We measure the SCAC mechanism on real-time traffic through simulation. We consider a simulated environment with an independent BSS. Two traffic models are considered and the traffic parameters are summarized in Table I.
CBR Voice Traffic: The voice traffic is modeled as a two-state Markov process with talk-spurt and silence states. The duration of these two states is assumed to be exponentially distributed with parameters 1s and 1.35s, respectively.
ABR Data Traffic: There are 20 MTs to generate asynchronous data traffic at a mean aggregate rate of 5 Mbps.
The performance measurements considered in our simulations are defined as follows:
Table I. Simulation Parameters
Parameter Symbol Value
Transmission Rate R 100 kbps
The SIFS interval S-IFS 80 µs
The PISF interval D-IFS 160 µs
The DIFS interval P-IFS 240 µs
System
Maximum duration of the
superframe TSF 48 ms
Mean ON Period 1 s
Mean OFF Period 1.35 s
Mean Total Connection Time 120 s
Mean Voice Request Rate 0.001 request/sec
Voice Coding Rate 8.5 kb/s
Voice Traffic Priority High
CBR Voice Traffic
Voice-IFS A-IFSVoice 240 µs
Mean Data Packet Arrival Rate 0.01 Packet/sec
Mean Packet Size 2 kb/s
Data Traffic Priority Low
ABR Data Traffic
Data-IFS A-IFSData 300 µs
Voice request delay: The slot time duration for a voice request from entering the local queue to the beginning of successful transmission.
Voice request blocking probability: The fraction of discarded requests caused by violating the delay bound.
We draw comparisons of performance with respect to voice request access delay, and blocking probability, between ECCA, EDCF and DCF. Simulation was terminated after reaching 95% confidence interval. Simulation results are depicted in Figures 13-16.
To show the fact that the usage of ECCA can increase the system performance, we perform the experiment with 20 CRB voice flows and increasing number of ABR data flows. Figure 13 shows that more ABR data flows can be accommodated in the system, given an acceptable packet drop delay bound (300 slot-times), when EDCF and ECCA are used separately. We further make comparisons of voice request delay among DEF, EDCF and ECCA. In Figure 14, we clearly observe that, DCF scheme has higher request delay than EDCF and ECCA. The reason is that the number high priority traffic (CBR voice traffic) is fixed and EDCF and ECCA can support the real-time request.
In Figure 15, we show the CBR voice request delay with the increasing number of real-time flows. The experiment is performed with 20 ABR data flows and increasing number of CRB voice flows. If the flows have been served and there is residual time in the CFP. In the simulation, when voice flow increasing, DCF and EDCF have the same high blocking probability. And, ECCA got better performance. Moreover, the voice request delay comparison showed in Figure 16 is the same result. From the simulation, we clearly observe that, ECCA significantly outperforms under the same priority traffic load increasing.
Figure 13. Comparison of voice request blocking probability by different methods.
0 50 100 150 200 250
0.0 0.2 0.4 0.6 0.8 1.0
Voice signaling request blocking probability
Number of MTs with data traffic Blocking condition:
request delay > 300 slots
DCF EDCF ECCA
0 50 100 150 200 250
102 103 104 105
Voice signaling request delay
Number of MTs with data traffic DEC
EDCF ECCA
Figure 14. Comparison of voice request delay by different methods.
0 50 100 150 200 250 0.0
0.2 0.4 0.6 0.8 1.0
Voice singaling request blocking probability
Number of MTs with voice signaling requests Blocking condition:
request delay > 300 slots DCF EDCF ECCA
Figure 15. Comparison of voice request blocking probability by different methods.
0 50 100 150 200 250
102 103 104 105
Voice signaling request delay
Number of MTs with voice signaling requests DEC
EDCF ECCA
Figure 16. Comparison of voice request delay without blocked control.
5. Conclusions
In this paper, we have proposed an efficient soft-guarantee-based CAC (SCAC), which combined Q-CAC as a limiter for restricting the number of contending signaling requests, a new MAC algorithm (ECCA) for supporting higher guarantee and better success contending performance to voice signaling traffic, and a pre-constructed database (RMDB) used search system information for Q-CAC. SCAC runs Q-CAC to estimate the MTs with signaling requests (Nˆ ) find out the optimal QoS guaranteed duration (DQoS) based on current system environments. Through searching RMDB, we can determine the admitted number of MTs (Nadm) to compute the admitted parameter (Padm Nadmˆ
= N ) and CCI(1)opt, which is a important parameter used for ECCA. And then, SCAC enter ECCA operation. In ECCA, each MT wishing to contend the channel plays a dice (Padm) to decide join ECCA two phases operation or not. The MTs, which win the dice, follow the two phase rule of ECCA to join AP’s polling table. Through simulation result, we find out that, SCAC can achieve pre-defined soft QoS guarantee for living MTs in AP’s polling table. And even more, when comparing with IEEE 802.11e, we also can observe that ECCA, which is a MAC mechanism in SCAC, provide better QoS guarantee and higher channel utilization for voice signaling requests.
