• About MIMO
  • Application
  • U of A System
History
 

 

MIMO HISTORY

MIMO, Multiple-Input Multiple-Output signaling was a groundbreaking development pioneered by Jack Winters of Bell Laboratories in his 1984 article "Optimum Combining in Digital Mobile Radio with Cochannel Interference". Since then, many academics and engineers have made significant contributions to the understanding of MIMO systems.

In 1996, radically novel approaches were invented to increase signaling efficiency over MIMO channels. Gregory G. Raleigh and V.K. Jones wrote a paper "Multivariate Modulation and Coding for Wireless Communication" arguing that multi-path channels can have a multiplicative capacity effect if the multi-path-signal propagation is used in an appropriate communications structure. In the same year, Foschini introduced the BLAST concept in his paper "Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multi-Element Antennas". BLAST is one of the most widely examined techniques today.

In 1999, the Shannon capacity of an isotropic fading MIMO channel was calculated by I. Emre Telatar in his paper "Capacity of multi-antenna Gaussian channels". He stated that the channel capacity increases with the number of antennas and is proportional to the minimum number of transmit or receive antennas. This basic information theoretic result drew widespread attention to MIMO communications.

Meanwhile, engineering research of MIMO systems also progressed. In 1998, Bell Lab performed the first successful technology demonstration under laboratory conditions. One year later, in 1999, Gigabit Wireless Inc. and Stanford Univeristy successfully held the first outdoor prototype demonstration. And Iospan Wireless Inc. (formerly Gigabit Wireless Inc., acquired by Intel) produced the first commercial product in 2002. As one of the first, a 4x4 MIMO academic test-bed started operation at University of Alberta in 2003.

 

Reference

  1. Jack Winters "Optimum Combining in Digital Mobile Radio with Cochannel Interference," Special Issue on Mobile Radio Communications IEEE Journal on Selected Areas in Communications, July 1984, IEEE Trans. on Vehicular Technology, August 1984.
  2. Raleigh, G. G. and Jones, V. K. "Multivariate modulation and coding for wireless communication", IEEE J. Selected Areas in Communication, vol. 17, no. 5, pp. 851-866, May 1999
  3. Gerard. J. Foschini "Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multi-Element Antennas". Bell Laboratories Technical Journal, pp: 41-59. October 1996
  4. I. Emre Telatar "Capacity of multi-antenna gaussian channels". European Transactions on Telecommunications , 10, pp: 585-595. November 1999
  5. Helmut Bolcskei, MIMO Systems, Communication Technology Laboratory, ETH Zurich October 13, 2005

 

 

 
Channels
 

 

CHANNELS

MIMO channels arise in wireless communications environment where multiple transmit and receive antennas are used (see Figure 1). MIMO channels work best in highly scattering transmission environment, where multiple multiple paths exist between transmitters and receivers.

 

Fig4

Figure 1: MIMO Channels

 

A communications system for a MIMO channel aims at exploiting the multi-path signal propagation to increase channel capacity. Under certain assumptions [1][2], the correlated partial channels created by the scattering environment can be accurately described by the matrix equation:

Fig3

 

Typical assumptions are:

  • The path gains hij are independent complex gain cofficients modeled as Gaussian random variables assuming a scatter-rich or radio environment.
  • The noise is complex additive Gaussian noise with variance N0 (that is N0/2 in each dimension).

 

For engineering uses, the following block diagram is preferred:

Fig1

Figure 2: Channel Model

 

The following simple models are often used to study and model basic MIMO channels:

  • Rayleigh Model: The entries hij of H are modeled as zero-mean Gaussian random variables with unit variance, and distributed independently and identically (i.i.d.).A mean matrix M is added to H.
  • Correlated Model: The entries hij of H are modeled as i.i.d.Gaussian random variable with unit variance, mean M, and correlation E[hijhkl*]= riktjl. H -> R^(1/2)*H*T^(1/2) where T models transmit weight or laod correlation and R models the receiver weight or load.
  • Degraded Channels: Keyhole channels, Relay channels. A Keyhole channel is a special MIMO channel, which models the presence ofsmall apertures such as holes, corridors, tunnels, etc. The channel is degraded from a capacity point of view, but can still provides increased diversity.

