82,685 research outputs found
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective)
Level Crossing Rate of Macrodiversity System in the Presence of Multipath Fading and Shadowing
Macrodiversity system including macrodiversity SC receiver and two microdiversity SC receivers is considered in this paper. Received signal experiences, simultaneously, both, long term fading and short term fading. Microdiversity SC receivers reduces Rayleigh fading effects on system performance and macrodiversity SC receiver mitigate Gamma shadowing effects on system performance. Closed form expressions for level crossing rate of microdiversity SC receivers output signals envelopes are calculated. This expression is used for evaluation of level crossing rate of macrodiversity SC receiver output signal envelope. Numerical expressions are illustrated to show the influence of Gamma shadowing severity on level crossing rate
Description and Experience of the Clinical Testbeds
This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of
the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies
in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with
deployment and usage of these applications, components and technologies, and their clinical service impact
Analyzing Delay in Wireless Multi-hop Heterogeneous Body Area Networks
With increase in ageing population, health care market keeps growing. There
is a need for monitoring of health issues. Wireless Body Area Network (WBAN)
consists of wireless sensors attached on or inside human body for monitoring
vital health related problems e.g, Electro Cardiogram (ECG), Electro
Encephalogram (EEG), ElectronyStagmography (ENG) etc. Due to life threatening
situations, timely sending of data is essential. For data to reach health care
center, there must be a proper way of sending data through reliable connection
and with minimum delay. In this paper transmission delay of different paths,
through which data is sent from sensor to health care center over heterogeneous
multi-hop wireless channel is analyzed. Data of medical related diseases is
sent through three different paths. In all three paths, data from sensors first
reaches ZigBee, which is the common link in all three paths. Wireless Local
Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX),
Universal Mobile Telecommunication System (UMTS) are connected with ZigBee.
Each network (WLAN, WiMAX, UMTS) is setup according to environmental
conditions, suitability of device and availability of structure for that
device. Data from these networks is sent to IP-Cloud, which is further
connected to health care center. Delay of data reaching each device is
calculated and represented graphically. Main aim of this paper is to calculate
delay of each link in each path over multi-hop wireless channel.Comment: arXiv admin note: substantial text overlap with arXiv:1208.240
Wireless technology and clinical influences in healthcare setting: an Indian case study
This chapter argues that current techniques used in the domain of Information Systems is not adequate for establishing determinants of wireless technology in a clinical setting. Using data collected from India, this chapter conducted a first order regrssion modeling (factor analysis) and then a second order regression modeling (SEM) to establish the determinants of clinical influences as a result of using wireless technology in healthcare settings. As information systems professionals, the authors conducted a qualitative data collection to understand the domain prior to employing a quantitative technique, thus providing rigour as well as personal relevance. The outcomes of this study has clearly established that there are a number of influences such as the organisational factors in determining the technology acceptance and provides evidence that trivial factors such as perceived ease of use and perceived usefulness are no longer acceptable as the factors of technology acceptance
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