12,507 research outputs found
Auction-based Bandwidth Allocation Mechanisms for Wireless Future Internet
An important aspect of the Future Internet is the efficient utilization of
(wireless) network resources. In order for the - demanding in terms of QoS -
Future Internet services to be provided, the current trend is evolving towards
an "integrated" wireless network access model that enables users to enjoy
mobility, seamless access and high quality of service in an all-IP network on
an "Anytime, Anywhere" basis. The term "integrated" is used to denote that the
Future Internet wireless "last mile" is expected to comprise multiple
heterogeneous geographically coexisting wireless networks, each having
different capacity and coverage radius. The efficient management of the
wireless access network resources is crucial due to their scarcity that renders
wireless access a potential bottleneck for the provision of high quality
services. In this paper we propose an auction mechanism for allocating the
bandwidth of such a network so that efficiency is attained, i.e. social welfare
is maximized. In particular, we propose an incentive-compatible, efficient
auction-based mechanism of low computational complexity. We define a repeated
game to address user utilities and incentives issues. Subsequently, we extend
this mechanism so that it can also accommodate multicast sessions. We also
analyze the computational complexity and message overhead of the proposed
mechanism. We then show how user bids can be replaced from weights generated by
the network and transform the auction to a cooperative mechanism capable of
prioritizing certain classes of services and emulating DiffServ and time-of-day
pricing schemes. The theoretical analysis is complemented by simulations that
assess the proposed mechanisms properties and performance. We finally provide
some concluding remarks and directions for future research
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
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
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