77 research outputs found
Dynamic NOMA-Based Computation Offloading in Vehicular Platoons
Both the mobile edge computing (MEC) based and fog computing (FC) aided
Internet of Vehicles (IoV) constitute promising paradigms of meeting the
demands of low-latency pervasive computing. To this end, we construct a dynamic
NOMA-based computation offloading scheme for vehicular platoons on highways,
where the vehicles can offload their computing tasks to other platoon members.
To cope with the rapidly fluctuating channel quality, we divide the timeline
into successive time slots according to the channel's coherence time. Robust
computing and offloading decisions are made for each time slot after taking the
channel estimation errors into account. Considering a certain time slot, we
first analytically characterize both the locally computed source data and the
offloaded source data as well as the energy consumption of every vehicle in the
platoons. We then formulate the problem of minimizing the long-term energy
consumption by optimizing the allocation of both the communication and
computing resources. To solve the problem formulated, we design an online
algorithm based on the classic Lyapunov optimization method and block
successive upper bound minimization (BSUM) method. Finally, the numerical
simulation results characterize the performance of our algorithm and
demonstrate its advantages both over the local computing scheme and the
orthogonal multiple access (OMA)-based offloading scheme.Comment: 11 pages, 9 figure
A Survey on UAV-enabled Edge Computing: Resource Management Perspective
Edge computing facilitates low-latency services at the network's edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent
advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new
opportunities for edge computing in military operations, disaster response, or
remote areas where traditional terrestrial networks are limited or unavailable.
In such environments, UAVs can be deployed as aerial edge servers or relays to
facilitate edge computing services. This form of computing is also known as
UAV-enabled Edge Computing (UEC), which offers several unique benefits such as
mobility, line-of-sight, flexibility, computational capability, and
cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices
are typically very limited in the context of UEC. Efficient resource management
is, therefore, a critical research challenge in UEC. In this article, we
present a survey on the existing research in UEC from the resource management
perspective. We identify a conceptual architecture, different types of
collaborations, wireless communication models, research directions, key
techniques and performance indicators for resource management in UEC. We also
present a taxonomy of resource management in UEC. Finally, we identify and
discuss some open research challenges that can stimulate future research
directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU
Power control optimization for large-scale multi-antenna systems
Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research
Resource Allocation in Energy Cooperation Enabled 5G Cellular Networks
PhD thesisIn fifth generation (5G) networks, more base stations (BSs) and antennas have been
deployed to meet the high data rate and spectrum efficiency requirements. Heterogeneous
and ultra dense networks not only pose substantial challenges to the resource allocation
design, but also lead to unprecedented surge in energy consumption. Supplying BSs
with renewable energy by utilising energy harvesting technology has became a favourable
solution for cellular network operators to reduce the grid energy consumption. However,
the harvested renewable energy is fluctuating in both time and space domains. The
available energy for a particular BS at a particular time might be insufficient to meet the
traffic demand which will lead to renewable energy waste or increased outage probability.
To solve this problem, the concept of energy cooperation was introduced by Sennur
Ulukus in 2012 as a means for transferring and sharing energy between the transmitter
and the receiver. Nevertheless, resource allocation in energy cooperation enabled cellular
networks is not fully investigated. This thesis investigates resource allocation schemes
and resource allocation optimisation in energy cooperation enabled cellular networks
that employed advanced 5G techniques, aiming at maximising the energy efficiency of
the cellular network while ensuring the network performance.
First, a power control algorithm is proposed for energy cooperation enabled millimetre
wave (mmWave) HetNets. The aim is to maximise the time average network data
rate while keeping the network stable such that the network backlog is bounded and the
required battery capacity is finite. Simulation results show that the proposed power control
scheme can reduce the required battery capacity and improve the network throughput.
Second, resource allocation in energy cooperation enabled heterogeneous networks (Het-
Nets) is investigated. User association and power control schemes are proposed to maximise the energy efficiency of the whole network respectively. The simulation results
reveal that the implementation of energy cooperation in HetNets can improve the energy
efficiency and the improvement is apparent when the energy transfer efficiency is high.
