Sensor based analysis of the WiFi interference

AdvancesonWirelessSensorNetworks2013 ConferenceProceedings

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Sensor Based Analysis of the WiFi Interference

Z. Gal*, T. Balla* and A. Sz. Karsai* * University of Debrecen, Debrecen, Hungary

Abstract The Internet is everywhere, now everyone has at least one device which is connected to some kind of computer network. Not only wired but wireless communication technologies have become part of everyday life, as well. Because of the dynamic evolution of the WiFi services, majority of the enterprises and higher education institutions have a sort of own wireless infrastructure on their sites or campuses. Such subsystems in most cases are central managed, 2nd generation solutions consisting of high-end network devices from the top manufacturers. The IEEE 802.11 widespread technology is affordable for everyone in the ISM (Industrial-Science-Medical) frequency band. Many users have access to WiFi devices and make its own SOHO wireless service in ad-hoc manner in the neighborhood of the enterprise sites. These devices cause interference and generate huge amount of noise in the wireless radio channels for others in the physical vicinity. Improvement of an existing enterprise system without overall coverage in interior and exterior environment needs important design consideration to extend the WiFi services conform to the user expectation level. Having a large number of mobile users with even real-time communication necessities, high-quality, and high-bandwidth WiFi system with QoS (Quality of Service) guarantees need to be developed henceforward. Improvement of the WiFi access point infrastructure to cover the wanting physical zones can be done after a thoughtful frequency analysis of the existing enterprise wireless system. The paper presents a frequency analysis method based on sensor facility of the intelligent WiFi access points for helping the determination of the optimal physical coordinates of several dozens of new access points in university or enterprise campus environment.

Keywords: WiFi, WLAN, QoS, spectrum, Poisson distribution, FFT.

I. INTRODUCTION The IEEE 802.11 wireless network services are

considered ubiquitous in academic and enterprise environment recently. Several manufacturers produce run-in access points and clients conforming to the standard. The IEEE 802.11 wireless communication technology evolved in the last sixteen years can be considered a mature solution both for enterprise and home users, as well. Based on the increasing QoS requirements of the multimedia services, development of stable and relatively high bandwidth communication mechanisms becomes critical challenges of these years.

Having a large possibility of wireless equipment procurement, sometimes are not taken in consideration the scaling necessity of such services. In SOHO (Small-Office Home- Office) environment with several tens of users the classical, isolated managed WiFi system with very cheap access points can have satisfactory level of

correspondence because the next expansion phase can be done easily with minimal cost of investment. The relatively small coverage area of the site makes the migration process of such production devices easy and rapidly. In enterprise or academic environment, where the number of active users is high and the access network infrastructure is based on these free-of-charge WiFi services, the service level of agreement (SLA) can be maintained only by on-demand scaling of the WiFi infrastructure.

Another factor affecting the quality of such services is the temporal agglomeration of the users in frequently used places like large auditoriums or discussion rooms where high numbers of persons are communicating through different mobile devices (smart phones, ultrabooks, palmtops, tablets, etc.) on the same time. The spatial locality and time locality of WiFi usage become proper characteristic of such communication services, involving prudence for the complex development with optimal and realistic hardware and software investment.

Applying the same WiFi development strategy in both, enterprise and SOHO environment creates risky and unsafe service for the large number of multimedia users with pretentions toward the enterprise communication services. Installing large number of cheap access points with reduced intelligence results in one or more a hot-zones with low bandwidth. The manual or semi-automated management of such WiFi infrastructure is very difficult, inducing bigger staff for the continuous monitoring and intervention to maintain the SLA. Such management increase the operation cost of the service.

Applying intelligent WiFi service provisioning devices makes the management more convenient and the scaling more easily. The leader WiFi equipment manufacturers integrate special service modules in their products to control strictly the running mode of the access points deployed in the same physical site. The dynamic switching of the active radio channels based on the communication activity of other access points and other sources of noise in the ISM (Industrial Science Medical) frequency bands is a very effective service of these WiFi systems [1].

Having intelligent WiFi system with high switching capacity controller, the access points together with the controller can be considered as a spatial distributed sensor network. Each access point is a sensor and is able to detect the noise level existing at his physical coordinates. The sensor network communication can be modeled favorable as a finite-source queuing system [2].

