21,704 research outputs found
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
This paper studies a deep learning (DL) framework to solve distributed
non-convex constrained optimizations in wireless networks where multiple
computing nodes, interconnected via backhaul links, desire to determine an
efficient assignment of their states based on local observations. Two different
configurations are considered: First, an infinite-capacity backhaul enables
nodes to communicate in a lossless way, thereby obtaining the solution by
centralized computations. Second, a practical finite-capacity backhaul leads to
the deployment of distributed solvers equipped along with quantizers for
communication through capacity-limited backhaul. The distributed nature and the
nonconvexity of the optimizations render the identification of the solution
unwieldy. To handle them, deep neural networks (DNNs) are introduced to
approximate an unknown computation for the solution accurately. In consequence,
the original problems are transformed to training tasks of the DNNs subject to
non-convex constraints where existing DL libraries fail to extend
straightforwardly. A constrained training strategy is developed based on the
primal-dual method. For distributed implementation, a novel binarization
technique at the output layer is developed for quantization at each node. Our
proposed distributed DL framework is examined in various network configurations
of wireless resource management. Numerical results verify the effectiveness of
our proposed approach over existing optimization techniques.Comment: to appear in IEEE J. Sel. Areas Commu
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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