recACK record whether ACK is missed;
recCT S record whether CTS is received;
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Figure 3.1: The average transmission time with respect to packet error probability for different MCSs.
3.3 Maximum Likelihood Estimator
There are two possible outcomes of a frame transmission, success s and failure f. The sample space contains two events, s and f. Let X be a random variable defined by X(s) = 1 and X(f) = 0. The frame success probability is defined as
3. PROPOSED ALGORITHM
Table 3.1: Main Steps of the proposed rate adaptive algorithm
Summary of the proposed algorithm
initialize: Num[i]=0,count=9,recACK=1,recCTS=0 1. repeat
2. if ACK is missed then 3. recACK = 0;
4. if CTS is received then 5. recCTS = 1;
14. else FSR[i] = ACK[i]/Num[i];
15. get T x time[i] from Fig.??;
3.3 Maximum Likelihood Estimator
the conditional probability that a frame transmission would be successful given a modulation and coding scheme MCSi. It is denoted as pi =P (X = 1|MCSi).
Suppose that there are m MCSs. When a station has a frame to transmit, we are interested in the frame success probabilities of this frame for allm MCSs.
Although this frame is not transmitted yet, an estimated frame success probability pˆi for i = 1,...,m could be obtained by a maximum likelihood estimation method.
The collected samples of X are separated according to each individual MCS, so that there are m sets, denoted as D1, ...Dm. Suppose that Di contains n samples, x1, ..., xn. In Di, α of the samples are 1’s, and the others are 0’s. Since the samples are obtained independently,
p(Di|MCSi) =
n k=1
p(X = xk|MCSi) =pαi × (1 − pi)(n−α) (3.9)
We define a log-likelihood function l(pi) as
l(pi)≡ ln p(Di|MCSi) =α ln pi+ (n − α) ln(1 − pi) (3.10)
The maximum likelihood solution could be written as the argumentpi that max-imizes the log likelihood, i.e.,
pi = arg max
pi l(pi). (3.11)
3. PROPOSED ALGORITHM
Chapter 4
Simulation Set-up
We evaluate the performances of the proposed algorithm via extensive computer simulations using Qualnet 4.0, which provide many wireless protocols and channel models. [16]. We implement several well-known algorithms, such as AARF [12], CARA [13], RRAA [9], SampleRate [11], SLA [17], and SARA [14]. We take 802.11b as MAC protocol. For reality, several time-varying wireless channel mod-els are considered. We use the two-ray ground reflection model as the large scale fading model and Rayleigh and Ricean fading model (with Ricean factor of 3) as the small scale fading model. We set the traffic CBR in a greedy mode, which means a station always has the data to send, at a duration of 100sec and the pay-load size is 1500 bytes. Four scenarios are devised to exploit the characteristics of rate adaptive algorithms: 1) single transmission link with the fixed distance 2) single transmission link with different distances; 3) several transmission links with the same distance in a infrastructure mode; 4) several transmission links with different distances and mobilities in an infrastructure mode. And then we evaluate the performance of each algorithms in 802.11-based mesh network. The topology of the wireless mesh network is shown in Fig. 4.1. Mesh Portal Point is
4. SIMULATION SET-UP
placed at the black node (in the center of the leftmost stations). Three different distances between stations are chosen in order to evaluate the decision flexibil-ity of various algorithms. Since the transmission range of 11 Mbps is about 340 meters, the experiments are performed in three scenarios of (1) 250 meters (the best fixed transmission rate is 11 Mbps), (2) 370 meters (the best fixed rate is 5.5 Mbps), and (3) the mixed distances of 250 and 370 meters. The distances are chosen because, for a single transmission link, the 11 Mbps performs the best in 250 meters and 5.5 Mbps is the best in 370 meters during a static channel condition.In addition, to exploit the responsiveness to sudden changes in channel conditions, three different channel models, including (1) large-scale fading, (2) Ricean fading with Ricean factor of 3 , and (3) Raleigh fading, are utilized. Con-stant Bit Rate traffic of a 1500-byte packet sent every 40 mili-second is adopted in the experiments. The simulation time for each scenario is 60 seconds and the routing protocol of AODV is applied.
