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1

Introduction

Wireless-system designers are faced with numerous challenges, including limitedavailability of radio-frequency spectrum and transmission problems caused by such factors asfading and multipath distortion. Meanwhile, there is increasing demand for higher data rates,

betterquality service, fewer dropped calls, and higher network capacity. Meeting these needsrequires new echniques that improve spectral efficiency and network links operationalreliability. Multiple-input-multiple-output technology promises a cost-effective way to

provide these capabilities.MIMO uses antenna arrays at both the transmitter and receiver. Algorithms in a radio

chipset send information out over the antennas. The radio signals reflect off objects, creatingmultiple paths that in conventional radios cause interference and fading. But MIMO sendsdata over these multiple paths, thereby increasing the amount of information the systemcarries. The data is received by multiple antennas and recombined properly by other MIMOalgorithms.

This technology promises to let engineers scale up wireless bandwidth or increasetransmission ranges. MIMO is an underlying technique for carrying data. It operates at the

physical layer, below the protocols used to carry the data, so its channels can work withvirtually any wireless transmission protocol. For example,MIMO can be used with the

popular IEEE 802.11 (Wi-Fi) technology.For these reasons, MIMO eventually will become the standard for carrying almost all

wireless traffic, according to Greg Raleigh, president and CEO of wireless vendor AirgoNetworks. MIMO still must prove itself in largescale, real-world implementations, and itmust overcome several obstacles to its success, including energy consumption, cost, andcompetition from similar technologies. Nonetheless, said Craig Mathias, an analyst with TheFarpoint Group, a wireless communications and computing consultancy, We think it will

become a core technology in wireless systems. It is really the only economical way toincrease bandwidth and range.

MIMO

Numerous companiesincluding Airgo, Intel, and Lucent Technologies have announced

plans to release MIMO-based products.

Airgo plans to release the first MIMO chips forincorporation in wireless LAN cards.

MIMO background

MIMO was originally conceived in the early 1970s by Bell Labs engineers trying to addressthe bandwidth limitations that signal interference caused in large, high-capacity cables. Atthe time, however, the processing power necessary to handle MIMO signals was tooexpensive to be practical. Advances to and cost reductions in signal-processing technology,coupled with increased demands to overcome the limits of existing mobile communicationsapproaches, have since led researchers to reconsider MIMO for wireless systems.


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Signals in a wireless system frequently reflect off objects en route to the recipient and bouncealong different paths. At various points, the signals become out of synch, thereby scramblingthe received transmission and decreasing bandwidth, creating a problem called multipathdistortion.

As Figure 1 shows, MIMO takes advantage of this situation by sending a singletransmission from two or more antennas to bounce along multiple paths to a receiver. Puttingdata on multiple signal paths increases the amount of information a system can carry and thenumber of users it can serve. In addition, this approach lets a system divide a single data setinto parts that are sent over multiple paths in parallel. This lets the system handle theinformationfaster than approaches that send data over a single path. For example, first-generation MIMO

products would double IEEE 802.11s theoretical maximum data rate from 54 to 108 Mbitsper second.

Figure 1. MIMO sends a transmission from multiple antennas to bounce over multiple paths to a

receiver, in this case using the overriding IEEE 802.11 wireless LAN technology. MIMO has several

benefits.

For example, it increases the amount of information a system can carry, which allows it to serve more

users. In addition, a system can divide a single data set into parts that it sends over multiple paths in

parallel, thereby handling the information more quickly than data sent over a single path.

The nature of the signals on each path is changed slightly based on the differentantennas from which they are sent, the spacing of the antennas, and the type of interferencethe signals encounter.The recipients system analyzes this information via matrix-manipulation signal-processing technology which cross-correlates the signals to detect theirvarious paths and reconstitute them properly.

This process also reduces the effects of interference.Moreover, by spreading atransmission signal across multiple paths, MIMO increases the chance that any given pathwill reach the destination, which improves link reliability.

In addition, MIMO systems can choose from the multiple antennas they work with touse those with the clearest signals. This reduces error rates and improves communicationquality.


