## 1 Introduction  
Ensuring clear communication through unreliable channels has always been a challenge in telecommunications. The rise of cellular networks and sensitive data transmission has amplified the challenge. Coding theory is crucial in achieving a lower signal-to-noise ratio (SNR) for the same desired bit-error-rate (BER). Two key metrics, BER and SNR, help evaluate how well a system can decode information. The SNR represents the ratio between the signal power and the interfering noise, while the BER indicates the proportion of errors in received data compared to the total transmitted bits. Imagine an uncoded system with a BER of \(10^{-4}\) (1 error in 1000 transmitted bits) at an SNR of 2.5 dB. A coded system could achieve the same BER at a lower SNR, say 1.75 dB. This difference, 0.75 dB, represents the coding gain. In simpler terms, coding allows us to transmit data with the same level of accuracy while requiring less signal power. Due to their efficient error correction, LDPC codes are preferred for implementing modern communication standards [1]. They were invented by [PERSON] as a family of linear Forward Error Correction (FEC) codes in 1962 [2]. LDPC codes are recognized for achieving near-shannon-limit performance in error correction for practical applications [3]. Following their rediscovery in the mid-1990s, they have garnered significant interest from both the academic community and industry [4]. LDPC codes have emerged as a powerful error-correction tool due to their impressive combination of features [5]. They offer excellent coding gain, meaning they require fewer redundant bits to achieve high accuracy, even at high transmission rates [6; 7]. Additionally, they boast a remarkably low error floor, ensuring reliable data delivery even in noisy environments [8]. Furthermore, they structured a parallel decoding scheme, making the process faster [9]. The flexibility of LDPC codes extends to their adaptable decoding architectures, enabling optimization for specific needs. This versatility is evident in their adoption by numerous industry standards like Wi-Fi, WiMAX, and even space data transmission protocols, solidifying their place as a key technology for modern communication systems [10]. 5G-NR's error correction capabilities benefit from an innovative block design (Figure 1). Techniques like the Cyclic Redundancy Check (CRC) verify data integrity using checksums, while block processing efficiently manages data by splitting streams and then reassembling them [11]. Rate matching ensures smooth data flow by synchronizing speeds across system components. Bit interleaving distributes errors for easier detection and correction by merging data streams with a specific pattern [12]. New decoding algorithms and architectures [2; 13; 14] have been used in recent research to make LDPC codes better at fixing errors for the next generation of wireless communication systems. However, current decoder designs exhibit limitations that compromise their throughput and adaptability. Our research introduces a significant advancement in LDPC decoder design, surpassing prior work like [2], which decoded an 8832-bit codeword at 153 MHz in 10 iterations. Notably, lower code rates often translate to better decoder performance. Our design tackles this challenge by efficiently decoding higher rate 2/3 codes with significantly longer codewords (13,440 bits). It achieves successful decoding in just six iterations. Furthermore, our design boasts a remarkable maximum frequency of 304.5 MHz, exceeding the 153 MHz reported in the previous approach [2]. The \"Improved Adapted Min-Sum (IAMS)\" algorithm presented in [14] exhibits promising error correction capabilities. However, significant decoding latency hinders its real-world applicability. A lot of simulations with the first base graph (BG1), a code rate of 2/3, and an expansion factor of 104 showed that 15 iterations are needed to get good decoding performance.