Paper review

Several effects on capacity and correlation coefficients on MIMO/UWB-MIMO capacity prediction/measurement have been explored. The effect of antenna spacing is reported in the literature [3] that the antenna spacing has little effect on capacity. The effect

of antenna orientation is reported in the literature [4, 8] that receiver antenna array, the one perpendicular to the direct-path direction obtains more capacity than the one parallel to the direct-path direction in a “wave-guiding” environment. The effects of Tx-Rx distance are reported in the literature [5] that for not normalized channel, the channel capacity usually rose when moving from NLOS into LOS since the loss in multipath was more than compensated for by an increase in SNR. The effect of unequal array spacing is reported in the literature [6] that unequal array spacing may obtain the optimum capacity. The effect of correlation of the antenna elements is reported in the literature [7] that correlations of the antenna elements are the limiting factor for the channel capacity. The above mentioned works are summarized in Table 1.

Several effects on UWB radio channel modeling have been explored. Characterization of UWB channels are reported in the literature [9] that present character UWB channels for outdoor office environment in LOS and NLOS cases. Reference [10] indicates that means angles of each cluster were found to be distribution uniformly over all angles. The distribution of arrivals within clusters was approximately Laplacian. In the literature [11]

that parameters of S-V model are compared with the parameters presented in the paper by Saleh and Valenzuela. The above mentioned works are summarized in Table 2

Several effects on MIMO/UWB MIMO radio channel modeling have been explored.

Reference [12] presents a set of channel models applicable to indoor MIMO WLAN systems. Reference [13] presents a simplified UWB-MIMO channel model that combine IEEE 802.15.3a channel model recommendation and a wideband MIMO channel model structure. In the literature [14] that UWB system is represented by a STDL model and MIMO system is represented by MIMO channel covariance matrix. The above mentioned works are summarized in Table 3.

1.2 Motivation

Recent years have seen the emergence of high data rate, third generation wideband wireless communication standards like wideband code division multiple access (W-CDMA) and UWB radio. Motivated by the ever increasing demand for higher wideband wireless data rates, we consider multiple antenna communication over the UWB wireless channel (UWB-MIMO). The spectral efficiency of UWB-MIMO system could be greatly increased when the channel is deployed in rich multipath condition such as indoor environment, where any two multipath components may have low correlation. Thus to investigate the capacity and correlation properties of the UWB-MIMO radio channel under various propagation and array arrangement will be an interesting and important subject. And few people do research on UWB-MIMO channel model.

1.3 Purpose

In this thesis, we will introduce the capacity and correlation properties of UWB-MIMO channels and analyze the measured UWB-MIMO channel data to investigate the effects of propagation range, local scatterer, antenna array spacing, array orientation and bandwidth.

And in order to propose a set of channel models applicable to indoor UWB-MIMO systems.

We first present the characterization of UWB channels for indoor environment with above measured data. Then we base on 802.11n channel model to develop the UWB-MIMO channel model.

1.4 Organization

This thesis is composed of 7 chapters as following: In chapter 2, the fundamental theory of UWB-MIMO systems will be introduced. Chapter 3 is the UWB-MIMO channels measurement. We will describe the UWB-MIMO channels measurement system and the measurement environments. In chapter 4, according to the measured data, the capacity,

EDOF, correlation coefficients and capacity loss of UWB-MIMO channel will be defined and calculated. The effects of propagation range, local scatterer, antenna spacing, array orientation and bandwidth effect will be considered. In chapter 5, character UWB channels for indoor environment with above measured data. The Saleh-Valenzuela (S-V) model [19]

is used as a basis for our UWB channel model. In chapter 6, we propose a set of channel models applicable to indoor UWB-MIMO systems. The newly developed UWB MIMO channel models are based on the 802.11n channel model [12]. A brief conclusion is provided in Chapter 7.

Table 1 Summary of related works on MIMO/UWB-MIMO capacity prediction and measurement

Spacing has little effect Prediction of MIMO

[4] 2.4 Corridor dimensions are

(100m×4m×3m)

Room dimensions are (10m×

10m×3m)

Array orientation that is

perpendicular to the direct-path obtains more capacity in the corridor

Prediction of MIMO

[5] 2.43 Corridor

Capacity decreases with distance increases

Measurement of MIMO

[6] 5.8 Indoor environment (virtual

transmitter and receiver antenna array)

Unequal array spacing may obtain the optimum capacity

Measurement of MIMO

[7] 5.14 -5.26

Indoor office The correlation of the antenna elements are the limiting factor for the channel capacity.

