윤지현 Jihyeon Yun

이승현 Seunghyun Lee

채준호 Junho Chae

김국진 Kookjin Kim

조해성 Haesung Jo

박대현 Daehyun Park

배따뜻한빛 Hanbit Bae

강선정 Sunjung Kang

Abylay Satybaldy

김민호 Minho Kim

지병규 Byoungkyu Ji

홍윤식 Yoonsik Hong

김유라 Yoora Kim

D. Manjunath

류지호 Jiho Ryu

The RAIN Lab conducts cutting-edge research focused on artificial intelligence (AI), machine learning (ML), and networks. Modern systems are characterized by fragmented and decentralized information, making it essential to collect and analyze the necessary data swiftly and accurately within complex network structures. Our lab focuses on developing Network for AI services to efficiently utilize such decentralized systems, while also exploring various approaches to enhance the efficiency and performance of learning in the network, developing mobile-friendly AI/ML techniques.

Inter-Satellite Link Technologies and Applications in Low Earth Orbit Satellite Networks

Economic and Strategic Perspectives on CDN Pricing: A Comprehensive Review

HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression

Demo: Feedback-Free Adaptive Multimedia Compression at Wireless Edge

Multi-Agent Adaptive Frequency Block Selection of LEO Satellites for Interference Avoidance

Haematologic Data Improves Long-Term Prediction Accuracy of Artificial Intelligence Models for Temporomandibular Disorders

Hierarchical Learning for Interference Management in Multi-user LEO Satellite Networks

Remote Vehicle Tracking with Location-dependent Communication Cost

Distributed Mobile Computing for Deep Learning Applications

Comparison of Decentralized and Centralized Update Paradigms for Distributed Remote Estimation

Remote Estimation for Dynamic IoT Sources under Sublinear Communication Costs

B-hop: Time-Domain Adjustment of BLE Frequency Hopping Against Wi-Fi Beacon Interference

AutoGAN-DSP: Stabilizing GAN Architecture Search with Deterministic Score Predictors

Toward Scalable and Efficient Visual Data Transmission in 6G Networks

CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

Online Service Request Duplicating for Vehicular Applications

Two-Side Pricing in the Content Distributed Network under Traffic Dynamics

Dynamic Downlink Interference Management in LEO Satellite Networks without Direct Communications

Agnostic Architecture for Heterogeneous Multi-Environment Reinforcement Learning

On the Economics Effects of CDN-Mediated Delivery on Content Providers

Downlink Spectrum Sharing of Heterogeneous Communication Systems in LEO Satellite Networks

A Learning-based Distributed Algorithm for Scheduling in Multi-hop Wireless Networks

Prediction of COVID-19 Infection Spread through Agent-based Simulation

Index-based Update Policy for Minimizing Information Mismatch with Markovian Sources

On the Economics of Network Interconnections and its Impact on Net Neutrality

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Sequencing Universal Quantum Gates for Arbitrary 2-Qubit Computations

Remote Tracking of Dynamic Sources under Sublinear Communication Costs

Comparison of Decentralized and Centralized Update Paradigms for Remote Tracking of Distributed Dynamic Sources

BLESS: BLE-aided Swift Wi-Fi Scanning in Multi-protocol IoT Networks

On the CDN Pricing Strategies in the Internet Traffic Delivery Chain

Tree Sampling for Detection of Information Source in Densely Connected Networks

Optimal CSMA scheduling with dual access probability for wireless networks

Information Update for Multi-source Remote Estimation in Real-time Monitoring Networks

BeaconRider: Opportunistic Sharing of Beacon Air-Time in Densely Deployed WLANs

SplitScan: Sharing Wi-Fi Scan Information through Bluetooth Low Energy

On the Economics of Network Interconnections and Net Neutrality

Wireless Scheduling for Information Freshness and Synchrony: Drift-based Design and Heavy-Traffic Analysis

Resource Sharing in Dual-stack Devices: Opportunistic Bluetooth Transmissions in WLAN Busy Periods

