capacity of mimo indoor

8/9/2019 Capacity of MIMO Indoor

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Capacity Analysis of MIMO-OFDM Broadband

Channels In Populated Indoor EnvironmentsJishu DasGupta1, Karla Ziri-Castro2 and Hajime Suzuki3

1Faculty of Sciences, University of Southern Queensland

Toowoomba, AustraliaTel: +61-7-4631 5564, Email: [email protected]

2Faculty of Engineering and Surveying, University of Southern Queensland

Toowoomba, Australia

Tel: +61-7-4631 2503, Email: [email protected] 3CSIRO ICT Centre

Sydney, Australia

Tel: 61-2- 9372 4121, Email: [email protected]

Abstract —This paper presents the results of dynamic channel

capacity measurements for a multiple-input multiple-output

orthogonal frequency division multiplexing (MIMO-OFDM)

system in two populated indoor environments with and without

line-of-sight (LoS). The experiment used 4 sending and 4receiving antennas and 114 sub-carriers at 5 GHz as per draft

IEEE 802.11n. MIMO-OFDM channel capacity is analysed both

with fixed receiver signal-to-noise ratio (SNR) and fixed

transmitter (Tx) power criteria. It is found that fixed SNR

capacity increased while fixed Tx power capacity decreased in

both environments by the presence of pedestrian. It is also

revealed that the spread of the capacity cumulative distribution

function (CDF) increased due to the pedestrians in both

environments with both criteria.

I. I NTRODUCTION

When multiple-input multiple-output (MIMO) systems are

deployed in suitable rich scattering environments, a

significant capacity gain can be observed due to the assuranceof multipath propagation [1], [2]. MIMO systems are

particularly attractive with narrowband channels wheremultipath tends to create independent channels even with

small antenna spacing (approximately a half-wavelength).

Hence the combination of MIMO with orthogonal frequency

division multiplexing (OFDM), where a wide frequency

bandwidth can be efficiently separated into multiple channels

at independent sub-carriers, is considered as a strong

candidate for the next generation bandwidth-efficient wireless

systems [3].

Sufficiently rich multipath signal propagation has been

found in MIMO or MIMO-OFDM channels operating within

indoor and their capacity has been analysed for staticenvironments [4], [5], [6]. However, temporal channel

variations can occur as a result of personnel, industrial

machinery, vehicles and various equipment moving within the

indoor environment. Recently, effort has been made to model

human body shadowing effects on MIMO channels [7], [8].

The time varying effects on the propagation channel within

populated indoor environments depends on different

pedestrian traffic conditions, and is related to the particular

type of environment considered [9]. Notwithstanding previous

studies, a systematic measurement campaign to characterize

time-varying pedestrian movement effects in MIMO-OFDM

channels has not yet fully investigated. Characterizing

channel variations caused by the relative positioning of pedestrians is essential in the study of indoor MIMO-OFDM

wireless networks.This paper investigates the dynamic channel capacity of a

MIMO-OFDM channel using four transmitters and four

receivers (4x4) within two populated indoor environments

through systematic experimental measurements. Thecharacteristics of actual temporal variation and correlation

analysis have been reported in [10] and [11], respectively.

This paper focuses on the analysis of capacity cumulative

distribution functions (CDFs). Following, Section 2 presents

an overview of the fundamentals of MIMO-OFDM channel

capacity. The description of the measurement equipment andmeasurement sites are presented in Section 3. Section 4

summarises the experimental set up. Section 5 provides the

time-varying results and analysis for the 4x4 MIMO-OFDMchannel capacity, followed by the conclusions in Section 6.

II. MIMO-OFDM CHANNEL CAPACITY

Characterisation of MIMO-OFDM channel capacity invarious indoor environments plays a key role to determine the

performance of MIMO-OFDM systems. The MIMO-OFDM

channel capacity is given by

( )∑∑

= =

+=

f t n

k

n

j t

k j

f n

f

nC

1 1

2 1log1 ρλ

(1)

Here C is the normalised capacity in bit/sec/hertz, n f is the

number of OFDM sub-carriers, nt is the number of Txantennas, ρ is the average signal to noise ratio (SNR) and λ j is

the eigenvalue of H( f k )H( f k ) H

. H( f k ) is the normalised channel

coefficient matrix at sub-carrier f k and H denotes Hermitian

transpose.

