A Comprehensive Investigation of UWB SensorNetworks for Industrial Applications

Frank Althaus∗, Francois Chin† Jurgen Kunisch‡, Jorg Pamp‡, Bozidar Radunovic§, Ulrich G. Schuster∗,Florian Trosch∗, Hong Linh Truong§, Martin Weisenhorn§, Rudolf Zetik¶

∗Communication Technology Laboratory, ETH Zurich, Switzerland, email: {althaus, schuster, troesch}@nari.ee.ethz.ch†Institute for Infocomm Research, Singapore, email: chinfrancois@i2r.a-star.edu.sg

‡IMST GmbH, Kamp Lintfort, Germany, email: {kunisch, pamp}@imst.de§IBM Zurich Research Laboratory, Ruschlikon, Switzerland, email: hlt@zurich.ibm.com

¶TU Ilmenau, Germany, email: rudolf.zetik@e-technik.tu-ilmenau.de

Abstract— We present an overview of physical and medium-access-control (MAC) layer research for communication and local-ization in ultra-wideband (UWB) sensor networks for industrialapplications, and provide a summary of our results obtained underthe umbrella of the EU Integrated Project PULSERS. The focus ofour work is on fundamental questions about UWB channel mod-eling, UWB channel capacity, efficient signaling, low-complexityMAC design, and localization and imaging.

I. INTRODUCTION

Data communication and localization and tracking of objectsbecome increasingly important technologies in factories, plants,warehouses, and other industrial environments, as they allow forefficient control and organization of manufacturing processesand logistics, increased workplace safety, and reduced environ-mental impact. We envision a scenario where a cluster of low-complexity sensor nodes (SNs) conveys information to severalaccess points (APs), some of which may be connected to awired backbone network. While individual SNs can be expectedto generate very modest traffic, often only unidirectional, APshave to cope with high aggregated traffic and strong multiaccessinterference; they may be, therefore, equipped with severalantennas. Ultra-wideband (UWB) radio technology seems tobe a well-suited physical layer to cater for the required robust,low-power, ubiquitous data communication; at the same time, ithas the potential to supply accurate location information. Yet, tosuccessfully design UWB sensor networks for industrial applica-tions, a number of fundamental problems need to be addressed:• characterization of the UWB radio channel in factory hallsand large open spaces, • assessment of fundamental limits onthe communication performance achievable over those channels,for point-to-point links as well as for entire wireless networks,• design of signaling schemes that make efficient use of thechannel capacity at low complexity and low power consump-tion, • efficient medium-access-control (MAC) protocols for alarge number of distributed SNs, • derivation of algorithms toprovide location information of wireless nodes and geographicinformation about the environment. We will present researchresults in all theses areas, obtained to a large extend in work-package 3b of the Integrated Project PULSERS, funded by theEuropean Commission under contract FP6-506897. Our goalis to provide an overview of physical and MAC layer issues

and their implications for the design of UWB communicationand localization systems for industrial applications. For specificdetails about our research results, we refer to the individualpublications [1]–[14] and the corresponding deliverables of thePULSERS project.

II. THE ULTRA-WIDEBAND RADIO CHANNEL

The design of reliable UWB communication systems requiresa robust and accurate model of the UWB channel. The propa-gation environment in factories and other open spaces can beexpected to be quite different from the already well-studied officeenvironment.

A. Channel Measurements

We conducted two different measurement campaigns, one in afactory hall [1], and another on in a public open space [2]. Bothmeasurement campaigns characterized time-invariant channels.Time-variant channel measurements will be possible in thesecond phase of the PULSERS project with a novel multiantennatime-domain UWB channel sounder [3].

1) Factory Hall: In our generic setup of an industrial sensornetwork described in the Introduction, SNs are assumed to beattached to or in close proximity to machines or other factoryequipment, while APs are assumed to be mounted sufficientlyhigh above the floor. This leads to three distinct propagationscenarios: (1) The channel between a singleantenna SN and amultiantenna AP, denoted S–A, mostly corresponds to a non-line-of-sight (NLOS) situation, because machines and otherequipment often block the direct path. (2) The multiple-inputmultiple-output (MIMO) channel between APs, referred to asA–A, mainly consists of LOS links. (3) If ad-hoc communicationbetween low-complexity SNs is desired, the channel will mostprobably have a single-input and a single-output (SISO). BothLOS and NLOS conditions are possible. This scenario is referredto as S–S.