There are several useful measurement and analyzing devices in practice which can measure the noise or the SNR (Signal to Noise Ratio) level and can give useful indications to the healthy and the expansion process of an existing WiFi system. The problem with these expensive


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measurement tasks is the temporal character, making necessary reevaluation the WiFi services periodically or in cases when the wireless system produces drop-outs and low-down the communication service level. Reevaluation of an enterprise or academic wireless network with several dozens or hundreds of access points installed is possible with specialized hardware tools in sequential way, without measuring the whole radio channel set in the same moment. It is obvious that this method involves other costs of the operation, making relatively difficult and expensive the WiFi service provisioning in sites with tens or hundreds of hot-zones running simultaneously.

In this paper we propose a solution of continuous evaluation of the WiFi QoS in enterprise and academic environment without any extra devices and extra costs. In the second section we introduce the basic characteristics in general of the controller based WiFi systems, highlighting special intelligence features of the access points as sensors. The third section presents the effects of the noise and the interference to the controller managed WiFi radio channels. In the fourth section the statistical analysis background utilized for noise and interference evaluation will be presented. The fifth section is about the measurement scenario for simultaneous detection of the noise and the interference in different radio channels of the IEEE 802.11g/n (2.4 GHz) and IEEE 802.11a/n (5 GHz) communication technologies. The frequency analysis results of the localized noise sources and the spatially distributed interference will be given in sixth section. Then, at the end we will conclude and we will give some possible directions and subjects related to this work to continue.

II. CHARACTERISTICS OF THE CONTROLLER BASED WIFI SYSTEMS

If we make a short survey of the WiFi communication mechanism, we can state that no more IEEE technology can be shown with such an intensively development and high number of revisions during the last sixteen years (see Table 1) [3]. Even the twenty eight years old IEEE 802.3 Ethernet technology with 10/100/1,000/10,000/100,000 Mb/s variants have no so many amendments to the basic standard [4].

In case of each Ethernet version the time interval between the Call for Interest announcement and the publication of the new standard was controlled by the IEEE standardization rule, setting this process to maximum four years.

The longer than ten years standardization process of the ATM technology supervised by the ATM Forum was an instructive caution in the new century relaying time period for the international scientific committees to not to lengthen too much the standardization process. Even with this generally valid standardization constraint lesson the IEEE 802.11 communication technology had several spectacular developments in relatively short time intervals. The amendment process is not finished yet. In the next year the theoretical maximum transmission rate (6 Gb/s) of the wireless PDU (Protocol Data Unit) in the 60 GHz band will be achieved [5].

As a prognosis can be concluded that in the next years the efficiency improvement will continue in this field of the wireless communication (see Table 1).

TABLE I. CHARACTERISTICS OF THE IEEE 802.11 WIRELESS TECHNOLOGY

Technology -Year Characteristics IEEE 802.11-

1997 1-2 Mb/s, Frequency Hoping, Direct Sequence; 2.4 GHz

IEEE 802.11a-1999

54 Mb/s, OFDM, 5 GHz (less coverage than IEEE 802.11b)

IEEE 802.11b-1999 11 Mb/s, 2.4 GHz

IEEE 802.11g-2003 54 Mb/s, 2.4 GHz

IEEE 802.11-2007

IEEE 802.11a, b, d, e, g, h, i, j (merging 8 amendments)

IEEE 802.11n-2009

-Input Multiple-Output (MIMO); 2.4 GHz, 5 GHz

IEEE 802.11-2012

IEEE 802.11k, r, y, n, w, p, z, v, u, s (merging 10 amendments)

IEEE 802.11ac-2013

Multi-station: 1 Gb/s, Single/link: 500 Mb/s, 5 GHz

IEEE 802.11ad-2014

WiGig: tri-band (2.4/5/60 GHz) WiFi: 6 Gb/s (theoretical maximum)

The WLAN services life cycle has several important

elements (see Table 2). The expansion steps of the WiFi infrastructure are following: - Site survey: It is the first and most important step in successfully implementing any wireless LAN. - Network design: Create a high-performance network - Deployment and verification: Verify that WiFi meets the requirements set for the service. - Troubleshooting: Identify and eliminate or neutralize WiFi issues.

