188 research outputs found
Precoder Design for Physical Layer Multicasting
This paper studies the instantaneous rate maximization and the weighted sum
delay minimization problems over a K-user multicast channel, where multiple
antennas are available at the transmitter as well as at all the receivers.
Motivated by the degree of freedom optimality and the simplicity offered by
linear precoding schemes, we consider the design of linear precoders using the
aforementioned two criteria. We first consider the scenario wherein the linear
precoder can be any complex-valued matrix subject to rank and power
constraints. We propose cyclic alternating ascent based precoder design
algorithms and establish their convergence to respective stationary points.
Simulation results reveal that our proposed algorithms considerably outperform
known competing solutions. We then consider a scenario in which the linear
precoder can be formed by selecting and concatenating precoders from a given
finite codebook of precoding matrices, subject to rank and power constraints.
We show that under this scenario, the instantaneous rate maximization problem
is equivalent to a robust submodular maximization problem which is strongly NP
hard. We propose a deterministic approximation algorithm and show that it
yields a bicriteria approximation. For the weighted sum delay minimization
problem we propose a simple deterministic greedy algorithm, which at each step
entails approximately maximizing a submodular set function subject to multiple
knapsack constraints, and establish its performance guarantee.Comment: 37 pages, 8 figures, submitted to IEEE Trans. Signal Pro
Exploiting Hybrid Channel Information for Downlink Multi-User MIMO Scheduling
We investigate the downlink multi-user MIMO (MU-MIMO) scheduling problem in
the presence of imperfect Channel State Information at the transmitter (CSIT)
that comprises of coarse and current CSIT as well as finer but delayed CSIT.
This scheduling problem is characterized by an intricate `exploitation -
exploration tradeoff' between scheduling the users based on current CSIT for
immediate gains, and scheduling them to obtain finer albeit delayed CSIT and
potentially larger future gains. We solve this scheduling problem by
formulating a frame based joint scheduling and feedback approach, where in each
frame a policy is obtained as the solution to a Markov Decision Process. We
prove that our proposed approach can be made arbitrarily close to the optimal
and then demonstrate its significant gains over conventional MU-MIMO
scheduling.Comment: Expanded version: Accepted WiOpt 201
Augmented Reality Marketing-Impact on Intrinsic Motivation and Optimal User Experiences
Augmented reality marketing (AR marketing) has emerged as a transformative tool with the promise of a captivating user experience through the use of technology. Through the lens of flow theory, this study examines and seeks to understand how AR marketing triggers intrinsic motivation and fosters optimal user experiences. Based on the concept of flow theory which elucidates the psychological state of deep engagement and enjoyment, this research-in-progress proposes to examine how AR marketing campaigns can cultivate flow experiences to enhance attitudes towards both, the advertisement and the brand. This research-in-progress will adopt a mixed-methods approach involving quantitative surveys and qualitative analyses, to explore the interplay between flow experiences, attitudes, and user engagement in AR marketing contexts. By examining key components of flow theory, such as clear goals, immediate feedback, and balance between skill and challenge, the research aims to identify strategies for designing AR marketing experiences that facilitate flow states and subsequently influence attitudes towards the advertisement and the brand. The findings of the study are expected to have significant implications for marketing and technology academicians and practitioners. Additionally, the findings will guide industry practitioners in leveraging AR technology to create immersive and impactful brand experiences, ultimately fostering positive attitudes and stronger consumer relationships in an increasingly digital landscape
Multi-user multiple input multiple output (MIMO) communication with distributed antenna systems in wireless networks
Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile
Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards
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