A novel measurementbased approach for modeling and computing interference factors for wireless channels
 Alper Rifat Ulucinar^{1}Email author,
 Ibrahim Korpeoglu^{1} and
 Ezhan Karasan^{2}
https://doi.org/10.1186/16871499201368
© Ulucinar et al.; licensee Springer. 2013
Received: 8 October 2012
Accepted: 7 February 2013
Published: 14 March 2013
Abstract
Wireless communication technologies divide their available spectrum into predefined channels. Some wireless technologies, such as the IEEE 802.11b/g, define their channels in such a way that adjacent channels share the spectrum. When two distinct channels share some part of their spectrum, simultaneous transmissions on these channels cause what is known as the adjacent channel interference. For problems that consider adjacent channel interference, such as the channel assignment problem for multichannel wireless mesh networks, we need a model that quantitatively describes adjacent channel interference. The interference factor is a concept defined to quantify the amount of the overlap, hence the interference, between two wireless channels. Analytical and experimental methods have been proposed in the literature to define the interference factors among channels of a wireless technology. In this article, we propose a physicallayermeasurementbased, technologyindependent and generic approach that is capable of determining interference factors between the channels of a wireless technology and also between the channels of two different wireless technologies, such as IEEE 802.11 and IEEE 802.15.4. We also report our measurement results for interference factors among 802.11b DSSS channels and between 802.15.4 and 802.11b channels. Our results show that our approach is practical, accurate and generic enough to compute the interference factors of radio channels belonging to various wireless communication technologies.
Keywords
Radio channels Overlapping and orthogonal channels Interference factor 802.11 802.15.4 Spectrum analyzer1 Introduction
Interference factor (Ifactor) [1] is used to model the amount of interference between two channels of a wireless communication standard, such as the IEEE 802.11 family of standards or the IEEE 802.15.4 standard. Spectral bands are allocated to wireless communication standards by regulation and standardization bodies such as the US Federal Communications Commission (FCC) or the European Telecommunications Standards Institute (ETSI). These standards further divide the band allocated to them into channels. Each standard has its own channel definition, some of which are compatible and some of which are not. For instance, the IEEE 802.11g standard is compatible with the 2.4 GHz DSSS channel definitions of the 802.11b standard for supporting legacy devices, whereas the channel definitions of the 802.11g and IEEE 802.15.4 standards are not compatible with each other. Furthermore, a standard may define more than one channel structure if it employs multiple PHY service specifications. As an example, the IEEE 802.11 standard defines different channel structures for the frequencyhopping spread spectrum (FHSS) PHY and the directsequence spread spectrum (DSSS) PHY services [2].
The definition of the channels of a wireless communication standard comprises the center frequencies and the bandwidths of the channels. For instance, the IEEE 802.11b and IEEE 802.11g standards use the same channel structure for the DSSS PHY. The 802.11b/g DSSS channels are in the 2.4 GHz industrial, scientific and medical (ISM) band. The center frequency of the first 802.11b/g DSSS channel (channel id 1) is 2412 MHz. The center frequencies of consecutive channels are separated by 5 MHz and the bandwidth of each channel is 22 MHz. The standard defines 14 channels, of which the first 11 are supported in the FCC domain.
For two radios to communicate with each other using a communication standard, they must be tuned to the same channel, which can be shared by other nearby transmitters. The 802.11 medium access control (MAC) sublayer coordinates transmissions on the same channel using the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol. A mediumaccess control protocol is needed for, among other functions, controlling and scheduling transmissions of contending radios. Two radios are said to be contending for the channel if they simultaneously have packets to send through the wireless medium. However, the distributed stochastic operation of the 802.11 MAC sometimes fails to properly schedule the contenders, so more than one transmitters concurrently attempt to send packets. This causes simultaneous transmissions from multiple radios that interfere with each other.
To further complicate the situation, the interfering radio might be operating on a different (but adjacent or overlapping) channel as the channel the receiver under discussion is using. Some wireless communication standards (for example, IEEE 802.11a) define their channel structures in such a way that it is impossible for a radio to receive any signal power from a transmitter not tuned to the same channel as itself. However, for other standards (such as the 802.11b/g DSSS PHY), it is possible for a receiver operating on channel i to receive interference power from a transmitter operating on one of the channels i,i ± 1,i ± 2,i± 3, or i ± 4. This occurs because of how DSSS channels as well as the transmit spectrum mask (TSM) are defined in 802.11b/g. The DSSS TSM [2] allows a transmission bandwidth of 22 MHz centered around the channel center frequency. Therefore, two transmitters tuned to channels i and j share the wireless medium (a common frequency range) as long as i and j’s center frequencies are separated by less than 22 MHz. Because the center frequencies of consecutive channels are 5 MHz apart from each other, this translates into a channel separation, i  j, of fewer than ⌈22/5⌉ = 5 channels.