 

Fig2

Figure 3: Keyhole Channels

 

Reference
  1. Gerard. J. Foschini "Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multi-Element Antennas". Bell Laboratories Technical Journal, October,1996. pp: 41-59.
  2. I. Emre Telatar "Capacity of multi-antenna gaussian channels". European Transactions on Telecommunications, pp: 585-595. November 1999.
  3. R.G. Gallager, Information Theory and Reliable Communication, John Wiley & Sons,Inc., New-York, 1968, Section 7.5, pp. 343 ff.

 

 

 

Capacity
 

 

CAPACITY

The value of the channel gain matrix H is affect by the fading in the channel. Whether or not a transmitter knows the value of H is crucial in how the MIMO system is operated: 1. Transmitters have Channel Knowledge (Waterfilling Capacity). If the value of the MIMO channel gain H is known at both transmitter and receiver, we can decompose the matrix H via the singular value decomposition SVD space [1]:

Figc12

where U and V are unitary, i.e. UU+=VV+=I (if Nr<Nt) and N=min(Nr,Nt). The matrix D contains the singular values of H, which are the positive square roots of the eigenvalues of HH+ and H+H .

Now the channel equation can be written in an equivalent form:

Figc13
Figc14

Here, if Nt>Nr only the first Nr signals of x will be received and the Nt-Nrremaining channels will be nulled. This leads to the following parallel Gaussian channel model

Figc15

 

Figc5

Figure 1: Parallel Gaussian channels

 

The capacity of N parallel complex channels can be computed following as [2]:

Figc4

 

where Figc16 is fixed and the maximizing distribution E=[E1,…,En] follows:

This is the celebrated Waterfilling Theorem, illustrated in Figure 2.

 

 

Figc19

Figure 2: Waterfilling theorem.

 

2. Transmitters without Channel Knowledge (Symmetric Capacity)

Channel knowledge is not always available at the transmitters, so the only choice we have is to distribute the energy uniformly over all component channels. This leads to the definition of the "Symmetric Capacity":

Figc17

 

The above formula can be written in terms the matrix eigenvalues of H as

Figc18

 

E. Biglieri and G. Taricco review closes form expressions of the above equation[3].The plot specific function tells us that the capacity C of the MIMO channel shows an almost linear increase with the number of receiving antenna or transmitting antennas for Nr=Nt.

Figc3

Figure 3: Capacity with Nr=Nt antennas

 

 

3. MIMO Channel Capacity via Random Matrix Theory

In a large MIMO system,, , with fixed aspect ratio alpha=Nt/Nr, the capacity per antenna tends towards a constant. This is shown via random matrix theory. Assume H is random Gaussian matrix and

 

Figc8

 

The eigenvalues of W are randomly distributed according to the Marcenko-Pastur Law with marginal probability density.

 

Figc9

 

An example of 1000 random matrices of size 20x 10 is shown below:

Figc6

Figure 4: Example of eigenvalues

 

For , the empirical eigenvalue distribution converges to . The capacity per dimension for the large Rayleigh MIMO channel is consequently given by

Figc10

 

For Ricean MIMO channels, modeled as

Figc11

 

where M is a low-rank "fixed-gain channel" and alpha is Rice factor. The Marcenko-Pastur law can still be applied, if . Then, as N approaches infinity, the empirical distribution of . And, in the limit, the capacity of the Ricean channel is given by (1) with adjusted SNR. Thus, the Ricean channel has an effective energy of loss of Figc1, since the low-rank average channel contributes nothing in the limit. The figures below compare the analytical eigenvalue distribution for a rank(W)=4/2 channel with those obtained by physical measurements:

 

Figc7

Figure 5: Eigenvalue analytical distribution

 

Figc3

Figure 6: Distribution of Squared Singular Values measured by the U of A testbed (see U of A system).

