Can Unlicensed Bands Be Used by Unlicensed Usage?

Since their introduction, unlicensed ISM bands have resulted in a wide range of new wireless devices and services. It is fair to say that the success of ISM was an important factor in the opening of the TV white space for unlicensed access. Further bands (e.g., 3550-3650 MHz) are being studied to support unlicensed access. Expansion of the unlicensed bands may well address one of the principle disadvantages of unlicensed (variable quality of service) which could result in a vibrant new group companies providing innovative services and better prices. However, given that many commercial mobile telephone operators are relying heavily on the unlicensed bands to manage growth in data traffic through the “offloading” strategy, the promise of these bands may be more limited than might otherwise be expected (Musey, 2013).\ud \ud Wireless data traffic has exploded in the past several years due to more capable devices and faster network technologies. While there is some debate on the trajectory of data growth, some notable reports include AT&T, which reported data growth of over 5000% from 2008 to 2010 and Cisco, who predicted that mobile data traffic will grow to 6.3 exabytes per month in average by 2015 (Hu, 2012). Although the data traffic increased dramatically, relatively little new spectrum for mobile operators has come online in the last several years; further, the “flat-rate” pricing strategy has led to declining Average Revenue Per User (ARPU) for the mobile operators. Their challenge, then, is how to satisfy the service demand with acceptable additional expenditures on infrastructure and spectrum utilization.\ud \ud A common response to this challenge has been to offload data traffic onto unlicensed (usually WiFi) networks. This can be accomplished either by establishing infrastructure (WiFi hotspots) or to use existing private networks. This phenomenon leads to two potential risks for spectrum entrants: (1) the use of offloading may overwhelm unlicensed spectrum and leave little access opportunities for newcomers; (2) the intensity of the traffic may increase interference and degrade innovative services.\ud \ud Consequently, opening more unlicensed frequency bands alone may not necessarily lead to more unlicensed usage. In this paper, we will estimate spectrum that left for unlicensed usage and analyze risks for unlicensed users in unlicensed bands in terms of access opportunities and monetary gain. We will further provide recommendations that help foster unlicensed usage in unlicensed bands

Q-CP: Learning Action Values for Cooperative Planning

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment