The RTS/CTS-based access mechanism provides positive control over the medium in order to minimize the collisions caused by the hidden stations. Fig.2-9 shows the hidden terminal problem. For example, B is in the transmission range of C, but the others are not. B and D are in the transmission range of C, but A is not. A and C are unaware of each other since their signal do not carry that far. So their frames may collide with each other at B. But unlike an Ethernet, neither A nor C can be aware of the collision. A and C are called “hidden nodes” with respect to each other.
Fig.2-9 hidden node problem
RTS/CTS-based access mechanism can solve the hidden node problem. If A has a frame to B, A will send a RTS frame first. When B receives the RTS frame, B will return a CTS frame that contains a time value that alerts other stations to hold off from accessing the medium. After A receives the CTS frame from B, A will begin to send its data frame. Adding the RTS/CTS access mechanism will increase redundancy.
But if the data frame is always large like aggregated frames, the RTS/CTS access mechanism can reduce the collision cost instead. Fig.2-10 shows the RTS/CTS access mode and Basic access mode.
RTS CTS DATA ACK
SIFS SIFS SIFS DIFS
RTS
SIFS
CTS_Timeout DIFS 1) RTS/CTS access mode
2) Basic access mode
DATA
SIFS
ACK
DIFS
DATA
SIFS
ACK_Timeout
DIFS
Successful Transmission
Collision Transmission
Successful Transmission
Collision Transmission
Fig.2-10: RTS/CTS access mode and Basic access mode
Chapter 3 Related Work
Chapter 3 Related Work
With the rapid deployment of the IEEE 802.11 WLANs, there are many studies of contention-based DCF medium access function. In order to reduce the collision probability, the DCF applies a collision avoidance mechanism called backoff procedure. Most of the studies are supposed a saturated WLAN. The saturated throughput or channel utilization are the maximum load that the system can carry in a saturated condition. The definition of a saturated WLAN can be found in the papers [4]
[8] [9]. This basic performance figure indicates the limit throughput when the offered traffic load increases.
In the paper [4], it presents an analytic model for computing the capacity of an infrastructure IEEE 802.11 WLAN enhanced with the support of the bidirectional MAC frame aggregation. The analytic model helps us to understand the performance gain of bidirectional aggregation and serve as the foundation for the future aggregation scheduler development.
In the paper [5], this paper uses an analytical model to study the channel capacity when using the basic access (two-way handshaking) method in this analysis. The important contribution in this paper is that it provides closed-form approximations for collision probability p, the maximum throughput S and the limit on the number of stations in a wireless cell. p and S depend on the minimum window size W and the number of stations n only through a gap g = W/(n-1). Consequently, halving W is like
doubling n. The maximum contention window size has minimal effect on p and S. The choice of W that maximizes S is proportional to the square root of the packet length.
The results of this paper can suggest guidelines on when and how W can be adjusted to suit the measured traffic.
In the paper [14], the author first considers the enhanced DCF access method of IEEE 802.11e. The analytical model can be used to calculate the traffic priority and throughput corresponding to the configuration of multiple DCF contention parameters under the saturated WLAN.
In the paper [15], it provides an analytical model to evaluate the saturation throughput of the IEEE 802.11e EDCA. The analytical model is based on the use of the mean value analysis. It also models accurately the effects of the change of the contention window size and Arbitration Interframe Space (AIFS). This model is applicable to real-time system tuning and on-line admission control algorithms that need a low computation complexity.
In the paper [14], most features of the EDCA such as virtual collision, different arbitration interframe space (AIFS) and different contention window are considered.
The throughput and mean delay of differentiated service traffics are analyzed with using the Markov chain model Fig.3-1.
Chapter 3 Related Work
Fig.3-1 Transition diagram of discrete time Markov chain model for one AC per station
Chapter 4
Unidirectional Traffic Flow
To reduce the complexity of analysis, the saturated WLAN [4] [8] [9] is considered in the following sections. A saturated WLAN has the following properties:
(1) All stations and the AP always have a nonempty queue of data frames to transmit.
(2) The traffic distribution to all stations from the AP is uniform. Its meaning is that each station has the same probability to be the destination when the AP wants to send a frame.
(3) The AP always has at least one frame destined to each station waiting in its queue.
Suppose there are N-1 stations and one AP in the saturated WLAN. They have the equal probability 1
N to contend for the channel successfully. Fig.4-1 and Fig.4-2 show the scenario of the unidirectional traffic flow. If one station sends an aggregated data frame to the AP. The AP will return the Ack frame to the AP. This is called unidirectional traffic flow. In this thesis, we focus on the case there is at most one aggregated frame as shown in Fig.4-2. Multiple MAC frames carried in a single physical frame is called aggregation or an aggregate. RTS/CTS-based contention access can reduce the collision cost. The channel access in the saturated WLAN is contention-based, e.g. distributed coordination function (DCF) and enhanced DCF (EDCF) as in [10] [11]. Centrally-controlled channel access, e.g. Point Coordination Function (PCF) and HCF Controlled Channel Access (HCF) as in [12] [13] is not considered.
