LDPC vs. Convolutional Codes for 802.11n Applications: Performance Comparison

1
LDPC vs. Convolutional Codes for 802.11n
Applications Performance Comparison

• January 2004
• Aleksandar Purkovic, Nina Burns, Sergey Sukobok,
  Levent Demirekler
• Nortel Networks
• (contact apurkovi_at_nortelnetworks.com)

2
Outline

• Background
• Simulation Methodology
• Simulation Results
• Packet Error Rate (PER) vs. SNR
• Throughput vs. SNR
• PHY data rate vs. distance
• LDPC Convolutional coding gain difference vs.
  Block size
• Demonstration of embedded interleaving capability
  of LDPC codes
• Summary and Conclusions
• References

3
Background

• Advanced coding has been identified as one of
  techniques to be considered in the process of
  802.11n standard development (among other
  considerations, such as MIMO, higher order
  modulations, MAC efficiency improvement, etc.)
• Advanced coding candidates Turbo coding, LDPC,
  Trellis Coded Modulation, more powerful
  convolutional codes, etc.
• Contribution IEEE 802.11-03/865 (Intel,
  Albuquerque meeting), 1 introduced Low-Density
  Parity-Check (LDPC) codes as candidate codes for
  802.11n applications. It showed potential
  advantages of those codes over existing
  convolutional codes used currently (802.11a/g).
• This submission compares performance of example
  LDPC codes and existing (802.11a/g) convolutional
  codes in a systematic fashion, with
• Various frame lengths
• Various code rates
• Various line conditions (channel models)
• At this time performance comparison is addressed
  only, in order to justify further consideration
  of LDPC codes. In the next related submission
  (planned for March 2004 meeting) emphasis will be
  on performance/complexity tradeoffs for both
  existing convolutional codes and candidate LDPC
  codes.

4
Simulation Methodology - General

• PHY model based on the 802.11a spec 2 expanded
  with 256-QAM constellation. Simulation included
• Channels simulated
• AWGN channel
• Fading Channel Model D with power delay profile
  as defined in 3, NLOS, without simulation of
  Doppler spectrum. This implementation utilized
  the reference MATLAB code 4.
• Simulation scenario assumed
• Ideal channel estimation
• All packets detected, ideal synchronization, no
  frequency offset
• Ideal front end, Nyquist sampling frequency

Data Rate (Mbits/s) 6 9 12 18 24 36 48 54 64 72
Modulation BPSK BPSK QPSK QPSK 16QAM 16QAM 64QAM 64QAM 256QAM 256QAM
Coding Rate (R) 1/2 3/4 1/2 3/4 1/2 3/4 2/3 3/4 2/3 3/4
5
Simulation Methodology - FEC

• General FEC
• Packet lengths 40, 200, 600, 1500 bytes, chosen
  based on distributions in 1 and 5
• Code rates 1/2, 2/3, 3/4 (as in 802.11a)
• Uniform bit loading
• Convolutional codes
• Viterbi decoding algorithm
• LDPC codes
• Iterative Sum-Product decoding algorithm with 50
  iterations
• Concatenated codewords for longer packets

6
Simulation Results PER vs. SNR

• AWGN

Channel Model D
7
Simulation Results Throughput vs. SNR
Throughput PHY_data_rate (1 - PER)

• AWGN

Channel Model D
8
Simulation Results PHY Data Rate vs. Distance
Channel Model D path loss Tx power 23dBm Noise
figure 10dB Implementation margin 5dB PER 10-1
9
Simulation Results LDPC Convolutional Coding
Gain Difference vs. Block Size
Modulation BPSK Code rate 1/2 Coding gain
difference measured at PER of 10-2
10
Simulation Results Embedded Interleaving
Capability Demonstration
Channel Model D Code rate 1/2
Block size 40 bytes
Block size 200 bytes
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Summary and Conclusions

• LDPC codes offer considerable performance
  advantages over the existing convolutional codes.
• With the proper design LDPC codes can be made
  flexible enough to satisfy demands of 802.11n
  applications.
• LDPC codes have an inherent feature which
  eliminates need for the channel interleaver (this
  was already pointed out in 1).
• Preliminary complexity analysis showed that
  reasonable solution is feasible. A submission on
  the performance/complexity analysis and potential
  real system design tradeoffs is planned for the
  March meeting.

12
References

• 1 IEEE 802.11-03/865r1, LDPC FEC for IEEE
  802.11n Applications, Eric Jacobson, Intel,
  November 2003.
• 2 IEEE Std 802.11a-1999, Part 11 Wireless LAN
  Medium Access Control (MAC) and Physical Layer
  (PHY) Specifications, High-speed Physical Layer
  in the 5 GHz Band
• 3 IEEE 802.11-03/940r1, TGn Channel Models,
  TGn Channel Models Special Committee, November
  2003.
• 4 Laurent Schumacher, WLAN MIMO Channel Matlab
  program, October 2003, version 2.1.
• 5 Packet length distribution at NASA Ames
  Internet Exchange (AIX), http//www.caida.org/anal
  ysis/AIX/plen_hist/.