 

Reference
  1. I. E. Telatar, "Capacity of multi-antenna gaussian channels", Europ. Trans. Telecommun. and Related Technol., vol.10, no.6, pp.585-596, Nov. 1999.
  2. R.G. Gallager, Information Theory and Reliable Communication, John Wiley Sons Inc., New-York, 1968, Section 7.5, pp. 343 ff.
  3. E. Biglieri and G. Taricco, Transmission and Reception with Multiple Antennas: Theoretical Foundations, Now Publishers Inc., 2004, Section 4.2, pp.25-35

 

 

 

Verification
 

 

VERIFICATION

In order to verify the MIMO potential in realistic propagation environments, researchers conducted measurement studies. Both indoor and outdoor measurements show promising throughput gain. There are 3 typical models observed.

 

1. Transmitter / Receiver correlation

Correlated channel path gains occur in the real-world environment. The widely used correlated model is given by

H=Rr(1/2)HwRt(1/2)

where Hw is an Nr x Nt matrix with independent entries, Rr and Rt denote Nr x Nr and Nt x Nt antenna correlation matrices at the transmitter and the receiver sides, respectively.

Based on the correlation properties of the receiver array response vector, or the singular values of the channels response matrix H, MIMO channel can be classified as high-rank or low-rank channels.

High-rank MIMO channels occur when there is a rich scattering environment and when there is little correlation among the channel path gains. The capacity of this channel can then be approximated by

C min(NrNt)log(1+SNR x Nr/Nt)

Low-rank MIMO channels occur under scatter-free or long-distance links, where there is strong correlation between the channel path gains. The capacity of the low-rank MIMO channel can be approximated as

C ≈ log(1+SNR x Nr)

Additionally, if the SNR is low, correlation in the channel has little effect on capacity. In this situation, the capacity will approach

C ≈ min(NrNt)xSNR x Nr/Nt

 

2. Keyhole Channels

In realistic propagation environments, rank deficiency of a channel matrix may severely reduce the performance of MIMO system because of keyhole effects. These describe special MIMO channels which are degraded from a capacity point of view, but provide diversity in fading environments.

A rank=1 channel model for a keyhole channels is given by

H=hNrhNt+

Keyhole channels have low capacity even though path correlations are low.

 

3. Waveguide channels

The waveguide channel uses guided wave theory to model propagation in waveguide-like environments such as hallways, corridor or streets. Considering negligible losses, the channel matrix can be presented by [11][12]:

H=VZA

where Z is an L x L diagonal matrix, L is the number of propagation modes, V is an Nr x L matrix containing the contribution of each mode to the signal received at each antenna and A represents the L x Nt excitation matrix.

Figure 1 is an example of what happens in a narrow corridor. Radio waves striking the walls reach the receiver via many reflections. Such multi-reflected rays will be heavily attenuated at the receiver because of the power loss with each reflection. In addition, smaller θ will cause lower reflection coefficients which will cause the additionally attenuation with every reflection. These effects eliminate multi-path wireless components and reduce the MIMO channel rank. Thus, channel capacity will decrease with the distance down the corridor.

 

Figd1
Figure 1: Corridor diagram

 

The following results were calculated and measured with the U of A testbed.

 

 
Station separation (meters)
Average capacity channel form measurements (bits/use)
Channel capacity from the model (bits/use)
Location 1
8
19.226
20.720
Location 2
20
12.270
11.187
Location 3
35
12.180
10.226

 

Reference:

  1. L. Yang and J. Qin "Performance of STBCs with antenna selection: spatial correlation and keyhole effects" IEE Proc.-Commun., Vol. 153, No. 1, February 2006
  2. P. Goud Jr., C. Schlegel, W.A. Krzymieñ, R. Hang, "Multiple antenna communication systems - an emerging technology", Can. J. of Electrical & Computer Engineering, Special Issue on Advances in Wireless Communications and Networking, vol. 29, no. 1/2, January/April 2004, pp. 51-59.
  3. Hyundong Shin, and Jae Hong Lee, "Capacity of Multiple-Antenna Fading Channels: Spatial Fading Correlation, Double Scattering, and Keyhole" IEEE Transactions on Information Theory, vol. 49, no. 10, October 2003
  4. David Gesbert, Helmut Bölcskei, Dhananjay A. Gore, and Arogyaswami J. Paulraj, "Outdoor MIMO Wireless Channels: Models and Performance Prediction" IEEE Transactions on Communications, vol. 50, no. 12, December 2002
  5. Nathan A. Goodman "MIMO Channel Rank via the Aperture-Bandwidth Product".
  6. Dmitry Chizhik, Gerard J. Foschini, Michael J. "Keyholes, Correlations, and Capacities of Multielement Transmit and Receive Antennas" IEEE Transactions on Wireless Communications, vol. 1, no. 2, April 2002.
  7. Almers, P., Tufvesson, F., Molisch, A.F.; "Keyhole Effects in MIMO Wireless Channels - Measurements and Theory", Global Telecommunications Conference, 2003. GLOBECOM '03. IEEE, vol. 3, 1-5 Dec. 2003 Page(s):1781 - 1785.
  8. Cui, x.W., and Feng, Z.M.: 'Lower capacity bound for MIMO correlated fading channels with keyhole', IEEE Commun. Letters., 2004, vol.: 8, issue: 8, pp. 500-502.
  9. Bonek, E., Herdin, M., Weichselberger, W.; Ozcelik, H., "MIMO - study propagation first!" Signal Processing and Information Technology, 2003. ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on 14-17 Dec. 2003 Page(s):150 - 153
  10. P. Goud Jr., R. Huang, D. Truhachev, C. Schlegel, "A Portable MIMO Testbed and Selected Channel Measurements", EURASIP Journal on Applied Signal Processing, Vol. 2006, Article ID 51490, 10 pages.
  11. Porrat, D., Kyritsi, P., Cox, D.C., "MIMO capacity in hallways and adjacent rooms"; Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE, Volume 2, 17-21 Nov. 2002, Page(s):1930 - 1934.
  12. Poitau, G., Kouki, A., "Analysis of MIMO capacity in waveguide environments using practical antenna structures for selective mode excitation"; Electrical and Computer Engineering, 2004. Canadian Conference on Volume 1, 2-5 May 2004, Page(s):349 - 352.
  13. Porrat D., Cox D. C., "A waveguide model for UHF propagation in streets", The 11th Virginia Tech/MPRG Symposium on Wireless Personal Communications, Blacksburg, Virginia, June 6-8,2001.

 

 
Other Channels
 

 

OTHER CHANNELS

 

1. Cable Channels

A cable bundle consisting of a number of twisted pair copper cables acts as a MIMO transmission system, particularly at high frequencies. MIMO processing can greatly reduce the extent of the far-end crosstalk (FExT). In a practical system, FExT works as an unwanted noise and reduces the capacity of the cable channel.

In the channel matrix, the diagonal terms represent the channel gains and the off-diagonal elements represent FExT. In a practical system, it is not too difficult to have knowledge of the MIMO cable channels because the environment of cable lines is mostly fixed. Thus, singular value decomposition (SVD) processing can be performed.

Experimental results based on physical measurements on a five pair cable show the capacity of the MIMO cable channels has a potential improvement compared to that of a conventional connection.[1] Capacities per pair of the MIMO cable channel increases 16 and 9 bits/sec/Hz at frequencies of 10,20MHz respectively.

 

2. MMF Optical Channels

The multimode causes mode propagation in the fiber, which limits the bandwidth, i.e. the capacity of the fiber. This is called modal dispersion. MMF Optical MIMO systems exploit the modal dispersion to increase the capacity by MIMO processing.

 

Fige2
Figure 1: MMF optical channel

 

Figure 2 illustrate a 2x 2 MMF optical channel. The system includes t lasers and r photo-detectors. The channel matrix can be shown to be given by [4]:

Fige3

where Q is the number of modes in MMF, gijk is the channel gain of the kth mode from the jth transmitter to the ith receiver, wc is the carrier frequency,Fige4 is the phase delay associated with the kth mode.

