Analysis of Error and Time Behavior of the IEEE 802.15.4 PHY-Layer in anIndustrial Environment

Andreas Vedral, Jorg F. WollertFachhochschule Bochum - University of Applied Science

Lennershofstr.14044801 Bochum, Germany

andreas.vedral, joerg.wollert @fh-bochum.de

Abstract

In this analysis a practical characterization of a trans-mission channel was performed based on the Gilbert-Elliott model (GE). The model’s parameters were deter-mined by the Baum-Welch algorithm and evaluated withthe Kolmogorov-Smirnov hypothesis test. In this connec-tion the burst error density was chosen as themain factor to characterize the channel. In the mea-surements a standard transceiver of the type MC 13192(manufacturer: Freescale Semiconductor) compliant tothe IEEE 802.15.4 PHY was used. Stationary as well asmobile measurements were performed in the environmentof an industrial high rack warehouse.

1 Introduction and Arrangement of thisAnalysis

Nowadays wireless transmission systems are an inte-gral part of communication systems for industrial envi-ronments. But especially in time critical domains of theautomation field the usage of wireless transmission sys-tems is normally not taken into consideration. Due tomultiple sources causing transmission errors [1, 2], e.g.small scale fading, multipath spread , multi-user in-terferences and interfering components of machines, thechannels behavior is hardly predictable. Generally, priorthe installation of a wireless transmission system, compre-hensive measurements have to been taken place in orderto guarantee for a safe functionality. From the analogouspoint of view the signal to noise ratio ( ) is often usedas a characterizing parameter. But digital characteristics,e.g. the mean bit error rate (BER) and its variance, aresignificant parameters to describe a transmission channelas well.

In comparison to the digital characteristics the deter-mination of the analogous characteristics is much moredemanding. Special measurement devices and thereforequalified skilled personnel are needed. In addition to that,the missing knowledge of the bit error distribution withinthe data packets makes it impossible to evaluate the in-

fluences of channel codes (minimum Hamming distance) and interleaving methods on the base of analogous

characteristics. In contrast to that, digital characteristicsare easier to determine and reflect the expected channelbehavior more precisely. However the mean bit error ra-tio and its variance aren’t sufficient enough for the de-tailed characterization due to the fact that the propertiesof channel codes or interleaving procedures could not betaken into a mathematical closed-form with the transmis-sion channel’s parameters.

By reason of these weak points you see that additionaldigital characteristics are required for a sufficient descrip-tion of the transmission channel which allows a mathe-matical closed-form expression between the properties ofchannel codes or interleaving procedures and channel’sparameters. Thereby, it would be possible to computethe packet error behavior and the rate of re-transmitteddata packets to get better knowledge of the expected per-formance of the wireless transmission system. With thismathematical description of the corresponding channelstructure, you can directly see which modifications are re-quired, if the wireless transmission system does not fulfillthe given requirements. Especially with respect to adap-tive procedures improving the deterministic transmissionof wireless systems, e.g. Deadline-Aware Hybrid II ARQ[3] or adaptive FEC [4, 5], a much more efficient selec-tion of error correcting codes and the interleaving degreerespectively could be realized by the precise knowledge ofthe channel characteristics.

Therefore, empirical analyses to characterize trans-mission channels in an industrial environment with ra-dio transceivers (compliant to the IEEE 802.15.4 -PHY[6, 7]) have in this publication - as far as the authorsknow - been performed for the first time. The remain-ing part of this paper is organized as follows. Chaptertwo deals with the methods to characterize a radio chan-nel. This includes a short presentation of the structure ofa digital transmission channel, the GE model, the Baum-Welch algorithm to determine the models parameters andthe Kolmogorov-Smirnov test to evaluate the determinedparameters. Chapter three describes the measurement sys-tems, the industrial environment and the defined measure-

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ment scenarios. After this the results of the analyses arepresented in chapter four. Chapter five gives an overviewof related scientific researches which inspired the authorsduring this analysis. Finally, a concluding evaluation andpossibilities to improve the description of a transmissionchannel are shown in chapter six.

2 Characterization of Digital Radio Chan-nels

As already mentioned in the introduction, this paperfocuses on the digital point of view of wireless communi-cation systems. All analogous components (de-, modula-tor) as well as error causing influences (small scale fading,multipath spread, etc.) are abstracted by the use of a digi-tal data source, digital data sink and a digital error source(see figure 1).

