Study on Quasi-regular NB-LDPC Codes

EXIT Chart Analysis of Puncturing for Non-binary LDPC Codes

5.3 Proposed EXIT Chart for Punctured NB-LDPC Codes

5.3.1 Random Selection of the VNs and Regular Puncturing

5.3.1.1 Study on Quasi-regular NB-LDPC Codes

We use the proposed EXIT chart model to investigate the effects of various puncturing patterns for quasi-regular NB-LDPC codes with different values of mean column weight (t). For a quasi-regular code of mean column weight t, the degrees of the VNs are restricted to {⌊t⌋,⌈t⌉}, where ⌊·⌋ and

⌈·⌉ are the floor and the ceiling functions respectively. The degrees of the CNs are restricted to nj t

1−r₀

k,^l_1−r^t

0

mo, wherer₀ is the rate. We calculate the thresholds for punctured quasi-regular codes over GF(2⁶), GF(2⁵) and GF(2⁴) with r₀ = 0.5 and rl ∈ {0.6,0.7,0.8}. We consider N = 142 to obtain the value ofpv.

Figure 5.6(a) shows the thresholds for punctured codes over GF(2⁶) at rl = 0.6. The threshold values are plotted against b_p for different values of t. Several interesting observations can be made from the figure. Whent= 2, puncturing a smaller number of bits per VN gives better thresholds. At t = 2.6, the scenario is completely opposite. In between these two contrasting cases, there exists a point aroundt= 2.2, where the thresholds appear to be unaffected by bp. We call this value oft as thecrossover point (tc). From Figure 5.6(b) and Figure 5.6(c), it can be observed that the values of

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2 3 4 5 6 0.9

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7

Number of bits punctured per VN,bp

Thresholds, Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

1 2 3 4 5

0.9 1 1.1 1.2 1.3 1.4 1.5

Number of bits punctured per VN,bp

Thresholds,

Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.38 t= 2.4 t= 2.5 t= 2.6

1 1.5 2 2.5 3 3.5 4

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Number of bits punctured per VN,bp

Thresholds, Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

(a): GF 2⁶ (b): GF 2⁵

(c): GF 2⁴

Figure 5.6: Thresholds for punctured quasi-regular codes withr₀= 0.5,N = 142 at rate r_l= 0.6

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5.3 Proposed EXIT Chart for Punctured NB-LDPC Codes

tc for the quasi-regular codes over GF(2⁵) and GF(2⁴) are 2.38 and 2.5 respectively.

2 3 4 5 6

1.6 1.8 2 2.2 2.4 2.6 2.8

Number of bits punctured per VN,bp

Thresholds,

Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

2 2.5 3 3.5 4 4.5 5

1.6 1.8 2 2.2 2.4

Number of bits punctured per VN,bp

Thresholds, Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.38 t= 2.4 t= 2.5 t= 2.6

2 2.5 3 3.5 4

1.8 2 2.2 2.4 2.6 2.8

Number of bits punctured per VN,bp

Thresholds,

Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

(a): GF 2⁶ (b): GF 2⁵

(c): GF 2⁴

Figure 5.7: Thresholds for punctured quasi-regular codes withr₀ = 0.5,N = 142 at rate r_l= 0.7

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3 3.5 4 4.5 5 5.5 6 2.6

2.8 3 3.2 3.4 3.6 3.8 4 4.2

Number of bits punctured per VN,bp

Thresholds, Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

2 2.5 3 3.5 4 4.5 5

2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4

Number of bits punctured per VN,bp

Thresholds,

Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.38 t= 2.4 t= 2.5 t= 2.6

2 2.5 3 3.5 4

2.5 3 3.5 4 4.5 5 5.5

Number of bits punctured per VN,bp

Thresholds, Eb N0

∗ (dB)

t= 2 t= 2.1 t= 2.2 t= 2.3 t= 2.4 t= 2.5 t= 2.6

(a): GF 2⁶ (b): GF 2⁵

(c): GF 2⁴

Figure 5.8: Thresholds for punctured quasi-regular codes withr₀= 0.5,N = 142 at rate rl= 0.8

Like any code ensemble having an optimum degree distribution, the quasi-regular codes also have

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5.3 Proposed EXIT Chart for Punctured NB-LDPC Codes

an optimum mean column weight (t_opt) for a given field. It is interesting to examine the relationship betweent_opt and tc. In [46], the authors have obtained the values oft_opt for quasi-regular NB-LDPC codes over a BI-AWGN channel at a rate of 0.5. For the codes over GF(2⁴), GF(2⁵) and GF(2⁶), the values oft_opt are 2.3, 2.2 and 2.1 respectively. The values oftc for the punctured codes at a rate rl = 0.6 over GF(2⁴), GF(2⁵) and GF(2⁶) are approximately 2.5, 2.38 and 2.2 respectively as found out from Figure 5.6. It can be seen thatt_c is higher than t_opt. The gap betweent_c and t_opt decreases with the increasing field order.

