170 research outputs found
On the Performance and Optimization for MEC Networks Using Uplink NOMA
In this paper, we investigate a non-orthogonal multiple access (NOMA) based
mobile edge computing (MEC) network, in which two users may partially offload
their respective tasks to a single MEC server through uplink NOMA. We propose a
new offloading scheme that can operate in three different modes, namely the
partial computation offloading, the complete local computation, and the
complete offloading. We further derive a closed-form expression of the
successful computation probability for the proposed scheme. As part of the
proposed offloading scheme, we formulate a problem to maximize the successful
computation probability by jointly optimizing the time for offloading, the
power allocation of the two users and the offloading ratios which decide how
many tasks should be offloaded to the MEC server. We obtain the optimal
solutions in the closed forms. Simulation results show that our proposed scheme
can achieve the highest successful computation probability than the existing
schemes.Comment: This paper has been accepted by IEEE ICC Workshop 201
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction
Data Augmentation through generating pseudo data has been proven effective in
mitigating the challenge of data scarcity in the field of Grammatical Error
Correction (GEC). Various augmentation strategies have been widely explored,
most of which are motivated by two heuristics, i.e., increasing the
distribution similarity and diversity of pseudo data. However, the underlying
mechanism responsible for the effectiveness of these strategies remains poorly
understood. In this paper, we aim to clarify how data augmentation improves GEC
models. To this end, we introduce two interpretable and computationally
efficient measures: Affinity and Diversity. Our findings indicate that an
excellent GEC data augmentation strategy characterized by high Affinity and
appropriate Diversity can better improve the performance of GEC models. Based
on this observation, we propose MixEdit, a data augmentation approach that
strategically and dynamically augments realistic data, without requiring extra
monolingual corpora. To verify the correctness of our findings and the
effectiveness of the proposed MixEdit, we conduct experiments on mainstream
English and Chinese GEC datasets. The results show that MixEdit substantially
improves GEC models and is complementary to traditional data augmentation
methods.Comment: Accepted to Findings of EMNLP 202
FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing
Integrating unmanned aerial vehicles (UAVs) into vehicular networks have
shown high potentials in affording intensive computing tasks. In this paper, we
study the digital twin driven vehicular edge computing networks for adaptively
computing resource management where an unmanned aerial vehicle (UAV) named
FlexEdge acts as a flying server. In particular, we first formulate an energy
consumption minimization problem by jointly optimizing UAV trajectory and
computation resource under the practical constraints. To address such a
challenging problem, we then build the computation offloading process as a
Markov decision process and propose a deep reinforcement learning-based
proximal policy optimization algorithm to dynamically learn the computation
offloading strategy and trajectory design policy. Numerical results indicate
that our proposed algorithm can achieve quick convergence rate and
significantly reduce the system energy consumption.Comment: 6 pages, 6 figure
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