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