A Radio Channel Estimation Scheme Using the CQI Info HSDPA

8/12/2019 A Radio Channel Estimation Scheme Using the CQI Info HSDPA

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A Radio Channel Estimation Scheme Using the

CQI Feedback Information in High Speed

Downlink Packet Access

Junsu Kim, Young-Jun Hong, and Dan Keun Sung

Department of Electrical Engineering and Computer Science

Korea Advanced Institute of Science and Technology (KAIST)


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Channel Quality Indicator (CQI) Mobile terminals do not report the measured SINR directly to base

station

Mobile terminals measure their own radio channel status and report thequality in uplink direction to base station using a Channel Quality

Indicator (CQI).

Channel Quality Indicator (CQI) which is a sampled and quantized

version of the measured SINR.

CQI is another version of Signal-to-Interference and Noise Ratio (SINR), more

information about radio channel states of mobile terminals can be extracted

CQI is an indicator carrying the information on how good/bad the

communication channel quality is.

Base station utilizes CQI feedback information for scheduling, Adaptive

Modulation and Coding (AMC)

Factors play important roles to CQI measurement.

Signal-to-Noise Ratio (SNR)

Signal-to-Interference plus Noise Ratio (SINR)

Signal-to-Noise plus Distortion Ratio (SNDR) 2


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Channel Quality Indicator (CQI) Based on careful observation of reported CQI information by base

station, the mobile speed and geographical environment of each

mobile terminal can be estimated. Depending which value User Equipment (UE) reports, network

transmit data with different transport block size.

If network gets high CQI value from UE, it transmits the data with larger

transport block size and vice versa. What if UE report high CQI even when the real channel quality is

poor?

In this case, network would send a large transport block size

according to the CQI value and it would become highly probablethat UE failed to decode it (cause CRC error on UE side) and UE

send NACK to network and the network have to retransmit it

which in turn cause waste of radio resources.

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An effective channel estimation scheme in which large scaleand small scale fading are estimated simultaneously at a

base station using the feedback information from mobileterminals.

The proposed scheme decomposes a radio channel modelinto a large-scale fading component and a small-scalefading component using the feedback information.

The Large-Scale fading component is determined by thedistance from base station and its surrounding geographicalenvironment.

The Small Scale fading component depends on the mobile

speed. These components affect the Signal to Interferenceand Noise Ratio (SINR) measured in a mobile terminal.

The analysis with the measured SINR values is still validfor CQI with some quantization errors.

Proposed Scheme for Channel estimation

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The mean of SINR represents the large-scale fading and the

level crossing rate (LCR) of the normalized SINR, which isthe SINR value divided by its average value, only depends

on the small-scale fading.

A two-dimensional 'Mean-LCR Plane' to efficiently estimate

the radio channel status of mobile terminals. Proposed estimation scheme is able to estimate the radio

channel status of mobile terminals including the small and

large-scale fading components

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Proposed Scheme for Channel estimation


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The HSDPA (High Speed Downlink Packet Access)

system consists of a single Node-B (base station) and

multiple user equipments (UE, mobile station) in a cell.

A UE measures its channel quality using a known pilot

channel and determines a CQI value to obtain the target

QoS, such as Frame Error Rate (FER) or BLock ErrorRate (BLER), and the CQI value is reported to Node-B.

Then, the scheduler in the Node-B selects a UE based on

an appropriate policy using the reported CQI values.

In HSDPA, the CQI value ranges from 0 ~ 30.

Value 30 indicates the best channel quality and 0.1

indicates the poorest channel quality.

Overview of HSDPA

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Measured CQI and SINR values in HSDPA

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Figure shows the relation between the SNR and CQI values, when

the UE's mobile speed is 5km/h.

In the figure, the solid line indicates the measured SINR and the

discrete steps indicate the corresponding CQI values.

Since only 30 CQI values are defined in HSDPA, the CQI values

cannot indicate the whole range of SINR values


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Mathematical Analysis of SINR and CQIA. Environment and Assumptions

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A cell model which consists of ahome-cell and outer cells.

We assume that all the Node-Bstransmit constant power and thetransmitted power from the outer-

cell Node-Bs becomes outer-cellinterference to a UE in the home-cell.

The cell radius is R km and thedistance from Node-B to the UEis r km.

Single path Rayleigh fading as asmall-scale fading model and the

path loss as a large-scale fading

model.


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Mathematical Analysis of SINR and CQIB. SINR Formulation

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The path loss from the Node-B in the cell (i, j)can be expressed as

where K is a constant factor, rois a reference distance in km,

and n is a path loss exponent.

Outer cell interference can be derived as

where Ntdenotes thenumber of tiers.


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The received signal power of a UE in the homecell can be written as:

SINR can be derived as

Where No is the gaussian thermal noise


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This equation includes random variables in both denominator and numerator,

it is very complex to derive the probability density function (PDF) of SINR

directly.

In order to simplify this the variances of the signal component and the

interference component are investigated.If the variance of the interference component is much smaller than that of the

signal component, then the interference component can be approximated as a

constant in the viewpoint of the signal component.

In other words, the signal component dominates the statistical characteristics

of the SINR.


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Where i,jis the mean power of fading coefficients i.e. 2i,jthus we can

assume i,j= 1.The ratio of the above variances is given by


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13Signal and Interference Variance Ratio

Simulation result in a 2-tier cell model,

where the cell radius is 1 km, and x-axisindicates the distance from the home-cell

Node-B to the UE.