 Optical MIMO channels behave like a complex Gaussian channel model used for wireless MIMO systems.[2][4] When Nr=Nt, the bandwidth achievable equals Nrx BSISO in an ideal environment, using the same length and same total energy as a SISO optical channel. [3].

 

 

3. Free-Space Optical (FSO) channels

Free-Space optical channels are used to build large-capacity links. It improves its performance in a similar way to the wireless RF MIMO system by means of spatial diversity. The system includes multiple laser transmitters and multiple photo-detector receivers. The channel with intensity modulation and direct detection can be expressed as [7]

Fige5

where Fige1 denotes convolution, r represents the photodetector (PD) responsivity, and h(t) represents the impulse response of the channel. The fading due to atmospheric turbulence is mitigated by MIMO processing and the channel capacity and SNR are increased. [5][7].

 

 

Reference:

  1. Chambers. P and Downing. C., "Experimental Study of the Capacity of a Multiple-Input / Multiple-Output (MIMO) Twisted-Pair Cable", Irish Signals and Systems Conference (ISSC), University of Limerick, July 2003.
  2. H. R. Stuart, "Dispersive multiplexing in multimode optical fiber,"Science, vol. 289, pp. 281-283, July 2000.
  3. A. Shah, R. C. J. Hsu, A. Tarighat, A. H. Sayed, and B. Jalali, "Coherent optical MIMO (COMIMO)," IEEE/OSA J. Lightwave Technology, vol. 23, pp. 2410-2419, August 2005.
  4. Rick C. J. Hsu, Alireza Tarighat, Akhil Shah, Ali H. Sayed, and Bahram Jalali, "Capacity Enhancement in Coherent Optical MIMO(COMIMO) Multimode Fiber Links" IEEE Communications Letters, vol. 10, no. 3, March 2006
  5. Cvijetic, N.; Wilson, S.G.; Brandt-Pearce, M.; "Optimizing system performance of free-space optical MIMO links with APD receivers" Optical Fiber Communication Conference, 2006 and the 2006 National Fiber Optic Engineers Conference 5-10 March 2006 Page(s):3
  6. K. Chakraborty "Capacity of the MIMO Optical Fading Channel", Information Theory, 2005. ISIT 2005. Proceedings. 4-9 September 2005, pp: 530- 534.
  7. Daisuke Takase,Tomoaki Ohtsuki, "Spatial Multiplexing in Optical Wireless MIMO Communications Over Indoor Environment",IEICE Transactions on Communications vol. E89-B, no. 4 pp. 1364-1371 , April 2006

 

 

 
Products
 

 

PRODUCTS

MIMO promises large increases in the throughput of wireless communication systems, therefore it has already been adopted in many standards such as 802.11g, 802.11n, 802.16, 3GPP, B3G and WCDMA.

Meanwhile, many companies are shipping wireless gateway, broadband router, and notebook adapter products based on the 802.11 specifications. Even though 802.11n is currently in draft status, end-user products are now entering the market. Figure 2 shows a 802.11n router, Wireless-N Broadband Router produced by Linksys.

figf1

Figure 1: 802.11n Router

 

Specification[1]:

 

Standards: Draft802.11n, 802.11g, 802.11b, 802.3, 802.3u

 

Number of Antennas: 3

 

Transmit Power: 17 dBm

 

Antenna Gain: 2 dBi

 

Speed: 12 times faster than 802.11g

 

Range: 4 times farther than 802.11g

 

Security Features: Up to 256-bit wireless encryption

 

Security Key Bits: 64, 128, 256

 

 

Reference:

  1. Product data of linksys WRT300

 

 
Testbed
 

 

TESTBED

The HCDC Lab at the University of Alberta has developed a flexible 4 x 4 MIMO testbed that allows real-time characterization of MIMO wireless channel. It consists of an independent transmitter and receiver that operate in the 902-928MHz Industry Scientific and Medical (ISM) band. The transmitter and receiver stations each consists of a GVA development board (manufactured by GV and Associates Inc.) for the baseband processing, It contains two xilinx Virtex-E 2000 field programmable gate arrays (FPGAs), four 12-bit Analog Devices AD9432 analog-to-digital converters (ADCs) , and four 12-bit Analog Devices AD9762 digital-to-analog converters (DACs).

