doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 1 LDPC vs. Convolutional Codes for n Applications: Performance Comparison January 2004 Aleksandar Purkovic, Nina Burns, Sergey Sukobok, Levent Demirekler Nortel Networks (contact:

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 2 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 Outline

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 3 Advanced coding has been identified as one of techniques to be considered in the process of n 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 /865 (Intel, Albuquerque meeting), [1] introduced Low-Density Parity-Check (LDPC) codes as candidate codes for n 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. Background

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 4 PHY model based on the a 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 Simulation Methodology - General Data Rate (Mbits/s) Modulation BPSK QPSK 16QAM 64QAM 256QAM Coding Rate (R) 1/23/41/23/41/23/42/33/42/33/4

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 5 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 a) –Uniform bit loading Convolutional codes: –Viterbi decoding algorithm LDPC codes: –Iterative Sum-Product decoding algorithm with 50 iterations –Concatenated codewords for longer packets Simulation Methodology - FEC

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 6 AWGN Simulation Results: PER vs. SNR Channel Model D

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 7 AWGN Simulation Results: Throughput vs. SNR Channel Model D Throughput = PHY_data_rate (1 - PER)

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 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

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 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

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 10 Simulation Results: Embedded Interleaving Capability Demonstration Block size: 200 bytes Block size: 40 bytes Channel Model D Code rate: 1/2

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 11 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 n 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. Summary and Conclusions

doc.: IEEE /0071r1 Submission January 2004 Aleksandar Purkovic, Nortel NetworksSlide 12 References [1] IEEE /865r1, “LDPC FEC for IEEE n Applications”, Eric Jacobson, Intel, November [2] IEEE Std a-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 /940r1, “TGn Channel Models”, TGn Channel Models Special Committee, November [4] Laurent Schumacher, “WLAN MIMO Channel Matlab program,” October 2003, version 2.1. [5] Packet length distribution at NASA Ames Internet Exchange (AIX),