TABLE II. WLAN SERVICES LIFE CYCLE

Element Tasks

Network design and requirements

- Define applications to be used - Define areas to be covered - Find locations for the Aps - Determine AP configurations - Find optimal antennas and their alignment

Deployment and verification

- Ensure coverage - Verify performance - Check correct AP configurations - Test end-to-end network operation

Management troubleshooting expansion

- Solve WiFi issues (802.11n issues; excessive interference; rogue APs, broken APs; AP misconfigurations; coverage holes/leakage

- Plan for added capacity or coverage Controller based WiFi system is a new generation

WLAN infrastructure. The central device connected to the wired LAN has continuous logical connection with the managed access points. A special inbound communication protocol is used for controlling the access point running modes. Depending on the manufacturer implementation, the traffic coming from the mobile devices on the radio channels should or should not be relayed to the controller. There are advantages and disadvantages depending on the physical placement of the switching functionality. If this function is executed exclusively by the controller, then the intelligence on the access points can be lower implying lower cost of the AP devices. However, the controller should have high capacity of the frame switching and of the subsidiary access point management. The wired LAN connecting the controller and access points will be loaded


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with the traffic between the mobile devices and between the mobile and wired devices, as well. The existing intelligent systems in practice use 150-300 access points, having 150-300 Mb/s throughput each, producing 10 Gb/s extra traffic on the wired LAN coming from the wireless devices.

If the controlling function is shared between the controller and the access points with higher intelligence, then lower amount of traffic should traverse the wired LAN and less switching computation capacity is needed at the central controller. Another important aspect of the controlling function centralization is the immunity to the failure. If all the switching functions of the wireless PDUs are concentrated in a single WiFi controller, then hardware redundancy is needed to eliminate the single point of failure produced by the controller.

Having the control function concentrated exclusively or partially in the WLAN controller, important extra services of the WiFi system are realized in this way:

- Workgroup bridging (VLAN segment management); - Radio resource (channels, time slots) management; - Secure transfer of PDUs between the AP and the

controller; - Access control lists: filtering different traffics on L2

and/or L3 layers; - Bidirectional rate limiting of the traffic from/to the

mobile devices; - Roaming in L2 and L3 layers: continuous logical

connection for mobile wireless nodes. The DFS (Dynamic Frequency Selection) mechanism

was implemented by different manufacturers so that access points and clients can share the radio channel with radar sensor functions of the access points [6]. DFS specifies how other signal source node can be detected and what should be done in this case. Access points operating on DFS channels are listening to a channel for 60 seconds to determine the eventual signal source node before transmitting energy. A access point operating on a DFS channel detects a signal source it must shut down operation on that channel. After 30 minutes this channel can be evaluated again. Client devices are not able to detect such sources. In general DFS certified access points have such integrated intelligence, but the client support for DFS channels is inconsistent today [7].

III. EFFECTS OF THE NOISE AND INTERFERENCE TO THE CONTROLLER MANAGED WIFI CHANNELS

Noise is the amount of non-802.11 traffic that is interfering with the currently assigned channel [6]. Noise can limit signal quality at the client and access point, too. An increase in noise reduces the effective cell size and degrades QoE (Quality of Experience). By optimizing channels to avoid noise sources, the controller can optimize coverage while maintaining system capacity. If a channel is unusable due to excessive noise, that channel can be avoided.

Interference is any 802.11 traffic that is not part of the own wireless LAN, including rogue access points and neighboring wireless networks [6]. Some advanced access points constantly scan all channels looking for sources of interference. If the amount of 802.11interference exceeds a predefined configurable threshold (e.g. 10 %), the access point sends an alert to the controller. The controller may

then dynamically rearrange channel assignments to increase system performance in the presence of the interference. Such an adjustment could result in adjacent own access points being on the same channel, but this setup is preferable to having the access points remain on a channel that is unusable due to an interfering foreign access point. If a channel has virtually no capacity remaining, the controller may choose to avoid this channel.

The QoS of a WLAN is affected by WiFi or non WiFi interference operating within the same site. Non WiFi interference has a much larger impact on throughput in a high-density environment than unmanaged WiFi energy [7]. This is because the IEEE 802.11 utilizes contention-based access mechanisms to coordinate station access to the channel, but non WiFi devices operating in the same channel do not share these rules. This creates a problem for normal WiFi operations, since a WiFi modem can only classify energy in two groups:

- WiFi (the energy detected can be demodulated), - Noise (all remaining energy).

The effect of non WiFi interference is logarithmic in its impact on WiFi network operations. The higher the utilization of the WiFi network, the more destructive non WiFi energy will be. The presence of a small amount of non WiFi interference can have a large and noticeable effect. Interference needs to be identified, managed, and eliminated to provide the required bandwidth for a high-density network to work properly [6].

