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Coordinated Multi-Point (CoMP) Adaptive Estimation and Prediction Schemes using Superimposed and Decomposed Channel Tracking Gencer Cili, Halim Yanikomeroglu, and F. Richard Yu Department of Systems and Computer Engineering, Carleton

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SLIDE 1

Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu

Coordinated Multi-Point (CoMP) Adaptive Estimation and Prediction Schemes using Superimposed and Decomposed Channel Tracking

Gencer Cili, Halim Yanikomeroglu, and F. Richard Yu

ICC 2013 June 09, 2013

Department of Systems and Computer Engineering, Carleton University, ON, Canada

1/19

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SLIDE 2

Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013

Introduction

Our contributions

  • Multi-point channel estimation and prediction framework to tackle the CoMP

system delays and inaccurate measurements

  • Comparison between superimposed versus decomposed channel estimation

schemes

  • CoMP adaptive switching/fallback between channel estimation schemes
  • Balance the clustering accuracy versus the channel estimation computation

complexity trade-off

  • Effects of estimation/prediction filter length increases on CoMP performance are

characterized according to users being served by various cluster sizes

2/19

Motivation:

  • Comparison of decomposed and superimposed channel tracking methods in

Coordinated Multi-Point (CoMP) networks is not studied in existing literature.

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SLIDE 3

3 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 3/19

CoMP Joint Transmission Procedures

CoMP Definition: Dynamic coordination among multiple geographically separated points referred as CoMP cooperating set for downlink transmission and uplink reception

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4 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 4/19

CoMP Joint Transmission Procedures

Parameter Assumption or Value

πŽπƒπ©π©πͺ CoMP coordinating set πŽπ§πŸπ›π­ CoMP measurement set πŽπŠπ” CoMP joint transmission set

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SLIDE 5

Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013

Threshold Based CoMP Joint Transmission Clustering

5/19

  • Actual measured received power from eNB n by user i at TTI t :

π‘„π‘†π‘Œ π‘œ, 𝑒, 𝑗 = π‘„π‘ˆπ‘Œ π‘œ βˆ’ 𝑄𝑀 π‘œ, 𝑗 βˆ’ π‘„πΊπ‘π‘’π‘—π‘œπ‘• π‘œ, 𝑗, 𝑒

  • Received feedback due to estimation error + system delay:

π‘„π‘†π‘Œ_𝑓𝑠𝑠 π‘œ, 𝑒, 𝑗 = π‘„π‘†π‘Œ π‘œ, 𝑒 βˆ’ βˆ†, 𝑗 + 𝑄

𝑓𝑠𝑠(𝜈, 𝜏)

  • Threshold based Decision to Form the CoMP Transmission Set:

( , ) argmax{ ( , , )}

err

RX

Best

n

n i t P n i t ο€½

n ∊ 𝑂

πΎπ‘ˆ 𝑗, 𝑒 𝑗𝑔 |π‘„π‘†π‘Œπ‘“π‘ π‘ (π‘œπΆπ‘“π‘‘π‘’, 𝑗, 𝑒) βˆ’ π‘„π‘†π‘Œπ‘“π‘ π‘ (π‘œ, 𝑗, 𝑒)| ≀ π›Όπ‘‚π‘‹βˆ’πΎπ‘ˆ

Note: Contents of the joint transmission set will be impacted by reported CSI feedbacks due to multi-point channel estimation errors and system delays !!!

