A Radio Channel Estimation Scheme Using the CQI Info HSDPA
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Transcript of 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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Mathematical Analysis of SINR and CQIB. SINR Formulation
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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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Mathematical Analysis of SINR and CQIB. SINR Formulation
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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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Mathematical Analysis of SINR and CQIB. SINR Formulation
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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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Mathematical Analysis of SINR and CQIB. SINR Formulation
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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Mathematical Analysis of SINR and CQIB. SINR Formulation
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Rewriting the SINR formula with only one random variable
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15Mean and Standard Deviation of SINR
Mathematical Analysis of SINR and CQIB. SINR Formulation
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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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Mathematical Analysis of SINR and CQIC. Normalized SINR Modeling and Level Crossing Rate
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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Mathematical Analysis of SINR and CQIC. Normalized SINR Modeling and Level Crossing Rate
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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Two Dimensional Channel Estimation
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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Two Dimensional Channel Estimation
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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Two Dimensional Channel Estimation
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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