27,605 research outputs found
Partially-Distributed Resource Allocation in Small-Cell Networks
We propose a four-stage hierarchical resource allocation scheme for the
downlink of a large-scale small-cell network in the context of orthogonal
frequency-division multiple access (OFDMA). Since interference limits the
capabilities of such networks, resource allocation and interference management
are crucial. However, obtaining the globally optimum resource allocation is
exponentially complex and mathematically intractable. Here, we develop a
partially decentralized algorithm to obtain an effective solution. The three
major advantages of our work are: 1) as opposed to a fixed resource allocation,
we consider load demand at each access point (AP) when allocating spectrum; 2)
to prevent overloaded APs, our scheme is dynamic in the sense that as the users
move from one AP to the other, so do the allocated resources, if necessary, and
such considerations generally result in huge computational complexity, which
brings us to the third advantage: 3) we tackle complexity by introducing a
hierarchical scheme comprising four phases: user association, load estimation,
interference management via graph coloring, and scheduling. We provide
mathematical analysis for the first three steps modeling the user and AP
locations as Poisson point processes. Finally, we provide results of numerical
simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on
Wireless Communication
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
EVEREST IST - 2002 - 00185 : D23 : final report
Deliverable pĂşblic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version
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