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SLIDE 6

Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013

CoMP Performance Metrics - Capacity

  • Joint PDSCH transmission (TM-9) mitigates the Inter-cell Interference

𝑇𝐽𝑂𝑆 = π‘„π‘‘π‘“π‘ π‘€π‘—π‘œπ‘• 𝑗=1

𝐿

𝑄𝑗 + 𝑄𝑂𝑝𝑗𝑑𝑓

Single Point Transmission

𝑇𝐽𝑂𝑆𝐷𝑝𝑁𝑄 = π‘„π‘‘π‘“π‘ π‘€π‘—π‘œπ‘• + 𝑄

π‘˜ + 𝑄 𝑛

𝑗=1

π‘—β‰ π‘˜,𝑛

𝐿

𝑄𝑗 + 𝑄𝑂𝑝𝑗𝑑𝑓

CoMP Downlink Transmission Total received Power from CoMP Transmission Set Perceived Downlink Capacity due to CoMP

6/19

2 \ ( )

( , ) ( , ) ( , )log 1 ( , , )

JT

JT RX noise n N N i

P i t C i t W i t P n i t P

οƒŽ

 οƒΆ  οƒ· ο€½   οƒ·   οƒ·  οƒΈ

οƒ₯

( , ) ( , , )

JT

JT RX n N

P i t P n i t

οƒŽ

ο€½ οƒ₯

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SLIDE 7

Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013

CoMP Performance Metrics – Energy Efficiency

CoMP Power Consumption Model Signal Processing Power 𝑄

π‘‡π‘„βˆ’π·π‘π‘π‘„ = 58 (0.87 + 0.1𝑂𝐷 + 0.03𝑂𝐷 2)

Backhauling Power 𝑄𝐢𝐼 = 𝐷𝐢𝐼 100𝑁𝑐𝑗𝑒𝑑/𝑑𝑓𝑑 50𝑋 Additional Data capacity for CoMP Backhauling 𝐷𝐢𝐼 = 𝑂𝑑 2𝑂𝐷 π‘žπ‘Ÿ

π‘ˆπ‘‡

𝑐𝑗𝑒𝑑/𝑑𝑓𝑑 Total Power Consumption

  • f an eNB using CoMP

𝑄𝐷oMP = 𝑂𝑑𝑂

𝑄𝐡 𝑑𝑓𝑑𝑒𝑝𝑠

π‘„π‘ˆπ‘Œ 𝑄𝐡𝑓𝑔𝑔 + 𝑄

𝑇𝑄

1 + 𝐷𝐷 1 + 𝐷𝐢𝐢 + 𝑄𝐢𝐼

𝑂𝑑 = Number of Sectors 𝑂

𝑄𝐡 𝑑𝑓𝑑𝑒𝑝𝑠

= Power amplifiers per sector π‘„π‘ˆπ‘Œ = DL Transmit Power, 𝐷𝐷 = Cooling Loss

𝐷𝐢𝐢 = Battery Backup 𝑂𝐷 = Number of points in Joint Transmission π‘ž = pilot density π‘Ÿ = CSI signalling π‘ˆ

𝑇 = Symbol Period 𝑄𝐡𝑓𝑔𝑔 = Power Amplifier Efficiency

Power Consumption Parameters

7/19

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Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013

CoMP Performance Metrics – Energy Efficiency

Energy Efficiency =

π·π‘π‘žπ‘π‘‘π‘—π‘’π‘§ (𝑐𝑗𝑒𝑑/𝑑𝑓𝑑) 𝑄𝑝π‘₯𝑓𝑠 π·π‘π‘œπ‘‘π‘£π‘›π‘žπ‘’π‘—π‘π‘œ(πΎπ‘π‘£π‘šπ‘“π‘‘/𝑑𝑓𝑑) = 𝑐𝑗𝑒𝑑 πΎπ‘π‘£π‘šπ‘“ Time Varying Energy Efficiency Joint Transmission CoMP Operation (𝑂𝐷 β‰₯ 2 ) 𝐹𝐹(𝑗, 𝑒) =

𝐷(𝑗, 𝑒) 𝑄𝐷𝑝𝑁𝑄 + 𝑂

πΎπ‘ˆ(𝑗,𝑒) βˆ’ 1

𝑄𝐷𝑝𝑁𝑄 βˆ’ 𝑄𝐢𝑏𝑑𝑓 Single Point Transmission (𝑂𝐷 = 1 ) 𝐹𝐹(𝑗, 𝑒) = 𝐷(𝑗, 𝑒) 𝑄𝐢𝑏𝑑𝑓 Notes: 1) 𝑄𝐢𝑏𝑑𝑓 has 𝑄𝐢𝐼 = 0 since there is not need for multi-point CSI transfer to serving cell 2) 𝑄

