ൌ
ೞ ቀ ೞൗΩ ቁ ೞ Γሺೞ ሻ
Ωೞ ିଵ ቀെ݉௦
Ω Ωೞ
ቁ ǡ Ω Ͳ(2)
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where ݉௦ Ͳ is shadow severity parameter and Ω > 0 is real, modified received signal power. Algorithm Procedure in selecting CCH interval And SCH Interval for each N T (CCH, SCH) do Detect the type of service packet N for each of service packet N if N S (SendIpPackets and SendWsmpPackets) Setting for N T (Nakagami Propagation Loss Model) if N T (Distance > 150m, Same Channel Number) Hidden node interference else Node not interferer // Executed by nodes at the beginning of the CCH interval if CIprev not equal zero then CIcurr = CIprev else CIcurr = 50 ms repeat Randomize SIcurr Update (CIvci_f2, CIwsa_f2, CIrfs_f2, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f) until CurrentDelay < = PreviousDelay and CurrentThroughput > PreviousThroughput if receive a VCI frame if it is the first time receiving a VCI frame Randomize SIcurr; Update (CIvci_f2, CIwsa_f2, CIrfs_f2, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f) else if CIcurr < CIvci_f then CIcurr = CIvci_f if receive a WSA/RFS/ACK frame and have not yet received a VCI frame Randomize SIcurr; Update(CIvci_f2, CIwsa_f2, CIrfs_f, CIcurr, CIvci_f, CIsi, CIwsa_f, CIack, CIrfs_f); else if the WSA/RFS/ACK frame is from the node will connect to then if CIcurr < CIwsa_f then // Under different RSUs CIcurr = CIwsa_f end if end if end for end for
III.
SCENARIO AND SIMULATION
By using ns 3.18 simulator, we evaluate the performance propose coordination scheme caused by mobility on IEEE 1609.4 standard. We also analysis Nakagami distribution parameter caused by Additive White Gaussian Noise (AWGN) using Matlab tools. We simulate the scenario with the number of cars range from 10 to 100 nodes and using Manhattan, Traffic sign, and IDM_IM model mobility. The channel configuration using variable values for control and service channel intervals, and the guard intervals value is 4 ms. Table I. presents all
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parameters used in our simulation.While some parameters stay fixed, others are varied in order for us to observe the changing behavior of the network. TABLEI.SIMULATION PARAMETERS Parameters MAC Protocol Number of vehicles Number of CCH Number of SCHs Service packet length Data rate of each channel PHY header MAC header WSA/RFS ACK Slot time SIFS DIFS
Values IEEE 1609.4 10-100 nodes 1 4 2000 bytes 3 Mbps 192 bits 256 bits 160 bits + PHY header 112 bits + PHY header 20 μs 10 μs 50 μs
Based on [12], the transmitted signal is assumed known in the estimation of the fading distribution parameters. This is the case for received signal samples taken during the transmission of training sequences (which may be for channel estimation, synchronizer training or equalize training) [12]. This is also the case when the receiver makes correct decisions which occur with high probability in a well designed [12]. The fading signal is corrupted by additive white Gaussian Noise (AWGN), which is independent of the fading. The received signal in the i-th symbol period can be expressed as
i (t) = ri
ି୨୧ si(t) + wi (t)
(3)
where si(t) is the transmitted signal in the i-th symbol period, ri is the fading amplitude in the i-th symbol period having a Nakagami-m distribution. ߠ݅ is the fading phase (phase fluctuation) in the i-th symbol period, whereߠ݅ א [െߨǡ ߨሿ. In this paper, the propose coordination scheme also considered the hidden node. Based on [11], hidden node refer to the nodes located within ri of the intended destination and out of rc of the sender. When a receiver is receiving a packet, if a hidden node tries to start a concurrent transmission, collisions can happen at the receiver [11]. For example as shown in Figure 5, node B is located within interference ranges of both nodes A and D so that node D’s transmission interferers with the transmission
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from node A to node B [11]. On the other hand, node A’s rt is not overlapped with node E’s ri, since node A and E are separated by a distance denoted by dsr [11]. Therefore, both nodes can concurrently transmit their packets without interfering with each other. dsr is called spatial reuse distance and should be larger than the sum of rt and ri [11].
Fig. 6. Average delay of Enhanced Variable CCH Interval
Figure 5. Hidden Node Problem [11]
IV.
RESULT AND EVALUATION
Base on the scenario above, in Figure 6, 7, and 8 the simulation was performed to obtain data according to three aspects to be measured, i.e the average delay, number of delivered packet, and throughput. 1) Performance of Enhanced Variable CCH Interval for Different Mobility on the IEEE 1609.4 standard a) Average Delay Fig. 5 shows the average of delay enhanced Variable CCH Interval (VCI) for different mobility models on the IEEE 1609.4 standard, by varying the number of nodes. We focus on the average access delay which calculate the MAC layer. Delay and access will be used interchangeably on this work by varying the number of nodes. This can be seen in Fig. 6. The following is the equation for the average delay (>G@ derived as [16,17] : (>G@ (>F@(>T@
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Fig. 6, we found that the average delay of Enhanced Variable CCH Interval lower compared with Variable CCH Interval caused by mobility models based on IEEE 1609.4 standard. We can evaluate from the performance simulation that the new propose coordination schemes, basically the variable CCH interval (safety and and WSA intervals) is calculated by roadside unit (RSU) and neighbor nodes. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be handle for heavy traffic, caused by hidden node, and also caused by high mobility on the IEEE 1609.4 standard. b) Throughput Fig. 7 shows the throughput of Enhanced Variable CCH Interval on the IEEE 1609.4 standard, by varying the number of nodes. Throughput Ti (t) is the rate of successful packet delivery through a network connection per unit time. We focus on the throughput calculated at the MAC layer, then Ti (t) derived as [16,17] : Throughput Ti (t) = x* (1-p)*d*data rate G WhereGd = DATA/(DIFS+PACKET+SIFS+ACK) GG x is the number of nodes Ti (t) is the throughputG a is the distance of nodes p is the collision probability for a transmission
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Fig.9 Probability Density Function Corrupted Nakagami Signal for m = 0.6, 1.1, 1.5, 2, 5
Using eqn. (6), we evaluate probability of signal error caused by corrupted Nakagami [15] : Fig. 7 System throughput of Enhanced Variable CCH Interval
Fig. 7 shows that the throughput of Enhanced Variable CCH Interval higher compared with Variable CCH Interval caused by mobility models based on IEEE 1609.4 standard. We can evaluate from the performance simulation that the new propose coordination schemes, calculates the optimal safety interval and WSA interval based on network traffic conditions for service packet in the CCH interval. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be enhance the saturated throughput under heavy traffic or congested traffic.
