After Each Unsuccessful Transmission of Packets

Next, we discuss the case of how to change the contention window value after each unsuccessful transmission of packets. Packet collisions of the IEEE 802.11e can be classified into two situations. One is virtual collisions between ACs in a station.

The other is real collisions between stations. We will explain how to deal with each situation.

Fig. 4 is used to illustrate virtual collisions and real collisions. We use an instance to explain. If AC_BK of STA 1 has a packet to transmit and AC_BE of STA 1 also has a packet to transmit, they will separately use their AIFS and the minimum contention window to enter the backoff procedure. If both the backoff timers count down to zero at the same time, a virtual collision occurs. In addition, if AC_BK of STA 1 has a packet to transmit and AC_BK of STA 2 also has a packet to transmit, they will separately use their AIFS and the minimum contention window to enter the backoff procedure. If both the backoff timers count down to zero at the same time, a real collision occurs. In the following, how to handle virtual collisions and real collisions will be described.

Fig. 4. Virtual collision vs. real collision.

Virtual collisions are resolved by allowing the packet with higher priority to transmit, while the packet with lower priority will not modify its contention window values after each unsuccessful transmission of packets. That is, we will not double the current contention window CW ⁱold when the packets with lower priority encounter collisions. Since the collisions are not real collisions and they will not contribute to the average collision rate, we will not double the current contention window CWⁱold. If we double the current contention window CWⁱ_old, the packet with lower priority will have more delay. In this way, the system performance can be improved. That is, after encountering a virtual collision, the CW ⁱ_new of the AC with lower user priority i in a station can be expressed as follows：

iold (6)

inew

CW CW =

Next, collisions occur between stations are referred as real collisions. Real collisions increase packet delay and decrease system performance. We double the current contention window CWⁱoldto avoid subsequent collisions; however, it can not exceed the CW ⁱ_max. Therefore, after encountering a real collision, the CW ⁱ_new of the AC with user priority i in a station can be expressed as follows [6]：

CW min(CW ,CWimax ) (7)

old i new

i = ×2

In sum, our I-EDCA dynamically changes the contention window value after each successful transmission of packets to reduce possible later collision. If collisions still occur, through our method we can reduce the impact to avoid the next collisions and decrease packet delay after each unsuccessful transmission of packets when virtual collisions occurred.

The flowchart of our I-EDCA protocol is illustrated in Fig. 5. When a new packet

is in the head-of-line queue and is ready to be transmitted, the station must check if a channel is idle. If the channel is busy, the station must enter the backoff procedure. On the contrary, if the channel is idle, the station can start to transmit a packet. In the meantime, the station must check if a virtual collision occurs. If the virtual collision occurs, it is resolved by allowing the packet with higher priority to transmit, while the packets with lower priority update their contention window values according to Eq. (6) after each unsuccessful transmission of packets. That is, we will not double the current contention window CWⁱold. On the contrary, if a virtual collision did not occur, the station must further check if a real collision occurs. If the real collision occurs, the station updates the contention window value according to Eq. (7) after each unsuccessful transmission of packets. That is, we must double the current contention window CWⁱold. If a real collision did not occur, the station can successfully transmit the packet and the station must update the contention window value according to Eq.

(5) after each successful transmission of packets. That is, we dynamically adjust the current contention window, CWⁱ_old, according to the average collision rate, R^j_avg.

Note that I-EDCA only requires small overhead in calculating the average collision rate, R^j_avg, and updating the contention window after each successful transmission of packets, and it also provides backward compatibility to the legacy 802.11 DCF.

Fig. 5. Flowchart of the proposed I-EDCA protocol.

Chapter 4

Evaluation and Discussion

We use a popular network simulator, ns-2 [25], running on the Linux platform to simulate our proposed approach. Ns-2 is an open source software and it supports wired and wireless networking protocols. In order to demonstrate the superiority of I-EDCA, we compare it with the other three classical approaches, which have been reviewed in section 2.2, in terms of throughput and packet delay.

4.1 Simulation Model

All simulation results were obtained by running the ns-2 simulator. As referred from [19][20][23][24], assume that there are from 5 to 50 stations in an ad hoc network and each station generates three different types of traffic, namely, high, medium, and low priority traffic. The high, medium, and low priority traffic types are corresponding to AC_VO, AC_VI, and AC_BE, respectively. We use three Constant Bit Rate (CBR) [10][18] sources to simulate the three different types of traffic. The MAC parameters used in the simulation, referred from [1][6][18][21][22], are shown in Table 4. The parameters, CWmin, CWmax, and AIFSN, are the default EDCA parameters, referred from Table 2. Besides, the slot time is set to 9 µs, the SIFS is set to 16 µs, the smoothing factor α is set to 0.8, and the size of a period to update the average collision rate, R ^j_avg, is set to 3000 time slots in this simulation [18].

Table 4：The MAC parameters used in the simulation.

Inter-frame interval (ms) 20 10 12.5

Sending rate (Kbytes/s) 8 128 16

4.2 Performance of I-EDCA

We first compare the throughput of I-EDCA with that of EDCA, AEDCF, and ADB. Simulation results under high, medium, and low priority traffic are shown in Fig. 6, Fig. 7, and Fig. 8, respectively. As shown in Fig. 6, we can see that the four approaches had almost equal throughput performance under high priority traffic.

When the number of stations exceeds 35, AEDCF, ADB, and I-EDCA achieved better throughput than EDCA. The reason is that these three approaches all dynamically adjust the contention window size, which can reduce the collision rate at high load condition.