References
[1] IEEE 802.11 WG, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications,” IEEE 802.11 standard,1997.
[2] Lindgren A, Almquist A, and Schelen O. Evaluation, “Evaluation of quality of service schemes for IEEE 802.11 wireless LANs,” Procedings of LCN 2001, Tampa, Florida, USA, Nobember 15-16, 2001.
[3] Y. Kwon, Y. Fan, and H. Latchman, “Design of MAC Protocols With Fast Collision Resolution for Wireless Local Area Networks,” IEEE Tran. Wireless Commu., vol. 3, no. 3, pp. 793-807, May 2004.
[4] G. Daqing, and Z. Jinyun, “QoS Enhancement in IEEE 802.11 Wireless Local Area Networks,”
IEEE Commun. Mag., vol. 41, no. 6, pp. 120-124, June 2003.
[5] IEEE Draft Std 802.11e, Medium Access Control (MAC) Enhancements for Quality of Service (QoS), D5.2, November 2003.
[6] S. S. Tsong, and S. T. Fang, “A Bandwidth Allocation/Sharing/Extension Protocol for Multimedia Wver IEEE 802.11 Ad Hoc Wireless LANs,” IEEE J. Select. Areas Commun., vol.
19, no. 10, pp. 2065-2080, Oct. 2001.
[7] IEEE Draft Std 802.11e, Medium Access Control (MAC) Enhancements for Quality of Service (QoS), D2.0a, November 2001.
[8] M. Veeraraghavan, N. Cocker, and T. Moors, “Support of voice services in IEEE 802.11 wireless LANs,” IEEE INFOCOM, vol. 1, pp. 488-497, April 2001.
[9] A. Veres, A. T. Campbell, and M. Barry, “Supporting Service Differentiation in Wireless Packet Networks Using Distributed Control,” IEEE J. Select. Areas Commun., vol. 19, no. 10, pp.
2081-2093, Oct. 2001.
[10] M. C. Yuang, B. C. lo, and J. Y. Chen, “Hexanary Feedback Contention Access with Pdf-based Multi-User Estimation for Wireless Access Networks,” IEEE Tran. Wireless Commu., vol. 3, pp.
278-289, Jan. 2004.
[11] M. Yuang, and P. Tien, “Multiple Access Control with Intelligent Bandwidth Allocation for Wireless ATM Networks,” IEEE JSAC, vol. 18, no. 9, Sept. 2000, pp. 1658-1669.
[12] S. Valaee, and B. Li, “Distributed Call Admission Control for Ad Hoc Networks,” Procedings of
VTC 2002, vol. 2, pp. 1244-1248, Sept. 2002.
[13] D. Pong, and T. Moors, “Call Admission Control for IEEE 802.11 Contention Access Mechanism,”Procedings of GTC 2003, vol. 1, pp. 174-178, Dec. 2003.
[14] L. Lenzini, M. Luise, and R. Reggiannini, “CRDA: A Collision Resolution and Dynamic Allocation MAC Protocol to Integrate Date and Voice in Wireless Networks,” IEEE JSAC, vol.
19, no. 6, June. 2001, pp. 1153-1163.
[15] N. Passas, S. Paskalis, D. Vali, and L. Merakos, “Quality-of-Service-Oriented Medium Access Control for Wireless ATM Networks,” IEEE Comm. Mag., vol. 35, no. 11, Nov. 1997, pp. 42-50.
[16] L. Alonso, R. Agusti, and O. Sallent, “A Near-Optimum MAC Protocol Based on the Distributed Queueing Random Access Protocol (DQRAP) for a CDMA Mobile Communication System,”
IEEE JSAC, vol. 18, no. 9, Sep. 2000, pp. 1701-1718.
[17] L. Georgiadis, and P. Papantoni-Kazakos, “A Collision Resolution Protocol for Random Access Channels with Energy Detectors,” IEEE Trans. Comm., vol. 30, no. 11, Nov. 1982, pp.
2413-2420.