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Error Detection Algorithm for Lempel-Ziv-77 Compressed Data
권범,이상훈 한국통신학회 2019 Journal of communications and networks Vol.21 No.2
In this study, we develop a novel error detection algorithmfor Lempel-Ziv-77 (LZ77) compressed data. In the proposedalgorithm, additional bits are not used to detect bit errors, unlike inconventional methods such as checksum, cyclic redundancy check,Hamming code, and repetition code. We also introduce eight specialfeatures of LZ77-compressed data for detecting the presenceof errors. We demonstrate the feasibility of the algorithm based onsimulations and evaluate it using two publicly available databasescomprising the Calgary and Canterbury corpora. The error detectionrate using the proposed algorithm is below those of conventionalmethods, but the compression ratio is better. The applicationof a parity bit in the algorithm improves the error detection performance. The number of redundant bits increases owing to theinsertion of the parity bit, but the code rate is still greater thanor equal to 0.9, whereas conventional methods obtain code ratesless than 0.9. Simulations demonstrate that the algorithm obtainssignificant performance improvements when a parity bit is periodicallyinserted. In particular, we achieve an error detection rate of100% using the parity bit when the number of bit errors is greaterthan seven.
권범,노언수 한국컴퓨터정보학회 2024 韓國컴퓨터情報學會論文誌 Vol.29 No.2
Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.