π‘‡π‘„βˆ’π·π‘π‘π‘„ = 58W since 𝑂𝐷 = 1

8/19

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9 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 9/19

  • 1. Lowered Joint Transmission Cluster Degree
  • Inaccurate multi-point CSI feedbacks can exclude a potential joint transmission

point from the CoMP cluster unnecessarily.

  • This decreases both the energy efficiency of the access network and the user

perceived quality of service in terms of received downlink data rates.

  • 2. Expanded Joint Transmission Cluster Degree
  • Inclusion of an incorrect point in the CoMP joint transmission cluster increases

the downlink data rates slightly; however, this causes significant bits/Joule energy efficiency losses since the increased power consumption of the access network is not compensated by an equal amount of downlink capacity gain for the served UEs.

CoMP Performance Metrics Summary

Technical challenges of CoMP systems due to channel estimation errors and system delays:

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10 10 10 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 10/19

Decomposed Channel Impulse Response (CIR) Estimation

l L οƒŽ

  • Track each multipath delay tap of every CoMP measurement

set member individually

  • Perform channel estimation separately for each path
  • Smoothened CIR at each path is then merged to report CSI

feedback for all points

1 ,

Λ† ( , ) ( ) ( , )

UE

M n i l l m

h t w m h t m  

ο€­ ο€½

ο€½ ο€­

οƒ₯

β„Žπ‘œ,𝑗 𝑒, πœπ‘š = [(π‘†β„Ž βˆ†π‘’, πœπ‘š + πœπ‘œπ‘π‘—π‘‘π‘“

2

𝐽𝑁𝑉𝐹𝑦𝑁𝑉𝐹)βˆ’1π‘ β„Ž βˆ†π‘’, πœπ‘š ]𝐼 π’Šπ‘’,…,π‘’βˆ’π‘π‘‰πΉ+1;πœπ‘š π’Šπ‘’,…,π‘’βˆ’π‘+1;πœπ‘š = β„Ž 𝑒, πœπ‘š … . β„Ž(𝑒 βˆ’ 𝑁𝑉𝐹 + 1, πœπ‘š)

π‘ˆ

π‘†β„Ž βˆ†π‘’ = 𝑁𝑉𝐹 βˆ’ 1, πœπ‘š = 𝐹 β„Ž 𝑒 βˆ’ 𝑁𝑉𝐹 + 1, πœπ‘š β„Ž 𝑒, πœπ‘š βˆ—

( ) ( ) w j w k j k ο€Ύ ο€’ ο€Ό

( ) ( ) w j w k j k ο€Ύ ο€’ ο€Ό

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11 11 11 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 11/19

Superimposed Channel Impulse Response (CIR) Estimation

  • Track the superimposed time-varying CIR coefficients instead of separate CIR

realizations at each path

  • Yields less accurate CSI estimates compared to decomposed multipath tracking
  • Multi-point channel estimation complexity of the UE is decreased significantly
  • Smaller auto-correlation filter and channel estimation filter input buffers

β„Žπ‘œ,𝑗 𝑒 = [(π‘†β„Ž βˆ†π‘’ + πœπ‘œπ‘π‘—π‘‘π‘“

2

𝐽𝑁𝑉𝐹𝑦𝑁𝑉𝐹)βˆ’1𝑠

β„Ž βˆ†π‘’ ]𝐼

π’Šπ‘’,…,π‘’βˆ’π‘π‘‰πΉ+1 π’Šπ‘’,β€¦π‘’βˆ’π‘+1;πœπ‘š =

π‘š=1 𝑀

β„Ž 𝑒, πœπ‘š , … ,

π‘š=1 𝑀

β„Ž 𝑒 βˆ’ 𝑁𝑉𝐹 + 1, πœπ‘š

π‘ˆ

π‘†β„Ž βˆ†π‘’ =

π‘š=1 𝑀

π‘†β„Ž βˆ†π‘’, πœπ‘š π‘’πœπ‘š

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12 12 12 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 12/19