We performed channel measurements according to all threescenarios in an industrial hall equipped with machinery. We mea-sured the channel in the frequency domain using omnidirectionalbiconcial antennas especially designed for this measurementcampaign. The measured frequency band ranged from 3 GHz to11 GHz [1].

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Fig. 1. Gain (r.m.s. average over the entire band) versus distance (dots) forthe S–S scenario, and fitted power-law. The dashed lines indicate the basic freespace path gain at 3 GHz and 11 GHz.

2) Open Space: We characterized the UWB channel in anindoor public open space, using frequency-domain and time-domain measurement methods. For the first method, the channelwas kept static and the receiving antenna was moved on avirtual array, while for the second method, both antennas werefixed, and channel variation was induced by moving people. Themeasurement band ranged from 2 GHz to 5 GHz.

B. Modeling Aspects

The power gain G for the S–S scenario in the factory hall,averaged over the band from 3 GHz to 11 GHz, is shown inFig. 1. It follows reasonably well a power law with an exponentmodulus slightly larger than two, which is quite surprising for themostly NLOS S–S settings, and is closer to the free space gainthat would be expected for a sinusoidal signal at 11 GHz than forthe same signal at 3 GHz. The two other scenarios, in contrast,show hardly any distance dependence of the received power, butthe variability of G over different pairs of transmitter–receiverlocations is high [1]. The delay spread in all three scenariosincreases with the distance between transmitter and receiver.

From the data measured in the public space environment, wefound that a complex Gaussian distribution is adequate to modelthe distribution of the channel taps. If the antennas are static andthe environment changes, a strong Ricean component is presentin all taps. The number of independent diversity branches inthe channel increases approximately linearly with bandwidth.Therefore, it seems as if UWB channels were not too differentfrom conventional wideband channels [4].

III. COMMUNICATIONS: CHANNEL AND SYSTEM CAPACITY

The channel capacity is the maximum rate at which reliabletransmission is possible. It is thus a benchmark for real-worldsystems, and provides design insight. We considered the A–Aand the S–S scenario from the envisioned industrial networkdescribed in the Introduction.

A. Multiantenna Systems with Co-Located Antennas

As discussed in Section II, it seems as if UWB channelsare not too different from conventional wideband channels, sothat many results on wideband capacity still hold for UWB

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Fig. 2. Upper bound on the system capacity, derived on the basis of space-frequency-unitary codewords. A 4×4 system and a 16×16 system are comparedfor the same typical UWB link budget with d = 100m and frequency-dependentattenuation. The coherence time of the channel is Tc = 5 µs in both cases.

channels. The capacity measure of interest is the noncoherentergodic capacity, where neither the transmitter nor the receiverhave access to channel state information (CSI). This capacityis unknown even for the SISO case. Additional aspects thatneed to be taken into account when analyzing the capacity ofMIMO UWB channels are regulatory constraints on peak powerand power spectral density (PSD) at the transmitter, frequencydependent link attenuation [15], and the large number of diversitybranches that result from the combination of large bandwidth andmultiple antennas. We adapted bounds on the system capacityof noncoherent wideband systems [16], introduced a simple linkbudget, included the frequency-dependent link attenuation [15],and numerically evaluated these bounds as a function of band-width, the number of antennas, and the channel parameters. Ourresults show that MIMO techniques increase system capacity ifthe received power is large enough, and there is not too muchvariation in the channel over time, frequency, and space; if theseconditions are violated, Fig. 2 illustrates that increasing thenumber of antennas might even reduce system capacity. Hence, asuccessful design needs to be adapted to the operating conditions;MIMO UWB seems to be suitable for short links with highreceived power only.