Figure 1. Block Diagram of a Digital Com-munication System

The whole communication system is now reduced toan input vector , an output vector and an error vector. While a ’0’ within the error vector corresponds to an

error free transmission of a bit, a ’1’ defines an erroneoustransmission of a bit. The error vector , which representsthe transmission channel, is computed on the basis of aXOR operation of the system’s input and output vectors,

. In order to mathematically describe the er-ror vector to characterize the channel, the parameteriza-tion of a digital channel model with memory was chosen.Well known channel models of this type are the Gilbert,the Gilbert-Elliott and the Fritchman model. Research ofWillig et. al. [8] showed some weakness of the Gilbertmodel [9] by modeling a radio channel based on the IEEE802.11. Therefore, during this analysis a closer look wastaken at the extended Gilbert-Elliott model [10].

The Gilbert-Elliott model defines two states, Good andBad (see figure 2), in which the channel remains duringa transmission of a bit. The probability of an erroneoustransmission of a bit while the channel remains in theGood state is , the probability of an er-roneous transmission of a bit while the channel remains inthe Bad state is . The probabilities

of the state transmissions or are re-spectively . The probabilities to remain in a stateor are respectively .

Figure 2. State Diagram of the Gilbert-ElliottModel

By means of the characteristics it is nowpossible to compute the transmission channel’s burst errordensity 1 [10]. Since it is not possible to deter-mine the state sequence directly on the basis of the mea-sured error vector , the GE Model belongs to the class ofHidden Markov Models [11]. In this research the param-eterization of the GE Model is done via the Baum-Welchalgorithm [12]. The Baum-Welch algorithm belongs tothe group of expectation-maximation algorithms. It opti-mizes the model’s parameters to maximize the probabilitythat the observed realization of the error vector was gen-erated with these parameters. After that the cumulativedistribution of the burst error density - generatedby these optimized parameters - is tested against the em-pirical measured cumulative burst error densityon the basis of the Kolmogorov-Smirnov [13] hypothesistest with a significance level of = 0.1.

3 Description of the Measurement Setup

3.1 IEEE 802.15.4 - Freescale MC 13192 EvaluationBoard

In this analysis an IEEE 802.15.4 compliant transceiverof the type MC 13192 [14] for the 2.4 GHz frequencyband (Manufacturer Freescale Semiconductor) mountedon the development board MC 13192-EVB was used.The transceiver’s nominal transmission power is specifiedwith 0 dBm, but can - by the use of software - be ad-justed within the range from -27 dBm up to a maximumof 4 dBm. In order to determine the exact transmissionpower, a spectrum analyzer was directly connected to theMC 13192-EVB. Adjusted to 0 dBm, a real transmissionpower of -4 dBm was measured. The sensitivity of thetransceiver is specified with a packet error rate ofby a given signal level of -92 dBm. Compliant to the IEEE802.15.4 specification for the 2.4 GHz frequency band,a 2 Mchip/s O-QPSK modulation with Direct SequenceSpread Spectrum (DSSS) was applied. The resulting max-imum data rate amounts 250 kBit/s.

To handle the data link layer procedures (channel ac-cess, ARQ), an additional external 8 bit micro controller(HCS08 - Freescale Semiconductor) was mounted on thedevelopment board. Since this analysis primarily dealswith the measurement of the error vector , the Simple

1Probability of erroneous bits in a block of the length .

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MAC (SMAC)2 [15] was appointed. With the usage ofthe SMAC all CRC checks and ARQ procedures could bedeactivated.

3.2 Measurement Setup for the Determination of theTransmission Delay

Besides the measurements to characterize the wirelesschannel, additional measurements to determine the trans-mission delay were performed on the system’s integrationinterfaces. In this case the RS 232 interfaces to the MC13192-EVB were used. The transmission delay was de-termined with an oscilloscope, which measured the timedifference between the TX and RX signals. An evalua-tion software based on Matlab automatically gathered thevalues of the transmission delay from the oscilloscope toperform further statistical analyses (see figure 3). Forthe determination of the transmission delay 10.000 datapackets were transmitted between both investigated MC13192-EVBs. In order to avoid additional ARQ cycles,the SMAC software was used on the MC 13192-EVB’sMAC processor.