Chapter 4 Unidirectional Traffic Flow
Fig.4-1: WLAN Scenario – Unidirectional Traffic flow
Fig.4-2: Unidirectional Traffic flow with aggregation
4-1 Our Method for Calculating the Channel Utilization
If a collision happens in the channel, the channel will pay for this collision as shown in
channel collision EmptySlots OurAnalysis RTS CTS
T = W × +T + T +SIFS+ τ +DIFS (4.1)
RTS_timeout CTS 2
where T =T +SIFS+ τ
Fig. 4-3 Tchannel_collision
If a successful transmission happens in the channel, the channel will pay for this successful transmission as shown in
_ channel success
T Fig. 4-4.
_ _ 20 _ 3 4
channel success EmptySlots OurAnalysis RTS CTS phy data ACK
T =W × +T +T +T +T + SIFS+ τ DIFS
Chapter 4 Unidirectional Traffic Flow
Fig. 4-4 Tchannel_successfor unidirectional traffic flow
channel
p is the probability that a collision happens to the medium. is the probability that a collision happens to a station. The channel utilization can be represented as below:
channel channel collision channel channel success
p T following sections. From the result in [5] and the equation (4.21),
channel
In the paper [5], the rsuccess is the rate of successful packet transmissions and the is the rate of packet transmissions (including packet collisions). Then the average number of transmission per packet is
rxmit
Suppose one channel_collision_num contains two packet_collision_num . So the
rate of channel collisions rcollision is given by
(4.6) rxmit −rsuccess =2rcollision
cycle
T is the time between two payload transmissions – and consist of successful and collided transmissions.
(4.7) 1
From equations (4.4)-(4.6), we can get a conclusion
(4.8)
Next we will explain how to estimate the average value ( ) of the backoff time between two transmissions in the medium in the chapter 4-2.
EmptySlots
Chapter 4 Unidirectional Traffic Flow
4-2 The Proposed Method of Paper [4] for Calculating the Channel Utilization
In this paper [4], the channel utilization can be expressed as below:
(4.12) 1
v paper
µ = t
Where is the average period between two successful transmissions, which is defined in [9]. It is also called and is the average data frame size (MAC data frame size in this thesis) successfully transmitted during
.
tv
virtual transmission time mv
tv Fig.4-5 shows the concept of a virtual transmission time.
Fig.4-5 a virtual transmission time
One STA contends for the channel successfully
_ _ 1
TTXOP is the time duration for a TXOP in a virtual transmission time.
(4.14) TTXOP =TRTS +TCTS +Tphy_data +TACK + ×3 SIFS+ ×4 τ
where Tphy_data is the average transmission time for a physical data frame
i
T T SIFS T
TColl is the duration of the i-th collision in a virtual transmission time.
(4.15) T T where
Chapter 4 Unidirectional Traffic Flow
4-3 How to Calculate WEmptySlots
A station would select the number of slots at random out of an interval between 0 and contention window (CW). The backoff time would be selected from a larger range when a transmission fails. Each time the retry counter increases, the CW moves to the next greatest power of two and the size of CW is like the equation as below:
(4.18) 2 CW = m CWmin+2m−1
where CWmin is the minimum contention window
Define be the average number of backoff slots experienced by a packet until it is transmitted successfully or discarded in the saturated WLAN with unidirectional traffic flow
1 1
where K is the maximal retransmission number
Define be the average transmissions experienced by a packet until it is transmitted successfully or discarded in the saturated WLAN with unidirectional traffic flow.
W = x , we get the average time that a station makes a transmission.
(4.21) 1 1
Chapter 4 Unidirectional Traffic Flow
Next we will discuss three types of WEmptySlots as below:
4-3-1 The Proposed Method of Paper [4]
In the long term, the AP and each STA will get a fair share of channel accesses for transmitting their frames, and in particular, the average period of virtual transmission time for the AP and each STA is N × . In this paper [4], it concludes a conclusion that the total empty slots in a virtual transmission time as below:
tv
Random variable is the number of collisions in a virtual transmission time and it is a geometrical distribution. So its mean can be calculated as below:
Nc
The backoff slots between two transmissions in the medium can be calculated from the equation as below:
(4.26)
4-3-2 The Proposed Method of Paper [5] [6]
At a saturated WLAN, most transmission are preceded by a minimum backoff of
; when N stations uniformly choose a time in , the separation between choices has mean
CWmin CWmin
min
1 CW
N+ . In particular, the station that picks the earliest slots breaks the channel silence after the time min
1 CW
N + . So we can express the equation as below:
(4.27)
min
_ 2
W = N
EmptySlots paper 1
CW +
Chapter 4 Unidirectional Traffic Flow
4-3-3 Our Analytic Method
Every station will make a transmission for every in average. After the DIFS time interval, some station must count down its backoff counter from . Please see the
unith W
Wuni
Fig.4-6. Suppose other stations will count down their backoff counter form r.v.
{
X X1, 2,",XN−1}
. We model{
X X1, 2,",XN−1}
by Uniform distribution from [0,Wuni]. Let Y = min{
X X1, 2,",XN−1}
and Y means the backoff slots between two transmissions in the medium. Its cumulative distribution function, , can be calculated as below:Its probability density function, fY( )y , can be calculated as below:
(4.29)
The mean of Y can be calculated as below:
Therefore, the backoff slots between two transmissions in the medium as below:
(4.31) EmptySlots OurAnalysis_ uni