The term overlapping channels [1] (or nonorthogonal channels) is used to describe a relation between at least two channels that share a frequency range. When we consider the 802.11 DSSS PHY specification, two channels i and j are overlapping channels if and only if i  j < 5. If i  j ≥ 5, channels i and j are called nonoverlapping or orthogonal channels.
The interference factor quantitatively defines the amount of overlap and interference between two wireless channels. In this article, we propose a novel and practical approach that can be used to compute the interference factor values between not only the channels of a single standard but also between the channels of two different standards sharing the same spectrum. Our approach is based on physicallayer measurements and has the advantage of being practically applicable to various wireless communication standards. Existing analytical or measurementbased approaches lack this important property because of their dependence on a specific wireless standard. The main contributions of our article are as follows:

To the best of authors’ knowledge, the physicallayermeasurementbased methods we propose are the first in the literature that are generic enough to model the interference between channels of any two wireless communication technologies, such as 802.11 and 802.15.4. They can also be used to obtain the Ifactor values between channels of the same wireless technology, such as 802.11 channels.

These methods are also capable of modeling interference on wireless communication devices caused by noncommunication devices such as microwave ovens.

Using the proposed methods and a 2.4 GHz spectrum analyzer, we give our measurement results for the proposed Ifactors between 802.11b DSSS channels and we compare our results with those of other analytical and measurementbased Ifactor models in the literature. We also report our measurement results on the interference from an 802.15.4 (ZigBee) transmitter on an 802.11b receiver.
The remainder of the article is organized as follows: In Section 2, we introduce the concept of the Ifactor in more detail. In Section 3, we discuss some related study on Ifactor modeling. In Section 4, we present and discuss our novel methods. In Section 5, we report our measurement results for the Ifactors between 802.11b DSSS channels and between 802.15.4 and 802.11b channels. Finally, in Section 6, we conclude the article.
2 Interference factor
The concept of an interference factor [1] is used as a model of how much interference power will leak from adjacent channels. An Ifactor is commonly defined as a value in the interval [0,1], where 0 means no interference, and 1 means maximum interference (when the interferer’s frequency band intersects maximally with the transmitter’s frequency band). Ideally, a transmitter operating on a nonoverlapping channel with respect to a receiver generates no interference on the receiver, therefore the Ifactor for two nonoverlapping channels is 0. When the interferer and receiver radios are operating on the same channel, 100% of the interferer signal power at the location of the receiver will pass through the receiver’s filter. Hence, the Ifactor is defined as 1 for two channels i and j if i = j.
Two important properties of the Ifactor definition given above are: (a) it is not a commutative operation, i.e., I (i,j) is not necessarily equal to I (j,i), and (b) I (i,j) ∈ [0,1] (assuming the transmitter is transmitting on channel j).
where P_{DSSS} (f,F_{c,t}) is the power spectral distribution function for the interferer DSSS signal, with a center frequency of F_{c,t}(in MHz), and B_{ R }(f,F_{c,r}) is the receiver bandpass filter’s frequency response, with F_{c,r}= F_{c,t}5 ij (in MHz). In this idealized discrete model, the transmitted signal’s power distribution (P_{DSSS}(f,F_{c,t})) and the receiver filter (B_{ R }(f,F_{c,r})) are approximated with the DSSS TSM defined by the standard, with center frequencies of F_{c,t}and F_{c,r}, respectively [1].
where P_{ i n t f }(f,F_{c,i}) is the interferer signal’s power spectral distribution trace captured with a spectrum analyzer, and P_{r e c v _c h}(f,F_{c,j}) is the power spectral distribution trace captured with a spectrum analyzer that belongs to a transmitted signal using the same standard as the receiver radio and that is on the same channel as the receiver (channel j). F_{c,i} is the center frequency of the interferer signal’s channel, which is specific to the standard the interferer radio belongs to. Similarly, F_{c,j}is the center frequency of the receiver’s channel, which is specific to the standard the receiver radio belongs to. The interferer and the receiver radios might belong to different wireless communication standards as well as to the same standard. The unit of power used in the calculation of I (i,j) in (3) is the unit used in the spectrum analyzer’s traces. The lower and upper limits of the integrations (f_{ l }and f_{ u }, respectively) are determined by the total spectrum band covered by the traces of the interferer and the receiver channel signals. We call this method the signal intersection area method (SIAM) in this article and detail it in Section 4.