For a deterministic channel, with a strong LOS and small angular spread, the horizontal orientation of the receiver antenna array can make a significant difference in channel capacity gain.

Measurement of UWB-MIMO

Table 2 Summary of related works on UWB radio channel modeling

Ref.

No.

Freq.

(GHz)

Meas. Sites Meas. Result

[9] 3 - 6 Outdoor office environment Present a characterization of UWB channels

for outdoor office environment in LOS and NLOS cases.

The mean angles of each cluster were found to be distribution uniformly over all angles.

The distribution of arrivals within clusters was approximately Laplacian.

[11] Indoor office environment

Indoor laboratory environment

The parameters of S-V model in this paper are compared with the parameters presented in the paper by Saleh and Valenzuela.

Table 3 Summary of related works on MIMO/UWB MIMO radio channel modeling

Ref.

Present a set of channel models applicable to indoor MIMO WLAN systems.

MIMO

[13] 4 - 10 A simplified UWB-MIMO channel model that combines

IEEE 802.15.3a channel model recommendation (S-V model) and a wideband MIMO channel model structure.

UWB MIMO

[14] UWB system is represented by a STDL model and MIMO

system is represented by MIMO channel covariance matrix

UWB MIMO

Chapter 2

Fundamental Theory of UWB-MIMO Systems

It is well known that using antenna arrays at both transmitter and receiver over a multiple-input–multiple-output (MIMO) channel can provide a very high channel capacity as long as the environment has sufficiently rich scattering. Under these circumstances, the channel matrix elements have low correlation and the channel realizations are high rank, leading to a substantial increase in channel capacity.

In this chapter we will introduce the fundamental theory of UWB-MIMO systems, complex spatial correlation coefficient and EDOF (Effective Degrees of Freedom).

2.1 Generalized UWB-MIMO capacity formula

Consider a UWB-MIMO system with M transmits elements and N receives elements.

The baseband input-output relationship is given by

y

r

( ) ^τ

=

H

v

( ) ( ) ( ) ^τ

×

s

r

^τ

+

n

v

^τ

where sv

( ) τ

is the transmitted signal, yv

( ) τ

is the receiver signal, nv

( ) τ

is AWGN (Additive White Gaussian Noise) and × denotes convolution. Each element of the channel impulse response matrix

H

v

( ) τ

is the impulse response from a transmit antenna to a receiver antenna.

When the transmitted power is equally allocated to each transmit element and frequency subchannel, the UWB-MIMO channel capacity can be expressed as [17], [31]

∫ ( ) ( )

⎟⎟⎠ frequency response matrix of each narrow-band subchannel, * is the complex conjugate, and

ρ

is the average SNR at each receiver branch over the entire bandwidth. Since the measured UWB-MIMO matrices include the pathloss, we have to do a normalization to set the average receiver SNR to a specific value. Here, we normalize the frequency response of every narrow-band subchannel using a common factor such that

_∫ ( ( ) )

⁼

where

N is frequency components and i is the time or snapshot index.

_f

The normalization factor for each UWB-MIMO measurement snapshot T _i

(i is the

time or snapshot index) was calculated separately. This removed the effect of large-scale spatial fading, which can be significant for dynamic measurements, and ensured that only the small-scale: spatial fading was observed. T has dimensions of _i

n

_R×

n

_T×

N

_f , where

nR, n_T,

N are the number of receive antennas, transmit antennas and frequency

_f components respectively. Each 4x4 measured channel snapshot had dimensions of (4×4×

801), thus providing a sufficient number of independent samples for normalization. The normalized UWB-MIMO channel H was calculated from (2-3) and (2-4), where _i η ^{^}_k is the normalization factor estimate.

_^

The goal of channel normalization is usually to scale the channel response so that the expectation of its power is unity. We refer to this as unity-gain normalization.

2.2 Complex Spatial Correlation Coefficient

In addition to capacity, we also considered the correlation at both transmit and receive side. To estimate the receive and transmit correlation matrix we let hij be the channel complex gain between j-th Tx element and i-th Rx element. The complex correlation coefficient between hij and hkl is given as [21]

]

where * denotes the complex conjugate operation and a = hij and b = hkl. The complex correlation coefficient is a complex number that is less than unity in absolute value. In the following figures we will only present its absolute value. Also, it is assumed that all antenna elements in the two arrays have the same polarization and the same radiation pattern. To describe the propagation environments around Tx and Rx, the correlation coefficients of Tx and Rx are explored and given by [21], respectively,

)

Receiver correlation describes the local scattering around the receivers, whereas transmitter correlation describes the correlation of the transmitted signals as seen at the

receiver and does not provide insight into the scattering of the environment close to the transmitter array.

