A new Approach for Interference Measurement in 802.11 WLANs

Murad Abusubaih

College of Engineering and Technology

Palestine Polytechnic University

Hebron - Palestine

murads@ppu.edu

Abstract

In Wireless Local Area Networks (WLANs)[3], the esti-

mation of interference conditions at a receiving node and

the determination of interference relations among different

wireless nodes is a challenging problem. Interference rela-

tions provide the information of which wireless links do in-

terfere if activated concurrently. This information is crucial

for the determination of which wireless links are allowed

to access the channel concurrently in a time slotted access

scheme. Estimate of interference conditions at a WLAN

node characterizes the impact of interference on the abil-

ity of this node to correctly receive packets. This is also

needed for numerous WLAN interference management poli-

cies. This paper proposes a method for interference mea-

surement and estimation. Interference measurement will be

accomplished through passive observation of the wireless

channel. The proposed approach is demonstrated through

extensive simulation and real experiments.

1 Introduction

In wireless communication, the signal attenuates with

distance as it propagates from the transmitter over the

channel. The signal also undergoes other different at-

tenuations while traveling in the wireless environment

(e.g. passing through different types of obstacles). At the

receiver side, the receiver attempts to decode the attenuated

signal. The ability of a receiver to correctly receive the

transmitted signal depends on the sum of the noise and the

total interfering power measured at the receiver (during

the reception of the desired signal) relative to the power

level of the desired signal being received. The Signal to

Interference and Noise Ratio (SINR) is a commonly used

value that determines whether a wireless node can correctly

decode a transmitted signal in presence of noise and some

other concurrent interfering ones. The transmission can be

successfully received only if this ratio is sufficiently high.

Nowadays, WLAN administrators deploy many ac-

cess points (APs) to better cover a given area and improve

users connectivity with APs. The closer the user to an AP,

the larger the physical transmission rate the WLAN adapter

is expected to employ. Due to the lack of non-interfered

channels the 802.11 standard supports, this kind of dense

deployment will likely result in the Inter-BSS interfer-

ence unless the network is carefully planned and tuned.

Inter-BSS interference is a phenomenon where signals

transmitted from one BSS spread to a neighboring BSS

that operate over the same channel. Such a coincident in

time signal may corrupt the frame under reception if its

strength is comparable relative to the signal strength of the

frame being received. Similarly, signals from neighboring

BSSs on the same channel can prevent local nodes from

transmitting their frames, even if intended receivers might

not interfere. This is known as Exposed Node Problem.

This problem stems from the fact that the CSMA/CA

provides information about potential collisions at the

transmitter side and not at the receiver.

In fact, the real impact of interference depends both

on the strength of interfering signal and the frequency of

the interference event (i.e. How active interferers are?).

The later strongly depends on the traffic profile.

In this paper, a realistic approach for interference

measurement is proposed. The approach is based on

passive measurements to be conducted while the network

is operating. This trend is in line with 802.11 standards

which develop mechanisms to facilitate measurements

during network operation (e.g. the 802.11k standard [1]).

The approach was used within our interference mitigation

framework recently published in [10]. In this paper, our

intention is to detail the measurement approach and publish

the results of the real experiments conducted to assess its

potential.

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

978-1-4244-8016-6/10/$26.00 ©2010 IEEE 2336

While the provided results are based on RSSI mea-

surements (in addition to other parameters), the Received

Channel Power Indicator (RCPI), standardized in 802.11k,

seems to be more accurate in representing the strength of a

received packet. The RSSI value is defined in the 802.11

standard as a measure of the power level observed at the

receiver antenna, measured during the PLCP (Physical

Layer Convergence Protocol) of an arriving packet [4]. In

contrast, the RCPI value is measured over the entire frame

at the antenna connector used to receive that frame.

The remainder of this paper is organized as follows:

Section 2 discusses the related work. Section 3 details the

proposed interference measurement approach. Experimen-

tal results are presented and discussed in section 4 before

we conclude the paper in section 5.

2 State of the Art

Interference measurement and modeling is critical for

the performance analysis of wireless networks. In fact,

measuring the interference level while a node is receiving a

signal is impossible. Therefore, studies in the literature fol-

low two common approaches to characterize the effect of

interference while modeling wireless communication sys-

tems:

• Approximating interference using simple models.

