Z
Zheng Chang
Researcher at University of Jyväskylä
Publications - 150
Citations - 3532
Zheng Chang is an academic researcher from University of Jyväskylä. The author has contributed to research in topics: Resource allocation & Efficient energy use. The author has an hindex of 28, co-authored 137 publications receiving 2574 citations. Previous affiliations of Zheng Chang include Information Technology University & Xidian University.
Papers
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Proceedings ArticleDOI
Two-Stage Matching for Energy-Efficient Resource Management in D2D Cooperative Relay Communications
TL;DR: A pricing-based two-stage matching algorithm to reduce dimensionality and provide a tractable solution to jointly optimizes relay selection, spectrum allocation, and power control, so that the total energy efficiency of D2D links is maximized while guaranteeing the quality of service (QoS) requirements of D1D and cellular links at the same time.
Proceedings ArticleDOI
Reliable and Privacy-Preserving Task Recomposition for Crowdsensing in Vehicular Fog Computing
TL;DR: Modified homomorphic Paillier encryption and superincreasing sequence are employed for aggregating hybrid subtasks into one ciphertext for reliable and privacy-preserving task recomposition for multiple subtasks sensing in VFC.
Proceedings ArticleDOI
A double auction mechanism for virtual resource allocation in SDN-based cellular network
TL;DR: A Software Defined Network (SDN) based wireless virtualization architecture for enabling multi-flow transmission in order to save capital expenses (CapEx) and operation expenses (OpEx) significantly with multiple Infrastructures Providers (InPs) and multiple Mobile Virtual Network Operators (MVNOs).
Journal ArticleDOI
Incentive Mechanism for Resource Allocation in Wireless Virtualized Networks with Multiple Infrastructure Providers
TL;DR: It can be observed that the proposed contract theoretic approach can effectively stimulate InPs’ participation, improve the payoff of the MVNO, and outperform other schemes.
Journal ArticleDOI
Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing
TL;DR: In this paper , a distributed deep learning based computation offloading and resource allocation (DDL-CORA) algorithm was proposed for SD-MEC IoT in which multiple parallel deep neural networks (DNNs) are invoked to generate the optimal offloading decision and resource scheduling.