The thresholds for the quasi-regular codes at ratesr_l= 0.7 andr_l= 0.8 are shown in Figure 5.7 and Figure 5.8 respectively. Observe that at these higher rates also, the mean column weight determines the performance of a puncturing pattern. If the mean column weight is very small, then the VNs should be punctured with the minimumbp satisfying (5.27). On the other hand, if the mean column weight is on the higher side, then the VNs should undergo symbolwise puncturing. However, the threshold curves take a peculiar shape for the values of t around tc. For the extreme puncturing patterns (i.e., with the minimum and the maximum possible values of b_p), the thresholds become high. The threshold curves experience a trough at an intermediate value of b_p. The puncturing

1 1.5 2 2.5 3 3.5 4

1.5 2 2.5 3 3.5 4 4.5 5

Number of bits punctured per VN,bp

Thresholds,

Eb N0

∗ (dB)

dv= 2, dc= 4 EXIT dv= 3, dc= 6 EXIT dv= 4, dc= 8 EXIT dv= 2, dc= 4 DE [30]

dv= 3, dc= 6 DE [30]

dv= 4, dc= 8 DE [30]

Figure 5.9: Comparison of thresholds for regular codes over GF 2⁴at rl= 2/3 pattern should be judiciously determined in order to achieve the minimum possible threshold.

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Note that the findings provided by the proposed EXIT chart model fully agree with those reported in [30] where the thresholds were computed via the DE. We compare these thresholds for (dv, dc) - regular codes over GF(2⁴) for rate rl= 2/3 in Figure 5.9.

Verification through Simulations

We verify the threshold-based analysis by performing simulations. We consider the threshold curves presented in Figure 5.6(a) over GF 2⁶at rl = 0.6. The motive is to validate the existence of the cross-over points for the quasi-regular codes. We considerN = 142,M = 71 parity-check matrices over GF 2⁶. A parity-check matrix with a particulart is generated by first forming a binary matrix according to the PEG algorithm [34] and then replacing the 1s by the randomly chosen elements from GF(2⁶)⁻. The equivalent binary streams for the codeword symbols are BPSK modulated and transmitted over a BI-AWGN channel. For decoding, we consider the FFT-QSPA with the maximum number of iterations set at 50.

For the puncturing patterns, we randomly select the required number of VNs for a givenbp. First, we show the symbol-error rate (SER) performances for a code with t = 2 in Figure 5.10(a). The SER performance gets worsened with increasing bp. Next, we show the SER results for t = 2.6 in Figure 5.10(b). In this case, the SER performance gets improved with increasingb_p. Finally, we show the SER performances for a code witht= 2.2 in Figure 5.10(c). For this code, the performances of all the puncturing patterns are almost similar. These results are in full agreement with the conclusions derived from the threshold plot in Figure 5.6(a). We also performed the simulations for the quasi- regular codes over GF 2⁵and GF 2⁴. The simulation results in these cases are also found to correlate strongly with the threshold analysis.

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5.3 Proposed EXIT Chart for Punctured NB-LDPC Codes

1 1.5 2 2.5 3

10⁻⁶ 10⁻⁵ 10⁻⁴ 10⁻³ 10⁻² 10⁻¹ 10⁰

Eb/N0(dB)

SER

bp= 2 bp= 3 bp= 4 bp= 5 bp= 6

1.5 2 2.5 3 3.5

10⁻⁶ 10⁻⁵ 10⁻⁴ 10⁻³ 10⁻² 10⁻¹ 10⁰

Eb/N0 (dB)

SER

bp= 2 bp= 3 bp= 4 bp= 5 bp= 6

1 1.5 2 2.5 3

10⁻⁶ 10⁻⁵ 10⁻⁴ 10⁻³ 10⁻² 10⁻¹ 10⁰

Eb/N0(dB)

SER

bp= 2 bp= 3 bp= 4 bp= 5 bp= 6

(a): t= 2 (b): t= 2.6

(c): t= 2.2

Figure 5.10: Simulation results of regular puncturing with random selection of the punctured VNs for quasi-regular codes over GF(2⁶) having different mean column weights (t) with r0 = 0.5 and rl = 0.6

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5.3.2 Selection of the VNs According to the Grouping Algorithm and Irregular