It is noted that ρTx and ρRx are first averaged over the index elements (j, l) or (I, k) then over the element indices i (receiving elements) and j (transmitting elements) respectively.

2.3 Effective Degrees of Freedom (EDOF)

The notion of multipath richness is less formal than capacity and there are several potential measures that could be used. Here, the concept of Effective Degrees of Freedom (EDOF) [2] will be used. This measure is based on the fact that for an N × N channel with rich multipath (fully decorrelated channel), a capacity increase of N bits is obtained when doubling the transmitted power. A correlated channel, i.e. a channel with fewer multipaths, will exhibit a smaller capacity increase. Hence, a convenient measure of the multipath richness is the slope of the capacity curve defined as

( )

2 ₌0

∂

= ∂ ^δ

ρ

_δ

δ ^C

EDOF

(2-7) By rewriting the capacity expression in (2-1) [9] as

( ) ∑

^{{ }}

Where

λ

_k denotes the singular values of the normalized channel matrix it is straightforward to calculate the derivative in (2-7)

( ) _∑

^{{ }}

The EDOF is then obtained as

{ }

Note that the EDOF is a real number in

[

^0,min

{

^{n ,}_T ⁿ_R

} ]

. A LOS channel with one dominant propagation path will yield an EDOF close to one while a rich NLOS channel will be close tomin

{

n ,_T n_R

}

. For channels in between these, the EDOF will essentially be the minimum of the number of transmit and receive antennas or the number of propagation paths with non-negligible strength [33]. Unfortunately, the EDOF measure depends on the SNR since the number of independent transmission channels that rise above the noise floor depends on the SNR. In this paper the EDOF will be calculated assuming a medium SNR of 10dB.

Chapter 3

Measurement System and Environment

In a typical indoor environment, due to reflection, refraction and scattering of radio waves by structures inside a building, the transmitted signal most often reaches the receiver by more than one path, resulting in a phenomenon known as multipath fading. In UWB pulse transmission, the effect is to produce a series of delayed and attenuated pulses (echoes) for each transmitted pulse.

In order to fulfill the requirement of higher data rates and capacities for future indoor wireless communications, numerous research programs are now underway and focused on evaluating and characterizing the wireless radio channel so that proper radio architectures with omni-directional UWB antennas on MIMO can be designed and implemented efficiently. This requires obtaining the channel characteristics in different environments, the UWB-MIMO channel measurement methods are proposed for analyzing each composition of multipath response. Later we will classify the propagation scenarios into following six categories:

1. LOS (Line-of-Sight) with light and heavy clutter (scenarios I and II).

2. NLOS (Non-Line-of Sight) with light and heavy clutter (scenarios III and IV).

3. LOS and NLOS in a guided environment such as corridors (scenarios V and VI).

3.1 Measurement System and Setup

In order to obtain the channel characteristics, the UWB channel measurement is performed to analyze the MPCs. An Agilent 8719ET Vector Network Analyzer (VNA) is

exploited to measure the channel response between two ends. The transmitted signal is sent from the VNA to the transmitting antenna through a low-loss 10-m coaxial cable.

For our measurement operation, we use a pair of omni-directional UWB antennas by Electro-Metrics (EM-6865), which frequency range is 2-18 GHz and antenna gain is 0dBi.

The signal from the receiving antenna is through a preamplifier (with a gain of 30 dB) via a low-loss 30-m coaxial cable and then returned to port 2 of the VNA. For UWB-MIMO application, the swept frequency band is from 3.5GHz to 4.5GHz (1GHz of frequency span).

With 1.25MHz steps corresponding to 801 points, we would be able to detect multipath with a time delay up to 800ns. Besides the network analyzer, the time-domain channel response can be obtained by taking the inverse Fourier transform (IFFT) of the frequency-domain channel response. Table 4 lists the main parameters in the measurement.