ר
ܲ ൌ
ଵ ಶ್ ൘ಿ ൛భశబ ൫మഏವ ೄ൯ൟ רబ శభ శభ ାଵ ಶ್ ൘ಿ ൛భషబ ൫మഏವ ೄ൯ൟ רబ శభ శభ
ר
2) Probability of signal error caused by corrupted Nakagami signal Based on [12], the m-parameter estimator over Nakagami-m multipath / gamma-shadowed noisy channel is obtained as : ר
݉
ͳ
ר
ൌ Ǥ σ݆ܭൌͳ ݆݉ ܭ
(6)
Where K represents total number of realizations of the shadow fading as a random process.
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ܲ ൌ
ଵ ಶ್ ൘ಿ ଶቌ רబ ାଵቍ శభ
ಶ್ ൘ಿ బ
ר
൮െ ಶ್ శభ ൲ (7) ൘ಿ బ ାଵ ר శభ
ಶ್ ൘ಿ బ
ר
൮െ ಶ್ శభ ൲ ൘ಿ బ ାଵ ר శభ
(8)
Fig.9 shows that the probability density function (pdf) on IEEE 1609.4 standard with different mparameter estimator over Nakagami-m (m= 0.6, 1.1, 1.5, 2, 5). We can evaluate from the performance simulation that m parameter characterise fading and shadowing on the desired signal disturbed by Additive White Gaussian Noise (AWGN). We also can evaluate corrupted signal at m=5 has the highest fluctuative probability function (pdf).
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Fig.10 Probability of signal error modeled versus simulation result (m=0.6)
Fig.12 Probability of signal error modeled Vs simulation result (m=1.5)
Fig.10 shows that the probability of signal error and Eb/No caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=0.6).
Fig.12 shows that the probability of signal error caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease with Eb/No. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=1.1).
V.
Fig.11 Probability of Signal Error modeled versus simulation result (m=1.1)
Fig.11 shows that the probability of signal error caused by Additive White Gaussian Noise (AWGN) on IEEE 1609.4 standard slightly decrease with Eb/No. We can evaluate from the performance simulation that the probability of signal error influnced by m-parameter estimator over Nakagami-m (m=1.1).
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CONCLUSION
Our performance simulation for the new propose coordination schemes show the enhancement of the saturation throughput and reduction of the delay because it basically the variable CCH interval (safety and and WSA intervals) is calculated by roadside unit (RSU) and neighbor nodes. If there are heavy traffic or congested traffic, the VCI packets may could be heard by other nodes using channel gossip. So the new propose scheme could be handled by heavy traffic, impact of hidden node, and also high mobility on IEEE 1609.4 standard. Finally, we evaluate the probability of signal error for m = 0.6, 1.1, and 2 using modeled by Nasuf H. et al. compared simulation (ns-3). We also evaluate the Probability Density Function (PDF) corrupted signal Nakagami caused by Additive White Noise Gaussian (AWGN) for m = 0.6, 1.1, 1.5, 2, and 5 using modeled compared simulation (ns-3).
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[16] Doan Perdana and Riri Fitri Sari, “Mobility Models Performance Analysis using Random Dijkstra Algorithm and Doppler Effect for IEEE 1609.4 Standard”, International Journal of Simulation, Systems, Science, and Technology, United Kingdom Simulation Society. [17] Ali J. Ghandour, Marco Di Felice, Hassan Artail and Luciano Bononi, “Modeling and Simulation of WAVE 1609.4-based Multi-channel Vehicular Ad Hoc Networks”, 5th ACM International Conference on Simulations Tools and Techniques (SIMUTools 2012), March 19-23, 2012, Sirmione-Desenzano, Italy. [18] M. Abramowitz and I.A. Stegun, Eds. Handbook of Mathematical Functions.New York: Dover, 1972.
Doan Perdana received his BSc and MSc degrees in Telecommunication Engineering, from the Institute of Technology Telkom in 2004 and 2012, respectively. He is currently undergoing doctoral study in Electrical Engineering Department, University of Indonesia. His interests include Telecommunication Systems and Computer Engineering. Riri Fitri Sari, PhD is a Professor of computer engineering at Electrical Engineering Department of Universitas Indonesia. She received her BSc degree in Electrical Engineering from Universitas Indonesia. She completed her MSc in Computer Science and Parallel Processing from the University of Sheffeld, UK. She has awarded a PhD in Computer Science received from the University of Leeds, UK. Riri Fitri Sari is a senior member of the Institute of Electrical and Electronic Engineers (IEEE).
