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Aggregated Circulant Matrix Based LDPC Codes

Aggregated Circulant Matrix Based LDPC Codes. Yuming Zhu and Chaitali Chakrabarti Department of Electrical Engineering Arizona State University, Tempe. Outline. Introduction to LDPC codes Iterative decoding of LDPC codes Aggregated Circulant Matrix (ACM) based LDPC codes

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Aggregated Circulant Matrix Based LDPC Codes

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  1. Aggregated Circulant Matrix Based LDPC Codes Yuming Zhu and Chaitali Chakrabarti Department of Electrical Engineering Arizona State University, Tempe

  2. Outline • Introduction to LDPC codes • Iterative decoding of LDPC codes • Aggregated Circulant Matrix (ACM) based LDPC codes • Construction algorithm • BER performance • Decoder architecture • Concluding remarks

  3. Introduction to LDPC codes • LDPC codes are linear block codes with sparse parity check matrix. (Low complexity) • Can be represented by bipartite (Tanner) graph. • LDPC codes were proposed by Gallager in 1962. Rediscovered in 90’s because of the success of Turbo codes. • Adopted in IEEE 802.16e (WiMax),10G BaseT, DVB-S2, IEEE 802.11n (in consideration)

  4. Introduction (contd.) • LDPC code is Shannon limit approaching. • Chung (Trans. IT, Feb 2001) reported 0.0045dB to AWGN channel Shannon limit with irregular LDPC code. • Very simple data path. • Potential to achieve massive parallelism and high throughput. • 1G bps LDPC decoder (Blanksby and Howland 2001)

  5. Related Work • High speed LDPC decoder architectures • C. Howland 2001(Fully Parallel) • T. Zhang 2001 (Partially Parallel) • M.M. Mansour 2003 (Partially Parallel) • D.E. Hocevar 2004 (Partially Parallel) • Partially Parallel decoding for sub-matrix with multiple shifted identity matrices • Z. Wang 2005 (With restriction on the shift values; for geometrical LDPC codes)

  6. Outline • Introduction to LDPC codes • Iterative decoding of LDPC codes • Aggregated Circulant Matrix (ACM) based LDPC codes • Construction algorithm • BER performance • Decoder architecture • Conclusion and future work

  7. Bit to check Check to bit + + + + Code graph and decoding • Iterative decoding • Belief Propagation (BP) • Bit nodes send their belief information (likelihood ratio, usually in LOG domain) • Check nodes gather the information and update the corresponding bit nodes. An example of (2,3) regular LDPC

  8. Iterative decoding algorithm BP: Min-Sum: λ-Min:

  9. Circulant matrix based LDPC code • Partial parallel implementation with ordered sub-matrix in H matrix. • Scheduling of the belief information update. • Check node based • Variable node based Each element in the Hb matrix is a circulant shifted version of identity matrix. (Tanner 2004)

  10. Layered BP algorithm • Layered BP algorithm schedules in row order. • The rows in one block row can be processed in parallel. • Pipelining is a common technique to increase the throughput. • However, the throughput increased by pipelining is limited if there are data dependencies.

  11. Outline • Introduction to LDPC codes • Iterative decoding of LDPC codes • Aggregated Circulant Matrix (ACM) based LDPC codes • Construction algorithm • BER performance • Decoder architecture • Conclusion and future work

  12. Aggregated Circulant Matrix (ACM) based LDPC • Idea: Remove the data dependency between the block rows in the parity check matrix. • Method: Perform aggregation to reduce the non-zero sub-matrix within each block column. • Outcome: Throughput is doubled with a small increase in data-path.

  13. I I II O I I O II Construction algorithm for ACM • First, construct Hb matrix with designed degree distribution. • “Aggregation”: • It does not change the degree distribution. • For Hb of size MxN, make sure (i,j) and (i+M/2,j) does not contain ‘I’ simultaneously. • The decoding of block rows is scheduled as: 0, M/2, 1, M/2+1, … , M/2-1, M.

  14. Hb matrix of ACM based LDPC Original Aggregated Reordered

  15. BER performance of ACM

  16. Bit Update Algorithm m1 m Regular Sub-matrix m2 ACM Sub-matrix n n • In parallel decoding of regular circulant matrix based LDPC, each bit node is updated only once. • However, in the parallel processing of ACM LDPC, the bit update information could come from different rows that are being processed simultaneously. • Some mechanism is needed to combine the multiple bit update information.

  17. Bit Update for ACM

  18. ACM LDPC decoder architecture

  19. Synthesis result • The decoder was synthesized with TSMC 90 nm lib. • The decoder achieved 930 Mb/s throughput when clocked at 300 MHz for a rate 4/5 code. • Compared with the regular LDPC code, the data-path of the ACM LDPC decoder increased by only 20%, while the throughput increased by a factor of 2. • The data-path contributed 25.6% of the area of the overall decoder.

  20. Outline • Introduction toLDPC codes • Iterative decoding of LDPC codes • Aggregated Circulant Matrix (ACM) based LDPC codes • Construction algorithm • BER performance • Decoder architecture • Conclusion and future work

  21. Concluding Remarks • The proposed ACM LDPC code has comparable performance with the regular LDPC codes. • The advantage is that it can be decoded with a two-fold increase in the throughput at the expense of only 20% increase in data-path complexity. • Efficient implementation of the aggregation algorithm with more than 2 identity matrices is still an open problem.

  22. Thank You!! Questions?

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