Distributionally Robust Chance-Constrained Backscatter Communication-Assisted Computation Offloading in WBANs

Ling, Zhuang; Hu, Fengye; Zhang, Yu; Fan, Lei; Gao, Feifei; Han, Zhu

doi:10.1109/tcomm.2021.3056714

Cited by 15 publications

(1 citation statement)

References 42 publications

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“…The authors of [20] proposed a robust anti-edge server fault task offloading scheme to overcome the dynamics of edge servers. For channel robustness design, the authors of [21] presented a hybrid offloading scheme with backscatter communication under imperfect CSI with the aim of minimizing the end-to-end system latency. In [22], the authors considered a robust offloading strategy against realistic channel estimation errors in fog-IoT systems and minimized the power consumption of UEs with the latency requirements.…”

Section: Related Workmentioning

confidence: 99%

Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multiagent DRL Approach

Li,

Yang,

Liu

et al. 2024

IEEE Internet Things J.

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For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multi-agent Markov decision process and propose a multi-agent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.

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Section: Related Workmentioning

confidence: 99%