1,323 research outputs found
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Formulation, implementation considerations, and first performance evaluation of algorithmic solutions - D4.1
Deliverable D4.1 del projecte Europeu OneFIT (ICT-2009-257385)This deliverable contains a first version of the algorithmic solutions for enabling opportunistic networks. The presented algorithms cover the full range of identified management tasks: suitability, creation, QoS control, reconfiguration and forced terminations. Preliminary evaluations complement the proposed algorithms. Implementation considerations towards the practicality of the considered algorithms are also included.Preprin
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 Autonomous Channel Selection Algorithm for WLANs
IEEE 802.11 wireless devices need to select a channel in order to transmit their packets. However, as a result of the contention-based nature of the IEEE 802.11 CSMA/CA MAC mechanism, the capacity experienced by a station is not fixed. When a station cannot win a sufficient number of transmission opportunities to satisfy its traffic load, it will become saturated. If the saturation condition persists, more and more packets are stored in the transmit queue and congestion occurs. Congestion leads to high packet delay and may ultimately result in catastrophic packet loss when the transmit queueâs capacity is exceeded. In this thesis, we propose an autonomous channel selection algorithm with neighbour forcing (NF) to minimize the incidence of congestion on all stations using the channels. All stations reassign the channels based on the local monitoring information. This station will change the channel once it finds a channel that has sufficient available bandwidth to satisfy its traffic load requirement or it will force its neighbour stations into saturation by reducing its PHY transmission rate if there exists at least one successful channel assignment according to a predicting module which checks all the possible channel assignments. The results from a simple C++ simulator show that the NF algorithm has a higher probability than the dynamic channel assignment without neighbour forcing (NONF) to successfully reassign the channel once stations have become congested. In an experimental testbed, the Madwifi open source wireless driver has been modified to incorporate the channel selection mechanism. The results demonstrate that the NF algorithm also has a better performance than the NONF algorithm in reducing the congestion time of the network where at least one station has become congested
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