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can be resolved over longer distances than technologies whose effective ranges are reducedby noise and signal diffusion.

Its theoretically possible to continue increasing data rates and transmission ranges byadding antennas to a system. In practice, though, engineers are limited by the nature of themultipath environment, such as the number and nature of obstacles encountered, and theincreased processing power required to handle the extra work generated by additionalantennas.Gee Rittenhouse, vice president of wireless research for Lucent TechnologiesBell Labs,said installing up to four antennas per transmitter or receiver in suburban environments and16 in urban environments, where a denser user population would require denser antennaconcentrations, is practical.

MIMOS ADVANTAGES

MIMOs higher speeds are critical for letting wireless networks handle data-intensiveultimedia files. The increased bandwidth also lets wireless networks serve more usersat a given data rate than they could without MIMO.

And the increased range of MIMO LANs base stations would let large businessesserve their entire organization with fewer stations, thereby saving them money.

Because the technology reduces the effects of interference and can focus on better-quality signals, MIMO networks use less radio-transmission power than otherwireless networks, so there is less battery drain on portable systems and less chance

of interference with or from other systems.

In addition, because MIMO sends transmissions along multiple paths,most of thesignals can avoid objects and other sources of interference that cause fading andinterruptions.And senders can adjust the power and phase given to antennas to steer signals towardthe paths with the best transmission quality. More precise steering couldminimize the interference a sender causes or receives, explained Andrea Goldsmith,associate professor at Stanford University.

According to Goldsmith, MIMOs signaling properties could also help create morerobust wireless security. It would be difficult for hackers to set up their receivers to

properly receive all of the signals that have been broken up and sent via multipleantennas along different paths.

IMPLEMENTING MIMO

MIMO has several important implementation issues. For example, users can achieve modestperformance gains by implementing MIMO only at the transmitter, but enabling both

transmitters and receivers to take advantage of the technology yields much greaterimprovements.


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For a big organization, integrating MIMO into a base station is much less expensive thanupgrading hundreds of LAN cards. Vendors such as Airgo are already incorporating MIMOchips into Wi-Fi LAN cards. Airgos Raleigh said that the early market will be for WLANs

but that companies eventually will integrate MIMO into almost all types of radio-basedwireless equipment.

New implementation approaches

Researchers are now focusing on two popular coding schemes for using MIMO to carrytraffic: orthogonal frequency-division multiplexing, supported by companies such as Airgoand Lucent, and code-division multiple access. OFDM increases bandwidth and data capacity

by splitting broad channels into multiple narrowband channelseach using a differentfrequency that can then carry different parts of a message simultaneously. To maximizecapacity, the channels are spaced closely together but avoid interference because neighboringchannels are orthogonal to one another and thus have no overlap.

CDMA is a type of multiplexing that lets multiple signals occupy a single transmissionchannel, optimizing the use of available bandwidth. The system varies a transmitted signalsfrequency according to a defined pattern, known as a code, so that only a receiver whosefrequency response is programmed with the same code can successfully intercept it. Thus,signals intended for multiple recipients can be coded differently and carried at the same timeon the same channel.

HURDLES TO CLEAR

Despite its promise, MIMO still faces several challenges.

Technical challengesDesigning MIMO systems, which send signals over multiple transmission paths, is a

challenge, particularly because most wireless engineers have worked only on systemsdesigned to use one transmission path, according to Raleigh. Also, MIMO has worked wellin a laboratory environment between two fixed nodes. However, said Stanfords Goldsmith,there are questions about how well it will work in a real-world environment between mobilenodes.She added, Many of the algorithms and the performance gains assume that you know thenature of the transmission channel perfectly or almost perfectly.

In real-world mobile environments, she said, the nature of the channel changes

regularly as users move about. MIMOs biggest technical challenge may be the increasedprocessor energy consumption caused by the processing complexity required to handlesignals traveling multiple paths between antenna arrays. First-generation MIMO vendors arereducing this problem by using small antenna arrays. In addition, researchers are working onCPU power efficiency.

