^{1}

^{2}

^{1}

^{2}

This paper is focused on the problem of optimizing the aggregate throughput of the distributed coordination function (DCF) employing the basic access mechanism at the data link layer of IEEE 802.11 protocols. We consider general operating conditions accounting for both nonsaturated and saturated traffic in the presence of transmission channel errors, as exemplified by the packet error rate

DCF represents the main access mechanism at the medium access control (MAC) layer [

Many papers, following the seminal work by Bianchi [

Let us provide a survey of the recent literature related to the problem addressed in this paper. Papers [

The behavior of the DCF of IEEE 802.11 WLANs in unsaturated traffic conditions has been analyzed in [

The effect of the contention window size on the performance of the DCF has been investigated in [

In [

Paper [

As a starting point for the derivations that follow, we adopt the bidimensional model proposed in a companion paper [

Compared to the works proposed in the literature, the novel contributions of this paper can be summarized as follow. Firstly, we investigate the behavior of the aggregate throughput as a function of the traffic load

The second part of this paper is focused on the optimization of the DCF throughput under variable loading conditions. We propose a cross-layer algorithm whose main aim is to allow the network to operate as close as possible to the link capacity

The rest of the paper is organized as follows. Section

The bidimensional Markov Process of the contention model proposed in [

Markov chain for the contention model accounting for unsaturated traffic conditions. The model considers

An idle state, identified by

The proposed Markov model accounts for packet errors due to imperfect channel conditions, by defining an equivalent probability of failed transmission, identified by

The Markov Process depicted in Figure

The stationary distribution of the Markov Model in Figure

Given

Let us spend few words on the evaluation of

The other terms involved in

Typical network parameters.

MAC header | 24 bytes | Slot time | 20 |

PHY header | 16 bytes | SIFS | 10 |

ACK | 14 bytes | DIFS | 50 |

EIFS | 300 | ||

32 | ACK timeout | 300 | |

m | 5 | CTS timeout | 300 |

The computation of the normalized system throughput relies on the numerical solution of the nonlinear system obtained by jointly solving (

The two rightmost subplots of Figure

Throughput for the 2-way mechanism as a function of the packet rate

We adopted the patch NOAH (

Let us focus on the curves noticed in the two rightmost subplots in Figure

Indeed, as

Once again, let us focus on the results shown in the rightmost subplots of Figure

On the other hand, the aggregate throughput tends to be upperbounded by

The critical value of

Let us investigate the behavior of the critical value

Figure

Behavior of

Behavior of the aggregate throughput as a function of

As long as the packet size increases, the aggregate throughput shows an increasing slope in the linear region characterized by traffic loads

Similar considerations may be derived from the behavior of

Behavior of

Figure

Behavior of

The considerations deduced in Section

The next two subsections address separately the two optimization strategies, while Section

The first optimization strategy proved to be effective for improving the aggregate throughput in the LC region, that is, for

Upon considering saturated conditions, that is, imposing

Finally, by equating (

Moreover, we notice that for

In the following, we present simulation results accomplished in NS-2 for validating the theoretical models, as well as the results presented in this section. The adopted MAC layer parameters for IEEE802.11b are summarized in Table

The main simulation results are presented in Figures

Saturation throughput as a function of the minimum contention window size

Some observations are in order. Let us focus on the results shown in Figure

Notice also that the value of

The key observation from the subplots of Figure

Let us focus on the results shown in Figure

The saturation throughput in each simulated scenario reaches a maximum corresponding to the abscissas ^{5} bps irrespective of

Similar considerations can be drawn from Figure

Saturation throughput as a function of the minimum contention window size

The analysis of the aggregate throughput in Section

Before proceeding any further, let us discuss two important issues in connection with the choice of the packet size

As long as the erroneous bits are independently and identically distributed over the received packet (We notice that wireless transceivers make use of interleaving in order to break the correlation due to the frequency selectivity of the transmission channel [

We notice that the relations (

From (

The second issue to be considered during the choice of

Let us discuss a simple scenario in order to reveal this issue. (Where not otherwise specified, we employ the network parameters summarized in Table

Based on the considerations deduced in the scenario A, the proposed optimization technique aims to optimize the payload size in two consecutive steps. In the first step, we find the size of the payload in such a way that the critical threshold

The second step verifies whether the packet error rate associated to this payload size, is below a predefined PER-target (identified by

Let us consider again the scenario A discussed previously with the application of this algorithm. Consider 10 stations transmitting packets of size

Since

Finally, the optimal payload size is

Simulation results of a sample network employing the algorithm described above, are presented in the next section, along with a sample code fragment summarizing the key steps of the optimization technique.

In this section, we present simulation results for a network of

The basic steps of the proposed optimization algorithm are summarized in Algorithm

(1)

(2) Estimation of PER and N

(3) Evaluate

(4)

(5)

(6)

(7)

(8) The station evaluates

(9)

(10)

(11) compute

(12) compute

(13) select

(14)

(15) send packet

The evaluation of the critical value

Finally, the value of the optimal

We have realized a C++ simulator implementing all the basic directives of the IEEE 802.11b protocol with the 2-way handshaking mechanism, namely exponential backoff, waiting times, post-backoff, and so on. In our simulator, the optimization algorithm is dynamically executed for any specified scenario. For the sake of analyzing the effects of the optimization, the instantaneous network throughput is evaluated over the whole simulation. The aggregate throughputs obtained in the investigated scenarios are shown in Figure

Aggregate throughput of a network with a maximum number of stations equal to 10, transmitting at the fixed bit rate 1 Mbps. Curve

In the first scenario, we considered a congested network in which 10 stations transmit packet of fixed size 1028 bytes at the packet load ^{5} bps. After 40 s, 5 out of the 10 stations turn their traffic off. Between 40 s and 80 s, the aggregate throughput increases to about 8.2 × 10^{5} bps because of the reduced effect of the collisions among the contending stations. After 80 s, the 5 stations turn on again and the aggregate throughput decreases to 7.6 × 10^{5} bps.

Consider again the same scenario over a time interval of 120s, whereby the contending stations adopt the optimal contention window. Upon using (

The aggregate throughput in the optimized scenario is about

Consider another scenario differing from the previous one in that the contending stations operate in the BLC region. As above, 10 stations contend for the channel in the time intervals [0,40] s and [80,120] s, while during the time interval [40,80] s 5 out of the 10 stations turn off. The traffic load is

Let us focus on the aggregate throughput depicted in Figure

Simulation results show that the proposed algorithm guarantees improved throughput performance on the order of 160 kbps when 10 stations are active, and about 400 kbps when only 5 stations contend for the channel. We notice in passing that, despite the optimization, the aggregate throughput could not reach the maximum

For the sake to verify that the optimization of the minimum contention window does not affect the aggregate throughput in the BLC region, Figure

This paper proposed an optimization framework for maximizing the throughput of the distributed coordination function (DCF) basic access mechanism at the data link layer of IEEE 802.11 protocols. Based on the theoretical derivations, as well as on simulation results, a simple model of the optimized DCF throughput has been derived. Such a model turns to be quite useful for predicting the aggregate throughput of the DCF in a variety of network conditions.

For throughput modeling, we considered general operating conditions accounting for both nonsaturated and saturated traffic in the presence of transmission channel errors identified by the packet error rate