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research project on physical layer downlink abstraction techniques for LTE (system level simulations)
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Physical layer abstraction forLTE downlink
PRESENTED BY RAJ PATEL
Introductionlink level simulator simulates a single radio link
system level simulator takes into accounta complete cell: time consuming
Physical layer abstraction : process of modeling the performance of the physical layer based on the current channel state and the physical layer parameters
IntroductionAWGN
MCS -> CQI
target SNR – 10% BLER
Plots : Target SNR vs CQI / MCS - linear
IntroductionExtrapolation of Reference curve to get effective SNR
choose MCS values belonging to same constellation.
Get the Target SNR value
•Calc. difference between the T.SNR values
We note down the effective code rate for the MCS used.
We use the reference curves to get the values of SNRusing the effective code rate of that MCS
•Calc. the difference between the SNR values
Observationsotheoretical difference and the difference calculated using interpolation are not the same
oPossible reason: C* = (TBS + CRC) / G. G: bits transmitted per second; C: Code Rate
o 40 <= Code Block Size(= TBS + CRC) <= 6144 ; CRC = 24 bits
oEg: 6126 bits TBC6120 + 24 // 6 + 24 + 10 ; 10 : padding
Delta SNR from
Lookup table values
C = TBS / G
4.237 4.3203 1.4398 2.8805 4.7258 6.6409 2.6672 3.9737
Delta SNR from look
up table using
C* = (TBS + CRC) / G
4.1689 4.3423 1.4366 2.9057 4.7415 6.684 2.6877 3.9963
Delta SNR from log
BLER curve2.86 3.446 0.788 2.668 4.2 3.742 2.412 2.33
Frequency Selective FadingCoherence Bandwidth
Signal Bandwidth
Flat fading: Just attenuation, no distortion
Frequency Selective (much more realistic): Distortion
If the attenuation happens in different amounts for the different parts of the signal, it is a distortion.
Condition: Coherence Bandwidth < Signal Bandwidth
Frequency selective fading channel model
Eg.: EPA
EPA : Extended Pedestrian A modelomultiple paths
osame signal copies arrive at the receiver delayed and different attenuations
o-g E –M1 –R1 –N 100 –n 10000
o-M1: Abstraction flag keeps channel coefficients constant over SNR range
o-R1: to reduce simulation time
o-g E: fading model
o-n: number of packets
o-N: number of channel realizations
oOUTPUT format:SNR, 50 channel coefficients, BLER1
Abstraction TechniquesEESM
MIESM
EESM: Exponential Effective SINR Mapping𝛾eff = 𝛽1 𝐼
−1 1
𝑁 𝑛=1𝑁 𝐼
𝛾𝑛
𝛽2
𝐼 𝛾𝑛 = 1 − exp (−𝛾𝑛) ; 𝛾𝑛 is the instantaneous SNR
Aim: to calculate SINR effective
Noise_var = 1 / SNR_linear; inst_snr = 10*log10 (h^2/Noise_var);
1. Calculate the instantaneous SNR corresponding to each value of channel realization
2. Use the I function with the instantaneous SNR and average it over N
3. Use the inverse function of I to calculate the effective SNR
PLOTS - EESM
MIESMMutual Information Effective SINR Mapping
No closed form expression
Calculate the instantaneous SNR
Using lookup tables, calculate normalized capacity for each instantaneous SNR
Calculate average normalized capacity per SNR
Calculate the effective SNR using average normalized capacity with lookup table
PLOTS MIESM
MSE calculation𝛾eff = 𝐼−1
1
𝑁 𝑛=1𝑁 𝐼(𝛾𝑛)
*N stands for the number of valuesof channel coefficients per SNR.
SNR interp: image of SNR effective on AWGN curve
𝑀𝑆𝐸=1
𝑁 𝑛=1𝑁
𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ −𝛾eff𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ
2
*N here, stands for the number of SNR values.