Because Agilent 8719ET is a SISO system with 2 omni directional antennas at both ends, we have simulated the 4x4 MIMO channels by moving the Tx and Rx to the ULA (Uniform Linear Array) fixed points. During the measurement, both the Rx and Tx antennas are at a height of 1.5m above the ground. And the measurement system that we used is shown in Figure 3-1, 3-2, 3-3.

Table 4 The main parameters in the measurement

Parameter Value

Frequency band 3.5GHz to 4.5GHz

Bandwidth (frequency span) 1GHz Number of points over the band 801

Transmitted power 10dBm

Preamplifier gain 30dB

Antenna gain 0dBi

Low loss cable Low loss cable

Low loss cable Low loss cable

Agilent 8719ET

Fig. 3-1 Block digram of the measured system

Fig. 3-2 A photo of the frequency domain channel sounding system

Fig. 3-3 A photo of the UWB antenna.

3.2 The Description of Measurement Environment

The measurement was performed in

1 floor (site A),^st 2 floor (site B and C), ^nd 7 ^th floor (site E), and 8 floor (site F) of the Microelectronics and Information System ^th Research Center (MISRC) and 2 floor (site D) of the 4^{nd th} Engineering Building at the National Chiao-Tung University, Hsinchu, Taiwan. The layout is shown in Fig. 3-4.

In order to compare the difference of capacity, EDOF and correlation for varied Tx-Rx distance in the scenarios I, II, III and IV, we do some measurement at site A, B and C. At site A, path 2 is measured at 1 floor of the MISRC and NLOS is always existed in path 2. ^nd At site B, path 1 is measured at 2 floor of the MISRC and LOS is always existed in path ^nd 1. At site C, both path 3 and path 4 are measured at Room 213 of the MISRC. And LOS is always existed in path 3. For instead of LOS, the NLOS exists in path 4. In the path 1, 2, 3, and 4, we take the samples when the Tx antenna array moves every 1m.

In order to analyze how the local scatterer affects the UWB-MIMO capacity and correlation in the scenarios I, II, III and IV, we carry out the measurement in site D. The Room 202 and 203 are all enclosed with concrete walls and wooden doors. All of them are clustered with wooden chairs. During the measurement, we don’t move these chairs to stand for the environment with local scatterers (scenarios II/IV). And then we move these chairs far away Tx, Rx to stand for the environment without local scatterers (scenarios I/III).

We adjust the element spacing of the virtual antenna arrays to investigate how the

capacity varies is with different antenna spacing in the scenarios I, II, III and IV. At P1, P2,

P3, P4, P5 and P6, both Tx and Rx antenna spacing is changed from 0.1λ to 2.0 λ . In order to know the difference between LOS and NLOS condition, we measure at P1, P3 &

P5 under the LOS condition, and P2, P4 & P6 under NLOS condition.

In order to compare with the difference of varied antenna array orientation in the scenarios II, IV, V and VI, we do some measurement at site E and site F. For each transmit antenna position, the complex transfer functions were recorded for 10 receive antenna positions, 5 positions with the broadside of the virtual antenna perpendicular to the LOS (orientation I) and 5 positions with the broadside parallel to the LOS (orientation II). The array broadside orientation is shown in Fig. 3-5.

The frequency response data have been exploited to analyze the UWB-MIMO channel characteristics. We observe the frequency response between 3.5GHz to 4.5GHz with 801 sweep points. Detailed measurement sites are shown in Table 5.

Fig. 3-4 (a) Site A: 1 floor layout of the Microelectronics and Information System ^st Research Center (MISRC)

Fig. 3-4 (b) Site B: 2 floor layout of the MISRC ^nd

v

Fig. 3-4 (c) Site C: Lab 213 layout of the MISRC

Fig. 3-4 (d) Site D: 2 floor layout of the 4th Engineering Building ^nd Rx

Fig. 3-4 (e) Site E: 7 floor layout of the MISRC ^th

Fig. 3-4 (f) Site F: Lab 810 layout of the MISRC

Tx Tx

Rx Rx

Tx Tx

Rx Rx

Fig. 3-5 Receiver antenna broadside (a) perpendicular (orientation I)

(b) parallel to the direct path (orientation II) (b)

(a)

Table 5 Measurement sites

Location Distance (Tx-Rx) Measurement

Scenarios

(Room 202, 203 of the 4th Engineering Building) Tx14-Rx6=3m Tx15-Rx6=7m

Chapter 4

Propagation, Array Arrangement and

Bandwidth on UWB-MIMO Capacity and Channel Correlations

To investigate the capacity and correlation properties of the UWB-MIMO channel under various propagation, array arrangement and bandwidth will be an interesting and important subject. In this chapter, the effects of propagation range, local scatterers, antenna spacing, array orientation and bandwidth on the UWB-MIMO capacity, EDOF and correlation are investigated through the measurement.