Decomposed multi-point channel estimation

  • Better performance for users being served

by large clusters

  • Increased computational burden on the UE

firmware to track each path separately

Performance Analysis of Multi-point Channel Estimation Schemes

Superimposed multi-point channel estimation

  • Energy efficiency and capacity degradation

for users being served by large clusters

  • Faster

channel estimation computation time and decreased CSI feedback delay

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SLIDE 13

13 13 13 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 13/19

CoMP Adaptive Channel Estimation Scheme Switching

CoMP cluster degree adaptive switching

  • Use Decomposed channel estimation when

the wireless device is receiving downlink CoMP joint transmission from a large cluster

  • Fallback to superimposed estimation if cluster

size decreases

CoMP received power adaptive switching

  • UEs

increase the channel estimation computation complexity only for the critical points that require extremely accurate CSI feedback for joint transmission set clustering decisions

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SLIDE 14

14 14 14 14 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 14/19

Multi-Point Channel Impulse Response (CIR) Prediction

1 , , , 1 1

( ) ( ) ( ) ( ) ( ) ,

NW

M p n i n i n i m m p p

h t p h t p m w m h t p m w m

ο€­ ο€½ ο€½ ο€Ύ

 ο€½  ο€­   ο€­

οƒ₯ οƒ₯

Parameter Representation Predicted CIR samples by the serving e-NB Estimated CIR samples by the UE Prediction range in terms of number of TTIs Prediction filter length used by the serving e-NB

, n i

h

, n i

h

[1,..., ] p P οƒŽ

𝑁𝑂𝑋

1. Serving e-NB performs the prediction at P steps using: 2. Filter inputs, predicted CIR autocorrelation matrix and filter coefficients are updated at every step 3. Currently predicted CIR sample replaces the most outdated CIR sample for the filter input at each step p

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15 15 15 15 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 15/19

  • Multi-point

channel estimation procedures performed by the UEs are enough to tackle the channel estimation errors

  • However,

CoMP system delays still create performance degradations due to outdated CSI feedbacks

  • Serving e-NB should predict how the multi-point

CIRs will change at the time of the joint data transmission

  • CIR prediction range P is set to the system delay
  • bserved

in the channel to maximize the performance gains due to prediction

  • Estimation and prediction filter lengths and the

prediction ranges should be adapted according to the served UE’s CoMP characteristics

Performance improvement of CoMP systems due to multi-point channel estimation and prediction schemes

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16 16 16 16 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 16/19

CONCLUSION & SUMMARY

  • UEs should choose to switch dynamically between the two channel

estimation schemes depending on the recently observed CoMP characteristics

  • UEs should balance the optimal trade-off between CoMP capacity and

energy efficiency performance gains versus computation complexity

  • UEs that are being served by larger clusters should use decomposed

CIR estimation and tracking, and fallback to superimposed tracking method when the cluster size decreases

  • Use decomposed CIR estimation tracking only for the points that have

received power values which are close to the joint transmission cluster threshold or for the points that are recently added to the CoMP measurement set

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17 17 17 17 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 17/19

FUTURE WORK

  • Utilize CoMP adaptive channel estimation scheme

switching method jointly with the CoMP adaptive filter length choices

  • Decomposed

channel estimation scheme will be further enhanced to have various filter lengths for each multipath component

  • f

every CoMP measurement set member

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18 18 18 18 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 18/19

REFERENCES

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19 19 19 19 Carleton University: G. Cili, H. Yanikomeroglu, F. R. Yu ICC 2013 June 09, 2013 19/19

THANK YOU !

QUESTIONS ?