Fig. 6. Throughput comparison among different approaches under high priority traffic.

The throughput comparison under the medium priority traffic is shown in Fig. 7.

For the medium priority traffic, we can see that the throughput of all four approaches begins to drop when the number of stations exceeds 15. The reason is that at this point the number of stations is too many and there is not enough traffic for handling under the medium priority traffic. We can see that the throughput of I-EDCA is better than that of EDCA, AEDCF, and ADB when the number of stations is more than 10.

Besides, although the throughput of I-EDCA begins to drop when the number of stations exceeds 15, the throughput of I-EDCA is still better than that of EDCA, AEDCF, and ADB even if the number of stations grows up to 50. The reason is that I-EDCA always adjusts the contention window whether each packet transmission is successful or not. In addition, the throughput of EDCA is the lowest due to its

contention window resetting mechanism.

Fig. 7. Throughput comparison among different approaches under medium priority traffic.

In Fig. 8, for the low priority traffic, we can see that the throughput of EDCA, AEDCF, ADB, and I-EDCA all begins to drop when the number of stations exceeds 10. The reason is that at this point the number of stations is too many and there is not enough traffic for handling under the low priority traffic. We can see that the throughput of AEDCF is the lowest compared to that of EDCA, ADB, and I-EDCA when the number of stations exceeds 15. The reason is that AEDCF adopts a large PF, 5, for the low priority traffic, and it increases the waiting time of the low priority packets. Besides, in order to improve the throughput of high priority traffic, I-EDCA sacrifices a little throughput of low priority traffic. This is why the throughput of I-EDCA is lower than that of EDCA when the number of stations is between 25 and

50.

Fig. 8. Throughput comparison among different approaches under low priority traffic.

We compare the aggregate throughput among different approaches in Fig. 9. The aggregate throughput represents the total throughput of high, medium, and low priority traffic. In Fig. 9, we can see that I-EDCA provides better aggregate throughput compared to EDCA, AEDCF, and ADB, especially when the number of stations exceeds 10. This is because for the medium priority traffic the throughput of I-EDCA is much better than that of EDCA, AEDCF, and ADB when the number of stations exceeds 10. In addition, we can see that the aggregate throughput of EDCA performs the worst among these approaches when the number of stations exceeds 10.

This is because for the medium priority traffic the throughput of EDCA is the worst among that of I-EDCA, AEDCF, and ADB when the number of stations exceeds 10.

Fig. 9. Aggregate throughput comparison among different approaches.

In Fig. 10, when the number of stations exceeds 10, we can see that I-EDCA had the smallest packet delay for the high priority traffic among these approaches. This is because it adjusts the contention window whether each packet transmission is successful or not.

The packet delay of the medium priority traffic is also shown in Fig. 10. For the medium priority traffic, due to having the highest collision rate among these approaches, EDCA performs the worst. Besides, we can see that I-EDCA achieves the smallest packet delay among these approaches. The reason is that I-EDCA will not double its contention window when the packets with lower priority encounter internal collisions in a station. Although AEDCF also dynamically adjusts its contention window after each successful transmission of packets, it adopts a large PF, 4, for the

low priority traffic. Consequently, AEDCF has longer packet delay than I-EDCA, especially when the network load is high. Note that we did not plot the packet delay of the low priority traffic. This is because the packet with low priority is delay tolerable.

Fig. 10. Packet delay comparison among different approaches.

Besides, we compare the average throughput of I-EDCA with that of EDCA, AEDCF, and ADB in Fig. 11. The average throughput is the average of aggregate throughput for the number of stations from 5 to 50. From the simulation results, we can see that our proposed I-EDCA performs better than the other three approaches, EDCA, AEDCF, and ADB. The average throughput of our proposed I-EDCA is 9%, 11%, and 15% greater than that of AEDCF, ADB, and EDCA, respectively. In addition, for high priority traffic, compared to ADB, AEDCF, and EDCA, I-EDCA

shortens the average packet delay by 22%, 26%, and 40%, respectively. In sum, the simulation results have shown that the proposed I-EDCA can indeed improve the throughput and packet delay of IEEE 802.11e.

Fig. 11. Average throughput comparison among different approaches.

Chapter 5

Conclusions and Future Work

5.1 Concluding Remarks

In this thesis, we have presented a better service differentiation scheme, I-EDCA for QoS enhancement to IEEE 802.11e EDCA. The basic idea is that I-EDCA dynamically adjusts the contention window size according to the average collision rate after each successful transmission of packets. Besides, once a virtual collision

occurs, I-EDCA retains its contention window size of the AC with lower priority after each unsuccessful transmission of packets. Although I-EDCA has the overheads of calculating the average collision rate and updating the contention window after each successful transmission of packets, it effectively reduces the contention window size according to different user priorities, and enhances the performance in terms of average throughput and packet delay. Simulations results have shown that the average throughput of our I-EDCA is 9%, 11%, and 15% better than AEDCF, ADB, and EDCA, respectively. In addition, for high priority traffic, compared to ADB, AEDCF, and EDCA, I-EDCA shortens the average packet delay by 22%, 26%, and 40%, respectively. Therefore, I-EDCA is effective in supporting the QoS of IEEE 802.11e wireless LANs.

5.2 Future Work

We will dynamically adjust Persistence Factor (PF) after each unsuccessful transmission of packets according to the previous calculated average collision rate to further improve the overall performance. Besides, we will use different MAC parameters, such as CW_min, CW _max, and frame size, to enhance the I-EDCA.

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