4.1 UWB-MIMO capacity, EDOF and correlations evaluation

From Eq. (2-2), the 4×4 MIMO capacity is given by

The capacity is calculated with

ρ

=10

dB

and the measured 4×4 UWB-MIMO channel matrix, T (i is the time or snapshot index), which is realized through the _i measurement by Agilent 8719 ET vector network analyzer. The normalized UWB

channel H was calculated from (2-3) and (2-4), where _i

η

^{^}_k is the normalization factor estimate.

From Eq. (2-10), the EDOF is then obtained as

{ }

And from Eq. (2-5) (2-6), the spatial correlation coefficient at Tx, Rx between elements are calculated.

4.2 Propagation Range Effect (Scenarios I/II/III/IV)

To investigate the propagation range effect on capacity, EDOF and correlations, we perform the measurement for a (4×4) UWB-MIMO system in two kinds of indoor environments: one in the lobby with light clutter (site A and site B) and another in the laboratory with heavy clutter (site C). We also consider the LOS and NLOS situation in two environments (scenarios I/II/III/IV). During the measurement, the antenna array broadside orientation direction is always perpendicular to the direct-path direction.

4.2.1 LOS with light/heavy clutter (scenarios I/II)

From Fig. 4-1 to fig. 4-2 illustrate the capacity, EDOF and correlations versus Tx-Rx distances in the LOS condition (scenarios I/II). By the chart of Fig. 4-1, we can find that in the scenario I, when Tx-Rx distance is in near distance, the capacity will be lower. But when distance is added to 10m, the capacity will not be changed obviously for distance increase. The reason is that because the Tx and Rx is very close, the correlations are relative higher at 1m, 2m and 3m (noted from Fig.4-1 (c) (d)). The LOS clutter strongly raises the correlation and then will cause the capacity reduced. In Fig. 4-1 (b) (EDOF vs.

distance) can find the same tread as Fig. 4-1 (a).

From fig. 4-2, we can find that capacity, EDOF and correlations are all similar for

various distances in the scenario II. The reason is that there are heavy clutters in the laboratory so that when the Tx-Rx is in near distance, multipath will increase (compare with the scenario I), then correlations are not as high as the scenario II.

(a) (b)

(c) (d) Fig 4-1 Measured results at site B (in LOS condition in scenario I)

(a) Capacity versus distance; (b)EDOF versus distance;

(c)

ρ

_Tx versus distance; (d)

ρ

_Rx versus distance

(a) (b)

(c) (d)

Fig 4-2 Measured results at site C (in LOS condition in scenario II)

(a) Capacity versus distance; (b) EDOF versus distance;

(c)

ρ

_Tx versus distance; (d)

ρ

_Rx versus distance;

4.2.2 NLOS with light/heavy clutter (scenarios III/IV)

Fig. 4-3, 4-4 illustrates the capacity, EDOF and correlations versus Tx-Rx distances in NLOS condition (scenarios III/IV). The main difference of Figs.4-1 (site B), 4-2 (site C) and Figs. 4-3 (site A), 4-4 (site C) is that there are many scatterers in the site C (compare with site A/B).

By the chart of Fig. 4-3, we note that in the near distance, there is not a great effect on distance change to capacity, but when distance is during 16~18m, the capacity is low. We can find the reason out in the LAYOUT chart (Fig. 3-5(a)). As Tx-Rx distance is in 16~18m, Tx enter to a wide space and the multipath which RX received is reduced, so that the correlation coefficient is increase and then capacity decreased. The chart of Figs.4-3(c) (d) is the correlation coefficient for Tx, Rx and from the chart we can find that when Tx-Rx distance is in 16-18m, the correlation coefficient is higher.

From fig. 4-4, we can find that capacity, EDOF and correlations are all similar for various distances in the scenario IV. The reason is same with situation II.

Due to above results, we can conjecture that capacity is dependent of Tx-Rx distance in LOS with light clutter, i.e. capacity is lower when Tx-Rx distance in small AS

Due to above results, we can conjecture that capacity is dependent of Tx-Rx distance in LOS with light clutter, i.e. capacity is lower when Tx-Rx distance in small AS

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