J
Jiandong Li
Researcher at Xidian University
Publications - 670
Citations - 9377
Jiandong Li is an academic researcher from Xidian University. The author has contributed to research in topics: Wireless network & MIMO. The author has an hindex of 37, co-authored 620 publications receiving 6970 citations. Previous affiliations of Jiandong Li include Cornell University & Nanjing University of Information Science and Technology.
Papers
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Changes in the default mode networks of individuals with long-term unilateral sensorineural hearing loss.
G.-Y. Zhang,Ming Yang,Bi-Cheng Liu,Zhichun Huang,H. S. Chen,P.-P. Zhang,Jiandong Li,Jing-ya Chen,Lijie Liu,Jian Wang,Jian Wang,Gao-Jun Teng +11 more
TL;DR: FMRI data and neuropsychological test scores suggest that long-term USNHL contributes to changes in the DMN, and these changes might affect cognitive abilities in patients with long-terms unilateral SNHL.
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Hop-by-Hop Routing in Wireless Mesh Networks with Bandwidth Guarantees
TL;DR: It is formally proved that the hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements and outperforms existing path metrics in identifying high-throughput paths.
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Distributed Subchannel Allocation for Interference Mitigation in OFDMA Femtocells: A Utility-Based Learning Approach
TL;DR: This paper investigates the distributed subchannel allocation (DSA) for cotier interference mitigation in OFDMA-based fem tocells, where the femtocells and macrocell transmit on orthogonal subchannels and develops a utility-based DSA algorithm that performs comparably or even better compared with the existing strategies.
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Orthogonal Power Division Multiple Access: A Green Communication Perspective
TL;DR: This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.
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Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning Based Approach
TL;DR: This work investigates the location error of a fingerprint-based indoor system with the application of hybrid fingerprints and proposes a hybrid received signal strength and channel state information localization algorithm (HRC), which is designed based on deep learning.