624 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
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
The combination of mobile edge computing (MEC) and radio frequency-based
wireless power transfer (WPT) presents a promising technique for providing
sustainable energy supply and computing services at the network edge. This
study considers a wireless-powered mobile edge computing system that includes a
hybrid access point (HAP) equipped with a computing unit and multiple Internet
of Things (IoT) devices. In particular, we propose a novel muti-user
cooperation scheme to improve computation performance, where collaborative
clusters are dynamically formed. Each collaborative cluster comprises a source
device (SD) and an auxiliary device (AD), where the SD can partition the
computation task into various segments for local processing, offloading to the
HAP, and remote execution by the AD with the assistance of the HAP.
Specifically, we aims to maximize the weighted sum computation rate (WSCR) of
all the IoT devices in the network. This involves jointly optimizing
collaboration, time and data allocation among multiple IoT devices and the HAP,
while considering the energy causality property and the minimum data processing
requirement of each device. Initially, an optimization algorithm based on the
interior-point method is designed for time and data allocation. Subsequently, a
priority-based iterative algorithm is developed to search for a near-optimal
solution to the multi-user collaboration scheme. Finally, a deep learning-based
approach is devised to further accelerate the algorithm's operation, building
upon the initial two algorithms. Simulation results show that the performance
of the proposed algorithms is comparable to that of the exhaustive search
method, and the deep learning-based algorithm significantly reduces the
execution time of the algorithm.Comment: Accepted to IEEE Open Journal of the Communications Societ
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