7,871 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control.
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
Privacy-preserving Security Inference Towards Cloud-Edge Collaborative Using Differential Privacy
Cloud-edge collaborative inference approach splits deep neural networks
(DNNs) into two parts that run collaboratively on resource-constrained edge
devices and cloud servers, aiming at minimizing inference latency and
protecting data privacy. However, even if the raw input data from edge devices
is not directly exposed to the cloud, state-of-the-art attacks targeting
collaborative inference are still able to reconstruct the raw private data from
the intermediate outputs of the exposed local models, introducing serious
privacy risks. In this paper, a secure privacy inference framework for
cloud-edge collaboration is proposed, termed CIS, which supports adaptively
partitioning the network according to the dynamically changing network
bandwidth and fully releases the computational power of edge devices. To
mitigate the influence introduced by private perturbation, CIS provides a way
to achieve differential privacy protection by adding refined noise to the
intermediate layer feature maps offloaded to the cloud. Meanwhile, with a given
total privacy budget, the budget is reasonably allocated by the size of the
feature graph rank generated by different convolution filters, which makes the
inference in the cloud robust to the perturbed data, thus effectively trade-off
the conflicting problem between privacy and availability. Finally, we construct
a real cloud-edge collaborative inference computing scenario to verify the
effectiveness of inference latency and model partitioning on
resource-constrained edge devices. Furthermore, the state-of-the-art cloud-edge
collaborative reconstruction attack is used to evaluate the practical
availability of the end-to-end privacy protection mechanism provided by CIS
Adaptive Semantic Communications: Overfitting the Source and Channel for Profit
Most semantic communication systems leverage deep learning models to provide
end-to-end transmission performance surpassing the established source and
channel coding approaches. While, so far, research has mainly focused on
architecture and model improvements, but such a model trained over a full
dataset and ergodic channel responses is unlikely to be optimal for every test
instance. Due to limitations on the model capacity and imperfect optimization
and generalization, such learned models will be suboptimal especially when the
testing data distribution or channel response is different from that in the
training phase, as is likely to be the case in practice. To tackle this, in
this paper, we propose a novel semantic communication paradigm by leveraging
the deep learning model's overfitting property. Our model can for instance be
updated after deployment, which can further lead to substantial gains in terms
of the transmission rate-distortion (RD) performance. This new system is named
adaptive semantic communication (ASC). In our ASC system, the ingredients of
wireless transmitted stream include both the semantic representations of source
data and the adapted decoder model parameters. Specifically, we take the
overfitting concept to the extreme, proposing a series of ingenious methods to
adapt the semantic codec or representations to an individual data or channel
state instance. The whole ASC system design is formulated as an optimization
problem whose goal is to minimize the loss function that is a tripartite
tradeoff among the data rate, model rate, and distortion terms. The experiments
(including user study) verify the effectiveness and efficiency of our ASC
system. Notably, the substantial gain of our overfitted coding paradigm can
catalyze semantic communication upgrading to a new era
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