Two different criteria are employed to estimate the MIMO-OFDM channel capacity in this paper. The first assumes an

interference-limited system where transmitting power can be

adjusted without a limit to provide a fixed average SNR at the

receivers. The averaging of SNR and normalisation of



channel coefficient matrix is performed over all MIMO sub-

channels and over all OFDM sub-carriers.

Fig. 1. CSIRO ICT center MIMO-OFDM channel sounder (Left: Tx,

Right Rx)

This corresponds to the system where co-channel

interference is the limiting factor for the system capacity, andthe enough Tx power is reserved to cater every locations

within the coverage area. The second assumes a power-

limited system where the transmitting power is fixed. In thiscase the averaging of SNR and normalisation of channel

coefficient matrix is performed over all MIMO sub-channels,

over all OFDM sub-carriers, over all 100 measurement

samples, and over all different number of pedestrian

(described in IV). Fixed Tx power criteria incorporates the

effects of the reduction of power due to body shadowing by

the pedestrian. This criterion is more suitable for the analysis

of WLAN system where the transmitting power is typically

fixed. Note that the actual transmitting power during the

measurement was fixed to one setting during LoSmeasurement and was changed to another setting during

NLoS measurement, in order to provide adequate SNR for

both measurements, and hence the comparison of fixed Tx

power LoS and NLoS measurement has an ambiguity.

However, this does not cause problems in analysing the

effects of pedestrian for each environment, which is the focus

of this paper.

III. MEASUREMENT EQUIPMENT AND SITES

The measurements were performed using the MIMO-

OFDM channel sounder developed by CSIRO ICT Centre [5].

The channel sounder operates at a carrier frequency of 5.24

GHz and has an operational bandwidth of 40 MHz. Thenumber of OFDM sub-carriers and the sub-carrier spacing are

114 (108 data and 6 pilots) and 312.5 kHz, respectively, as

per the IEEE 802.11n draft standard [12]. It has 4 transmitters

with a maximum gain of 23 dB per channel and 4 receivers

with 3 dB noise figure over the 40 MHz bandwidth. A

photograph of the equipment is shown in Fig. 1.Measurements were performed on the furniture free ground

floor rooms in the CSIRO ICT Centre, Marshfield, Sydney.

Two different Rx locations were considered (1) line-of-sight

(LoS), where Tx and Rx are located inside the same 57 m2

laboratory, and (2) non-LoS (NLoS), where Rx is located in

an adjacent 30 m2

office, see Fig. 2.

IV. EXPERIMENT DESCRITPION

Pedestrian trajectories for LoS and NLoS experiments are

shown in Fig. 2. During the LoS scenario pedestrians walked

along a 6 m trajectory within the laboratory while during the

NLoS scenario pedestrians walked along the adjacent 12 m

corridor.

Tx

12m

5m

Rx

6m

6m

Office

NLoS

LOS

Laoratory

Atrium5m

2.5 m

Rx

16 m

10m

Standing Pedestrain

Walking Pedestrian

Walking Path

Plaster Board

Window

Fig. 2. Measurement sites.

Data have been collected under controlled pedestrian traffic

conditions. Four different scenarios were considered: vacant,

one, two and three pedestrians walking along the indicated

trajectories. Complex channel coefficients for each of 16MIMO sub-channels at 114 OFDM sub-carriers werecollected at 100 time samples while pedestrians were walking,

standing, or vacant.

V. RESULTS

A sample of MIMO-OFDM channel coefficient power is

shown in Fig. 3 (NLoS) and Fig. 4 (LoS), where x axis is the

frequency (MHz) and y axis is the relative power (dB). Fromthe graphs more severe frequency selective fading is observed

in NLoS when comparing with LoS.