MSE resultsMCS MSE EESM using
'linear','extrap'
NORMALIZED
Linear, log
MSE_MIESM
'linear','extrap'
NORMALIZED
Linear, log
3 58.695, 0.3663 108.92, 0.2975
15 1.5247, 0.4958 0.3202, 0.3395
15 _n = 1000, N =1000 1.1699, 1.3596 0.3403, 1.9242
20 * 0.3869, 0.2304 0.1067, 0.5900
23 0.2551, 0.4954 0.0823, 0.3636
25 0.0897, 0.7444 0.0672, 0.7858
MSE – With 𝛽1, 𝛽2𝛾eff = 𝛽1 𝐼
−1 1
𝑁 𝑛=1𝑁 𝐼
𝛾𝑛
𝛽2
𝑀𝑆𝐸argmin𝛽1,𝛽2
=1
𝑁 𝑛=1𝑁
𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ −𝛾eff 𝛽1,𝛽2𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ
2
MSE Results – With 𝛽1, 𝛽2
MCS B values MSE EESM
calibrated
3 [0.0334,0.6226] 0.7683
15 [3.975e+02,4.7833e+03] 0.0037
15 _n = 1000, N
=1000
[3.991e+02,5.581e+03] 0.0041
20 (erroneous) [41.3997,58.1240] 0.0466
23 [6.862e+02,1.241e+04] 1.64e-04
25 [7.469e+02,1.318e+04] 1.20e-04
MCS B values MSE MIESM
calibrated
3 [0.2051,17.348] 0.9835
15 [0.7490,0.6111] 0.2887
15 _n = 1000, N
=1000
[0.7903,0.7440] 0.3339
20 (erroneous) [0.6041,0.7456] 0.0430
23 [0.8813,0.7282] 0.0567
25 [0.8398,0.8028] 0.0645
EESM – calib.
MCS- color3-Red, 15- Yellow, 20*- Sky blue,23- Blue, 25- Pink
Conclusions and Observations
Calibration factors work better with EESM
The resultant MSE after using calibration factor with EESM are around 10^3 times better
Where as for MIESM, it is 10 times better.
MCS 25: EESM MIESM
MSE Without calibration 0.7444 0.7858
MSE With calibration 1.20e-04 0.0645
Conclusions and ObservationsCalculations done in the log scale don’t make
𝑀𝑆𝐸argmin𝛽1,𝛽2
=1
𝑁 𝑛=1𝑁
𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ −𝛾eff 𝛽1,𝛽2𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ
2
Division in log scale?MCS MSE EESM using
'linear','extrap'
NORMALIZED
Linear, log
MSE_MIESM
'linear','extrap'
NORMALIZED
Linear, log
3 58.695, 0.3663 108.92, 0.2975
15 1.5247,0.4958 0.3202, 0.3395
20 (erroneous) 0.3869, 0.2304 0.1067, 0.5900
23 0.2551, 0.4954 0.0823, 0.3636
25 0.0897, 0.7444 0.0672, 0.7858
NOTE: Calculations in Linear scale show a gradual Decrease in MSE value, unlike the log scale
Thus operate with linear valuesif we are using Normalization
But why does Lower MCS have weird MSE values?
Conclusions and ObservationsIssues with the lower MCS values any ideas??
Working on Linear scale, why is it that the Lower MCS has higher values of MSE compared to higher MCS values?
Reason: Normalization while calculating MSE
𝑀𝑆𝐸argmin𝛽1,𝛽2
=1
𝑁 𝑛=1𝑁
𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ −𝛾eff 𝛽1,𝛽2𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ
2
𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ − 𝛾eff 𝛽1, 𝛽2 : more or less remains the same, say around 5-10 dB
But, 𝛾𝑖𝑛𝑡𝑒𝑟𝑝 BLER𝑐ℎ changes according to MCS value, stays close to -2 to 2 dB
Conclusions and Observations
Conclusions and ObservationsMCS MSE EESM using
'linear','extrap'
NORMALIZED
Linear
MSE_MIESM
'linear','extrap'
NORMALIZED
Linear
3 58.695 108.92
15 1.5247 0.3202
15 _n = 1000, N =1000 1.1699 0.3403
20 (erroneous) 0.3869 0.1067
23 0.2551 0.0823
25 0.0897 0.0672
Table with the calculations done in Linear scale.