Figg2

Figure 1: MIMO testbed transmitter

 

Figure 1 shows the MIMO transmitter. From left to right, it consists of a GVA290 board, inline filters, a four-channel RF up-converter module (from SignalCraft Technologies Inc.), and a multi-antenna structure. GVA 290 board creates 4 baseband signals, up-converts them to an intermediate frequency (IF) of 12.5MHz and then converts these IF digital signals to analog waveforms, The outputs of the GVAs are transmitted to the four-channel RF up-converter module through inline low-pass filters with a cutoff frequency of 15MHz. The RF board then up-converts these four independent IF waveforms to the 902-928MHz band for transmission over the air through the "multi-antenna structure".

 

Figg1

Figure 2: MIMO testbed receiver

 

Figure 2 shows the MIMO receiver. From left to right, it consists of the same multi-antenna structure as used by the transmitter, an RF down-converter board (manufactured by SignalCraft Technologies Inc.) with four independent receive paths, inline filters, and a GVA290 board. The four signals received from the antennas are amplified and down-converted from the ISM band down to an IF of 12.5 MHz by the RF module. The four received IF signals are then sampled by the ADCs of the GVA290 board. These sample streams are processed by the FPGAs at a clock rate of 50MHz. The measurements presented in Section Measurement and Result were obtained with this platform.

 

 

 

 
Measurement Results
 

 

MEASUREMENT RESULTS

Note: To calculate the channel capacity it is always a SNR=20 dB assumed. The SNR results in the tables below show that this assumtion was fulfilled!

 

1. Indoor Results

 
 

1.1 Parkade P1 of ECERF Building, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab, C-function (3, 30 H-Matrices/sec)
Description of Location: concrete walls, metal pipes at ceilling,receiver is mostly surrounded by cars.

 

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

15.972

37.657

0.823

Location 2

17.616

37.656

1.147

Location 3

14.231

35.354

0.814

Location 4

18.463

36.059

3.024

Location 5

18.752

35.025

0.615

Location 6

---

< 20

---

 

Fig

Fig

 

 

 

1.2 3rd Floor of ETLC Building, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: lecture rooms and laboratories, middle is open to floor above and below, concrete walls.

 

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

20.575

38.730

0.665

Location 2

20.654

38.551

2.015

Location 3

22.998

33.969

0.938

Location 4

22.484

39.162

1.018

Location 5

21.009

40.258

1.164

Location 6

21.603

38.539

1.623

Location 7

21.771

35.678

1.811

 

Fig

Fig

 

 

 

1.3 5th Floor of Civil Engineering Building, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: long corridor, 4 meter wide, 3 meter high, ceilling covered by wooden plates, no people.

 

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

19.226

40.805

0.587

Location 2

12.270

39.835

0.719

Location 3

12.180

38.503

0.210

 
 

 

 

1.4 V Wing Basement, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: long corridor, 4 meter wide, 3 meter high, concrete ceilling with metal and plastic pipes, concrete walls with metal lockers.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

18.182

37.818

0.981

Location 2

18.708

39.258

0.847

Location 3

19.333

40.910

1.648

 

 

 

1.5 2nd Floor of ECERF Building, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: typical office environment, a lot of moving people, dividing walls mostly thin wooden plates with embedded windows.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

20.464

37.819

1.967

Location 2

18.138

35.730

1.482

Location 3

19.289

27.574

3.545

Location 4

20.959

35.196

0.823

Location 5

20.801

33.876

2.985

Location 6

17.648

40.337

1.193

Location 7

no sync.

no sync.

no sync.