Pricing and Revenue Sharing between ISPs under Content Sharing

Pricing for Past Channel State Information in Multi-Channel Cognitive Radio Networks

Low-Delay Broadband Satellite Communications with High-Altitude Unmanned Aerial Vehicles

ORGMA: Reliable Opportunistic Routing with Gradient Forwarding for MANETs

Optimal Real-time Monitoring of an Information Source under Communication Costs

Content Sponsoring with Inter-ISP Transit Cost

Low-complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Special Issue on V2X Communications and Networks

Joint Scheduling of Data Transmission and Wireless Power Transfer in Multi-Channel Device-to-Device Networks

Combinatorial Multi-Armed Bandits in Cognitive Radio Networks: A Brief Overview

A Novel Coupled Queueing Model to Control Traffic via QoS-Aware Collision Pricing in Cognitive Radio Networks

Resource Allocation in Full-Duplex OFDMA Networks: Approaches for Full and Limited CSIs

Receiver-side TCP Countermeasure to Bufferbloat in Wireless Access Networks

Distributed Greedy Approximation to Maximum Weighted Independent Set for Scheduling with Fading Channels

A Reliable and Scalable Broadcast Protocol for Wireless Multi-hop Networks using Subcarrier-level Tone-signals

Queue-Affectance-based Scheduling in Multi-hop Wireless Networks under SINR Interference Constraints

Understanding the capacity region of the greedy maximal scheduling algorithm in multihop wireless networks

Performance of random access scheduling schemes in multi-hop wireless networks

Delay-based back-pressure scheduling in multihop wireless networks

Joint subcarrier assignment and power allocation in full-duplex OFDMA networks

Wireless scheduling for information freshness and synchrony: Drift-based design and heavy-traffic analysis

Finite-horizon energy allocation and routing scheme in rechargeable sensor networks

Joint congestion control and distributed scheduling for throughput guarantees in wireless networks

Longest-queue-first scheduling under SINR interference model

Challenges for efficient and seamless space-terrestrial heterogeneous networks

A simple asymptotically optimal joint energy allocation and routing scheme in rechargeable sensor networks

Delay performance of scheduling with data aggregation in wireless sensor networks

Greedy maximal matching: Performance limits for arbitrary network graphs under the node-exclusive interference model

Local greedy approximation for scheduling in multihop wireless networks

Distributed greedy approximation to maximum weighted independent set for scheduling with fading channels

Throughput-optimal scheduling in multihop wireless networks without per-flow information

Optimal real-time monitoring of an information source under communication costs

Low-delay broadband satellite communications with high-altitude unmanned aerial vehicles

A local greedy scheduling scheme with provable performance guarantee

Performance limits of greedy maximal matching in multi-hop wireless networks

Active queue management algorithm considering queue and load states

Ness Shroff

Saewoong Bahk (박세웅)

Xiaojun Lin

Jihyeon Yun

Jihwan Choi

Atilla Eryilmaz

Sunjung kang

Taegun An

Bo Ji

Gaurav Sharma

Changwon Nam

Ravi R Mazumdar

Jiho Ryu

Daeho Kang

Long Bao Le

Eytan Modiano

Dong Zheng

Hyung-Sin Kim

Taekyoung (Ted) Kwon

YANGHEE CHOI

Changhee Joo

In this paper, we characterize the performance of an important class of scheduling schemes, called greedy maximal scheduling (GMS), for multihop wireless networks. While a lower bound on the throughput performance of GMS has been well known, empirical observations suggest that it is quite loose and that the performance of GMS is often close to optimal. In this paper, we provide a number of new analytic results characterizing the performance limits of GMS. We first provide an equivalent characterization of the efficiency ratio of GMS through a topological property called the local-pooling factor of the network graph. We then develop an iterative procedure to estimate the local-pooling factor under a large class of network topologies and interference models. We use these results to study the worst-case efficiency ratio of GMS on two classes of network topologies. We show how these results can be applied to tree …

The scheduling problem in multi-hop wireless networks has been extensively investigated. Although throughput optimal scheduling solutions have been developed in the literature, they are unsuitable for multi-hop wireless systems because they are usually centralized and have very high complexity. In this paper, we develop a random-access based scheduling scheme that utilizes local information. The important features of this scheme include constant-time complexity, distributed operations, and a provable performance guarantee. Analytical results show that it guarantees a larger fraction of the optimal throughput performance than the state-of-the-art. Through simulations with both single-hop and multi-hop traffics, we observe that the scheme provides high throughput, close to that of a well-known highly efficient centralized greedy solution called the greedy maximal scheduler.

