submission doc.: ieee 11-13/1080r0 september 2013 joseph levy, interdigital communications...

Submission

doc.: IEEE 11-13/1080r0September 2013

Joseph Levy, InterDigital Communications Inc..Slide 1

Markov Modeling of the Channel for HEW System Level Simulations

Date: 2013-09-17

Name Affiliations Address Phone email Joseph LEVY InterDigital 2 Huntington

Quadrangle, Melville, NY 11747

+1 631 622 4139

[email protected]

Frank LA SITA InterDigital

Pengfei XIA InterDigital

Fengjun XI InterDigital

Authors:

Submission


Joseph Levy, InterDigital Communications Inc..Slide 2

Abstract

This contribution proposes that a Markov Model be used in system level simulations to provide an accurate and efficient means of including fast fading of outdoor channels for HEW system level modelling. Similar methods have been used by 3GPP for LTE modelling and 802.16 for 802.16e modelling.

Submission

doc.: IEEE 11-13/1080r0

Motivation

To provide an accurate and efficient outdoor physical channel model for HEW system level simulations.

Markov modeling techniques can provide such a system level simulation model. ([2], [5], [10])

The Markov modeling technique can be used for any agreed channel model (e.g. WINNER2, ITU, or customized)

The modeling approach and channel model(s) used for system level simulations are important considerations which should be agreed to allow for meaningful comparison of performance results.

Slide 3 Joseph Levy, InterDigital Communications Inc..

September 2013

(10^5) (10^5)

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Slide 4

This contribution provides:• A description of the proposed Finite State

Markov Chain (FSMC) system level modeling• Examples of calculated transition probability

matrix (TPM) for some channel models of interest.

Joseph Levy, InterDigital Communications Inc..

Discussion

(10^5)

Submission

doc.: IEEE 11-13/1080r0


Description of the proposed Finite State Markov Chain (FSMC) system level

modeling

September 2013

Slide 5

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doc.: IEEE 11-13/1080r0


Finite State Markov Chain

Model the channel SNR as a finite-state Markov chain (FSMC)

Each state represents a given value (range) of the SNR

The following example has four different SNR states

Slide 6

September 2013

0 1 2 3 4 5 6 7 8 9 10

x 104

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Sample index

inst

anta

neou

s S

NR

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doc.: IEEE 11-13/1080r0


Use of FSMC in System Level Simulations

Slide 7

September 2013

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doc.: IEEE 11-13/1080r0


Transition Probability Matrix (TPM)

Transition probability matrixpi,j : prob that the jth state is next, given that the ith state is current

be the prob distribution function when current state is the ith state

The underlying physical channel is fully characterized by the corresponding TPM matrix.

Slide 8

September 2013

NNNN ,,1

3,22,21,2

2,11,1

00

.........0

0

00

Submission

doc.: IEEE 11-13/1080r0


In modeling the physical channels, we need to convert multiple SNR values (one per subcarrier) into one single SNR value

Potential approaches

Throughput averaging (algo 1)

Straightforward, not MCS dependent

effective channel amplitude square

SNR averaging (algo 2)

Used by OPNET for LTE downlink/uplink

Exponential effective SNR mapping (EESM), received bit mutual information (RBIR), Mean mutual information per bit (MMIB) [10]

MCS dependent (not studied herein)

Multi-Carrier SNR Mapping

Slide 9

September 2013

22

22 ||1log||1log

1o

ii HH

N

2|| oH

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doc.: IEEE 11-13/1080r0


Examples of calculated transition probability matrix (TPM) for some channel

models of interest.

