6,309 research outputs found
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
Hybrid 3D Localization for Visible Light Communication Systems
In this study, we investigate hybrid utilization of angle-of-arrival (AOA)
and received signal strength (RSS) information in visible light communication
(VLC) systems for 3D localization. We show that AOA-based localization method
allows the receiver to locate itself via a least squares estimator by
exploiting the directionality of light-emitting diodes (LEDs). We then prove
that when the RSS information is taken into account, the positioning accuracy
of AOA-based localization can be improved further using a weighted least
squares solution. On the other hand, when the radiation patterns of LEDs are
explicitly considered in the estimation, RSS-based localization yields highly
accurate results. In order to deal with the system of nonlinear equations for
RSS-based localization, we develop an analytical learning rule based on the
Newton-Raphson method. The non-convex structure is addressed by initializing
the learning rule based on 1) location estimates, and 2) a newly developed
method, which we refer as random report and cluster algorithm. As a benchmark,
we also derive analytical expression of the Cramer-Rao lower bound (CRLB) for
RSS-based localization, which captures any deployment scenario positioning in
3D geometry. Finally, we demonstrate the effectiveness of the proposed
solutions for a wide range of LED characteristics and orientations through
extensive computer simulations.Comment: Submitted to IEEE/OSA Journal of Lightwave Technology (10 pages, 14
figures
IR sensors array for robots localization using K means clustering algorithm
The position of multi-robot system in an indoor localization system is successfully estimated using a new algorithm. The localization problem is resolved by using an array of IR receiver sensors distributed uniformly in the environment. The necessary information about the localization development is collected by scanning the IR sensor array in the environment. The scheme of scanning process is done column by column to recognize and mention the position of IR receiver’s sensors, which received signals from the IR transmitter that is fixed on the robot. This principle of scanning helps to minimize the required time for robot localization. The k-means clustering algorithm is used to estimate the multi-robot locations by isolating the labeled IR receivers into clusters. Basically the multi-robot position is estimated to be the middle of each cluster. Simulation results demonstrate the advances algorithm in estimation the multi-robot positions for various dimensional IR receiver’s array
Indoor assistance for visually impaired people using a RGB-D camera
In this paper a navigational aid for visually impaired people is presented. The system uses a RGB-D camera to perceive the environment and implements self-localization, obstacle detection and obstacle classification. The novelty of this work is threefold. First, self-localization is performed by means of a novel camera tracking approach that uses both depth and color information. Second, to provide the user with semantic information, obstacles are classified as walls, doors, steps and a residual class that covers isolated objects and bumpy parts on the floor. Third, in order to guarantee real time performance, the system is accelerated by offloading parallel operations to the GPU. Experiments demonstrate that the whole system is running at 9 Hz
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