where the definitions of P_{ i n t f }(f,F_{c,i}), F_{c,i}, and F_{c,j}are as in (3). B_{ R }is the receiver bandpass filter’s frequency response. In (4), we use this response twice, once for a receiver tuned to the channel with the center frequency, F_{c,j}, and once for another receiver tuned to the same channel as the interferer itself, F_{c,i}. We obtain P_{ i n t f }(f,F_{c,i}) experimentally using a spectrum analyzer. The lower limit of integration, f_{ l }, for the receiver channel j is ${f}_{l}={F}_{c,j}\frac{\mathit{\text{Bw}}}{2}$, and the upper limit of integration, f_{ u }, for the receiver channel j is ${f}_{u}={F}_{c,j}+\frac{\mathit{\text{Bw}}}{2}$, where Bw is the width of the bandpass filter’s response. This method, which we call percentage of maximum interference energy (PMIE), has a more concrete physical interpretation that we discuss in detail in Section 4.
3 Related study
There are two main classes of Ifactor models in the literature. The first class comprises analytical models [1], which are generally applied to relatively simple modulation techniques, such as the DSSS, because of the complexities of the models. The second class comprises a set of experimental measurementbased methods [3], which are more flexible than the analytical methods because they are not built upon the specifics of a physicallayer technique; they involve measurements in any of the various layers of the open systems interconnection (OSI) stack [4]. The most direct and more commonly adopted experimental method of obtaining an Ifactor model is to perform signaltonoise ratio (SNR) [5] measurements. In these models, a receiver is kept fixed at a channel and its transmitter is operated on nonoverlapping and overlapping channels. For each channel of the transmitter, SNR is measured on the receiver and normalized to a scale of [0,1] as in [3]. This method mandates that the interferer (transmitter) and the receiver must be using the same wireless communication standard, so that SNR readings (where the signal belongs to the interferer) are available at the receiver. If the interferer uses a different wireless communication standard than the receiver (such as the interferer being a Bluetooth radio and the receiver being an 802.11b/g radio), then there will be no links between these two radios and no SNR measurements will be available at the receiver radio.
In [6, 7], Feng and Yang use numerical methods to analyze network capacity improvements that can be gained by using partially overlapping channels. While defining the carrier sensing range between two nodes operating on channels i and j, they perform a set of testbed experiments that involve two pairs of nodes. One pair communicates with each other on channel i and the other pair communicates on channel j. The authors define the carrier sensing range as “the maximum distance that these two can affect each other’s communications” [7]. Then they give statistical and numerical models of capacity improvements when overlapping channels are used compared to using only orthogonal channels in onehop and multihop wireless networks. In [7], the authors also discuss the cases where no improvement can be gained by using partially overlapping channels.
In [8], Zhou et al. envision that in the very near future, the world will be full of lowpower wireless sensors sharing the same spectrum. As an illustrative example, they measure the 2.4 GHz spectrum with their HP 8593E spectrum analyzer in the coexistence of a microwave oven, a cordless 2.4 GHz presenter, and a MICAz sensor network. They also report the reception ratios of the MICAz motes when the microwave oven is on and when it is off. However, they do not model interference using these measurements. The authors propose the dimensions along which new wireless sensor network protocols should be designed to cope with the crowded spectrum issue.
In [9], Fuxjäger et al. pose the fundamental question of whether there really is no interference between the nonoverlapping channels of IEEE 802.11. To investigate this, the authors use a testbed consisting of four laptops, each equipped with an Intel PRO 2200BG minipci card and running Linux. They place the laptops on a linear lineofsight topology, each raised 1.5 m above the ground. Using this testbed, the authors measure the MAC and transport layer throughputs and MAC frame loss ratios. They also measure the goodput of a TCP flow. The authors conclude that due to the nearfar effect [10], crosschannel interference exists between nonoverlapping channels of IEEE 802.11 when the receiver and the interferer radios are placed only tens of centimeters away from each other. They also conclude that offtheshelf IEEE 802.11 chipsets may not be ready to be placed in the same box for use in multiradio wireless mesh networks.
In [11], Petrova et al. investigate the performance of IEEE 802.15.4 networks under the interference caused by IEEE 802.11g and prestandard IEEE 802.11n networks through measurements. They use a testbed consisting of an 802.11g/n access point, a laptop used as the 802.11g/n traffic sink and equipped with an 802.11g/n adapter, a PC used as the 802.11g/n traffic generator, and two TelosB motes. They also monitor the 2.4 GHz spectrum with an Agilent E4440A spectrum analyzer. Using this testbed, the authors measure the packet delivery ratios of the 802.15.4 network. They use the spectrum analyzer to report the average power spectral densities of the 802.11n signals for different alignments of the 802.11n nodes. However, they do not model interference using these measurements.