Scheduling is a critical and challenging resource allocation mechanism for multihop wireless networks. It is well known that scheduling schemes that favor links with larger queue length can achieve high throughput performance. However, these queue-length-based schemes could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets belonging to some flows may be excessively delayed due to lack of subsequent packet arrivals. Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. However, characterizing throughput optimality of these delay-based schemes has largely been an open problem in multihop wireless networks (except in limited cases where the traffic is single-hop.) In this paper, we investigate delay-based scheduling schemes for multihop traffic …

Recent advances in the physical layer have demonstrated the feasibility of in-band wireless full-duplex which enables a node to transmit and receive simultaneously on the same frequency band. While the full-duplex operation can ideally double the spectral efficiency, the network-level gain of full-duplex in large-scale networks remains unclear due to the complicated resource allocation in multi-carrier and multi-user environments. In this paper, we consider a single-cell full-duplex OFDMA network which consists of one full-duplex base station (BS) and multiple full-duplex mobile nodes. Our goal is to maximize the sum-rate performance by jointly optimizing subcarrier assignment and power allocation considering the characteristics of full-duplex transmissions. We develop an iterative solution that achieves local Pareto optimality in typical scenarios. Through extensive simulations, we demonstrate that our solution …

We consider the problem of scheduling in wireless networks with the aim of maintaining up-to-date and synchronized (also called, aligned) information at the receiver across multiple flows. This is in contrast to the more conventional approach of scheduling for optimizing long-term performance metrics such as throughput, fairness, or average delay. Maintaining the age of information at a low and roughly equal level is particularly important for distributed cyber-physical systems, in which the effectiveness of the control decisions depends critically on the freshness and synchrony of information from multiple sources/sensors. In this paper, we first expose the weakness of several popular MaxWeight scheduling solutions that utilize queue-length, delay, and age information as their weights. Then, we develop a novel age-based scheduler that combines age with the interarrival times of incoming packets in its decisions …

In this paper, we investigate the problem of maximizing the throughput over a finite-horizon time period for a sensor network with energy replenishment. The finite-horizon problem is important and challenging because it necessitates optimizing metrics over the short term rather than metrics that are averaged over a long period of time. Unlike the infinite-horizon problem, the fact that inefficiencies cannot be made to vanish to infinitesimally small values, means that the finite-horizon problem requires more delicate control. The finite-horizon throughput optimization problem can be formulated as a convex optimization problem, but turns out to be highly complex. The complexity is brought about by the “time coupling property,” which implies that current decisions can influence future performance. To address this problem, we employ a three-step approach. First, we focus on the throughput maximization problem for a …

We consider the problem of throughput-optimal cross-layer design of wireless networks. We propose a joint congestion control and scheduling algorithm that achieves a fraction 1/ d I ( G ) of the capacity region, where d I ( G ) depends on certain structural properties of the underlying connectivity graph G of the wireless network, and also on the type of interference constraints. For a wide range of wireless networks, d I ( G ) can be upper bounded by a constant, independent of the number of nodes in the network. The scheduling element of our algorithm is the maximal scheduling policy. Although this scheduling policy has been considered in several previous works, the challenges underlying its practical implementation in a fully distributed manner while accounting for necessary message exchanges have not been addressed in the literature. In this article, we propose two algorithms for the distributed implementation of the …

We investigate the performance of longest-queue-first (LQF) scheduling (i.e., greedy maximal scheduling) for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless interference more precisely than binary interference models. By employing the ρ-local pooling technique, we show that LQF scheduling achieves zero throughput in the worst case. We then propose a novel technique to localize interference which enables us to decentralize the LQF scheduling while preventing it from having vanishing throughput in all network topologies. We characterize the maximum throughput region under interference localization and present a distributed LQF scheduling algorithm. Finally, we present numerical results to illustrate the usefulness and to validate the theory developed in the paper.

