7,610 research outputs found
Resource Allocation with Reverse Pricing for Communication Networks
Reverse pricing has been recognized as an effective tool to handle demand
uncertainty in the travel industry (e.g., airlines and hotels). To investigate
its viability for communication networks, we study the practical limitations of
(operator-driven) time-dependent pricing that has been recently introduced,
taking into account demand uncertainty. Compared to (operator-driven)
time-dependent pricing, we show that the proposed pricing scheme can achieve
"triple-win" solutions: an increase in the total average revenue of the
operator; higher average resource utilization efficiency; and an increment in
the total average payoff of the users. Our findings provide a new outlook on
resource allocation, and design guidelines for adopting the reverse pricing
scheme.Comment: to appear in IEEE International Conference on Communications (ICC)
2016, Kuala Lumpur, Malaysia (6 pages, 3 figures
Sum-Rate Maximizing Cell Association via Dual-Connectivity
This paper proposes a dual-connectivity (DC) profile allocation algorithm, in
which a central macro base station (MBS) is underlaid with randomly scattered
small base stations (SBSs), operating on different carrier frequencies. We
introduce two dual-connectivity profiles and the differences among them. We
utilize the characteristics of dual-connectivity profiles and their applying
scenarios to reduce feasible combination set to consider. Algorithm analysis
and numerical results verify that our proposed algorithm achieve the optimal
algorithm's performance within 5 \% gap with quite low complexity up to
times.Comment: 10 pages, 5 figures, conferenc
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
Currently, the most widely used approach for speaker verification is the deep
speaker embedding learning. In this approach, we obtain a speaker embedding
vector by pooling single-scale features that are extracted from the last layer
of a speaker feature extractor. Multi-scale aggregation (MSA), which utilizes
multi-scale features from different layers of the feature extractor, has
recently been introduced and shows superior performance for variable-duration
utterances. To increase the robustness dealing with utterances of arbitrary
duration, this paper improves the MSA by using a feature pyramid module. The
module enhances speaker-discriminative information of features from multiple
layers via a top-down pathway and lateral connections. We extract speaker
embeddings using the enhanced features that contain rich speaker information
with different time scales. Experiments on the VoxCeleb dataset show that the
proposed module improves previous MSA methods with a smaller number of
parameters. It also achieves better performance than state-of-the-art
approaches for both short and long utterances.Comment: Accepted to Interspeech 202
Worst-case User Analysis in Poisson Voronoi Cells
In this letter, we focus on the performance of a worst-case mobile user (MU)
in the downlink cellular network. We derive the coverage probability and the
spectral efficiency of the worst-case MU using stochastic geometry. Through
analytical and numerical results, we draw out interesting insights that the
coverage probability and the spectral efficiency of the worst-case MU decrease
down to 23% and 19% of those of a typical MU, respectively. By applying a
coordinated scheduling (CS) scheme, we also investigate how much the
performance of the worst-case MU is improved.Comment: Accepted, IEEE Communications Letter
Asymmetric-valued Spectrum Auction and Competition in Wireless Broadband Services
We study bidding and pricing competition between two spiteful mobile network
operators (MNOs) with considering their existing spectrum holdings. Given
asymmetric-valued spectrum blocks are auctioned off to them via a first-price
sealed-bid auction, we investigate the interactions between two spiteful MNOs
and users as a three-stage dynamic game and characterize the dynamic game's
equilibria. We show an asymmetric pricing structure and different market share
between two spiteful MNOs. Perhaps counter-intuitively, our results show that
the MNO who acquires the less-valued spectrum block always lowers his service
price despite providing double-speed LTE service to users. We also show that
the MNO who acquires the high-valued spectrum block, despite charing a higher
price, still achieves more market share than the other MNO. We further show
that the competition between two MNOs leads to some loss of their revenues. By
investigating a cross-over point at which the MNOs' profits are switched, it
serves as the benchmark of practical auction designs
Extreme coefficients in Geographically Weighted Regression and their effects on mapping
This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.extreme coefficient, fixed and adaptive calibrations, geographically weighted regression, Mapping, Research Methods/ Statistical Methods,
Spatial Reasoning for Few-Shot Object Detection
Although modern object detectors rely heavily on a significant amount of
training data, humans can easily detect novel objects using a few training
examples. The mechanism of the human visual system is to interpret spatial
relationships among various objects and this process enables us to exploit
contextual information by considering the co-occurrence of objects. Thus, we
propose a spatial reasoning framework that detects novel objects with only a
few training examples in a context. We infer geometric relatedness between
novel and base RoIs (Region-of-Interests) to enhance the feature representation
of novel categories using an object detector well trained on base categories.
We employ a graph convolutional network as the RoIs and their relatedness are
defined as nodes and edges, respectively. Furthermore, we present spatial data
augmentation to overcome the few-shot environment where all objects and
bounding boxes in an image are resized randomly. Using the PASCAL VOC and MS
COCO datasets, we demonstrate that the proposed method significantly
outperforms the state-of-the-art methods and verify its efficacy through
extensive ablation studies.Comment: Pattern Recognition, Vol.120, 202
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