It is widely accepted assumption that multiple IEEE 802.11b/g transmissions in physical proximity can coexist without interfering each other [6]. This can happen when using separate channels with a minimum distance of 25 MHz, e.g. channel 1 and 6, which are often referred to as non-overlapping. But it was shown that in practice cross-channel interference can be present also between non-overlapping channels if the interfering transmitter is in the proximity of the receiver. While the interference between non-overlapping channels might be negligible for larger distances, it identifies a serious problem for the design of wireless multichannel mesh networks [8].

The IEEE 802.11b/g systems are both affected by interference when the desired and interfering signals are separated by less than 20 MHz [6]. It was shown that for 802.11g systems the performance degradation caused by interference is essentially independent of the frequency separation whereas for 802.11b systems it reduces significantly with increasing separation [6].

The performance degradation caused by interference between coexistent 802.11b and 802.11g WiFi systems cannot be excluded. It was shown that channel overlap degrades their performance by differing amounts. For similar interference levels this degradation is slightly worse than for systems that are exclusively 802.11b but slightly better than for ones that are exclusively 802.11g. Overall, for WiFi systems operating in environments where channel overlap exists, increasing the channel separation is generally of greater benefit for 802.11b than 802.11g [9] [10].

Even the WiFi controller has several useful functions for managing the WLAN system, some very important tasks needs more services. The controller cannot see things from the ground level or locate all rogue access points. It cannot detect all network failures situations, such


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as AP bridging failures, coverage holes or controller/AP system failures. In general the controller basic software has no mapping possibility of the physical environment modules like walls, floors, and cannot offer real time location service to the network management staff. To view in detail the WLAN services characteristics in a given physical site further processing of the detected values is needed. For this purposes dedicated and expensive software tools exists which depend strictly by the controller hardware and other characteristics of the manufacturer. These intelligent software tools take in consideration not only the signals and the SNR (Signal to Noise Ratio), but the placement of physical obstacles (walls, buildings, etc.) introduced manually by the management staff.

IV. SYSTEM MODEL OF THE WIFI PROPAGATION In wireless network the communication is affected by

propagation effects (path loss, shadowing, and multipath fading), network interference (accumulation of signals radiated by other transmitters) and thermal noise (modeled as additive white Gaussian noise AWGN). Interference modeling has increased interest in the ultrawideband (UWB) and narrowband (NB) technologies (GSM, GPS, WiFi, WiMAX). The interference is modeled usually as a Gaussian random process. Such situation happens when high number of independent signals is accumulated and no term dominates the sum. However, in the time hopping UWB systems with large number of dominance interferers the central limit theorem cannot be applied. In many cases the probability density function (pdf) of the interference has a heavier tail than what is belongs to the Gaussian model.

The 2D model we are using in this paper is described in detail in framework [11]. The interference is affected by three physical parameters: - Spatial distribution of the scattered interferers; - Transmission characteristics of the interferers (modulation, power and synchronization); - Propagation characteristic of the medium (path lost, shadowing, multipath fading).

When the mobile terminal positions (interferers) are not

known, it can be considered randomly as a homogeneous Poisson point process (see Figure 1) [11]. The probability of n nodes being inside a region R depends on the total area AR of the region and is given by

0n,e!n

ARinn RA

nR (1)

where is constant spatial density of interfering nodes, in nodes per unit area [11]. It is supposed that the model depends only on the [11]. The probe link with length r0 connects the probe receiver node RX to the probe transmitter node TX. All other nodes (i 1) are interfering nodes, whose random distances from the origin are R1 R2 R3

The power PRX received at a distance R from a transmitter is given by

,R

ZPP

b2k kTX

RX (2)

where PTX is the average power measured 1 m away from the transmitter; b is the amplitude loss exponent (with the corresponding power loss exponent 2b); and {Zk} are independent random variables, which account for propagation effects such as multipath fading and shadowing [11]. The amplitude loss exponent b is dependent of the environment: b = 0.8 for hallways inside buildings, b = 1 for free-space propagation, and b = 4 for dense urban environments [11]. This model is general enough to account for various propagation scenarios [11]:

- Path loss only: Z1 = 1. - Pass loss and Nakagami-m fading: Z1 2, where

2~G(x, , (x=m, is a gamma distribution with mean =x =1, and variance x 2 = 1/m.