4 Our proposed interference factor calculation methods
The method we follow to compute the Ifactor is based on physicallayer measurements in the frequency domain, taken with a spectrum analyzer. Using the spectrum analyzer, we obtain signal traces showing the power spectral distribution of a transmitted wireless signal. To capture channel activity, we generate and analyze 802.11b/g packets using the multiradio mesh nodes in our testbed BilMesh, which is a multihop, multiradio wireless mesh networking testbed we have established at Bilkent University. To analyze the 802.15.4 interference on 802.11 receivers, we add ZigBee motes to our testbed. We use a YellowjacketTablet 2.4/5 GHz WiFi spectrum analyzer [12] to obtain the signal traces. The setup required for the measurements consists of a single wireless transmitter. However, as explained in Section 5.3, two or more transmitters operating on the same channel can be used to shorten the time needed to collect the signal traces. The transmitter(s’) channel is set in succession to one of the channels of interest between which the Ifactor is to be calculated.
 1.
We obtain the spectrum analyzer traces for the two wireless signals between which the Ifactor is to be calculated. Because Ifactor is defined as the ratio of the received energy on a receiver channel to the radiated energy on a transmitter (interferer) channel, we have to obtain the interferer’s power spectral distribution using the spectrum analyzer. In our current implementation, we obtain, store, and process this information with device independent bitmap files. We use bitmap files because the spectrum analyzer available to us can export this data in bitmap format; however any other form of representation for the power spectral density data can be used with our method.
 2.
Once the two signal traces are obtained, the area of their intersection in the frequency domain over a reference power level is calculated. In our current implementation, the reference power level is determined by the user, considering the noise power level available in the spectrum analyzer data. The user also determines the interval in the frequency domain over which the integration will be performed.
 3.
The area in the frequency domain below the interferer signal and above the reference power level used in the previous step is calculated. The same frequency interval as in the previous step is used for the integration.
 4.
The Ifactor is obtained by dividing the area below the intersection of the two signals by the area below the interferer signal.
If we have a better estimation for the filter being used by the receiver radio, there is no need to collect the traces for the receiver in the first step. Instead, after collecting the interferer traces, we can directly calculate the ratio of the interferer signal power falling below this filter to the total interferer power to estimate the Ifactor, as in Figure 1b.
Algorithm 1 outlines the steps of our proposed method. The output of the algorithm, I (c h_{ i n t f },c h_{ r e c v }), is the Ifactor calculated between channels c h_{ i n t f }and c h_{ r e c v }. If there is an analytical model for the receiver bandpass filter’s frequency response, such as the one in (5), it must be converted into a suitable representation for processing. In our implementation, this corresponds to adding the filter model onto the bitmap where the interferer signal trace resides, using a different color than the interferer signal trace’s color. To calculate the total interferer power and the amount of interferer power that overlaps with the receiver radio’s filter on the frequency interval [f_{ l },f_{ u }], we use the procedure given in Algorithm 2. The inputs [f_{ l },f_{ u }] and referencePowerLevel should be specified in terms of the pixels of the bitmap B.
Algorithm 1 SIAM Ifactor model and computation method
Algorithm 2 Total interferer power and overlap power calculation on bitmap
The method we propose above defines Ifactor as the ratio of the area below the intersection of two frequency domain signal traces to the total area below the trace that belongs to the interferer signal. We now discuss another definition for the Ifactor that has a more concrete physical interpretation. Equation (4), introduced earlier, models the percentage of the maximum interference energy radiated on channel i and received by a receiver on channel j, where F_{c,i}is the center frequency of channel i and F_{c,j}is the center frequency of channel j. The maximum interference energy is defined as the interference energy that would be received by another receiver operating on channel i in the same location as the receiver on channel j.
Algorithm 3 outlines the steps of Ifactor calculation based on (4). We call this method described with Algorithm 3 as the PMIE method: P ercentage of M aximum I nterference E nergy. While calculating totalReceivedIntfEn and maximumIntfEn, Algorithm 4 is called twice, first with a receiver channel signal trace (or the receiver filter’s frequency response centered around F_{c,j}) and then with an interferer channel signal trace (or the receiver filter’s frequency response centered around F_{c,i}).