B. Distributed Multiantenna Systems

Cooperative diversity is a transmission technique where mul-tiple terminals pool their resources to form a virtual antennaarray that realizes spatial diversity gain in a distributed fash-ion. These systems are far less understood than conventionalMIMO systems, so that we had to use very simple channel andsystem models to gain initial insight. We studied the diversityperformance and ergodic and outage capacities of three differentcooperative protocols in both amplify-and-forward (AF) anddecode-and-forward (DF) modes [5], and found that the capacitydepends on the amount of broadcast and receive collision, i.e.the degree to which signals from different users occupy the samedegrees of freedom in signal space. The cooperative protocolscan be analyzed in terms of conventional MIMO gains, whereonly the protocol that implements full broadcast and receivecollision exhibits multiplexing gain. The interpretation in terms

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Fig. 3. Pairwise error probability Ppep of a receiver using partial MMSE channelestimation, as a function of the number of estimated taps and the PDP decayexponent; Es/N0 = 10 dB.

of conventional MIMO gains illustrates how to extend the pre-sented results to larger networks with more cooperating nodes.Full spatial diversity can be achieved by some of the protocols,provided that appropriate power control is used. The design ofspace-time codes to realize these diversity gains for AF relaysystems relies on the same design criteria as for conventionalMIMO channels.

IV. COMMUNICATIONS: SIGNAL DESIGN

Real-world signaling schemes might not be able to achievecapacity, especially if stringent constraints on complexity andpower consumption have to be adhered to, as is the case in SNs.

A. Channel Estimation

The power-delay profile (PDP) of most UWB channels followsan exponential decay characteristic, so that the first few tapsconvey most of the total energy, and can, therefore, be estimatedwith higher precision than later taps in the PDP. For SISOpulse-position modulation (PPM) with guard intervals betweensymbols, it is possible to analytically characterize the impactof the channel estimation error on uncoded error probabilityand achievable rates as a function of SNR, PDP, and delayspread [6]. For exponential PDPs, we find that in general onlya small number of taps needs to be estimated, as illustratedin Fig. 3 for an exponential PDP with decay exponent γ and30 taps. Although our analysis used PPM for simplicity, it can bereadily extended to any modulation scheme that provides accessto individual channel taps, e.g., direct sequences spreading withorthogonal spreading sequences. Consequently, rake receiversin such systems typically need a small number of fingers only.

B. Precoding

Precoding is a viable option to transfer hardware complexityfrom the SNs to the AP. We investigated how to adjust the pulsetrain of a pulse-based transmitter in such a way that the strongestreceived echoes of the signal align coherently at the SN receiver,so that a selective rake receiver with a very small number offingers is sufficient to capture a large part of the total energy.

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Precoding can also be used to efficiently exploit regulatory peak-and average-power limits. If the pulse-repetition rate is low, aconstraint on the peak power leads to an average transmit powerthat is well below the allowed level. To fully exploit both peakand average power constraint, we propose to transmit a train ofseveral pulses for each symbol, where the temporal separation ofpulses is set to the minimum allowable value that still satisfies thepeak power constraint [7]. This increases the total radiated energyper symbol, while it keeps the peak power constant. We call thecombination of these two techniques pulse position precoding(PPP). It significantly reduces receiver complexity at the expenseof increased transmit power and partial CSI at the transmitter.With a single-finger rake receiver, we demonstrated a precodinggain of up to 9 dB compared with single-pulse transmission [7].A further reduction in receiver complexity can be achieved bydispensing of channel estimation and channel state feedbackfrom SN to AP: the AP transmits a peak- and average-power-optimal pulse train designed to maximize the energy in theintegration window of the energy detector at the receiver [8].

V. NETWORK ORGANIZATION AND MEDIUM ACCESS

Traffic generated by SNs is often bursty, with long idle timesbetween transmissions. The most efficient way to save power isthus to shut down most of the SNs during phases of inactivity.

A. Access-Point-Aided Wake-Up

As the receiver consumes a significant part of the overallenergy in a SN, it should be switched off whenever possible. Butto be able to respond to external wake-up signals, the receiverneeds to be active. These contradicting requirements can bereconciled to some extend by implementing a special wake-upradio—a very simple energy detector with low sensitivity. Ifmultiple APs are present, they can be used to selectively wakeup individual nodes by beamforming [9]: the received energy ata given SN is increased, so that the sensitivity, and consequentlythe power consumption, of the wake-up radio can be reduced.Fig. 4 illustrates the operation of this wake-up scheme for asimple scenario. Regions where the wake-up procedure failsare not completely avoidable, but can be reduced if a sufficientnumber of APs are available.