Figure 3. Measurement Setup for the Deter-mination of the Transmission Delay

3.3 Measurement Setup for the Determination of er-ror vector

The determination of the error vector was performedby the measurement software VisualBER which is alsobased on Matlab. VisualBER was developed by Altrockand Buda [16] in the scope of a diploma thesis at the Uni-versity of Applied Science in Bochum, Germany. Thefunctional operation can be described as follows. Visu-alBER generates uncorrelated bit sequences based on aBernoulli generator which are separated into data packetsand directly passed to the transmitting MC 13192-EVBvia the RS 232 interface. The transmitting MC 13192-EVB embeds the data packets one-to-one into the pay-load data field of the PPDU. Afterwards these data packetsare transmitted over the wireless channel and forwardedto the receiving laptop (host processor of the receivingMC 13192-EVB) on the receiver side. The laptop passes

2A light weighted MAC implementation based on the well knownAloha channel access procedure.

these data packets unchanged over the TCP/IP based re-turn channel back to VisualBER. After the statistical eval-uation and the subsequent parameter estimation using theBaum-Welch algorithm, the error vector is stored in atrace database (see figure 4).

Figure 4. Measurement Setup for Determi-nation of

3.4 Definition of the Measurement ScenariosThe measurements to characterize the wireless chan-

nel were performed in a small automatic high rack ware-house (10 m x 6 m) located in the Laboratory for Soft-ware Technology and Computer Networks at the Univer-sity of Applied Science in Bochum, Germany. The trans-mitting device was stationary mounted on the master com-puter of the high rack warehouse. The receiving devicewas directly assembled on the mobile storage and retrieval(S&R) machine. As potential error sources the electricdrive of the S&R machine as well as a frequency converterare present in the environment of the high rack warehouse.

In the scope of this research two different kinds ofmeasurements were accomplished. On the one hand,four short term measurements (SHORTTERM - ST), eachwith a number of 10.000 transmitted data packets (equiv-alent to 9.840.000 Bits), on the other hand, two longterm measurements (LONGTERM - LT) each with a mil-lion transmitting data packets (equivalent to 984.000.000Bits). Besides the stationary short term measurementST STAT 7m, three further mobile scenarios ST HOR,ST VERT and ST RAND were defined. During thesescenarios the S&R machine moved with a maximum ve-locity of through the high rack. The scenarioST HOR defines a horizontal movement with a variabledistance in the range of 0.5m - 7m between transmitterand receiver. In the scenario ST VERT the S&R machinevertically moved with a variable distance in the range of7m - 9m. A random generated motion which simulates thenormal operation mode of the high rack is defined by thescenario ST RAND. The following table 1 gives a tab-ulated overview of the different short term measurementscenarios.

To perform measurements, which give significant ex-pertise even in the case of a low number of erroneousbits, two long term (LONGTERM - LT) scenarios weredefined. In the long term measurement one million data

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Scenario ST STAT 7m ST HOR ST VERT ST RANDProperty

Device Freescale MC 13192-EVB Development BoardDistance T-R 7 m 0.5-7 m 7-9 m 0.5-9 mMotion fixed movedLine of Sight permanent obstructedTransmit Power -4 dBm (max.)Packet Type IEEE 802.15.4 PPDU - 123 Byte payload# Packets 10.000

Table 1. Description of the short term mea-surement scenarios

packets were transmitted instead of 10.000. The scenarioLT STAT 7m is equivalent to ST STAT 7m as well asLT RAND and ST RAND.

Scenario LT STAT 7m LT RANDProperty

Device Freescale MC 13192-EVB Development BoardDistance T-R 7 m 0.5-9 mMotion fixed movedLine of Sight permanent obstructedTransmit Power -4 dBm (max.)Packet Type IEEE 802.15.4 PPDU - 123 Byte payload# Packets 1.000.000

Table 2. Description of the long term mea-surement scenarios

4 Presentation of the Measurement Results

4.1 Transmission DelayFigure 5 depicts the histogram of the transmission de-

lay. With a jitter of 465 s the MC 13192-EVBs showeda very good deterministic behavior. The measured min-imum value was , the maximum value

and the computed mean value was 2.414ms, its variance amounted 164.5 s.

