1. Code and Decoder Design of LDPC Codes for Gbps Systems Jeremy Thorpe Presented to: Microsoft Research 2002.11.25

2. Talk Overview  Introduction (13 slides)  Wiring Complexity ( 9 slides)  Logic Complexity (7 slides)

3. Reliable Communication over Unreliable Channels  Channel is the means by which information is communicated from sender to receiver  Sender chooses X  Channel generates Y from conditional probability distribution P(Y|X)  Receiver observes Y channel P(Y|X) Y X

4. Shannon’s Channel Coding Theorem  Using the channel n times, we can communicate k bits of information with probability of error as small as we like as long as  as long as n is large enough. C is a number that characterizes any channel.  The same is impossible if R>C.

5. The Coding Strategy  Encoder chooses the m th codeword in codebook C and transmits it across the channel  Decoder observes the channel output y and generates m’ based on the knowledge of the codebook C and the channel statistics. Decoder Encoder Channel

6. Linear Codes  A linear code C can be defined in terms of either a generator matrix or parity-check matrix.  Generator matrix G (k×n)  Parity-check matrix H (n-k×n)

7. Regular LDPC Codes  LDPC Codes linear codes defined in terms of H.  The number of ones in each column of H is a fixed number λ.  The number of ones in each row of H is a fixed number ρ.  Typical parameters for Regular LDPC codes are (λ,ρ)=(3,6).

8. Graph Representation of LDPC Codes  H is represented by a bipartite graph.  nodes v (degree λ) on the left represent variables.  Nodes c (degree ρ)on the right represent equations:... Variable nodes Check nodes

9. Message-Passing Decoding of LDPC Codes  Message Passing (or Belief Propagation) decoding is a low-complexity algorithm which approximately answers the question “what is the most likely x given y?”  MP recursively defines messages m v,c (i) and m c,v (i) from each node variable node v to each adjacent check node c, for iteration i=0,1,...

10. Two Types of Messages...  Liklihood Ratio  For y 1,...y n independent conditionally on x:  Probability Difference  For x 1,...x n independent:

11. ...Related by the Biliniear Transform  Definition:  Properties:

12. Variable to Check Messages  On any iteration i, the message from v to c is:... v c

13. Check to Variable Messages  On any iteration, the message from c to v is:... v c

14. Decision Rule  After sufficiently many iterations, return the likelihood ratio:

15. Theorem about MP Algorithm  If the algorithm stops after r iterations, then the algorithm returns the maximum a posteriori probability estimate of x v given y within radius r of v.  However, the variables within a radius r of v must be dependent only by the equations within radius r of v, v r...

16. Wiring Complexity

17. Physical Implementation (VLSI)  We have seen that the MP decoding algorithm for LDPC codes is defined in terms of a graph  Nodes perform local computation  Edges carry messages from v to c, and c to v  Instantiate this graph on a chip! Edges →Wires Nodes →Logic units

18. Complexity vs. Performance  Longer codes provide: More efficient use of the channel (eg. less power used over the AWGN channel) Faster throughput for fixed technology and decoding parameters (number of iterations)  Longer codes demand: More logic resources Way more wiring resources

19. The Wiring Problem  The number of edges in the graph grows like the number of nodes n.  The length of the edges in a random graph also grows like. A random graph

20. Graph Construction?  Idea: find a construction that has low wire- length and maintains good performance...  Drawback: it is difficult to construct any graph that has the performance of random graph.

21. A Better Solution:  Use an algorithm which generates a graph at random, but with a preference for: Short edge length Quantities related to code performance

22. Conventional Graph Wisdom  Short loops give rise to dependent messages (which are assumed to be independent) after a small number of iterations, and should be avoided.

23. Simulated Annealing!  Simulated annealing approximately minimizes an Energy Function over a Solution space.  Requires a good way to traverse the solution space.

24. Generating LDPC graphs with Simulated Annealing  Define energy function with two components: Wirelength Loopiness  traverse the space by picking two edges at random and do:

25. Results of Simulated Annealing  The graph on the right has nearly identical performance to the one shown previously A graph generated by Simulated Annealing

26. Logic Complexity

27. Complexity of Classical Algorithm  Original algorithm defines messages in terms of arithmetic operations over real numbers:  However, this implies floating-point addition, multiplication, and even division!

28. A modified Algorithm  We define a modified algorithm in which all messages are their logarithms in the original scheme  The channel message λ is similarly replaced by it's logarithm.

29. Quantization  Replaced a product by a sum, but now we have a transcendental function φ.  However, if we quantize the messages, we can pre-compute φ for all values!

30. Quantized MP Performance  The graph to the following page shows the bit error rate for a regular (3,6) of length n=10,000 code using between 2 and 4 bits of quantization.  (Some error floors predicted by density evolution, some are not)

32. Quantization Tradeoffs  A quantizer is characterized by its range and granularity  For fixed channel quantization: A finely granulated quantizer (Q 1 ) performs well at low SNR. However, the quantizer must be broadened (Q 2 ) to avoid saturation, and resulting error floor. Q1Q1 Q2Q2

33. Conclusion  There is a tradeoff between logic complexity and performance  Nearly optimal performance (+.1 dB = × 1.03 power) is achievable with 4-bit messages.  More work is needed to avoid error-floors due to quantization.