B. Unidirectional Communication

In some applications, the complexity of SNs can be furtherreduced by stripping them off their receive capability. This will

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Fig. 5. Performance comparison of proposed receiver architectures.

allow for low-cost implementation and very low power operation.Without receiver, however, channel sensing is impossible, andnodes cannot cooperate. Interfering uncoordinated transmissionsmay thus lead to packet errors, especially if traffic intensity ishigh.

The number of successfully received data packets can beincreased significantly by introducing a detection threshold atthe AP receiver. Packets with received signal strength below thethreshold will be ignored, so that the receiver only attempts todecode packets that stand a high chance to “survive” possibleinterference. The detection threshold is adapted to the trafficintensity to capture as many packets as possible [10].

This “adaptive threshold receiver” can be further improvedupon by adding another acquisition circuit, which constantlymonitors the channel for new packets. If a new packet is detectedthat has a higher signal strength than the packet currently beingreceived, the receiver stops receiving the current packet andswitches to the new, stronger, one. In this way, even packetswith very low signal strength can be received as long as thereis no interference in the channel. We call this type of receiver”switching receiver”.

Fig. 5 shows the performance of the two proposed receiversand the performance of a ”simple” receiver, i.e., a receiver withfixed threshold that can receive only one packet at a time. Thenetwork considered consisted of 50 SNs distributed uniformlyin a 40 m×40 m square, with AP located at the center. All SNsgenerated Poisson traffic with the same intensity. When the trafficintensity is low, there is no difference between the three receivertypes; but with increased traffic, and consequently increasedinterference, the fraction of the total number of packets receivedby the adaptive receiver is twice that of the simple receiver, andthe switching receiver even achieves a four-fold increase.

VI. LOCALIZATION

Communication is a necessity for industrial sensor networks,but the unique advantage of UWB arises from the combination ofcommunication with localization. Algorithms based on measure-ments of the time of arrival (TOA) of a signal at several receiversare able to yield precise location information. In addition, weenvision two rather different approaches to obtain geographicinformation.

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A. Signal Design for TOA Ranging

To precisely estimate the TOA of a signal under severe mul-tipath conditions with various types of detectors, the receivedand filtered signal should exhibit a single precisely located peak.Thus, the transmitted signal should have perfect autocorrelation.To distinguish ranging signals from different transmitters, a setof signals with (almost) vanishing cross-correlation is desirable.To this end, we proposed Ternary Base Sequences [11], whichwere adopted in the IEEE 802.15.4a draft standard.

B. Geo-Regioning

For many applications, it is not the exact position of the sensornode that matters, but one wants to know if a given node islocated, say, in shelve three or in shelve five of a high-rise rack.Such rough localization might be possible solely by means of thePDP of the channel between a given node and an AP. We assumethat the PDPs of different spatial regions are known, and try toclassify the node location as belonging to one of these regions bycomparing the channel estimated from the received signal withthe different PDPs. We refer to this approach as “geo-regioning”.It is particularly suited for low-complexity nodes, because nosignal processing is required at the transmitter. In contrast toTOA estimation, the algorithm benefits from rich multipathpropagation conditions. To study feasibility and performance, wemeasured about 1800 impulse responses in each of 22 regions ina warehouse-like scenario; the base of each region has size lessthan one square meter. The performance of a first algorithm [12]on the basis of the maximum likelihood principle is shown inFig. 6, where the probability of error in deciding between a givenregion R03 and other regions is plotted. The most difficult regionto distinguish is region R02, which is a direct neighbor of regionR03. Error probabilities < 10−2 can be achieved for almost allmeasured regions at reasonable SNR.