Marketplace challengesThere are concerns that businesses and consumers wont pay the added cost of

incorporating MIMO into their networks. Raleigh estimated that the first MIMO chips willadd about $20 to a LAN cards price but that more efficient integration and higher sales

volumes will reduce this cost over time.

In some applications in which MIMO reduces the number of necessary base stations,


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For now, though, Mathias said, MIMO doesnt make sense for most home and small-officedeployments because their Internet connections cant take advantage of the technologysincreased bandwidth. However, he added, MIMO could benefit new deployments at largerorganizations with higher-bandwidth networks. Other multi-antenna technologies may alsothreaten MIMO. For example, ArrayComm has developed a proprietary system that usesmultiple antennas to improve the range of cellular-system base stations without requiringchanges to existing mobile phones. This technology is already being deployed on mobilesystems in Japan and China, with other implementations on the way, said AdamKerr,ArrayComms vice president of engineering research.

Already, MIMO has become part of the IEEE 802.16d wireless networking standard.Numerousvendors, such as Airgo and Lucent, are promoting MIMO as the IEEEs next 802.11standard, 802.11n, which the organization expects to complete by 2006. In addition, the

Third GenerationPartnership Project, a collaboration of telecommunications standards organizations, isevaluating MIMO for cellular networks. Some sources say MIMO holds promise for cellularnetworks but is limited by the heavy cost of upgrading base stations. Farpoints Mathias said,MIMO in cell phones is a little more difficult proposition because there are so many cellsites without it.

According to Stanfords Goldsmith, MIMO may not be widely used in cellular phonesbecause the cost of implementing it outweighs the benefits. However, Mathias predicted widedeployment of MIMO in WLANs within three years. I think part of it is simple marketing,he explained, letting people know that this is not exotic technology and that it works.


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2

Physical Interpretation of MIMO Transmissions

2.1 Introduction

In the previous chapter, we already introduced the MIMO concept as acommunication technique that exploits the spatial dimension by applying multipleantennas at both the transmitter and receiver side. This MIMO principle has been

thoroughly studied by mathematical evaluations in literature, but to the author'sknowledge, it has never been explained by a physical interpretation. In this chapter,such a physical interpretation is presented providing a fundamental understandingof the MIMO concept in radio communication. Moreover, it gives an intuitiveexplanation why the spectral efficiency and stability of MIMO are especially high in rich-scattering environments.

In Section 2.2, the MIMO communication principle is explained and a detection techniquecalled Zero Forcing (ZF) is described. In Sections 2.3, 2.4, and 2.5, the effect ofthe environment on the antenna array pattern of the receiver (after ZF detection isapplied) is evaluated by considering in each section a different number of reflecting

planes. Section2.6 describes the effect on the antenna array patterns of the receiver when the receiver doesnot perfectly know the communication channel, but only has a noisy estimate ofthe channel. Finally, in Section 2.7 conclusions are drawn.

2.2 Multiple-InputMultiple-OutputCommunicationConsider a wireless communication system with Nt transmit (TX) and Nr receive(RX) antennas. The idea is to transmit different streams of data on the differenttransmit antennas, but at the same carrier frequency. The stream on the p-th transmitantenna, as function of the time t, will be denoted bysp(t). When a transmission occurs, thetransmitted signal from thep-th TX antenna might find different paths to arrive at the q-thRX antenna, namely, a direct path and indirect paths through a number of reflections.This principle is called multipath. Suppose that the bandwidth B of the system is chosensuch that the time


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TXNt

s x h

NN

t

delay between the first and last arriving path at the receiver is considerably smaller than1/B. In this case the system is called a narrowband system. For such a system, allthe multipath components between thep-th TX and q-th RX antenna can be summed up toone term, say hqp(t). Since the signals from all transmit antennas are sent at the samefrequency, the q-th receive antenna will not only receive signals from the p-th, butfrom all Nttransmitters. This can be denoted by the following equation (in this chapter,

the additive noise at the receiver is omitted for clarity, but will be introduced in Section3.4)

Nt

xq (t) = hqp (t)sp(t) .

p = 1

(2.1)