-40

-20

0

20Tx1-Rx1

-40

-20

0

20Tx2-Rx1

-40

-20

0

20Tx3-Rx1

-20 - 10 0 10 20-40

-20

0

20Tx4-Rx1

Tx1-Rx2

Tx2-Rx2

Tx3-Rx2

-20 -10 0 10 20

Tx4-Rx2

Tx1-Rx3

Tx2-Rx3

Tx3-Rx3

-20 - 10 0 10 20

Tx4-Rx3

Tx1-Rx4

Tx2-Rx4

Tx3-Rx4

-20 - 10 0 10 20

Frequency (MHz)

R e l a t i v e p o w e r ( d

B )

Tx4-Rx4

Fig. 3. A sample of the 4x4 MIMO-OFDM channels (NLoS)

-40

-20

0

20Tx1-Rx1

-40

-20

0

20Tx2-Rx1

-40

-20

0

20Tx3-Rx1

-20 - 10 0 10 20-40

-20

0

20 Tx4-Rx1

Tx1-Rx2

Tx2-Rx2

Tx3-Rx2

-20 -10 0 10 20

Tx4-Rx2

Tx1-Rx3

Tx2-Rx3

Tx3-Rx3

-20 - 10 0 10 20

Tx4-Rx3

Tx1-Rx4

Tx2-Rx4

Tx3-Rx4

-20 - 10 0 10 20

Frequency (MHz)

R e l a t i v e p o w e r ( d B )

Tx4-Rx4

Fig. 4. A sample of the 4x4 MIMO-OFDM sub-channels (LoS)

0 20 40 60 80 10012

12.5

13

13.5

14

14.5

15

15.5

Sample index

A v e r a g e c a p a c

i t y ( b p s / H z )

0

1

2

3

Fig. 5. MIMO channel capacity for LoS measurements with fixed Tx power

in the presence of various numbers of walking pedestrians.

0 20 40 60 80 10013

13.5

14

14.5

15

15.5

16

16.5

Sample index

A v e r a g e

c a p a c i t y ( b p s / H z )

0

1

2

3

Fig. 6. MIMO channel capacity for LoS measurements with fixed SNR at the

receiver in the presence of various numbers of walking pedestrians.

Figs. 5 and 6 show the dynamic MIMO-OFDM channel

capacity with fixed Tx and fixed SNR criteria, respectively,

for LoS experiments assuming average SNR of 15 dB. There

is a significant variation in channel capacity with the number

of pedestrians present in the environment. For both figures,variations in channel capacity are more noticeable at sample

index 60-80 when pedestrians were directly obstructing the

LoS between Tx and Rx. During this time when there were

three pedestrian present, a decrease of 2 bps/Hz or an increase

of 2 bps/Hz of channel capacity was observed relative to the

vacant scenario depending on the different capacity criteria.

The decrease of the capacity is mainly due to the reduction of

receivable power by the body-shadowing effects while the

increase of the capacity is due to the removal of dominant

LoS path causing decorrelated channels. For the following

analysis, the MIMO-OFDM capacity is calculated for an

average SNR of 15 dB at the receiver.

Fig. 7 shows CDFs of MIMO-OFDM capacity for the

Walking LoS scenario using the fixed SNR (a) and fixed Tx

power (b) criteria. As expected, the variation of the capacity

for Vacant case is minimal while the introduction of even one

pedestrian changes the CDF dramatically. The effects of having more pedestrians seem less significant, having similar

CDFs for one, two, or three pedestrians. The presence of the

pedestrian tends to increase the capacity for fixed SNR (this

can be observed by the fact that the capacity with the

pedestrian always surpasses that for the vacant scenario above

20% CDF), while it appears to increase at one location whiledecreasing at another location for fixed Tx power, with a

larger area suffering from the decrease of the capacity. This is

due to the blocking of the direct LoS path by the pedestrians

which causes the reduction of receivable power.



13 13.5 14 14.5 15 15.5 16 16.5 170

10

20

30

40

50

60

70

80

90

100

MIMO-OFDM capacity (bits/ s/Hz)

C D F ( % )

Vacant

One

Two

Three

(a) Fixed SNR.