Figure 5. Histogram Transmission Delay

4.2 Error BehaviorDuring the analysis of the error behavior, the MC

13192-EVBs showed an unexpectedly good bit error be-havior. No bit errors were measured while performingthe scenario ST STAT 7m. In addition to that, a lostpacket rate of was measured. The maximum num-ber of 280 erroneous bits was measured in the scenario

ST RAND. Hence, the bit error ratio was computed to. The maximum lost packet ratio of in

the scenario (ST VERT) was still significantly lower thanthe specified sensitivity of the transceiver (PER @-92 dBm). In comparison to all other configurations thehighest bit error rate of caused by erroneousdata packets was measured in the scenario ST RAND(normal operation mode of the high rack). With respectto wireless communication systems, the bit error ratio’smagnitude was still very low. Surprisingly, the amount ofbit errors caused by erroneous packets was significantlylower than the amount of bit error caused by lost datapackets. Interferences with other 2.4 GHz radio devices,which could be reasonable for the relative high rate of lostdata packets, could be excluded by the employment of aspectrum analyzer. For this reason, the failing synchro-nization on packet’s preamble seems to be responsible forthe lost data packets. The following table 3 contains acomplete overview of the short term analyses.

Scenario ST STAT 7m ST HORResults

# of tx packets 10.000 packets (9.840.000 bits)# of lost packets 5 7# of error packets 0 3# of bit errors 0 93bit error rate with LPbit error rate without LP 0

Scenario ST VERT ST RANDResults

# of tx packets 10.000 packets (9.840.000 bits)# of lost packets 17 8# of error packets 0 6# of bit errors 0 280bit error rate with LPbit error rate without LP 0

Table 3. Short Term Statistics for the ErrorBehavior

Within the scope of the long term analyses(LONGTERM), more emphasis was put on the in-vestigation of the bit error behavior during the stationaryscenario LT STAT 7m and the normal operation modescenario LT RAND. By this, meaningful statisticalstatements on the distribution of the distances betweenneighboring bit errors and burst errors were gathered,which were necessary for the evaluation of the Gilbert-Elliott model. Corresponding to the short term analysisof the stationary configuration ST STAT 7m, the longterm analysis LT STAT 7m showed a lost packet rate of

. In addition to that, again no erroneous datapackets were detected. The comparison of the long termand short term analysis of the normal operation mode(ST RAND, LT RAND) yielded in similar results.

The following table 4 shows a complete overview ofthe performed long term analyses.

Describing the error behavior of wireless communica-tion channels the burst error behavior is of particular inter-est. Besides the mentioned phenomenon of the lost datapackets, the main reason for a re-transmission of an erro-

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Scenario LT STAT 7m LT RANDResults

# of tx packets 1.000.000 packets (984.000.000 bits)# of lost packets 640 704# of error packets 0 560# of bit errors 0 20.880bit error rate with LPbit error rate without LP 0

Table 4. Long Term Statistics for the ErrorBehavior

neous data packet are strong burst errors, which could notbe repaired by weak forward error correcting codes. Toimprove the representation of the burst error behavior inthis work, the error vector was separated into random biterrors and burst errors by means of the burst error weights

(ratio of errors within a burst and the burst length ).The error vector of the scenario LT RAND was classi-fied for the weights . Figure 6 depictsthe corresponding burst error lengths classified by theseburst error weights.

Figure 6. Histogram burst length for

0 10 20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35burst error lengths (W = 0.4 )

lenghts

# of

bur

st e

rror

s in

per

cent

max = 739; mean = 22.93; variance = 10956; # of burst errors = 1856; # of bit errors = 160

0 10 20 30 40 50 60 700

5

10

15

20

25

30burst error lengths (W = 0.6 )

lenghts

# of

bur

st e

rror

s in

per

cent

max = 65; mean = 7.4; variance = 89.16; # of burst errors = 3888; # of bit errors = 1552

0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60burst error lengths (W = 0.8 )

lenghts

# of

bur

st e

rror

s in

per

cent

max = 17; mean = 3.55; variance = 6.19; # of burst errors = 4816; # of bit errors = 5440

0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60

burst error lengths (W = 1 )

lenghts

# of

bur

st e

rror

s in

per

cent

max = 9; mean = 2.58; variance = 0.97; # of burst errors = 5696; # of bit errors = 6192

The relation of bit errors and burst errors was about 1:1( ). For the weight we could discoverburst error lengths between . Since the IEEE802.15.4 standard only uses error detecting codes (16 BitCRC-CCITT) each bit error leads to the re-transmissionof the whole data packet.