To capture allNr received signals into one equation, the matrix notation can be used. With

s (t)

x(t)

h(t)

h (t) h (t)

1

1

11 12 1Nt

s(t) = s2 (t)

, x(t) = x2 (t

) and H(t) =

h21 (t)h

22 (t) h2N (t) t , (2.2)

Nt

(t)

N

r

(t)

Nr1

(t)

hNr2

(t

) hNr

Nt

(t)

this results in

x(t) = H(t)s(t) . (2.3)

A schematic representation of a MIMO communication scheme can be found in Figure 2-1.

s1 RX1 x1

s2 RX2 x2

sH

RXNrx

r

Figure 2-1: A schematic representation of a MIMO communication system.

Mathematically, a MIMO transmission can be seen as a set of equations (the recordings oneach RX antenna) with a number of unknowns (the transmitted signals). If every equationrepresents a unique combination of the unknown variables and the number of equations is

equal to the number of unknowns, then there exists a unique solution to the problem. If thenumber of equations is larger than the number of unknowns, a solution can be found by

f i j i i h l h d ( ) l k h


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Suppose the coefficients of the unknowns are gathered in the channel matrix H(t) and thenumber of parallel transmit signals (unknown variables) equals to the number of received


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1

2

qp

N

2

s

signals (equations), i.e., Nt = Nr, then the equations are solvable when H(t) is invertible.Under this condition, the solution of (2.3) can be found by multiplying both sides with theinverse ofH(t):

H1 (t)x(t) = H1 (t)H(t)s(t

)

=

I

t

s(t) = s(t) , (2.4)

where IN is theN Ndimensional identity matrix. Thus, to estimate the transmitted signalsat the receiver, the vectorx(t) must be multiplied by the inverse of the channel matrix H(t).To that end, the channel matrix must be known at the receiver. This can be done by, e.g.,sending a training sequence, that is known to the receiver, to train the channel.

In the next sections, a system with two transmit antennas (Nt = 2) and two receive antennas

(Nr = 2), or shortly, a 2 2 system is considered. It will be assumed that thereceiver perfectly knows the channel. With this assumption, we may write the twosolutions s1(t) ands2(t) as

s (t) = w1 (t)x(t) , (2.5)

s (t) = w 2 (t)x(t) , (2.6)

where wi(t) denotes the weight vector that is applied at the receiver to estimate the

i-th transmitted signal and can be shown to be equal to the i-th row of H1

(t). Inthe next sections, for a specific antenna setup in different environments (withand withoutreflections), we will determine the channel coefficients and the weights, and show what the

influence of these weights is on the RX antenna array pattern.

2.3 FreeSpaceAspectsA free-space scenario is considered where a 2 2 system is placed in an(artificial) environment where no reflections occur. Both the antenna set-up and theenvironment are assumed static and, therefore, the channel coefficients are constant overtime. Hence, the time index can be omitted. Since no reflections take place, the channelcoefficient between thep-th TX antenna and the q-th RX antenna, hqp, only consists of thedirect path between these antennas. Denote the length of this path by dqp in metres, then

both the power and phase of the channel coefficient are a function ofdqp. Since thesystem is operation in free space, the power at a distance dqp from the p-th transmitter isgiven by the Friis free space equation ([92]):

P(d ) = PtGtGr Watts , (2.7)r qp (4 )2d2L

where Pt is the transmitted power per TX antenna, Gt and Gr are, respectively, the

transmitter and receiver antenna gains, Ls is the system loss factor not related topropagation and is the wavelength in metres. In the next analysis, we assume that there isno system loss (Ls = 1) and that unity gain antennas are used (Gt = Gr = 1). The phase at adistance dqpequals 2 dqp/ rad. This results in the following channel coefficient


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qp

2

P

t

dqp h

qp

= (4 )2

d2

exp j2

. (2.8)

Once the four elements of the channel matrix H are known, the weights for the ZeroForcing MIMO processing can be determined. The weight vectors w

1and w

2are obtained

as described in Section 2.2. We want to see what the effect of these weights is. To that end,a dummy antenna is placed at a given two dimensional spot (x,y) and the received vector asfunction of (x,y) is determined: x(x,y). This vector is multiplied by the weights w

1and w

2,

respectively, and we now can, e.g., show what the power is of the resulting signalsas function of (x,y). These plots can be seen as the RX antenna array patterns afterapplyingthe weights.