C. Imaging of the Environment

Instead of obtaining information about the location of sensornodes, it is also possible to use sensor nodes and APs to gatherinformation about the environment surrounding them [13]. Tothis end, one or more sensor nodes are moved on known trajec-tories through the environment and illuminate it from different

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positions with their transmitted signals. The channel impulseresponses recorded by fixed APs provide enough information forthe geometrical reconstruction of the actual propagation environ-ment in terms of location, shape, and size of the scattering objects.Fig. 7 shows the image of an environment reconstructed fromsignals emitted along the indicated path by a mobile UWB trans-mitter and measured by two APs. The Kirchhoff migration algo-rithm was used to reconstruct the image of the environment [14].

VII. CONCLUSIONS

Industrial sensor networks enable a great variety of applica-tions, each of which comes with specific requirements on per-formance, power consumption, and cost. Deployment scenarioswill be vastly different for different applications, so that a widerange of channel conditions may be encountered. We have shownhow many of these particularities can be used to optimize thesystem: Simple energy detector receivers, or even no receivers atall at the SNs reduce power consumption but decrease achievablerates, while node cooperation or the use of co-located antennascan lead to an increase in throughput in situations where thereceived power per degree of freedom is sufficiently high. Ifthe frequency-dependent attenuation of the link is significant, itmight be advisable not to use the full bandwidth allocated byregulatory bodies. Because this attenuation depends to a largeextend on the antennas, it becomes evident that sensible antennamodels are needed early on in the design of the system. If thenodes hardly move, the Ricean component in the channel canbe exploited, channel estimation simplified, and coding for time-diversity becomes dispensable. For battery-powered SNs thatgenerate low average traffic, high burst data rates help to keep thenode in deep sleep mode most of the time. Here, using multipletransmit antennas might actually help to boost the peak rate,while only a single receive chain of low sensitivity, and thus lowpower consumption, needs to be active at all times if the proposedwake-up scheme is used. The high temporal resolution of UWBsignals can be used in several ways to obtain information about

the location of SNs or about the environment. TOA techniquesand the geo-regioning approach can be combined with datatransmission; no provision for geo-regioning is necessary at theSNs, as all signal processing is performed at the AP.

In summary, an industrial UWB sensor network that is tailoredto the specific application at hand has the potential to offerreliable communication at low power consumption, and provideadditional location information. The big challenge is now tointelligently combine the presented techniques into devices thatcan be produced at low cost.

ACKNOWLEDGEMENT

We would like to acknowledge the work of our colleaguesYuen-Sam Kwok, Zhongding Lei, Rohit Nabar, Christophe Rob-lin, and Alain Sibille, who also contributed significantly to theresults of work-package 3b in the PULSERS project.

REFERENCES

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[8] ——, “Modified pulse repetition coding boosting energy detector perfor-mance in low data rate systems,” in Proc. Int. Conf. Ultra-Wideband (ICU),Zurich, Switzerland, Sep. 2005, pp. 508–513.

[9] ——, “A simple wake-up scheme based on ultra-wideband beamforming,”in Proc. Workshop Smart Antennas, Ulm, Germany, Mar. 2006, to appear.

[10] B. Radunovic, H. L. Truong, and M. Weisenhorn, “Receiver architecturesfor UWB-based transmit-only sensor networks,” in Proc. Int. Conf. Ultra-Wideband (ICU), Zurich, Switzerland, Sep. 2005, pp. 379–384.

[11] Z. Lei, F. Chin, and Y.-S. Kwok, “UWB ranging with energy detectionusing ternary preamble sequences,” in Proc. IEEE Wireless Commun. Netw.Conf. (WCNC), Las Vegas, NV, U.S.A., 2006, to appear.

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[13] R. Zetik, J. Sachs, and R. Thoma, “Distributed UWB MIMO sounding forevaluation of cooperative localization principles in sensor networks,” inProc. Eur. Signal Process. Conf. (EUSIPCO), Florence, Italy, Sep. 2006,accepted for publication.

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[15] J. Kunisch and J. Pamp, “Measurement results and modeling aspects forthe UWB radio channel,” in IEEE Conf. Ultra Wideband Syst. Technol.(UWBST) Dig. Tech. Papers, Baltimore, MD, U.S.A., May 2002, pp. 19–23.

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