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 $10^6$ 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