Following on that, a novel resource allocation for energy cooperation enabled nonorthogonal
multiple access (NOMA) HetNets is presented. Two user association schemes
which have different complexities and performances are proposed and compared. Following
on that, a joint user association and power control algorithm is proposed to maximise
the energy efficiency of the network. It is confirmed from the simulation results that the
proposed resource allocation schemes efficiently coordinate the intra-cell and inter-cell
interference in NOMA HetNets with energy cooperation while exploiting the multiuser
diversity and BS densification.
Last but not least, a joint user association and power control scheme that considers
the different content requirements of users is proposed for energy cooperation enabled
caching HetNets. It shows that the proposed scheme significantly enhances the energy
efficiency performance of caching HetNets
An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution
Nowadays, data caching is being used as a high-speed data storage layer in
mobile edge computing networks employing flow control methodologies at an
exponential rate. This study shows how to discover the best architecture for
backhaul networks with caching capability using a distributed offloading
technique. This article used a continuous power flow analysis to achieve the
optimum load constraints, wherein the power of macro base stations with various
caching capacities is supplied by either an intelligent grid network or
renewable energy systems. This work proposes ubiquitous connectivity between
users at the cell edge and offloading the macro cells so as to provide features
the macro cell itself cannot cope with, such as extreme changes in the required
user data rate and energy efficiency. The offloading framework is then reformed
into a neural weighted framework that considers convergence and Lyapunov
instability requirements of mobile-edge computing under Karush Kuhn Tucker
optimization restrictions in order to get accurate solutions. The cell-layer
performance is analyzed in the boundary and in the center point of the cells.
The analytical and simulation results show that the suggested method
outperforms other energy-saving techniques. Also, compared to other solutions
studied in the literature, the proposed approach shows a two to three times
increase in both the throughput of the cell edge users and the aggregate
throughput per cluster
Efficient and Secure Resource Allocation in Mobile Edge Computing Enabled Wireless Networks
To support emerging applications such as autonomous vehicles and smart homes and to build an intelligent society, the next-generation internet of things (IoT) is calling for up to 50 billion devices connected world wide. Massive devices connection, explosive data circulation, and colossal data processing demand are driving both the industry and academia to explore new solutions.
Uploading this vast amount of data to the cloud center for processing will significantly increase the load on backbone networks and cause relatively long latency to time-sensitive applications. A practical solution is to deploy the computing resource closer to end-users to process the distributed data. Hence, Mobile Edge Computing (MEC) emerged as a promising solution to providing high-speed data processing service with low latency.
However, the implementation of MEC networks is handicapped by various challenges. For one thing, to serve massive IoT devices, dense deployment of edge servers will consume much more energy. For another, uploading sensitive user data through a wireless link intro-duces potential risks, especially for those size-limited IoT devices that cannot implement complicated encryption techniques. This dissertation investigates problems related to Energy Efficiency (EE) and Physical Layer Security (PLS) in MEC-enabled IoT networks and how Non-Orthogonal Multiple Access (NOMA), prediction-based server coordination, and Intelligent Reflecting Surface (IRS) can be used to mitigate them.
Employing a new spectrum access method can help achieve greater speed with less power consumption, therefore increasing system EE. We first investigated NOMA-assisted MEC networks and verified that the EE performance could be significantly improved. Idle servers can consume unnecessary power. Proactive server coordination can help relieve the tension of increased energy consumption in MEC systems. Our next step was to employ advanced machine learning algorithms to predict data workload at the server end and adaptively adjust the system configuration over time, thus reducing the accumulated system cost. We then introduced the PLS to our system and investigated the long-term secure EE performance of the MEC-enabled IoT network with NOMA assistance. It has shown that NOMA can improve both EE and PLS for the network. Finally, we switch from the single antenna scenario to a multiple-input single-output (MISO) system to exploit space diversity and beam forming techniques in mmWave communication. IRS can be used simultaneously to help relieve the pathloss and reconfigure multi-path links. In the final part, we first investigated the secure EE performance of IRS-assisted MISO networks and introduced a friendly jammer to block the eavesdroppers and improve the PLS rate. We then combined the IRS with the NOMA in the MEC network and showed that the IRS can further enhance the system EE
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