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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Proceedings ArticleDOI
A new algorithm for network probabilistic connectivity
TL;DR: A new algorithm for network probabilistic connectivity is presented, and a new method to construct sub-graphs for the obtained events is proposed, which can greatly reduce the dimensions of network states space.
Journal ArticleDOI
Elevation of cortical C26:0 due to the decline of peroxisomal β-oxidation potentiates amyloid β generation and spatial memory deficits via oxidative stress in diabetic rats
TL;DR: This work suggests that in addition to decreases in EPA, increases in C26:0 by impaired peroxisomal β-oxidation can be a potential risk factor contributing to the progression of AD in diabetic brains via inducing oxidative stress.
Journal ArticleDOI
Dynamic Sensing Strategies for Efficient Spectrum Utilization in Cognitive Radio Networks
TL;DR: Three dynamic sensing strategies are developed that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model and can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in thePU traffic pattern.
Journal ArticleDOI
Interference Alignment for MIMO Downlink Multicell Networks
TL;DR: The proposed GEVD-based IA scheme can reduce the dimension of subspace occupied by the intercell interference (ICI) more efficiently, hence providing achievable DoF higher than or equal to that of the existing IA scheme.
Journal ArticleDOI
A Transformed Conflict Graph-Based Resource-Allocation Scheme Combining Interference Alignment in OFDMA Femtocell Networks
TL;DR: A transformed conflict graph-based resource-allocation scheme, where the users are selected according to the effect of IA on the requirement under the feasibility constraint and the subchannel assignment can be modeled as a classical color problem, which can approximate the optimal scheme in a small network.