108 research outputs found
Coordinated Beamforming with Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control
In this paper, coordinated beamforming based on relaxed zero forcing (RZF)
for K transmitter-receiver pair multiple-input single-output (MISO) and
multiple-input multiple-output (MIMO) interference channels is considered. In
the RZF coordinated beamforming, conventional zero-forcing interference leakage
constraints are relaxed so that some predetermined interference leakage to
undesired receivers is allowed in order to increase the beam design space for
larger rates than those of the zero-forcing (ZF) scheme or to make beam design
feasible when ZF is impossible. In the MISO case, it is shown that the
rate-maximizing beam vector under the RZF framework for a given set of
interference leakage levels can be obtained by sequential orthogonal projection
combining (SOPC). Based on this, exact and approximate closed-form solutions
are provided in two-user and three-user cases, respectively, and an efficient
beam design algorithm for RZF coordinated beamforming is provided in general
cases. Furthermore, the rate control problem under the RZF framework is
considered. A centralized approach and a distributed heuristic approach are
proposed to control the position of the designed rate-tuple in the achievable
rate region. Finally, the RZF framework is extended to MIMO interference
channels by deriving a new lower bound on the rate of each user.Comment: Lemma 1 proof corrected; a new SOPC algorithm invented; K > N case
considere
Outage Probability and Outage-Based Robust Beamforming for MIMO Interference Channels with Imperfect Channel State Information
In this paper, the outage probability and outage-based beam design for
multiple-input multiple-output (MIMO) interference channels are considered.
First, closed-form expressions for the outage probability in MIMO interference
channels are derived under the assumption of Gaussian-distributed channel state
information (CSI) error, and the asymptotic behavior of the outage probability
as a function of several system parameters is examined by using the Chernoff
bound. It is shown that the outage probability decreases exponentially with
respect to the quality of CSI measured by the inverse of the mean square error
of CSI. Second, based on the derived outage probability expressions, an
iterative beam design algorithm for maximizing the sum outage rate is proposed.
Numerical results show that the proposed beam design algorithm yields better
sum outage rate performance than conventional algorithms such as interference
alignment developed under the assumption of perfect CSI.Comment: 41 pages, 14 figures. accepted to IEEE Transactions on Wireless
Communication
ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions
Unfamiliar decisions -- decisions where people lack adequate domain knowledge
or expertise -- specifically increase the complexity and uncertainty of the
process of searching for, understanding, and making decisions with online
information. Through our formative study (n=14), we observed users' challenges
in accessing diverse perspectives, identifying relevant information, and
deciding the right moment to make the final decision. We present ChoiceMates, a
system that enables conversations with a dynamic set of LLM-powered agents for
a holistic domain understanding and efficient discovery and management of
information to make decisions. Agents, as opinionated personas, flexibly join
the conversation, not only providing responses but also conversing among
themselves to elicit each agent's preferences. Our between-subjects study
(n=36) comparing ChoiceMates to conventional web search and single-agent showed
that ChoiceMates was more helpful in discovering, diving deeper, and managing
information compared to Web with higher confidence. We also describe how
participants utilized multi-agent conversations in their decision-making
process
Determinants of Competitive Advantage for Sport Firms: Using Public Big Data in Korea
This study examines the determinants of competitive advantage with respect to economic performance of sport firms. Logit regressions estimated dependent variables of economic performance measures based on sales per capita of firms. Determinants of competitive advantage were estimated by efficiency indicators, organization characteristic indicators, and industry classification indicators. Increase in efficiency was a significant determinant of competitive advantage as well as organizational type, size of human resource, diversification of products, and sales growth rate. Operationalizing competitive advantage as outperforming the market average and better than the top 10%, the logit regression model provides means for sport firms to analyze industry data to evaluate their own performance. In particular, including efficiency estimates showed practical significance for market analysis
AN EFFICIENT PARAMETERIZATION FOR PARETO-OPTIMAL BEAMFORMERS FOR K-USER MIMO INTERFERENCE CHANNELS
ABSTRACT In this paper, Pareto-optimal beamforming in the K-pair Gaussian multiple-input multiple-output (MIMO) interference channel is considered. Under the assumption of Gaussian signaling at transmitters and single-user decoding at receivers, a necessary condition for any transmit signal covariance matrix to achieve a Pareto boundary point of the achievable rate region is derived. Based on the necessary condition for Pareto-optimality, an efficient parameterization for Pareto-optimal transmit signal covariance matrices is obtained. The obtained parameter space is given by the product manifold of a Stiefel manifold and a subset of a hyperplane, which is a low dimensional embedded submanifold of the original high dimensional beam search space. The new parameterization enables us to devise very efficient beam design algorithms for the K-pair MIMO interference channel
Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level
Large language models (LLMs) with chat-based capabilities, such as ChatGPT,
are widely used in various workflows. However, due to a limited understanding
of these large-scale models, users struggle to use this technology and
experience different kinds of dissatisfaction. Researchers have introduced
several methods such as prompt engineering to improve model responses. However,
they focus on crafting one prompt, and little has been investigated on how to
deal with the dissatisfaction the user encountered during the conversation.
Therefore, with ChatGPT as the case study, we examine end users'
dissatisfaction along with their strategies to address the dissatisfaction.
After organizing users' dissatisfaction with LLM into seven categories based on
a literature review, we collected 511 instances of dissatisfactory ChatGPT
responses from 107 users and their detailed recollections of dissatisfied
experiences, which we release as a publicly accessible dataset. Our analysis
reveals that users most frequently experience dissatisfaction when ChatGPT
fails to grasp their intentions, while they rate the severity of
dissatisfaction the highest with dissatisfaction related to accuracy. We also
identified four tactics users employ to address their dissatisfaction and their
effectiveness. We found that users often do not use any tactics to address
their dissatisfaction, and even when using tactics, 72% of dissatisfaction
remained unresolved. Moreover, we found that users with low knowledge regarding
LLMs tend to face more dissatisfaction on accuracy while they often put minimal
effort in addressing dissatisfaction. Based on these findings, we propose
design implications for minimizing user dissatisfaction and enhancing the
usability of chat-based LLM services
CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI
Graphic designers often get inspiration through the recombination of
references. Our formative study (N=6) reveals that graphic designers focus on
conceptual keywords during this process, and want support for discovering the
keywords, expanding them, and exploring diverse recombination options of them,
while still having room for designers' creativity. We propose CreativeConnect,
a system with generative AI pipelines that helps users discover useful elements
from the reference image using keywords, recommends relevant keywords,
generates diverse recombination options with user-selected keywords, and shows
recombinations as sketches with text descriptions. Our user study (N=16) showed
that CreativeConnect helped users discover keywords from the reference and
generate multiple ideas based on them, ultimately helping users produce more
design ideas with higher self-reported creativity compared to the baseline
system without generative pipelines. While CreativeConnect was shown effective
in ideation, we discussed how CreativeConnect can be extended to support other
types of tasks in creativity support
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