Here, this is worked out for an antenna set-up as depicted in Figure 2-2. Assume that the

TX antennas and RX antennas are centred on the y-axis, with an antenna spacingof respectively dTX = 1 and dRX = 1 , furthermore, assume that the distance

between the transmitter array and receiver array equalsD = 100 , and that the power

per TX antenna equals 0.035 Watts1. Then, the channel matrix can be shown to be

0.035 exp( j2 100) 0.035 exp( j2

10001) (4 )2 10000

(4 )2

10001 H

=

0.035 exp( j2

10001) 0.035 exp( j 2 100) ,

(2.9)

(4 )2

10001

(4 )2 10000

from which the weight vectors can be determined by taking the rows of the inverse ofH.

y

TX1 TX2

dTX

h21

h11

D

h12

h22

RX1 dRX RX2 x

Figure 2-2: Antenna set-up.

Applying these weight vectors results in the RX antenna array patterns as given in Figure


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grid of 180/ tan( dTX/D) 0.143 degrees and a radius grid of 1 . To smooth theplots,interpolation is applied. Note that the TX antenna positions are denoted by the whitecrosses and the RX antenna positions by the black ones. We clearly see that, when weightw

1is used, the signal from the second antenna (and all spots in line with that TX antenna

and the receiver array) is suppressed, and vice versa when w2

is applied. Clearly,

the undesired signal is forced to zero. Furthermore, it can be seen that the larger thedistance between a given spot (x,y) and the receiver array, the weaker the signal that isreceived. This is the result of applying the free-space path loss model.

(a) (b)

Figure 2-3: RX antenna array patterns after applying the

first (a) and second (b) weight vector in free space.

2.4 OnePerfectlyReflectingPlaneHere, the scenario of the previous section is extended with one perfectly reflecting plane,

parallel to the transmitter-receiver line. In addition to the direct paths of the free-space case, one indirect path per channel element has to be taken into account dueto the reflection. At the receiver side, this indirect path can be seen as if it would be adirect path from the image of the transmitter, mirrored in the reflecting plane (see Figure2-4). So, for the channel between the p-th TX and the q-th RX antenna this meansthat, besides the direct path, an extra path must be added, virtually being the direct path

from the image of thep-th TX antenna to the q-th receiver (see Figure 2-4).

Using the same parameters as in Section 2.3 (dTX = 1 , dRX = 1 ,D = 100 andPt =0.035

Watts) and with the extra information that Drefl is chosen to be 8 , the channel matrixand the weight vectors can be determined. The antenna patterns after applying bothweights are given in Figure 2-5.

From these figures, we can see that the reflecting plane at x = 8 causes aninterference pattern. Again, we see that applying the right weight vector suppresses thesignals from the antenna that is by this weight vector considered as interferer.


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y

TX1 TX2 TX1' TX2'

dTX

D

RX1 dRX RX2 x RX1' RX2'

Drefl image

Figure 2-4: Antenna set-up with a perfectly reflecting plane. Only the extra paths thathave to be taken into account in addition to the direct paths of Figure 2-2 are shown.

(a) (b)

Figure 2-5: RX antenna array patterns after applying the first (a) and second (b)

weight vector in a scenario with one perfectly reflecting plane atx= 8 .

2.5 TwoPerfectlyReflectingPlanesIn the final scenario that is considered, another perfectly reflecting plane is added to the

scenario of Section 2.4. Again, the following parameters are used: dTX = 1 , dRX =

1 ,

D = 100 and Pt = 0.035 Watts. Furthermore, we assume that the first reflecting plane is positioned atx = 8 , whereas the other plane is positioned at x = 6 . Since the

tworeflecting planes are parallel to each other, there will be paths that only arrive at thereceiver after a multiple of bounces between the two planes. Here, we will only consider a

maximum of one bounce and two bounces, respectively. The channel matrix and weightvectors can be determined for both cases. The RX antenna array patterns after application


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of the weight vectors in case of a maximum of one and two bounces are shown,respectively, in Figure 2-6 and Figure 2-7.

