Figure 9 From Coded Beam Training Semantic Scholar

Figure 3 From Coded Beam Training Semantic Scholar This work establishes the duality between hierarchical beam training and channel coding, and the proposed coded beam training scheme serves as a general framework, and presents two specific implementations exemplified by coded beam training methods based on hamming codes and convolutional codes. Specifically, we establish the duality between hierarchical beam training and channel coding, and build on it to propose a general coded beam training framework.

Figure 6 From Coded Beam Training Semantic Scholar In this paper, we introduce channel coding theory into hierarchical beam training and propose a beam training scheme called coded beam training. Google scholar provides a simple way to broadly search for scholarly literature. search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. Simulation results have demonstrated that, the proposed coded beam training method can enable reliable beam training performance for remote users with low snr, while keeping training. We attempt to depict a clear comparison across exhaustive beam training, traditional binary search based hierarchi cal beam training, and the proposed adaptive non adaptive coded beam.

Figure 10 From Coded Beam Training Semantic Scholar Simulation results have demonstrated that, the proposed coded beam training method can enable reliable beam training performance for remote users with low snr, while keeping training. We attempt to depict a clear comparison across exhaustive beam training, traditional binary search based hierarchi cal beam training, and the proposed adaptive non adaptive coded beam. Specifically, we establish the duality between hierarchical beam training and channel coding, and build on it to propose a general coded beam training framework. Simulation results have demonstrated that the proposed coded beam training method can enable reliable beam training performance for remote users with low snr while keeping training overhead low. Simulation results have demonstrated that, the proposed coded beam training method can enable reliable beam training performance for remote users with low snr, while keeping training overhead low. Google scholar semantic scholar internet archive scholar citeseerx pubpeer share record twitter reddit bibsonomy linkedin facebook persistent url: dblp.org rec journals corr abs 2401 01673 tianyue zheng, jieao zhu, qiumo yu, yongli yan, linglong dai: coded beam training.corrabs 2401.01673 (2024) home blog statistics update feed xml dump.

Figure 2 From Coded Beam Training Semantic Scholar Specifically, we establish the duality between hierarchical beam training and channel coding, and build on it to propose a general coded beam training framework. Simulation results have demonstrated that the proposed coded beam training method can enable reliable beam training performance for remote users with low snr while keeping training overhead low. Simulation results have demonstrated that, the proposed coded beam training method can enable reliable beam training performance for remote users with low snr, while keeping training overhead low. Google scholar semantic scholar internet archive scholar citeseerx pubpeer share record twitter reddit bibsonomy linkedin facebook persistent url: dblp.org rec journals corr abs 2401 01673 tianyue zheng, jieao zhu, qiumo yu, yongli yan, linglong dai: coded beam training.corrabs 2401.01673 (2024) home blog statistics update feed xml dump.