Figure 4.1: The simulation topology of a WMN.
Chapter 5
Simulation Results
5.1 Single Transmission Link with the Fixed Dis-tance
Table 5.1: The throughput of each algorithms in Fig.5.1, Fig.5.2, Fig.5.5, and Fig.5.7
Algorithms (1) (2) (3) (4) (5) (6)
AARF 3.81 Mbps 0.73 Mbps 0.97 Mbps 0.85 Mbps 0.83 Mbps 0.39 Mbps CARA 3.69 Mbps 0.75 Mbps 0.89 Mbps 0.56 Mbps 0.35 Mbps 0.23 Mbps RRAA 2.53 Mbps 1.48 Mbps 0.29 Mbps 2.51 Mbps 0.29 Mbps 0.07 Mbps SampleRate 3.85 Mbps 1.06 Mbps 5.24 Mbps 3.23 Mbps 0.25 Mbps 0.14 Mbps Proposed 3.86 Mbps 1.39 Mbps 5.80 Mbps 4.04 Mbps 2.04 Mbps 0.78 Mbps SARA 3.07 Mbps 1.18 Mbps 3.84 Mbps 2.27 Mbps 0.30 Mbps 0.20 Mbps SLA 2.93 Mbps 1.17 Mbps 3.77 Mbps 2.26 Mbps 0.30 Mbps 0.20 Mbps a. (1) (2) is the throughput of Fig.5.1 and Fig.5.2.
b. (4) is the throughput of Fig.5.4 with 15 nodes and (3) is (4) without Ricean fading.
c. (6) is the throughput of Fig.5.6 with 15 nodes and (5) is (6) without Rayleigh fading.
In this scenario, two stations are placed with the distance of 280 meters. In this situation, the rate 5.5 Mbps is the best choice. In this experiment, we want to investigate the rates chosen by different algorithms when only the large scale
5. SIMULATION RESULTS
AARF CARA RRAA SampleRate Proposed SARA SLA
0
Figure 5.1: The percentage of every rate chosen by algorithms without small scale fading.
5.1 Single Transmission Link with the Fixed Distance
AARF CARA RRAA SampleRate Proposed SARA SLA
0
Figure 5.2: The percentage of every rate chosen by algorithms with Rayleigh fading.
5. SIMULATION RESULTS
fading, without the small scale fading, is used. Fig.5.1 shows the result of the percentage. It can be observed that the proposed algorithm and SampleRate can select 5.5 Mbps above 90%, and the proposed algorithm is even a little higher than SampleRate. SARA and SLA use stochastic automata, so both of them rarely choose 2Mbps and 1Mbps. One thing worth mentioning is that the rate chosen by RRAA would oscillate between 5.5 Mbps and 11 Mbps and that is the reason why RRAA has large percentage to choose 11 Mbps. This phenomenon is consistent with the problem of ping-pong effect for RRAA as reported in the literature [19].
Then the channel, not only the large scale fading but also Rayleigh fading, is considered. The purpose is to evaluate the decision flexibility and responsive-ness of various algorithms to explore the short term characteristics of channel dynamics. Because the channel is varying dramatically with Rayleigh fading, the rate of 5.5 Mbps is not always the best choice at some time. When the channel is getting better, 11 Mbps would become the best choice. While the channel is getting worse, transmissions may be failed with the rate 5.5Mbps. From Fig.5.2, we can see that this time the proposed algorithm doesn’t choose the rate of 5.5 Mbps with a high percentage, but SampleRate still selects 5.5 Mbps for almost 90%. When the channel is better, the proposed algorithm would choose 11 Mbps, and select 2 Mbps or 1 Mbps instead while the channel is bad. This can explain why, in Fig.5.3, it shows that the performance the proposed algorithm does ac-tually better than SampleRate does. AARF and CARA take 1Mbps for most of the time because it is difficult for their mechanism to select a high data rate in a noisy environment.