Conclusions and ObservationsFor 15 _n = 1000, N =1000 case, the calculations are not in synchronization with the other cases.
Reason: too many values: may be it gives us a better estimate.
MCS B values MSE EESM
calibrated
3 [0.0334,0.6226] 0.7683
15 [3.975e+02,4.7833e+03] 0.0037
15 _n = 1000, N
=1000
[3.991e+02,5.581e+03] 0.0041
20 (erroneous) [41.3997,58.1240] 0.0466
23 [6.862e+02,1.241e+04] 1.64e-04
25 [7.469e+02,1.318e+04] 1.20e-04
MCS B values MSE MIESM
calibrated
3 [0.2051,17.348] 0.9835
15 [0.7490,0.6111] 0.2887
15 _n = 1000, N
=1000
[0.7903,0.7440] 0.3339
20 (erroneous) [0.6041,0.7456] 0.0430
23 [0.8813,0.7282] 0.0567
25 [0.8398,0.8028] 0.0645
Note: The MSE of EESM is lower than the MSE of MIESM
NOTE: Calculations in Linear scale show a gradual Decrease in MCS value
Conclusions and ObservationsNote: The MSE of EESM is lower than the MSE of MIESM
Reason? High values of Beta using EESM?
MCS B values MSE EESM
calibrated
3 [0.0334,0.6226] 0.7683
15 [3.975e+02,4.7833e+03] 0.0037
15 _n = 1000, N
=1000
[3.991e+02,5.581e+03] 0.0041
20 (erroneous) [41.3997,58.1240] 0.0466
23 [6.862e+02,1.241e+04] 1.64e-04
25 [7.469e+02,1.318e+04] 1.20e-04
MCS B values MSE MIESM
calibrated
3 [0.2051,17.348] 0.9835
15 [0.7490,0.6111] 0.2887
15 _n = 1000, N
=1000
[0.7903,0.7440] 0.3339
20 (erroneous) [0.6041,0.7456] 0.0430
23 [0.8813,0.7282] 0.0567
25 [0.8398,0.8028] 0.0645
Issues and Future WorkThe calibration factors are a bit high for some MCS values for EESM!
WHY!?
Is that the only reason why we see the performance of EESM is better than MIESM??
Thank You!Questions if any
LTEOFDM
OFDMA
Cyclic Prefix
ISI
RE
RB
OAIEurecom
Physical layer stimulations
Resource Elements Allocation•N_PILOTS = 6*N_RB*TM
•N_RB - by default set to 25
•N_RE = (OFDM symbols – Prefix length) * (N_RB*sub-carriers per block) - N_PILOTS
•Example: -x1 –y1 –z1 ; Normal cyclic prefix
•N_RE= (14-1)*(25*12) – (6*25*1) = 3750
Map CQI --> MCS•CQI – feedback
•MCS – chosen
CQI (1-15) MCS(1-28)
3 3
8 15
10 20
13 25 (with extended prefix)
AWGN reference curves•BLER vs SNR plots
•Monte Carlo stimulations
•Step size
•SNR range
•Interpret .csv
•Target SNR
Plots•Target SNR vs CQI
•Target SNR vs MCS
•Target SNR vs Code rate
•Observation
Extrapolation of curves•ΔSNR (db) = f -1(r2) – f-1(r1)
•Normalized capacity is the
effective code rate
•Code rate/ bits per symbol
Extrapolation method•Choose MCS values belonging to same constellation.
•Stimulate for those MCS values and get the Target SNR value. Target SNR is the SNR value for log BLER= -1
•ΔSNR value of two MCS schemes from stimulation
•We note down the effective code rate for the MCS used.
•We use the reference curves to get the values of SNR using the appropriate curve (taking into consideration the Modulation scheme used for that MCS)
•ΔSNR values found from the reference curves by extrapolation
Conclusions•Extrapolation important
•Needs to be improved