• Performing active interference measurements that are

shifted in time.

The most widely used models are proposed by Gupta and

Kumar [11]. Next, we discuss the features of each model.

1. The Simple Interference Model:

With this model, the euclidean distance between wire-

less nodes determines whether a transmission can be

correctly received or not. Specifically, successful re-

ception of a frame at receiver Nj from a transmitter

Ni is assumed, if and only if there is no other simulta-

neous sender within a guard zone, determined by a fac-

tor d, from the receiver Nj . Mathematically, a trans-

mission from node Ni to node Nj is successful if, for

every other node Nk,

|Nk − Nj | > (1 + d)|Ni − Nj | (1)

The factor d models the radius of the guard zone and is

specified by a protocol. Hence, the model is named as

the protocol interference model. Note that this model

only accounts for the path loss as the source of sig-

nal attenuation. It applies under same transmit power

levels. Further, the model is sensitive to the factor d

on which the protocol depends. The selection of this

factor is a challenging open issue.

2. The Physical Interference Model:

This model predicts that a transmission can be suc-

cessful if the signal to interference ratio SINR exceeds

some threshold. Particularly, a sender Ni transmits a

frame successfully to receiver Nj in presence of K

other simultaneous transmissions, if and only if:

Pi

|Ni−Nj |α∑K

k=1

Pk

|Nk−Nj |α+ Pn

> SINRTH (2)

where Pi is the power level used by any sender Ni,

|Nk − Nj| is the distance between any nodes Nk, Nj ,

α is the path loss exponent which is usually supposed

to be greater than 2, and Pn is the noise power level.

SINRTH is the threshold value necessary for a suc-

cessful decoding of Ni’s transmission at receiver Nj .

Obviously, the physical interference model is less restric-

tive than the protocol model. With this model, a receiver

may successfully receive a packet, even if another node

located within the interference range of this receiver is

simultaneously transmitting. Further, this model is more

related to physical layer effects, as it considers attenuation

sources like fading other than the path loss.

Though the two interference models described above

simplify interference modeling, it is shown in [7] that their

use in realistic networks is erroneous. This is because the

real signal strength is not a simple function of distance.

Additionally, the coverage area of a wireless node is

irregular.

Recently, a number of measurement-based interfer-

ence models for 802.11-based wireless networks have been

proposed [5, 9, 6, 8]. While these empirical models are

more realistic when compared to the simple mathematical

models described above, unlike our proposed approach,

they only focus on power level measurement and neither

consider the frequency of interference event nor the

duration of each event.

3 Passive Interference Measurement

Shifted in time measurement is another approach for

characterizing interference conditions. Measurements can

be performed actively at the network deployment phase, or

passively while the network is operating. The measurement

of throughput or signal strength is the core of active mea-

surements [5]. With the throughput-based approach, two

links i and j are assumed to interfere if and only if the

throughput of one degrades when the other is active. This

is known as pairwise interference. With the signal strength

based approach, each node sends in turn a series of broad-

cast packets. All other nodes measure the signal strength of

2337

received packets. The signal strength is used to indicate the

potential interference strength from the transmitting node

to each other node. These measurements deliver, however,

only an estimate of the real interference. This is due to the

followings:

1. It is known that the signal strength varies in time.

This depends on environmental changes of the wire-

less channel. Therefore, initial measurements may not

be valid all the time.

2. The estimated interference level is usually valid in the

scenario used to estimate it. Due to the variable na-

ture of traffic, the real interference level is not observed

all the time. Additionally, interference pattern changes

dynamically dependent on the node state (transmitting,

receiving).

An important factor that determines the impact of an inter-

ferer on a receiving node is the frequency of the interference

event and the event duration. This is captured in our passive

measurement approach. The approach does not only rely on

whether some power is measured from an interfering node,

but also on the time span an interferer occupies the medium.

It works as follow:

• Each AP requests all associated stations (STAs) to

monitor the wireless channel for a time period of T .

The STA can do this by setting its radio interface in the

promiscuous mode of the 802.11 standard during the

monitoring period. This mode allows it to capture and

pass to the MAC all packets on the medium, whether

they are addressed to it or not.

• While monitoring the wireless medium, a measuring

STA captures all transmitted frames and records the

following information elements: The number of trans-

mitted frames from each source address, the length of

each frame, the rate at which each frame was trans-

mitted, and the power level at which each frame is re-

ceived.