Variance of the signal component is 10 times

larger than that of the interference component,

at a location of 0.9 km apart from the home-

cell Node-B, and even 100 times larger at alocation of 0.6 km.

This means that the characteristics of the

signal component dominate the statistical

characteristics of entire SINR in the inner area

of the home cell.

IOC(r) ~ E[IOC(r)].Since No is relatively smaller than the

interference component, we can neglect the

thermal noise component.


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Rewriting the SINR formula with only one random variable


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15Mean and Standard Deviation of SINR



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Mathematical Analysis of SINR and CQIC. Normalized SINR Modeling and Level Crossing Rate

PDF of the approximated SINR

The LCR of a random variable with a threshold level of th isdetermined by the Rice formula

Normalized SINR is independent of distance

between the home cell Node-B and UE.


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Mathematical Analysis of SINR and CQIC. Normalized SINR Modeling and Level Crossing Rate PDF of the approximated SINR

Normalized SINR is independent of distance between the home cell Node-B and UE.

It only depends on the small scale fading coefficients

LCR of the normalized SINR would be determined by small-scale fading regardless of

the large scale fading in other words, regardless of the geographical characteristics

The time derivative of Y(r) can be derived as


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Mathematical Analysis of SINR and CQI

C. Normalized SINR Modeling and Level Crossing Rate

Joint PDF of normalized SINR and time derivative of SINR

LCR of Y(r)


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This equation is a function of threshold, Yth,

and the mobile speed inherent in .

It always has the maximum value at Yth= 0.5

regardless of mobile speeds.

Figure shows the analytical LCR value of Y(r)

for various thresholds and various mobilespeeds.

LCR always has the maximum value at a

threshold of 0.5. Therefore, it is reasonable to

use the threshold value of 0.5 to classify the

mobile speeds.

LCR of Y(r)


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LCR of Y(r)

When the distance between the Node-B andthe UE is less than 0.3km, the LCR of CQI

becomes zero regardless of mobile speeds.

This is due to the accuracy of CQI. Since

there are only 30 CQI values, all the

measured SINR values should be classifiedinto one of 30 CQI values. Therefore, the

UEs near the Node-B tend to have very high

probabilities to report a maximum CQI value

of 30.

Although the SINR fluctuates as the mobile

speed increases, the CQI value has amaximum value of 30. Therefore, the level

crossing of CQI values hardly happens.

However, if the distance between the Node-B

and the UE is larger than 0.3km,


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Two Dimensional Channel Estimation

A. Channel Estimation in the Mean-LCR Plane Mean of SINR depends on the large-scale fading including the

distance from the Node-B to the UE.

The LCR of the normalized SINR depends on the small-scale

fading including the mobile speed.

Mean SINR and the LCR of the normalized SINR together, can be

used to estimate the wireless channel status of a certain UE.


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A. Channel Estimation in the Mean-LCR Plane Channel Classification based on the Mean and LCR values of SINR

Figure shows the LCR values of the

normalized SINR according to the mean values

of SINR for 20 UEs with different mobile

speeds and different locations.

In this figure, we can classify each UE using

the position on the plane of both the mean and

the LCR values of SINR.

UEs in the right-bottom corner of the plane

have better channel status than those in other

parts of the plane.


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A. Channel Estimation in the Mean-LCR Plane Channel Classification based on the Mean and LCR values of CQI

The mean values of SINR are replaced by the

mean values of CQI and the LCR values of the

normalized SINR are substituted by the LCR

values of CQI.

Here the dots concentrated in the right-bottom

corner of the plane. These dots are for the UEs

located near the Node-B. The UEs near the

Node-B have very low LCR values of CQI.Although these UEs have relatively higher

mobile speeds, their channel status is good

enough to be served by the Node-B.


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A. Channel Estimation in the Mean-LCR Plane Mean LCR plane

Here the plane is divided into 9 regions. The x-

axis indicates the distance and the y-axis

indicates the mobile speed.

Each region is numbered according to the

mean and LCR values of CQI/SINR about

current wireless channel status.UEs in the smaller numbered regions have

better wireless channels. Therefore, if the Mean-

LCR plane is managed in the Node-B, the Node-

B can estimate not only the UE's current channel

status but also the channel variation history in

the past.This information is very valuable in HSDPA

systems.


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Conclusions A UE specific radio channel is mainly characterized by small-scale fading due to

its geographical conditions and large scale fading due to the mobile speed of theUE.

In this paper, it is possible to estimate the small-scale and large-scale fadingusing the mean values of SINR and the LCR values of the normalized SINR,respectively.

The approximated SINR distribution is found out and analyze the mean andLCR values.

A two-dimensional channel estimation scheme based on a 'Mean-LCR Plane' is

proposed which is used to represent the UE's current radio channel status and itshistory.

In HSDPA, CQI values are reported to Node-B instead of the measured SINR.

CQI is a sampled and quantized version of the measure SINR.

The analysis based on SINR is still valid for CQI with some quantization errorby comparing this result with the simulation result. Therefore, we can efficiently

manage the UE's radio channel status based on the 'Mean-LCR Plane' of the CQIversion at the Node-B.

Finally, we verify the performance of the proposed scheme using intensivesystem-level simulations. It is possible to develop more efficient schedulingalgorithms or handoff schemes using the proposed channel estimation schemefor further work.

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Thank You

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A Radio Channel Estimation Scheme Using the CQI Info HSDPA