Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

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

583 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d273b8a945d635a50d1c0c9c7b33335070f8c148

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, background): “Studies, such as [18] and [19] jointly considered content caching and offloading for MEC, and implied that moving popular contents close to end users can reduce replicated transmission and improve users’ quality of experience.”
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks (2019, background): “obtain the optimal offloading decisions and resource allocation policies [28].”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, methodology): “[5] have proposed an ADMM-based algorithm to optimize the computing resource allocation and data uploading in wireless networks with MEC.”
A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things (2019, background): “time overhead of the backhual link can be ignored [27], [28].”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, background): “Here, the time and energy consumption of outcome from the MEC server to the SMD are ignored in this case, due to the fact that the size of outcome data is much smaller than the size of input data, which is similar to the studies [30], [31].”
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (2018, methodology): “[7] developed an alternating direction method of multipliers-based algorithm to solve the problem of revenue maximization by optimizing computation offloading decision, resource allocation, and content caching strategy.”
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “The works in [5]–[7] focused on optimizing a single objective, which over-emphasizes the importance of one metric and may not achieve a good tradeoff among multiple metrics.”
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “limited mobile users to offload tasks for remote execution at the more powerful MEC server in their proximity, which brings the benefit of improved computation capacity and reduced latency [126].”
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, background): “, the decision making delay due to request queuing and decision making is omitted [23].”
Resource Scheduling in Edge Computing: A Survey (2021, background): “Likely, the work in [67] addressed the optimization problem for joint computation offloading, resource allocation, and content caching, in which computing,”
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, background): “Computation Offloading [7], [64]: In cloud computing, cloud servers have to perform heavy computational tasks for maintaining services and applications for users.”
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future (2020, background): “In this regard, efficient computational offloading will be a necessity [46], [47].”