September 2013

Slide 10

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doc.: IEEE 11-13/1080r0


Proposed HEW Use Cases [9]

Slide 11

September 2013

1High density of APs and high density of STAs per AP

a stadiumb airport/train stationsc exhibition halld shopping mallse E-Education

f Multi-media Mesh backhaul

2 High density of STAs – Indoor

a dense wireless officeb public transportation

c lecture hall

d Manufacturing Floor Automation

3High density of APs (low/medium density of STAs per AP) – Indoor

a dense apartment building

b Community Wi-Fi

4High density of APs and high density of STAs per AP – Outdoor

a Super dense urban Street

b Pico-cell street deployment

c Macro-cell street deployment

5High throughput demanding applications

a surgery/health care (similar to 2e from 11ac)

b production in stadium (similar to 1d-1e from 11ac)

c smart car

Submission

doc.: IEEE 11-13/1080r0


Urban Micro Channels in HEW

• Urban Micro cellular environment fits well in HEW [8]• “The microcellular test environment focuses on small cells and high

user densities and traffic loads in city centers and dense urban areas. The key characteristics of this test environment are high traffic loads, outdoor and outdoor-to-indoor coverage. This scenario will therefore be interference-limited, using micro cells.” [7]

Slide 12

September 2013

WINNER 2 modelMetropolitan (C2)Typical Urban (B1, B4)Indoor to outdoor (A2)Rural macro (D1)

ITU modelUrban macro (UMa)Urban micro (UMi)Indoor (InH)High speed (RMa)

Submission

doc.: IEEE 11-13/1080r0


TPM Generation Algorithm

Generate multiple (10^5) pairs of channel samples, each pair are separated by N OFDM symbols (N * 3.2 ms)

Each consists of multiple taps according to the channel model, e.g. WINNER 2 or ITU

Will serve as current state and next state in statistics collection

For each sample in the channel sample pair,

Convert it to freq domain using DFT

Convert multiple subcarrier SNRs into one effective SNR (either SNR averaging or throughput averaging)

Quantize the effective SNRs into P (16) equi-prob states

Find the probability of each state transitioning into other states accordingly

Slide 13

September 2013

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Slide 14

Simulation Assumptions1) Channel Scenarios: WINNER 2 B1, ITU UMi

Directly comparable channel scenarios

2) Single transmit and single receive antenna

3) OFDM symbol separation between current and next state: 10 or 100

4) Large scale signal to noise ratio: 0 or 20dB

5) Mobile velocity: 3 or 30 km/hr

6) Two algorithms considered for determination of effective SNR:

1) Throughput averaging

2) SNR averaging


Submission



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ITU Channel

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WINNER 2 Channel

Diagonal max diff 0.10

Diagonal mean diff 0.05Off-diagonal max diff 0.09

Off-diagonal mean diff 0.01

ITU/WINNER 2 difference, 100 symbol, 20 dB snr, 30 km/hr, algo 1

The generated TPM are more or less similar for WINNER 2 B1 and

ITU UMi

ITU/WINNER 2 Comparison, 100 symbols. 20dB SNR, 30 km/h, Throughput Averaging

Submission



The generated TPM are more or less similar for WINNER 2 B1 and

ITU UMi

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ITU Channel

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WINNER 2 Channel


Diagonal mean diff 0.06

Off-diagonal max diff 0.07


ITU/WINNER 2 difference, 100 symbol, 20 dB snr, 30 km/hr, algo 2ITU/WINNER 2 Comparison, 100 symbols. 20dB SNR, 30 km/h, SNR Averaging

Submission



Large scale SNR does not change the TPM noticeably (throughput averaging)

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20 dB

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20dB vs 0 dB comparison, 100 symbol, 30 km/hr, WINNER 2 Channel B1, algo 1SNR Comparison, 20dB vs. 0dB, 100 symbols, 30 km/h, WINNER 2 B1 Throughput Averaging

Submission


Slide 18

Discussion on Multi-Carrier SNR MappingSNR averaging

Simple, independent of large scale SNR

SNR averaging occurs in the linear domain

Throughput averagingStrictly speaking depending on large scale SNR

This dependence is weak though (see comparison on previous page) and may be removed for simplicity

The mapping may thus be approximated by SNR averaging in the dB domain

22

22 ||1log||1log

1o

ii HH

N

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1o

ii HH

N


Throughput averaging

SNR averaging in the dB domain

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Slide 19

Simulation SummaryMarkov modeling of PHY multipath channels in HEW

TPM more or less similar for ITU UMi and WINNER 2 B1 channel

Faster velocity leads to more state changes in TPM

Larger separation leads to more state changes in TPM

Converting multiple subcarrier SNRs into a single value

SNR averaging in the linear domain

Throughput averaging

may be approximated by SNR averaging in the dB domain

More complex averaging may be usedThe general method may be applied to any other indoor or outdoor channels

for HEW system level simulations

ITU channels

WINNER 2 channels

and othersJoseph Levy, InterDigital Communications Inc..