- Path loss and log-normal shadowing: Z1=e G, where G~N( , 2), is a Gaussian distribution with mean =0 and variance . The term e has a log-normal distribution with the shadowing coefficient .

- Path loss, Nakagai-m fading and log-normal shadowing: Z1 2, with 2~G(m,1/m), and Z2=e G with G~N(0,1).

The time variation of the distances {Ri of the interferers is strongly dependent of the shadowing {Gi},

affecting those sources [11]. It is considered that Ri(t) Ri and Gi(t) Gi over at least the duration of a protocol data unit transmission, Lp.

The distances {Ri} and propagation effects {Zi,k} associated with node I are slowly varying and remain constant during the packet transmission [11]. It is defined term SINR (Signal-to-Interference-Plus-Noise Ratio) with the following form [11]:

NI

SSINR (3)

where S is the power of the desired signal received from the probe node, I is the aggregate interference power received from all other nodes in the network, and N is the constant noise power [11]. Both S and I depend on a given realization of {Ri} and {Zi,k}. Using (2), the desired signal power S can be written as [11]:

b20

k k,00

r

ZPS (4)

where the subscript 0 refers to the probe link. The aggregate interference power I is given with the following relation [11]:

Figure 1. WiFi AP coordinates and campus dimensions


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1ib2

i

i

1ib2

i

k k,iiI

R

P

R

ZPI (5)

where PI is the transmitted power associated with each interferer [11]. i [0,1] and Pi is the random duty-cycle factor and the arbitrary quantity (incorporating propagation effects such as multipath fading or shadowing) associated with interferer i, respectively [11]. The random variable i accounts for the traffic patterns of nodes and is equal to the fraction of the duration Lp (length of packet) during which interferer i is effectively transmitting. Since S and I depend on the random node positions and random propagation effects, they can be seen random variables. The distribution of the I has a skewed stable distribution depending on the traffic pattern of the nodes only through i)1/b} [11]. There is no outage on the radio channel during the transmission if the

[11].

V. MEASUREMENT SCENARIO In our environment we measured the noise level of a

given university campus with 54 access points installed in different buildings. All the APs are set in the interior environment (see Figure 1).

The physical size of the campus is 240 m x 580 m. The

WiFi controller communicates with the APs through CAPWAP protocol (see Figure 4), and the transmission between the controller and sampling node was a UDP based SNMP (Simple Network Management Protocol).

The Requesti command on the sampling node was

executed once in each 30 seconds (Ti and the measurement was done during 24 hours starting at 16:45 on 19-April-2013 (N = 24 x 60 x 2 = 2880 epochs). It is important to note that this measurement interval is weekend time with minimum number of active users on the campus. The response time (Tri, i WiFi controller was not constant because the wired network and controller load modification during the measurement.

There were sampled 54 APs having 13 and 16 active

radio channels in the 2.4 GHz and 5 GHz frequency band, respectively. Because of the huge amount of Responsei information coming from the controller in each epoch (54 x 29 = 1566 values), the measured interference values were transmitted by a cluster of SNMP messages. In this way we got 1566 traces, each trace having N values. These traces are time series of the measured interference distributed in physical space and WiFi radio channels k:

29,1k;54,1a;n,1t),t(I ka (6)

We are considering this set of access points as a spatially distributed sensor network, where the sensors are the APs and the sink node is the WiFi controller.

Figure 5. Timeline of the noise measurement in controller based

WiFi system environment

Figure 4. Scenario of the noise measurement in controller based

WiFi system environment

Figure 3. WiFi AP coordinates and campus dimensions

Figure 2. WiFi AP placement in the buildings


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Each access point has 29 active sensors, one for each radio channel. The sensors are measuring the noise level in all 29 channels simultaneously. In each epoch we collected the spatially distributed noise on the campus with the physical characteristics above.

In our case potential interference sources are rogue and foreign APs, mobile devices like notebooks, smart phones of the users, interference of the APs, and other electrical devices (boosters, air conditioners, engines, etc.) belonging to the eight buildings noted with characters A, B, C, D, E, F, G, H. Building A is the newest (8 years old) with lot of reinforced concrete and building B is the oldest (80 year old) one with thick (1 m) walls without metal inside.