• Because frames have different sizes and sending nodes

may employ different physical transmission rates, our

proposed interference metric accounts for these facts.

A STA k estimates the interference level from a source

address l as follows:

InterferenceLevelkl =1

T

N∑

i=1

LliPli

Rli

(3)

where Lli and Pli denote the length in bits and power

level in dBm of frame i received from interferer l, re-

spectively. Pli is captured from RCPI or RSSI. Rli

denotes the physical rate in bits/second at which frame

i is received from interferer l, and T denotes the length

of the measurement period.

• Each STA k sends aggregated measurement informa-

tion to its AP. From this report, the AP identifies the

set of potential interferers for each STA as well as the

impact of each interferer on this STA.

• The measurement duration is a fundamental issue.

This period should be small in order to reduce the time

a STA spends operating in the promiscuous mode but

large enough to assure that transmissions from interfer-

ers fall within the measurement time and consequently

produce accurate estimations. Note that the promiscu-

ous mode of operation introduces processing overhead

at the MAC for the extraction of information elements

a measuring STA is collecting (i.e power level, phys-

ical transmission rate, and packet length). In order to

produce accurate estimation and avoid continuous op-

eration in the promiscuous mode for a long period, the

observation period T can take place over short non-

contiguous observation slots.

• The measurements are periodically conducted and re-

ported to the AP. In order to reduce the measurement

overhead, the time between successive measurements

shall be adaptively set based on differences between

measurement results which obviously depends on traf-

fic patterns.

• Similarly, an AP can measure interference level com-

ing from nodes that belong to neighboring BSSs.

We make the following notes on the above interference

measurement approach:

1. The approach jointly considers the power levels of

interfering frames and the duration of transmissions.

This is important because the probability of collision

due to interfering transmissions and the collision cost

depend on the time period collided packets occupy the

medium.

2. By considering the duration of each frame and divid-

ing over the whole measurement duration, we capture

the activity level of an interferer and hence differenti-

ate between interferers that permanently or transitory

transmitting.

3. As the proposed approach also accounts for the inter-

ferer activity, it is expected to influence the perfor-

mance of slotted time operation. Note that considera-

tion of inactive nodes as interferers increases the num-

ber of time slots required for link scheduling and con-

sequently leads to performance degradation due to ca-

pacity wasting.

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4 Evaluation of the Proposed Passive Mea-

surement Approach

4.1 Experiment Objective

In this experiment, we would like to see how the inter-

ference level estimated using equation 3 characterizes in-

terference impact captured by observation of the number of

corrupted packets. The experiment is performed with real

equipments as well as through simulation.

4.2 Simulation Setup

The simulation experiments are conducted using the NC-

TUns simulation package [2]. As with real experiments, the

configuration of the simulation scenario is comprised of a

topology of 2 APs and 10 STAs. The APs are deployed

and configured over the same channel. The APs are con-

nected to a server (via an 802.3 switch) through cables of

100 Mbit/s bandwidth. The latency for packets between

APs and the server was set to 10s. APs and STAs imple-

ment the 802.11b technology and use the DCF MAC proto-

col. Depending on the distance between AP and STA, the

wireless channel is attenuated more ore less severely. How-

ever, we assume that radio signals are not only attenuated by

path loss, but are also affected by fading due to multi-path

propagation. In order to accurately model these effects, a

path loss component as well as a Rayleigh-distributed fad-

ing component is considered. For the path loss, a two-ray

ground reflection model has been used. Depending on the

distance between AP and STA, the wireless channel is at-

tenuated more ore less severely. However, we assume that

radio signals are not only attenuated by path loss, but are

also affected by fading due to multi-path propagation. In

order to accurately model these effects, a path loss compo-

nent as well as a Rayleigh-distributed fading component is

considered. For the path loss, a two-ray ground reflection

model has been used

4.3 Real Experiment Setup

The real measurements were carried on a soccer field of

100m x 80m dimensions. Two APs are deployed as shown

figure 1. They are configured on the same channel. 10

STAs are distributed across the coverage area of both APs

at the distances shown the figure. APs and STAs use the

802.11b technology. The RTS/CTS mechanism was turned

off. STAs were equipped with Atheros (5006x) cards and

the MadWiFi-ng driver, while the well known sniffer TCP-

DUMP was used to sniff packets. STAs and APs transmit

with a fixed power level of 100mw. The 10 STAs download

packets from a server connected to the APs via an Ethernet

switch. Two traffic profiles are used as shown in tables 1

and 2. In the first traffic profile, we fix the packet size and

decrease the offered load after half of the experiment dura-

tion. In the second traffic profile, the offered load is fixed

and the packet size is reduced over the experiment time.