4.3 Capability of the GE Model to Characterize Ra-dio Channels

On the basis of the Baum-Welch algorithm the pa-rameters of the GE Model were determined for the sce-narios ST HOR, ST RAND, and LT RAND 3. The ac-curacy of these parameters was verified based on the

3Because of the absence of erroneous data packets in ST STAT 7mand ST VERT an estimation of the GE parameters was unnecessary.

Kolmogorov-Smirnov test (significance level )between the empirical measured burst error density dis-tribution of and the computed burst error densitydistribution of .

Scenario ST HOR ST RAND LT RANDGE Parameter

0.531 0.418 0.432

Table 5. Estimated and Proofed GE Parame-ters

In particular, the high values of the parameter (prob-ability of the occurrences of bit errors in the state bad)clearly shows a strong statistical binding of bit errors.Therefore the measured channel in this work can be labelas bursty.

5 Related Work

In this chapter other research that is related to bit andpacket error behavior investigations is mentioned. Firstdigital analyses about channel behavior should be ex-tracted from the work of Eckhardt, Steenkiste[17, 18] andNguyen et.al [19]. These analyses just showed simplestatistics (mean value and variance) on the channel behav-ior without respect to the correlation of adjacent bit errors.Willig et al.[8] included these statistics and did researchon the statistical behavior of bit and burst errors in an in-dustrial environment for the first time in more detail. Toidentify burst errors the characteristic parameter (theallowed number of consecutive zeros (no error) within theerror burst) was introduced. An IEEE 802.11-compliantradio transceiver was used to measure the wireless chan-nel in an industrial environment. Different error models,including the Gilbert model were parameterized and com-pared afterwards. Furthermore Osmann’ treatise [20] of-fers a good overview about the advantages and disadvan-tages of error models as well as an exact mathematicaldescription.

6 Conclusion

Comparing the measurement results of the MC13192transceivers with Bluetooth systems [21], a very good de-terministic behavior was observed. A fast transmissionin the range of about 2 ms with a jitter of about 500 scould be determined. Nevertheless one has to consider thefact that the implemented SMAC is not compliant withthe IEEE 802.15.4 MAC layer. By implementing an IEEE802.15.4 compliant MAC layer you have to expect a boostof the transmission delay up to about 5 ms.

On the basis of the GE model, the measured radio chan-nels could be classified successfully. We have measureda low number of erroneous data packets. The distribution

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of the occurrences of erroneous data packets showed ran-dom character within a trace. In this case the appearanceof erroneous data packets in a trace could be modeled bya memory less process. In contrast to that, a really strongstatistical binding between successive bit errors within adata packet was determined. Often more than 20 percentof erroneous data bits occur within a single data packet,which makes compensation by the usage of forward cor-recting codes difficult. So a sufficient modeling on bit er-ror level is only possible on the basis on channel modelswith memory. A drawback of the in this work appliedBaum-Welch algorithm concerned the high computationtime to determine the GE parameters. That’s the reasonwhy a direct implementation of this channel classificationin a MAC processing microcontroller is not possible, yet.Therefore, further analyses on more efficient methods tocharacterize a radio channel are still in progress. Further-more the analysis of the error behavior showed a low rateof lost data packets. Nonetheless this packet loss cannotbe ignored in order to design realistic radio channel mod-els. The work on a hybrid channel model, dealing withbits and packets, based on a Markov-Chain of first orderwith the states Lost packet, Error Free packet and Erro-neous packet, is still in progress. Within the erroneouspacket state, the GE model could be applied to model theburst error characteristics.

7 Acknowledgment

The authors appreciate the graduating student’s RobinAltrock and Aurel Buda effort and support during the de-velopment of the evaluation environment VisualBER andthe statistical evaluation of the measurement results. Fur-thermore the authors thank Dr. Kupris (Freescale Semi-conductor) for the provision of the MC 13192-EVB de-velopment boards as well as the anonymous reviewers forthe helpful comments to improve the quality of this work.

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