Lot of engines is placed in the buildings C and E because of the high number of laboratories and lecture rooms. Building D is equipped with several exterior WiFi access points, because it is the tallest point of the campus. The rest of the buildings have ordinary character from the signal propagation and absorption point of view. Around the campus there are roads and flats with several floors. On the campus site the vegetation is rich with dozens of 10 m tall trees.

VI. FREQUENCY ANALYSIS OF THE WIFI NOISE As we indicated above, the response time, Tr is not

constant because of the stochastic character of the wired network and WiFi controller load. Tr has statistical characteristics listed in Table III. The periodogram power spectra density of the Tr shows heavy tail character (see Figure 5). Because the response time relative variation is less than 2%, we deem the sampling process equidistant in time.

TABLE I. STATISTICAL CHARACTERISTICS OF THE RESPONSE TIME

Mean(Tr) [s]

STD(Tr) [s]

Min(Tr) [s]

Range(Tr) [s]

0.594 0.493 0,322 2

We considered two cases of the interference sources

distribution in the horizontal plane for each building: i) Poisson; ii) uniform. This assumption is realistic because the majority of fixed electrical accessories are placed on the ground floor. The interference sources are considered nodes with the same transmitted power. Based on relation (5) we search the closest value of the amplitude loss

exponent b for each building separately for each radio channel k. Based on (5) the interference power Ia(t), received in a given moment t, by the sensor access point ainside the current building can be calculated with

m

1ii,a

b2i,aa )b,t(P)t(R)t(I (7)

where m is the number of interference sources inside the same building, and Pa,i (t,b)= Pa(t,b) is a random factoraffecting the interference components, independent of the physical space. We get following relation for the interference:

)b(R)b,t(PR)b,t(P)t(I m,aa

m

1i

b2i,aaa (8)

where Ra,m(b) is independent of the time and characterizesthe effect of the relative position of m interference sources to the sensor access point a:

m

1i

b2i,am,a R)b(R (9)

We are interested about the dependence of random factor Pa(t,b) on the amplitude loss exponent b and number of interference sources. We considered five cases for the number of interference sources at each building: m = 10, 20, 50, 100, 200.

Figure 6. Poisson and uniform distributed interference sources in the

buildings (m = 200)

In case of access point AP9 in building B the dependence of the propagation factor Ra,m(b) by the variables m and b is presented on Figure 7.

Independent of the interference distribution type the propagation factor Ra,m(b) depends exponentially on the amplitude loss exponent b and linearly on the number of interference sources m. It was found that he rate of the two surfaces corresponding to the Poisson and uniform distribution is ~2 for b and m small values and increases to ~5.4 for b = 4 and m = 200 large values (see Figure 8).

The dependence of the random factor Pa(t,b) on the amplitude loss exponent b and number of interference sources m is presented in Figure 9.

The histogram of the random factor log(Pa(t,b)) is presented in Figure 10 for IEEE 802.11b and IEEE 802.11a channels in case of access point 9 situated in building B.

Figure 5. Periodogram of the response Time, Tr


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Figure 7. Dependence of the propagation factor Ra,m(b) for AP9

Figure 8. Rate of the propagation factor for Poisson and uniform

distributed noise sources

Figure 9. Dependence of the factor log(Pa(t,b)) for Poisson distributed interference sources (m,b,Ch) = (50,0.8,1), and (m,b,Ch) = (50,2,17),

AP9

It can be observed that both histograms are different but suggest Gaussian character of the logarithm of Pa(t,b).

VII. CONCLUSIONS In this paper we presented a frequency analysis of the

WiFi radio channel interference. Intelligent WiFi access points installed in a LAN environment were utilized as sensor nodes for sampling the interference signals in the internal environment of different buildings. Radio channels in both frequency domains of the IEEE 802.11 technology (2.4/5 GHz) were analyzed in the same manner. Two-dimensional model was applied for the common discussion of the interference phenomenon.

Utilization of the sensor function of the controller based WiFi system makes possible future extension planning of an existing WiFi system without expensive measurement devices.

More analysis is needed for the characterization of amplitude loss exponent in function of the radio channel frequency and the building physical environment.

ACKNOWLEDGEMENT -

4.2.2.C-11/1/KONV-2012-0001 (FIRST Future Internet Research, Services and Technology) project. The project has been supported by the European Union, co-financed by the European Social Fund.

The research was supported partially by the European Union with the help of the GOP-1.3.2-08-2009-0007 program.

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Sensor based analysis of the WiFi interference