Algorithm 3 PMIE Ifactor model and computation method
Algorithm 4 Received energy calculation on bitmap
PMIE has a more concrete physical interpretation when compared to SIAM because PMIE defines the Ifactor as the ratio of the total received interference energy from an interferer on the receiver’s channel to the total received energy from the same interferer on the interferer’s channel (the maximum interference energy). PMIE Ifactor value is, consequently, strictly 1 when the receiver captures all of the interferer’s energy and 0 when the receiver filters all of the interferer’s energy. However, because of the way SIAM is defined, these may not always hold for SIAM Ifactor values. PMIE, on the other hand, requires the knowledge of the receiver radio’s filter (the B_{ R }function). If B_{ R }is not known, SIAM, however, can approximate it from transmissions on the receiver radio’s channel by a transmitter radio using the same wireless standard and PHY layer specification as the receiver radio. This gives more flexibility to SIAM and makes it possible to obtain approximated Ifactor values using SIAM when B_{ R }is not known. If B_{ R }is known, PMIE can be used to obtain more accurate Ifactor values.
Our measurement results for the interference factor between two DSSS signals and between the DSSS TSM and an interferer DSSS signal are given in Section 5.1. We have written a separate Java program to implement PMIE (Algorithms 3 and 4). In Section 5.2, we report on the Ifactor calculations using the PMIE method. In Section 5.3, we report our results on the Ifactor values between an 802.15.4 OQPSK interferer and an 802.11 DSSS receiver.
5 Measurement results and comparisons
5.1 Measurements for modeling interference between 802.11 DSSS signals using SIAM
Ch.  Ifactor based on  Ifactor based on  Ifactor based on  IFactor based on  Ifactor based on 

separation  signal traces  TSM assumption  signal traces  analytical model  SNR Measurements 
(SIAM)  (SIAM)  (PMIE)  
0      1     
1  0.61  0.82  0.68  0.60  0.96 
2  0.33  0.56  0.18  0.30  0.77 
3  0.14  0.27  0.008  0.11  0.66 
4  0.09  0.1  0.003  0.01  0.39 
5  0.03  0.03  0.002  0  0 
6  0.01  0.01  0.002     
5.2 Measurements for modeling interference between 802.11 DSSS signals using the PMIE method
5.3 Measurements for modeling interference between 802.11 DSSS and 802.15.4 OQPSK signals
To model the interference between an 802.15.4 (ZigBee) transmitter and an 802.11b receiver, we perform another set of experiments and take measurements with our spectrum analyzer. In this set, we use five Crossbow TelosB motes [14] and two 802.11b radios in adhoc (IBSS) mode. The TelosB motes have IEEE 802.15.4 compliant Texas Instruments CC2420 radios with integrated onboard antennas, and communicate with a data rate of 250 Kbps. They are commonly employed in wireless sensor networks. One of the TelosB motes is used as the base station in our experiment. The other four motes periodically sample their sensors and transmit their readings to the base station. We use four ZigBee transmitters to increase the chance of an 802.15.4 signal being registered on the spectrum analyzer. The 802.11b radios are operated on (802.11) channel 3, whose center frequency is 2422 MHz. The channel of the ZigBee network is varied from (802.15.4) channel 11 (central frequency 2405 MHz) to channel 17 (central frequency 2435 MHz).
where F_{ c }is in MHz. The first channel on channel page 0 in the 2450 MHz band is channel 11 and the last channel is channel 26.
6 Conclusion
In this article, we have proposed a novel measurementbased approach for computing the interference factor between two wireless radio channels. Our approach is generic because it is capable of computing the Ifactor between channels of different wireless technologies, such as IEEE 802.11 and 802.15.4, as well as between channels of the same wireless technology. We have also proposed two specific measurementbased Ifactor definitions and presented their computation methods following a measurementbased approach.
In our wireless testbed, we performed experiments with 802.11b and 802.15.4 radios and computed Ifactor values between 802.11 channels and between 802.15.4 and 802.11 channels. We compared our results with the existing results in the literature and we reported new results on ZigBee to WiFi interference. According to our findings, an 802.15.4 radio does not interfere with an 802.11b receiver if their center frequencies are separated by at least 17 MHz. If the center frequencies are separated by 12 MHz, the interference from the 802.15.4 radio on the 802.11 receiver is still negligible. However, if the center frequencies are closer than 12 MHz, the interference from the 802.15.4 radio becomes significant in terms of the 802.15.4 radio’s total power.
Declarations
Acknowledgements
This study was supported in part by European Union FP7 Programme with project FIRESENSE, FP7ENV20091244088FIRESENSE.
Authors’ Affiliations
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