12 12.5 13 13.5 14 14.5 15 15.5 160

10

20

30

40

50

60

70

80

90

100

MIMO-OFDM capacity (bits/s /Hz)

C D F ( % )

Vacant

One

Two

Three

(b) Fixed Tx power.

Fig. 7. CDFs of MIMO-OFDM capacity for Walking LoS.


Walking NLoS scenario. Compared to Fig. 7, the mediancapacity is increased for NLoS than LoS for both fixed SNR

or fixed Tx power. This indicates de-correlation of channels

by removing direct LoS component. Note that the typically

larger Tx power is required for NLoS case. Compared to LoS

cases, it can also be seen that the spread of CDFs is less in the

case of NLoS. This is attributed to the fact that blocking of a

particular radio path within many paths is less significant than blocking of dominant direct LoS path in LoS case.

14 14.5 15 15.5 16 16.5 170

10

20

30

40

50

60

70

80

90

100


C D F ( % )

Vacant

One

Two

Three

(a) Fixed SNR.

13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 180

10

20

30

40

50

60

70

80

90

100

MIMO-OFDM capacity (bits/s/Hz)

C D F ( % )

Vacant

One

Two

Three

(b) Fixed Tx power.

Fig. 8. CDFs of MIMO-OFDM capacity for Walking NLoS.


Standing LoS scenario. From the graph it is found that there isa capacity increase in presence of more people in the LoS

path. Comparing the spread of CDFs for Vacant case and One,

Two, or Three pedestrian case, the pedestrians are found to

cause some temporal variation even when they are simply

standing. Compared to Fig. 7, the effect of a larger number of

the pedestrians is more profound. This is attributed to the fact

that pedestrians were lined up parallel to the direct LoS in the

case of Walking, while they were lined up perpendicular to

the direct LoS in the case of standing (see Fig. 2).



13 13.5 14 14.5 15 15.5 160

10

20

30

40

50

60

70

80

90

100


C D F ( % )

Vacant

One

Two

Three

(a) Fixed SNR.

13 13.5 14 14.5 15 15.5 160

10

20

30

40

50

60

70

80

90

100


C D F ( % )

Vacant

One

Two

Three

(b) Fixed Tx power.

Fig. 9. CDFs of MIMO-OFDM capacity for Standing LoS.

Fig. 10 shows CDFs of MIMO-OFDM capacity for

Standing NLoS cases. Unlike LoS case shown in Fig. 9, thespread of CDFs is similar between Vacant case and One, Two,

or Three pedestrian cases. This indicates that the main cause

of temporal variation in NLoS when pedestrians are simply

standing come from environment (fans, motor, fluorescent

lights, etc.)

15 15. 2 15. 4 15. 6 15.8 16 16. 2 1 6. 4 16. 6 16. 8 170

10

20

30

40

50

60

70

80

90

100


C D

F ( % )

Vacant

One

Two

Three

(a) Fixed SNR.

14 14.5 15 15.5 16 16.5 170

10

20

30

40

50

60

70

80

90

100


C D F ( % )

Vacant

One

Two

Three

(b) Fixed Tx power.

Fig. 10. CDFs of MIMO-OFDM capacity for Standing NLoS.

VI. CONCLUSIONS

The channel capacity of an indoor MIMO-OFDM system

was measured in the presence of nearby pedestrian trafficeffect. It has been observed that pedestrian effectssignificantly affect the theoretical maximum channel capacity

of indoor MIMO-OFDM systems. From the study of LoS and

NLoS scenarios with up to three pedestrians, the results

presented demonstrate that the spread become higher and

MIMO-OFDM channel capacity decreased or increased in the

presence of more pedestrian depending on the capacitycriteria.

Future effort should be directed at the analysis of different

types of environment, consistent data collection and

pedestrian traffic conditions, including corridors and larger

populated areas such as malls. Base on the collected data we

will design an improved model for the MIMO-OFDMchannels in the indoor environment in the presences of human.

ACKNOWLEDGMENT

The authors would like to acknowledge the CSIRO ICT

Centre personnel at Marshfield, Sydney, for providing the

MIMO-OFDM channel sounder and measurement sites.

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