Figure 1. Experiment Setup

Time Offered Load (Pkt/s) Packet Size (B)

0 - 150 50 1500

151 - 300 8 1500

Table 1. Traffic Profile 1

Time Offered Load (Pkt/s) Packet Size (B)

0 - 150 20 1500

151 - 300 20 500

301 - 450 20 200

Table 2. Traffic Profile 2

4.4 Real Measurement Results

We start presenting and discussing the results of the real

experiments. Figure 2 plots the number of corrupted pack-

ets during each second of the measurement time with traffic

profile of table 1. As expected, the results show that less

corruptions occurs at the low load. Figures 3 and 4 plot

0 50 100 150 200 250 300−50

0

50

100

150

Time (Seconds)

Number of Corrupted Packets

Number of Corrupted Packets

Offered Load: 50 Packets / Second

Offered Load: 8 Packets / Second

Figure 2. Real Experiments with Passive In-

terference Measurement: Number of Cor-

rupted Packets with two different loads.

the total power and total interference level during each sec-

ond of the measurement time, respectively. The power level

is computed from the received signal strength while the in-

terference level is computed using equation 3. The results

2339

show that both the power level and interference level met-

rics reflect the changes in the number of corrupted pack-

ets and consequently characterize interference conditions.

Note that under low load, measuring STAs will receive less

number of packets during the observation period. Hence,

the power level metric is lower for lower load, though it

does not consider activity level. Now, we discuss the re-

0 50 100 150 200 250 3000

0.5

1

1.5

2x 10

−3

Time (Seconds)

Power Level (mw)

Power Level

Offered Load: 8 Packets /second

Offered Load: 50 Packets /second

Figure 3. Real Experiments with Passive In-

terference Measurement: Total Power with

two different loads.

0 50 100 150 200 250 3000

1

2

3

4

5x 10

−7

Time (Seconds)

Interference Level (mw)

Interference Level

Offered Load: 50 Packets / Second

Offered Load: 8 Packets / Second

Figure 4. Real Experiments with Passive In-

terference Measurement: Interference Level

with two different loads.

sults for the traffic profile of table 2. Figure 5 plots the total

number of received corrupted packets observed by all STAs

versus the measurement time. Figures 6 and 7 plot the to-

tal power level and the total interference level, respectively.

Note that in this traffic profile the load is fixed while the

packet size is variable. The results show that the changes in

power level does not reflect changes in the number of cor-

rupted packets which becomes less as the packet size de-

creases. Thus, measurements based only on power levels

may lead to erroneous characterization of interferers’ im-

pact. On the contrary, small increase in the power can be

seen as the packet size decreases. In contrast, the interfer-

ence level computedwith equation 3 is able to track changes

in the number of corruptions. This is because it considers

the time an interferer packet occupies the medium which

influence the probability of packet collision and corruption.

Hence, it better characterizes the impact of an interferer.

0 50 100 150 200 250 300 350 400 450−50

0

50

100

150

200

250

Timee (Seconds)

Number of Corrupted Packets

Number of Corrupted Packets

Packet Size = 1500 Bytes

Packet Size = 500 Bytes

Packet Size = 200 Bytes

Figure 5. Real Experiments with Passive In-

terference Measurement: Number of Cor-

rupted Packets for different packet lengths.

0 50 100 150 200 250 300 350 400 4500

0.5

1

1.5x 10

−3

Time (Seconds)

Power Level (mw)

Power Level

Packet Size = 1500 Bytes

Packet Size = 500 Bytes

Packet Size = 200 Bytes

Figure 6. Real Experiments with Passive In-

terference Measurement: Total Power for dif-

ferent packet lengths.

0 50 100 150 200 250 300 350 400 4500

0.5

1

1.5

2

2.5

3x 10

−7

Time (Seconds)

Interference Level (mw)

Interference LevelPacket Size : 1500 Bytes

Packet Size : 200 Bytes

Packet Size: 500 Bytes

Figure 7. Real Experiments with Passive In-

terference Measurement: Interference Level

for different packet lengths.

