791 research outputs found
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
Distributed multimedia systems
A distributed multimedia system (DMS) is an integrated communication, computing, and information system that enables the processing, management, delivery, and presentation of synchronized multimedia information with quality-of-service guarantees. Multimedia information may include discrete media data, such as text, data, and images, and continuous media data, such as video and audio. Such a system enhances human communications by exploiting both visual and aural senses and provides the ultimate flexibility in work and entertainment, allowing one to collaborate with remote participants, view movies on demand, access on-line digital libraries from the desktop, and so forth. In this paper, we present a technical survey of a DMS. We give an overview of distributed multimedia systems, examine the fundamental concept of digital media, identify the applications, and survey the important enabling technologies.published_or_final_versio
Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one
Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction
Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation
Quantum computing networks enable scalable collaboration and secure
information exchange among multiple classical and quantum computing nodes while
executing large-scale generative AI computation tasks and advanced quantum
algorithms. Quantum computing networks overcome limitations such as the number
of qubits and coherence time of entangled pairs and offer advantages for
generative AI infrastructure, including enhanced noise reduction through
distributed processing and improved scalability by connecting multiple quantum
devices. However, efficient resource allocation in quantum computing networks
is a critical challenge due to factors including qubit variability and network
complexity. In this article, we propose an intelligent resource allocation
framework for quantum computing networks to improve network scalability with
minimized resource costs. To achieve scalability in quantum computing networks,
we formulate the resource allocation problem as stochastic programming,
accounting for the uncertain fidelities of qubits and entangled pairs.
Furthermore, we introduce state-of-the-art reinforcement learning (RL)
algorithms, from generative learning to quantum machine learning for optimal
quantum resource allocation to resolve the proposed stochastic resource
allocation problem efficiently. Finally, we optimize the resource allocation in
heterogeneous quantum computing networks supporting quantum generative learning
applications and propose a multi-agent RL-based algorithm to learn the optimal
resource allocation policies without prior knowledge
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