From comparing these figures with Figure 2-5, it becomes clear that the more reflectionsoccur, the more chaotic the interference patterns are. In Figure 2-6 and Figure 2-7, we cansee that the undesired antenna is nulled with a spot, instead of with a beam (like in Figure2-3), and that the desired antenna is (almost) located at a local maximum. This maximalseparation between the wanted and unwanted antenna shows that the signals from

both antennas can be treaded as uncorrelated (or independent). This observationspeaks in favour of the robustness of MIMO systems in environments with manyreflecting objects, i.e., rich-scatteringenvironments.

(a) (b)


weight vector in a scenario with two perfectly reflecting planes (atx= 6 andx= 8 ), where only paths with a maximum of one bounce are taken into account.

(a) (b)


weight vector in a scenario with two perfectly reflecting planes (atx= 6 andx= 8 ), where only paths with a maximum of two bounces are taken into account.


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2.6 ChannelEstimationErrorsThe observation of the previous section that MIMO is more robust in rich-scatteringenvironments can be confirmed by adding white Gaussian noise to thechannel observation. This provides insight in the MIMO performance when the

MIMO system experiences noise. More concrete, it illustrates how the antenna patternsare altered when the channel estimation is corrupted by noise. To include the influence ofnoise, we can add independent and identically distributed (i.i.d.) complex Gaussian noise

to the four channel elements of the 2 2 cases of the previous sections. With an averagenoise power of 10%of the average channel element power (i.e., the Signal-to-Noise Ratio (SNR) = 10 dB), andthe assumption that the average power per channel element is normalised to one, anexample of the Additive White Gaussian Noise (AWGN) is given by

0.0091 j

0.0186

0.0464 j 0.0858

0.0826 j 0.0933 . (2.10) 0.0326 + j 0.0658

When adding this noise to the channel coefficients of the free space (pure LOS) case ofSection 2.3 and applying correct scaling to maintain the SNR of 10 dB, the resulting RXantenna array patterns after applying the weight vectors are given in Figure 2-8. Addingthe same noise to the case with two reflecting planes where up to two bounces areconsidered (see Section 2.5), results in the antenna patterns of Figure 2-9.

(a) (b)


weight vector in free space with noise added to the channel observation.

When comparing the results of Figure 2-8 and Figure 2-9 with Figure 2-3 and Figure 2-7,respectively, we clearly see that the pure-LOS case strongly suffers from theadditive noise. This can be explained by the fact that for this case the columns of thechannel matrix have a strong resemblance (i.e., are highly correlated), see (2.9),resulting in a big error when noise is added. For the "richly-scattered" case, the

channel matrix is highly orthogonal and this property is hardly changed when noiseis added. As a result, the antenna patterns for the latter case are barely altered.Similar results are achieved when


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2.6 Channel Esti m ation Errors 23

other noise realisations are investigated, from which we can conclude that a MIMO systemis indeed more robust in environments with many reflecting objects.

(a) (b)


weight vector in a scenario with two perfectly reflecting planes (at x= 6 andx= 8 ), where only paths with up to two bounces are considered, and noise is added

to the channel observation.

2.7 Conclusions

In this chapter, we showed that, for a communication system with multiple transmit andmultiple receive antennas, the different signals from the different TX antennas (sent at thesame frequency) can be separated at the receiver, under the assumption that the rightweights can be found and applied. The ability of separating the different streams from thedifferent transmit antennas, results in a linear growth in data rate with the number of TXantennas, by which the potential capacity enhancement of MIMO is intuitively explained.Furthermore, for cases with many reflecting paths, it is shown that the undesired antenna isnulled by a spot, whereas a local maximum is placed at the position of the desired antenna.This maximal separation between the two antennas speaks in favour of the robustness ofMIMO systems in rich-scattering environments.

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