4.5 Simulation Results

Simulation results for the traffic profile of table 2 are

shown in figures 8 and 9. The results are in agreement with

2340

the real measurement results. As seen in the figures, the in-

terference measurement metric provided in this paper better

characterizes interference conditions.

0 50 100 150 200 250 300 350 400 4501

1.2

1.4

1.6

1.8

2x 10

−4

Simulation Time (Seconds)

Power Level (mw)

Power Level

Packet Size: 200 Bytes

Packet Size: 500 Bytes

Packet Size: 1500 Bytes

Figure 8. Simulation Experiments with Pas-

sive Interference Measurement: Total Power

for different packet lengths.

0 50 100 150 200 250 300 350 400 4501

1.5

2

2.5

3

3.5

4x 10

−7

Simulation Time (Seconds)

Interference Level (mw)

Interference Level

Packet Size: 1500 Bytes Packet Size: 500 Bytes

Packet Size: 200 Bytes

Figure 9. Simulation Experiments with Pas-

sive Interference Measurement: Interference

Level for different packet lengths.

5 Conclusions

In this paper, we proposed a method for estimating inter-

ference conditions at a node receiver. The measurements

can be used for identifying Interference relations among

WLAN nodes. It is shown that interference impact does

not only depend on the received signal power from the in-

terferer. Rather, other parameters such as packet size, mod-

ulation used by interfering nodes, and activity level of inter-

ferers are strong factors which characterize the impact of an

interferer on the ability of a receiving node to correctly re-

ceive packets. By observing the number of corrupted pack-

ets, simulation and experimental results have shown that the

proposed passive approach better characterizes impact of

interfering nodes than the commonly used approach that is

just based on total power level of received interfering pack-

ets. We have shown that this approach may produce im-

proper conclusion about interference conditions.

6 Acknowledgment

The authors would like to thank Proximetry Inc. (Both

in USA and Poland) for conducting the experiments and

preparing the raw results. Many thanks also to Mr. Berthold

Rathke at TKN (TU-Berlin) for the valuable and helpful dis-

cussions.

References

[1] IEEE 802.11k, Radio Resource Measurement, IEEE Draft

2.0.

[2] NCTUns Simulator. http://nsl.csie.nctu.edu.tw/nctuns.html.

[3] IEEE Std. 802.11-1999. Part 11: Wireless LAN Medium

Access Control (MAC) and Physical Layer (PHY) Spec-

ifications, Reference number ISO/IEC 8802-11:1999(E).

IEEE Std. 802.11, 1999.

[4] IEEE Std. 802.11-2007, Part 11: Wireless LAN Medium

Access Control (MAC) and Physical Layer (PHY) Speci-

fications. IEEE Std. 802.11, 2007 edition.

[5] A. Kashyap, S. Ganguly, and S. R. Das. A measurement-

based approach to modeling link capacity in 802.11-based

wireless networks. In Proceedings of Mobicom, 2007.

[6] C. Reis, R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahor-

jan. Measurement-based models of delivery and interference

in static wireless networks. In Proceedings of Sigcom, 2006.

[7] D. Kotz and C. Newport and R. S. Gray and J. Liu and Y.

Yuan and C. Elliott. Experimental evaluation of wireless

simulation assumptions In Proceedings of MSWiM, October

2004.

[8] Guoliang Xing and Mo Sha and Jun Huang and Gang Zhou

and Xiaorui Wang and Shucheng Liu. Multi-channel Inter-

ference Measurement and Modeling in Low-Power Wireless

Networks. In Proceedings of 30th IEEE Real-Time Systems

Symposium, 2009.

[9] L. Qiu, Y. Zhang, F. Wang, M. K. Han, and R. Mahajan. A

general model of wireless interference. In Proceedings of

Mobicom, 2007.

[10] M. Abusubaih and B. Rathke and A. Wolisz. A Framework

for Interference Mitigation in Multi-BSS 802.11 Wireless

LANs. In Proceedings of 10th IEEE WoWMoM, June 2009.

[11] P. Gupta and P. R. Kumar. The Capacity of Wireless Net-

works. In IEEE Transactions on Information Theory, 46(2),

2000.

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