By interconnecting satellites in the sky with ground stations on earth, space-terrestrial networks can extend the coverage area and increase the throughput for both commercial and nonprofit applications. To realize the seamless and economic sky-to-earth networks, we still have to advance the technologies that bridge different types of channels, and find killer-applications that are essential for our life. In this article we review the current state-of-the-art technologies of multibeam satellites and relaying as fundamental cornerstones, and then address the issues of low-latency random access, mobility support, and multipath protocols for realizing a seamless heterogeneous network. We then illustrate two impactful applications: for emergency communication as a near-future and non-profit application, and for simultaneous transmission of information and power as a long-term application.

In this paper, we investigate the utility maximization problem for a sensor network with energy replenishment. Each sensor node consumes energy in its battery to generate and deliver data to its destination via multihop communications. Although the battery can be replenished from renewable energy sources, the energy allocation should be carefully designed in order to maximize system performance, especially when the replenishment profile is unknown in advance. In this paper, we address the joint problem of energy allocation and routing to maximize the total system utility, without prior knowledge of the replenishment profile. We first characterize optimal throughput of a single node under general replenishment profile and extend our idea to the multihop network case. After characterizing the optimal network utility with an upper bound, we develop a low-complexity online solution that achieves asymptotic …

In-network aggregation has become a promising technique for improving the energy efficiency of wireless sensor networks. Aggregating data at various nodes in the network results in a reduction in the amount of bits transmitted over the network, and hence, saves energy. In this paper, we focus on another important aspect of aggregation, i.e., delay performance. In conjunction with link scheduling, in-network aggregation can reduce the delay by lessening the demands for wireless resources and thus expediting data transmissions. We formulate the problem that minimizes the sum delay of sensed data, and analyze the performance of optimal scheduling with in-network aggregation in tree networks under the node-exclusive interference model. We provide a system wide lower bound on the delay and use it as a benchmark for evaluating different scheduling policies. We numerically evaluate the performance of …

Greedy maximal matching (GMM) is an important scheduling scheme for multi-hop wireless networks. It is computationally simple, and has often been numerically shown to achieve throughput that is close to optimal. However, to date the performance limits of GMM have not been well understood. In particular, although a lower bound on its performance has been well known, this bound has been empirically found to be quite loose. In this paper, we focus on the well-established node-exclusive interference model and provide new analytical results that characterize the performance of GMM through a topological notion called the local-pooling factor . We show that for a given network graph with single-hop traffic, the efficiency ratio of GMM (i.e., the worst-case ratio of the throughput of GMM to that of the optimal) is equal to its local-pooling factor. Further, we estimate the local-pooling factor for arbitrary network graphs …

In recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multihop wireless networks. A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because its empirically observed performance is close to optimal in a variety of network settings. However, its distributed realization requires high complexity, which becomes a major obstacle for practical implementation. In this paper, we develop simple distributed greedy algorithms for scheduling in multihop wireless networks. We reduce the complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulation results show that the new algorithms approximate the performance of GMS, and outperform the state-of-the-art distributed scheduling policies.

It has been known that scheduling algorithms designed to achieve throughput optimality and good delay performance often require solving the Maximum Weighted Independent Set (MWIS) problem. However, under most realistic network settings, the MWIS problem is known to be NP-hard. In non-fading environments, low-complexity scheduling algorithms have been provided that converge either to the MWIS solution in time or to a solution that achieves at least a provable fraction of the achievable throughput. However, in more practical systems the channel conditions can vary at faster time-scales than convergence occurs in these lower-complexity algorithms. Hence, these algorithms cannot take advantage of opportunistic gains, and may no longer result in achieving good performance. In this paper, we propose a low-complexity scheduling scheme that performs provably well under fading channels and is …