Submission


Slide 20

Potential Items of Agreement

1. Use of Markov modeling of PHY multipath channels• Channel model(s) to be used (e.g. ITU UMi and WINNER 2 B1)

• Velocities to be considered

• SNR to be considered

• Number of Symbols to be averaged

2. Method of converting multiple subcarrier SNR• Throughput averaging

• More complex averaging

3. TPM for each agreed configuration• Generate multiple (e.g. 10^5) pairs, number of symbols separating pairs,

N

• Number of equi-probable states, P (e.g. 16)


Submission


Slide 21

References[1] IEEE 802.11-13/0722r1, “HEW SG Evaluation Methodology”, Intel.

[2] IEEE 802.11-04/0184r0, “802.11n TGn proposal for PHY abstraction in MAC simulators,” ST Microelectronics.

[3] R. Yaniv et. Al., “CINR measurements using the EESM method”, IEEE C802.16e-05/141r3.

[4] L. Hentilä, P. Kyösti, M. Käske, M. Narandzic , and M. Alatossava. (2007, December.) MATLAB implementation of the WINNER Phase II Channel Model ver1.1 [Online]. Available: https://www.ist-winner.org/phase_2_model.html

[5] OPNET Technologies Inc., “LTE PHY Multipath Fading Models – Design Document”.

[6] Software implementation of IMT.EVAL channel model, doc num: IST-4-027756

[7] Report ITU-R M.2135-1 (12/2009) Guidelines for evaluation of radio interface technologies for IMT Advanced

[8] IEEE 802.11-13/0996r1, . “Outdoor Channel Model Candidates for HEW”, K. Josiam, R. Taori, and F. Tong,

[9] IEEE 802.11-13/0657, “Usage models for IEEE 802.11 High Efficiency WLAN study group (HEW SG) – Liaison with WFA”, Laurent Cariou

[10] IEEE 802.16m-08/004r5, “IEEE 802 16m Evaluation Methodology Document (EMD)”


Submission

doc.: IEEE 11-13/1080r0


Additional TPM plots

September 2013

Slide 22

Submission



Faster velocity leads to more state transitions

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3 km/hr

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Velocity comparison 30km/hr vs 3km/hr, 100 symbol, 20 dB snr, WINNER 2 Channel B1, algo 1Velocity Comparison, 30 km/h vs. 3 km/h, 100 symbols, 20dB, WINNER 2 B1 Throughput Averaging

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Larger separation leads to more state transitions

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Symbol duration comparison 10 vs 100 symbols, 20 dB snr, 30 km/hr, WINNER 2 Channel B1, algo 1Packet Duration Comparison, 10 vs. 100 symbols, 20dB, 30 km/h, WINNER 2 B1 Throughput Averaging

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Algo 1: Throughput Averaging

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Algo 2: SNR Averaging


Diagonal mean diff 0.17Off-diagonal max diff 0.09


Algo difference, 100 symbol, 20 dB snr, 30 km/hr, WINNER 2 Channel B1Algorithm Comparison,100 symbols, 20dB, 30 km/h, WINNER 2 B1

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Algo 1: Throughput Averaging

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Algo difference, 100 symbol, 20 dB snr, 30 km/hr, ITU Channel UMiAlgorithm Comparison,100 symbols, 20dB, 30 km/h, ITU Channel UMi

submission doc.: ieee 11-13/1080r0 september 2013 joseph levy, interdigital communications...

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