 

 

 

1.6 5th Floor of ECERF Building, U of A

Antenna Spacing: Lambda/2 (16cm), Lambda/4 (8cm), Lambda(32cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: concrete walls, office environment, people moving.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

22.056

40.401

0.967

Location 2

18.370

34.689

2.389

Location 3

---

< 20

---

Location 4

22.174

28.664

1.935

Location 5

21.454

35.219

2.696

Location 6

21.074

39.672

1.895

Location 7

21.428

40.538

1.238

Location 8

21.029

39.945

1.105

 
       
 

Lambda/4

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

20.115

40.730

0.635

Location 2

17.398

35.605

2.056

Location 3

---

< 20

---

Location 4

19.258

29.851

0.767

Location 5

18.355

33.450

0.341

Location 6

19.337

38.051

0.763

Location 7

19.342

38.470

1.125

Location 8

17.352

39.776

1.945

 

 

 

1.7 Indoor Moving measurement

1.7.1 Parkade P1 of ECERF Building, U of A

Environment: Office Environment, thin walls, people moving.
Recording Type: C-function (250 samples / sec)
Moving Speed: ca. 1 meter per second

Antenna Spacing: Lambda/2 (16 cm)

The moving speed was about 1 meter per second (walking speed). The recording of the H-matrices has been done with a C-function, which can record 250 H-matrices per second.

M1: The starting and end point is 5 meter away from Location 4 of the stationary measurement and 25 meter from Location 5. A complete turn of about 60 meters has taken 1 minute and 30 seconds where the velocity for this turn has been almost linear.

M2: In contrast to M1 this measurement is done on the lower level of Parkade P1 where the starting and end point is 6 meter away from Location 3 of the stationary measurements.

 

 

 

 

Channel Capacity M1

Channel Capacity M2

Power Plot M1

Power Plot M2

 

 

 

 

2. Outdoor Results

2.1 Quad, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: open area, buildings around, people moving.

Measurement Result:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

16.470

39.480

0.635

Location 2

12.817

31.933

0.345

Location 3

14.474

38.545

0.672

Location 4

no sync.

no sync.

no sync.

Location 5

20.683

34.677

1.731

Location 6

21.210

36.051

1.338

Location 7

22.564

32.684

0.863

 

 

 

2.2 Corbett Field, U of A

Antenna Spacing: Lambda/2 (16cm), Lambda (32cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: typical sports field, wired mesh fence arround.

Measurement Results:

 
 

Lambda

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

13.097

40.385

0.675

Location 2

17.419

35.676

0.458

Location 3

17.376

35.496

0.379

Location 4

18.890

30.504

0.295

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

11.097

40.385

0.138

Location 2

17.097

35.676

0.353

Location 3

18.079

35.46

0.323

Location 4

19.439

30.504

0.526

 

 

 

2.3 U of A Farm

Antenna Spacing: Lambda/2 (16cm)
Recording Type: C-function (250 H-Matrices/sec)
Description of Location: completely open area.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

11.314

39.275

0.086

Location 2

9.383

35.021

0.057

Location 3

9.860

23.031

0.250

Location 4

---

< 20

---

 

 

2.4 Hawrelak Park

Antenna Spacing: Lambda/2 (16cm)
Recording Type: C-function (250 H-Matrices/sec)
Description of Location: typical park envirnment with some trees.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

10.277

41.049

0.279

Location 2

13.181

38.284

1.144

Location 3

17.980

27.797

1.033

Location 4

15.265

37.085

1.108

 

 

 

3. Indoor/Outdoor Results

1st Floor V Wing, U of A

Antenna Spacing: Lambda/2 (16cm)
Recording Type: Matlab (3 H-Matrices/sec)
Description of Location: Transmitter inside, Location 2 to 5 outside; Inside: concrete walls, some vending machines, entrance glazed; Outside: trees, buildings around.

Measurement Results:

 
 

Lambda/2

Average
Channel Capacity
(bps/Hz)

SNR
(dB)

Variance
(bps/Hz)

Location 1

20.438

40.340

0.729

Location 2

14.418

39.781

0.554

Location 3

18.827

34.407

0.854

Location 4

21.286

34.786

2.145

Location 5

16.668

33.156

0571