Simulation Results - QoS control in wireless IP access networks

We have carried out several simulations in OPNET which is an event-driven simulation tool.

The date rate is²⁰Mb/s and the carrier frequency is⁵

:

²GHz. As before, the data and control packets have⁵⁴ and⁹bytes respectively. We use a BPSK modulation scheme. The average SNR is ³⁴ dB corresponding to an average bit error probability of¹⁰^;4. All the receivers are located within a distance of ²³ meters from the AP. The wireless channel is modeled by a 3 state Markov model in the OPNET environment. The state

s

⁰ of the Markov model corresponds to a Bad state with

e

⁰ ¹, the state

s

¹corresponds to an intermediate state with a non-zero error probability

e

¹ ²¹⁰^;5 and the state

s

²corresponds to a Good state with a zero error probability

e

² ⁰. In order to have an error probability of

e

⁰ ¹^,

¹ ^must

be equal to²dB in BPSK. For a zero error probability

e

² ⁰ ^{in state}

s

², we also need to fix

² ^at ³⁴dB in BPSK. Knowing the threshold values of the Markov model, all the other parameters can be easily found as in Section 7.2.

1 2 3 4 5 6 7 8 9 10

Figure 7.2: Simulation results for

PLR

⁼^50%

For each scenario, we have carried out ten different simulations, each with a different seed. Figure 7.2 compares the efficiency and the packet loss rate of our proposed adaptive scheme with a pure ARQ protocol, an ARQ/FEC protocol using

RSE

⁽⁶⁰

;

⁵⁰⁾ and another hybrid protocol using

RSE

⁽⁷⁰

;

⁵⁰⁾. The number of receivers is fixed at ¹⁰⁰⁰. This figure corresponds to a scenario where non-real-time traffic such as data is transmitted over the network. The protocols try to retransmit a packet until it is correctly received by all receivers.

We have chosen a

PLR

⁼ ^50%in order to reduce the retransmission rate by a half in case

7.7. CONCLUSION 129 of adaptive scheme. From this figure, we can observe that the adaptive scheme provides the best efficiency. It also has a

PLR

^{less than}^50% as it was expected. Although other fixed hybrid protocols provide better packet loss rates, they have a lower efficiency compared to our adaptive scheme.

Figure 7.3: Simulation results for a packet life time of 100 msec

Figure 7.3 compares the efficiency and the average delay of our proposed adaptive scheme with the same error control mechanisms as in the previous figure. The number of receivers is again ¹⁰⁰⁰. This scenario corresponds to a traffic with delay constraints. The maximum lifetime of each packet of this traffic is fixed at 100 msec. We observe that our proposed adaptive scheme provides the lowest average delay while maximizing the efficiency. The other hybrid ARQ/FEC protocols also provide an average delay lower than 100 msec but they have a lower efficiency than our protocol.

Figure 7.4 depicts the average delay and the packet loss rate of our adaptive algorithm and the same error control protocols as before as a function of the number of receivers. The packet life time is fixed at⁵⁰msec and the packet loss rate is^10%. Our adaptive mechanism can guarantee an average delay of ⁵⁰msec up to⁶⁰⁰ receivers while the ARQ mechanism, for example, can guarantee this delay up to ³⁰ receivers as it can be seen in the first plot.

The packet loss rate is also less than^10%up to²⁰⁰receivers in our adaptive scheme and less than²⁰receivers in the case of ARQ protocol.

7.7 Conclusion

In this chapter, we proposed an adaptive algorithm capable to switch between an ARQ and a set of ARQ/FEC error control protocols. The coding scheme used in the ARQ/FEC protocols is based on RSE codes. The adaptive algorithm chooses the best error control mechanism as a function of the channel bit error rate, the channel state of the receivers and the desired QoS metric of the session while maximizing efficiency. We used a finite state Markov chain as

130 CHAPTER 7. QOS-BASED ADAPTIVE ERROR CONTROL

Figure 7.4: Simulation results for a packet life time of 50 msec and a

PLR

^of^10%

our wireless channel model. This model allowed us to predict the future states of the channel for each receiver based on its current channel state. Simulation results showed that the use of FEC improves significantly the performance of error control mechanisms specially when there is a high number of receivers listening to the same session. It also showed that the use of adaptive mechanism is quite useful in order to save bandwidth while maintaining the QoS metrics of a session below their thresholds.

Several issues need more investigation:

What is the effect of the feedback losses? During our analysis and simulations, we assumed that no control message is lost.

What is the effect of other QoS metrics? We considered efficiency, packet loss rate and average delay for our analysis. Other QoS metrics such as jitter, dropping rate and power consumption of mobile terminals must also be considered.

How is the channel state information of the receivers available at the sender? During our analysis and simulations, we assumed that the sender is informed about the channel state of all the receivers. However, we did not specify the exact mechanism. This information may be available at the sender either by the receivers informing the sender about their SNR or by the sender itself making an estimation of the number of receivers at each state. In the first case, the format of NAK messages can be modified in order to include the receiver SNR at the end of the reception of a block. In case a receiver has received all the packets correctly, it has to send an extra control message to the sender in order to inform it about its channel state. In the second case, the sender may estimate the channel state of all receivers based on their number of lost packets in a block.

How does our proposed adaptive algorithm perform in a real wireless channel model?

Within the WAND project, a Stochastic Radio Channel Model (SRCM) for broadband

7.7. CONCLUSION 131

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