## 2 Literature Review  
This literature review aims to synthesize and critically examine previous research on the efficacy of Convolutional Codes and the Viterbi Algorithm in GPS systems, in comparison to other coding techniques. Despite a paucity of studies specifically addressing this topic, it is important to note that much research has been conducted on the efficiency of error detection and correction codes, including comparisons between various codes within the context of communication systems. This study will draw upon these prior investigations to elucidate the rationale for the selection of Convolutional Codes in GPS systems. NASA has conducted a study that aims to achieve dependable communications at lower signal-to-noise ratios [5]. The research demonstrates simulation outcomes outlining the comparison between modified Convolutional codes with sequential decoding and NRBO (Non-Return-to-Zero Bit-Level One) codes. Showing that the Convolutional codes with sequential decoding achieve nearly the same performance as Turbo codes, but with improved computational complexity, as well as other aspects such as free and open architectures. Nevertheless, they did need large block sizes and high computational complexity to attain this performance. These advantages mentioned in the results of this paper illustrate one of the reasons that makes Convolutional codes with sequential decoding a promising option for GPS communications systems, which require reliable and efficient communication over long distances. Another study by [PERSON], [PERSON], [PERSON] and [PERSON] (2016) [6] provides a detailed analysis of different types of channel coding techniques, including block coding and Convolutional coding, and their performance in reducing bit error rates. The authors compared the performance of the Hamming code, [PERSON] code, and Convolution codes using bit error rate (BER) versus Eb/No performance with Binary Phase Shift Keying (BPSK) modulation. The simulation results showed that Convolution codes have better error controlling and correction capabilities in comparison to block codes, and, among the block codes, the performance of the Reed Solomon code is comparatively better. Another study by [PERSON] et al. (2021) [7] proposed a novel deep-learning-based approach for identifying Convolutional codes with high accuracy and robustness, particularly in low signal-to-noise ratio (SNR) scenarios. The approach employs deep residual networks, eliminating the need for manual feature extraction. The experimental results indicate a recognition accuracy exceeding 88% for 17 distinct forms of Convolutional codes. The approach is advantageous for applications such as cognitive radios and signal interception, where low SNR is a prevalent issue. Further investigation is necessary to ascertain its applicability to GPS systems. In another study by [PERSON] and [PERSON] (2015) [8], a comparative analysis of three types of error correction codes was conducted: BCH, Hamming, and convolution codes. Their results showed that, for larger block sizes, Convolution codes outperformed both the BCH and Hamming codes in terms of bit error rate (BER). However, for smaller block sizes, the Hamming code performed better than both BCH and Convolution codes. The results of this paper have highlighted a number of advantages that lend credence to the viability of adopting Convolutional codes with sequential decoding as a viable solution for GPS communications systems that demand dependable and efficient communication across long distances. In another study by [PERSON] and [PERSON] (2020) [9], the authors concluded that the implemented Convolutional codes with a Viterbi decodingscheme can effectively improve the BER performance in satellite communication systems. The coding gain obtained for different coding rates demonstrates the effectiveness of the design.