In this paper, we consider the problem of link scheduling in multihop wireless networks under general interference constraints. Our goal is to design scheduling schemes that do not use per-flow or per-destination information, maintain a single data queue for each link, and exploit only local information, while guaranteeing throughput optimality. Although the celebrated back-pressure algorithm maximizes throughput, it requires per-flow or per-destination information. It is usually difficult to obtain and maintain this type of information, especially in large networks, where there are numerous flows. Also, the back-pressure algorithm maintains a complex data structure at each node, keeps exchanging queue-length information among neighboring nodes, and commonly results in poor delay performance. In this paper, we propose scheduling schemes that can circumvent these drawbacks and guarantee throughput …

In this work, we study the real-time monitoring of an information source over a communication link with transmission cost. As the most basic nontrivial setting, we consider a single source governed by a random walk process and design the scheduling policy that minimizes the cost sum of the transmission costs and the inaccuracy of the state information at the monitoring center. This problem closely relates to the emerging area of Age-of-Information (AoI) scheduling that focuses on the timely transfer of fresh information over resource-constrained networks. When the information updates are controlled by the scheduler, we can incorporate the internal dynamics of the information source into the transmission decisions. For a general cost function of estimation errors, we first establish the optimality of a threshold rule for minimizing a weighted sum of the transmission cost and the tracking inaccuracy for the information …

The large number of users and the long propagation delay of communication satellites make it difficult to deploy a random access scheme that performs well in terrestrial networks. The aim of this paper is to demonstrate that a relay architecture with unmanned aerial vehicles (UAV) between the ground and satellites can facilitate satellite access with a low overhead. We consider a TDMA-based two-level relay structure, in which users transmit data to UAVs through a random access scheme, and the UAVs forward it to the satellite with either a coordinated or uncoordinated access scheme. We show that the coordinated access for the UAV- to-satellite links can substantially improve the throughput and delay performance. We conclude that a proper scheme for resource allocation between ground-to-UAVs and UAVs-to-satellite is critical to optimize the relay network, which includes the UAV altitude, and should be based …

In recent years, there have been many efforts to develop low-complexity scheduling schemes that can approximate optimal performance in multi-hop wireless networks. A centralized sub-optimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because it achieves high throughput. However, its distributed realization requires O(|V|) complexity, which becomes a major obstacle for practical implementation, where |V| is the number of nodes in the network. In this paper, we develop a simple distributed scheduling policy for multi-hop wireless networks. It achieves O(log |V|) complexity by relaxing the global ordering requirement of GMS. Instead, it deterministically schedules only links that have the largest queue length among their local neighbors. We show that, it still guarantees a fraction of the optimal performance, which is no smaller than GMS. We also further improve its performance …

In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multi-hop wireless networks. For simplicity, we focus on the well-established node-exclusive interference model, although many of the stated results can be readily extended to more general interference models. The study of the performance of GMM is intriguing because although a lower bound on its performance is well known, empirical observations suggest that this bound is quite loose, and that the performance of GMM is often close to optimal. In fact, recent results have shown that GMM achieves optimal performance under certain conditions. In this paper, we provide new analytic results that characterize the performance of GMM through the topological properties of the underlying graphs. To that end, we generalize a recently developed topological notion called the local …

In this paper, we propose a new AQM algorithm that considers both the average queue length and the estimated packet arrival rate together in order to detect incipient congestion. The proposed algorithm predicts the average queue length and controls it to maintain a certain reference value to achieve high link utilization and low queueing delay. It also has low complexity and easy configuration. Simulation results validate the performance of the proposed algorithm under various environments and show its advantage over other existing AQM algorithms.

RAIN Lab.

교수님 소개

주창희

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Inter-Satellite Link Technologies and Applications in Low Earth Orbit Satellite Networks

HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression

Inter-Satellite Link Technologies and Applications in Low Earth Orbit Satellite Networks

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

주창희

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Inter-Satellite Link Technologies and Applications in Low Earth Orbit Satellite Networks

HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression

Inter-Satellite Link Technologies and Applications in Low Earth Orbit Satellite Networks

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집