## 3 Entropy Feature-Based LNMS LDPC Decoding Optimization
### Serial Entropy Feature-Based LNMS (S-EFB-LNMS) LDPC Decoding Optimization Scheme
4:if\(LLR_{\text{ch},j}<0\)then
5:\(c_{j}^{(0)}=1\)
6:else
7:\(c_{j}^{(0)}=1\)
8:else
9:\(c_{j}^{(0)}=1\)
10:else
11:\(c_{j}^{(0)}=1\)
12:else
13:\(c_{j}^{(0)}=1\)
14:else
15:\(c_{j}^{(0)}=1\)
16:else
17:\(c_{j}^{(0)}=1\)
18:else
19:\(c_{j}^{(0)}=1\)
20:else
21:\(c_{j}^{(0)}=1\)
22:else
23:\(c_{j}^{(0)}=1\)
24:else
25:\(c_{j}^{(0)}=1\)
26:else
27:\(c_{j}^{(0)}=1\)
28:else
29:\(c_{j}^{(0)}=1\)
30:else
31:\(c_{j}^{(0)}=1\)
32:else
33:\(c_{j}^{(0)}=1\)
34:else
35:\(c_{j}^{(0)}=1\)
36:else
37:\(c_{j}^{(0)}=1\)
38:else
39:\(c_{j}^{(0)}=1\)
39:else
40:\(c_{j}^{(0)}=1\)
41:else
42:\(c_{j}^{(0)}=1\)
43:else
44:else
45:\(c_{j}^{(0)}=1\)
46:else
47:\(c_{j}^{(0)}=1\)
48:else
49:\(c_{j}^{(0)}=1\)
50:else
51:\(c_{j}^{(0)}=1\)
52:else
60:\(c_{j}^{(0)}=1\)
53:else
54:\(c_{j}^{(0)}=1\)
55:else
61:\(c_{j}^{(0)}=1\)
62:else
63:\(c_{j}^{(0)}=1\)
64:else
65:\(c_{j}^{(0)}=1\)
66:else
67:\(c_{j}^{(0)}=1\)
68:else
69:\(c_{j}^{(0)}=1\)
70:else
80:\(c_{j}^{(0)}=1\)
81:else
82:\(c_{j}^{(0)}=1\)
83:else
84:\(c_{j}^{(0)}=1\)
85:else
86:\(c_{j}^{(0)}=1\)
87:else
88:\(c_{j}^{(0)}=1\)
89:else
90:\(c_{j}^{(0)}=1\)
91:else
92:\(c_{j}^{(0)}=1\)
93:else
94:\(c_{j}^{(0)}=1\)
95:else
96:\(c_{j}^{(0)}=1\)
97:else
98:\(c_{j}^{(0)}=1\)
99:else
99:\(c_{j}^{(0)}=1\)
99:else
100:\(c_{j}^{(0)}=1\)
11:\(c_{j}^{(0)}=1\)
12:\(c_{j}^{(0)}=1\)
13:\(c_{j}^{(0)}=1\)
14:else
15:\(c_{j}^{(0)}=1\)
16:\(

+++ ==WARNING: Truncated because of repetitions==
c_{j}^{(0)}=1\)



++
```
5:\(c_{j}^{(0)}=0\)
6:endif
7: Get RLPU and URLPU with \(CS(\mathbf{f}_{\text{eff}},\mathbf{c}_{\text{cmp}})\) calculated by Equation (4). 8:for\(l\) from 1 to \(T_{\text{max}}\)do
9:if\((l-1)\) % \(\beta==0\)then
10: Process the RLPUs. 11:else
12: Process the URLPUs. 13:endif
14: Update VN information, CN information, and posterior information calculated by Equations (5)-(7), respectively. 15:if\(v_{j}^{(l)}<0\)then
16:\(c_{j}^{(l)}=1\)
17:else
18:\(c_{j}^{(l)}=0\)
19:endif
20:if\(\mathbf{c}^{(l)}\cdot\mathbf{H}^{\text{T}}==\mathbf{0}\)then
21: break
22:else
23: Perform an XOR operation on \(\mathbf{c}^{(l)}\) and \(\mathbf{c}^{(l-1)}\) to find the flipped bit, and subsequently set its corresponding entropy feature value to 0 in the \(\mathbf{f}_{\text{eff}}\). 24: Refresh RLPUs and URLPUs with \(CS(\mathbf{f}_{\text{eff}},\mathbf{c}_{\text{cmp}})\) calculated by Equation (4). 25:endif
26:endfor
```
**Algorithm 1**_Cont_. ### Parallel Entropy Feature-Based LNMS (P-EFB-LNMS) LDPC Decoding Optimization Scheme

In Section 3.2, this paper discusses in detail the use of the entropy feature vector of codewords to optimize the serial decoding framework and reduce the frame decoding latency. Based on this, this section will focus on the development of a parallel decoding framework and its potential advantages, which is named P-EFB-LNMS LDPC decoding. Parallel processing plays a key role in improving computational efficiency, especially in the LDPC decoding process. Using the entropy feature vector as an efficient analysis tool, this study effectively classifies the LPUs into two groups of high reliability and low reliability, namely, RLPU and URLPU, by calculating \(CS(\mathbf{f}_{\text{eff}},\mathbf{c}_{\text{cmp}})\). This classification strategy ensures that cross-information interactions between VNs are avoided during the decoding process. In the simulations, we design parallel multi-threads, each independently processing LPUs of a different reliability. This parallel strategy not only ensures that both high and low reliability tasks are effectively processed but also optimizes resource allocation by processing similar tasks in a targeted manner. With this parallel design, the system can process multiple LPUs simultaneously, which significantly shortens the decoding latency and improves the overall system performance. In addition, this design also supports flexible adjustment of the workload between threads, so that it can dynamically optimize the execution strategy of threads according to the actual running performance data.