Adaptive modulation and multiuser scheduling gains in adaptive TDMA/OFDMA systems in the WINNER framework

1
Adaptive modulation and multiuser scheduling
gains in adaptive TDMA/OFDMA systems in the
WINNER framework

• Sorour Falahati , Mikael Sternad,
• Tommy Svensson, Daniel Aronsson
• Uppsala University
• Chalmers University of Technology

2
Outline

• Introduction
• FDD downlink and uplink structure
• Timing events in DL/UL transmission
• Key techniques
• Channel prediction
• Scheduling
• Link adaptation
• Compression of feedback information
• Simulation results
• Summary

3
Introduction

• Predictive adaptive resource scheduling using
  TDMA/OFDMA
• Providing fast link adaptation in an OFDM system
  based on the predicted channel state information
  of time-frequency chunks
• Providing multi-user scheduling gain by
  allocating the resources to the flows with the
  potential of improving the throughput based on
  their channel status.

4
FDD downlink and uplink structure
FDD downlink
freq
Chunk
Chunk BW
D D P U U P
P pilots symbols D DL control symbols U UL
control symbols
8 sub-carriers
D D P U U P
6 TOFDM
time
freq
FDD uplink
O O O O O O O O
C C C C C C C C
O overlapping pilots C DL control feedback
T chunk
time
5
Timing events in DL/UL transmission
DL
DL
UL
O O O O O O O O
D D P U U P
D D P U U P
1. DL control symbols Report which present
chunks belong to which flows
C C C C C C C C
D D P U U P
D D P U U P
2. DL pilot symbols Used for channel
prediction Used for channel estimation
Dl prediction horizon 2.5X0.3372ms0.843ms
UL prediction horizon 2.5X0.3372ms0.843ms
3. UL control symbols Report which next UL
chunks appointed to which uplink flows
5. DL control feedback symbols Carry DL channel
prediction report
4. Pilot symbols Used for coherent detection And
updating predictor states
6. UL overlapping pilot symbols Used for
prediction
6
Channel prediction

• Prediction in frequency domain
• A set of linear predictors, one for each sub-band
• Kalman predictor
• Predict the complex channel and its power
• Using pilots in parallel sub-carriers
• Utilizing correlation in frequency and time
  domain
• Generalized Constant Gain (GCG) algorithm
• No need to update a sate-space Riccati difference
  eq.
• Moderate complexity and negligible performance
  loss as compared to Kalman algorithm

7
Channel prediction

• SINR and prediction horizon limit at 5 GHz
  downlink

30 km/h 50 km/h 70 km/h
lt0 dB, 0.117 6 dB, 0.195 12.5 dB, 0.273
8
Channel prediction

• SINR and prediction horizon limits at 5 GHz
  uplink

2 users
8 users
30 km/h 50 km/h 70 km/h
lt0 dB, 0.117 7 dB, 0.195 15 dB, 0.273
30 km/h 50 km/h 70 km/h
3.5 dB, 0.117 11dB, 0.195 20 dB, 0.273
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Scheduling

• Resource scheduling
• Proportional fair strategy
• Allocating resources (chunks) to the user with
  the highest SINR relative to its average
• For users with the same average SINR, this
  strategy reduces to Max. Throughput strategy.
• Allocating chunks to users with the highest MC
  rate.
• Due to curvature within the chunk, MC scheme is
  determined based on

Chunk Average SINR
Chunk minimum SINR
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Link adaptation

• Each user selects a modulation and coding (MC)
  scheme for each chunk in competition based on the
  prediction SINR
• The rate limit for a set of MC schemes are
  adjusted based on the TBER, average SNR and
  prediction error variance
• Based on the predicted chunk SINR, a MC scheme
  which fulfills the BER requirement and maximized
  the throughput is selected.

11
Link adaptation

• BER performance of MC schemes for perfect and
  imperfect prediction (NMSE0.1)

12
Link adaptation

• Variation of rate limits of MC schemes with
  prediction quality
• SNR10 dB and TBER0.001

13
Compression of feedback information

• Tricks or tools to reduce downlink overhead
• Use implicit signaling of utilized modulation
  rate whenever possible
• Contention-band The active terminals are in
  competition for only a part of the total BW
• Use short-hand addresses to indicate identities
  of active users whenever possible.

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Compression of feedback information

• Tricks or tools to reduce uplink overhead
• Contention-band
• Compression of feedback information
• Discrete cosine transform utilizing correlation
  in frequency
• Sub-sampling of transform coefficients in the
  time domain

15
Compression of feedback information

• THP as a function of feedback rate
• ITU VA channels, v50 km/h, sub-sampling factor
  of 2

10 users
5 users
1 user
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Simulation results

• Simulation set-up
• Wide-area full-duplex FDD downlink
• WINNER Urban Macro channel model
• Single cell (sector) and SISO
• Users with equal velocities and average SINRs

Center frequency 5.0/-0.384 GHz
Number of OFDM sub-carriers 1024
FFT BW 20 MHz
Signal BW 16.25 MHz paired
Number of used sub-carriers 832
Sub-carrier spacing 19531 Hz
OFDM symbol length (exc. CP) 51.20 microseconds
Cyclic prefix (CP) length 5.00 microseconds
Physical chunk size 156.24kHz x 337.2 microseconds
Chunk size in symbols 8 x 648
17
Simulation results

• Multi-user diversity, channel variations
• THP versus SNR for 2 and 8 users

18
Simulation results

• Multi-user diversity, channel variations
• BER versus SNR for 2 and 8 users

19
Simulation results

• Prediction quality, multi-user diversity, channel
  variation
• THP versus number of users (19 dB)

20
Simulation results

• Prediction quality, multi-user diversity, channel
  variation
• BER versus number of users (19 dB)

21
Simulation results

• Prediction quality, multi-user diversity, channel
  variation
• THP versus number of users (10 dB)

22
Simulation results

• Prediction quality, multi-user diversity, channel
  variation
• BER versus number of users (10 dB)

23
Simulation results

• TDMA/OFDMA versus use of TDMA
• THP versus number of users (19dB)

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Summary

• An adaptive transmission based on TDMA/OFDMA
  using multiuser scheduling is investigated.
• Predictive adaptation to the short-term fading
  and frequency-domain channel variability leads to
  significant multi-user diversity gain.
• With TDMA instead of TDMA/OFDMA, only half of
  these gains are realized for channels with Urban
  Macro scenarios.
• Predictive adaptation can use MC rate boundaries
  adjusted so that BER constraints are fulfilled in
  the presence of SINR prediction uncertainty.

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Summary

• Feasibility of adaptive transmission is limited
  by prediction accuracy.
• Prediction accuracy is determined by SINR and
  terminal velocity.
• For realistic SINR values, transmission at 50
  km/h is feasible at 5 GHZ in FDD DL.
• A solution to reduce the required feedback rate
• To feed back the required SINR and source code it
  by a combination of transform coding in the
  frequency direction and sub-sampling in the time
  direction.