Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

By Chen-meng Wang, F. Yu, Chengchao Liang, Qianbin Chen, Lun Tang. IEEE Transactions on Vehicular Technology, 2017

303 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, background): “[24] formulated the computation offloading decision, physical resource block allocation, and MEC computation resource allocation as the optimization problem to minimize the overall consumption of the entire system, in terms of time and energy.”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, methodology): “In [132], the MEC server makes the offloading decisions and physical resource block allocation to the UEs using the graph”
A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things (2019, background): “When the SeNB is in the proximity of the MEC server in local regions, the transmission time can be omitted [27] [28].”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, background): “Several other works, such as [9] and [11] attempted to find a tradeoff between energy consumption and execution delay.”
Deep reinforcement learning based computation offloading and resource allocation for MEC (2018, methodology): “We assume that each UE n has a computation-intensive task Rn , (Bn, Dn, τn), which can be executed on the local CPU of UE or on the MEC server via computation offloading [12].”
Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications (2018, unknown): “optimized energy and latency jointly [15], [26].”