• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1879-1888
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• 2015

This paper evaluates the adjacent channel coexistence issues between LAA(License Assisted Access) system and other system (e.g. Wi-Fi) system in 5 GHz unlicensed spectrum. LAA is a technology to achieve enhanced data rate by aggregating licensed and unlicensed spectrum using CA(Carrier Aggregation). The coexistence study is essential before deploying LTE in unlicensed spectrum to verify the impact of LTE on the existing system such as Wi-Fi including system in throughput and regulatory aspects. This paper evaluates and analyzes the RF requirements of LTE system using interference analysis of coexistence study when operating at adjacent frequency channel of the Wi-Fi system in order to minimize the impact of LTE system into Wi-Fi system.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.5
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• pp.17-23
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• 2016

LTE Licensed-Assisted Access(LAA)는 3GPP에서 진행되고 있는 비면허대역을 활용하여 LTE 시스템을 동작시키는 표준기술이다. LTE-LAA의 필요성과 표준의 진화 과정을 알아보고, Listen-Before-Talk(LBT) 등 핵심 표준기술들을 소개하였고, 특히, WiFi 등 기존의 비면허대역에서 동작하는 시스템과의 공정한 공존(fair coexistence)를 위해 어떠한 노력이 이루어졌는지를 중점적으로 살펴보았다. 마지막으로, 현재 3GPP 표준에서 논의되는 enhanced LAA를 통해 비면허대역을 활용하는 LTE 시스템 기술의 진화를 전망해 보았다.

• Journal of IKEEE
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• v.23 no.1
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• pp.91-98
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• 2019

LTE's extension for unlicensed spectrum called Licensed Assisted Access (LAA) is equipped with Listen-Before-Talk (LBT) designed similar with the backoff mechanism of Wi-Fi for coexistence. However, Wi-Fi's backoff mechanism has not evolved from its old design for compatibility with legacy devices, thus LAA's LBT is not efficient either in utilizing spectrum. If LAA operates with no Wi-Fi systems in proximity, it can run more efficient LBT. In this paper, we propose Narrowband Clear Channel Assessment (NCCA) for narrowband transmission. In NCCA, an LAA node performs LBT in either wide or each narrow bandwidth segment. This allows multiple LAA nodes to perform simultaneous transmissions in orthogonal bandwidth segments in the same time slot. We design four variants of NCCA implementation and model their performance using a mathematical model. The coexistence performance of NCCA with conventional wideband nodes and the accuracy of the model are shown via simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.783-788
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• 2016

An LTE enhancement for licensed-assisted access (LAA) to unlicensed spectrum has been studied recently and the first version of its design has been released in early 2016. As both 2.4 and 5GHz ISM bands have multiple channels and Wi-Fi devices already support multi-channel operation, LTE LAA has also been designed such that an eNB can access multiple channels at the same time. In this paper, we review several design variants of listen before talk (LBT) procedure for multi-channel transmission in downlink and perform simulation to investigate coexistence of Wi-Fi and LAA as well as the performance of both.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• Information and Communications Magazine
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• v.32 no.11
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• pp.58-64
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• 2015

본고에서는 최근 이동통신분야에서 많은 주목을 받고 있는 비면허대역(unlicensed-band)을 활용한 3GPP Long Term Evolution (3GPP-LTE) 시스템의 표준화 및 상용화 동향을 알아본다. 먼저 비면허대역과 관련 전파규정을 알아보고, LTE 시스템이 비면허대역을 활용할 수 있는 3가지 방법인 LTE-U, LTE-LAA, LTE-H를 각각 알아본다. 각각의 방식의 장단점과 표준화 방향 및 향후 LTE 발전 방향에 미치는 영향을 논의한다.

• Information and Communications Magazine
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• v.31 no.11
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• pp.73-78
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• 2014

본고에서는 현재 급증하고 있는 모바일 트래픽을 수용하기 위하여 제안된 LTE-U기술의 개념 및 특성, 요구사항 등을 살펴보고자 한다. 비면허 대역을 이용한 LTE-U 혹은 LTE-LAA 기술은 WiFi와 동일한 주파수 대역을 이용하기 때문에 두 방식간의 공존문제가 중요하게 다루어져야 한다. 또한 주파수 정책과 기술의 밀접한 관련성을 고려하여 다양한 LTE-U 기술 채용 시나리오와 후보 주파수 대역을 비교, 분석하여 5G 이동통신시스템 개발 및 연구에 있어서 비면허 대역을 이용한 기술 관련한 현황 및 시사점을 도출해보고자 한다.

• ETRI Journal
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• v.37 no.5
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• pp.845-855
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• 2015

To enable the coexistence of Licensed Assisted Access (LAA) and Wi-Fi in 5 GHz unlicensed bands, a new channel access mechanism is proposed. Accounting for the fairness between LAA and Wi-Fi, the proposed mechanism finds the optimal transmission time ratio by adaptively adjusting the transmission durations for LAA and Wi-Fi. In addition, we propose a new analytical model for the distributed coordination function of IEEE 802.11 through some modifications of conventional analytical models for saturation and non-saturation loads. By computing the activity ratio of Wi-Fi, the proposed analytical model is able to control the time ratio between LAA and Wi-Fi, which is required for practical implementation of the proposed access mechanism. Through numerical simulations, the proposed channel access mechanism is compared with conventional methods in terms of throughput and utility.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.208-211
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• 2016

Device-to-Device communication(D2D) enables devices in proximity to communicate directly without going through the network infrastructure. In particular, D2D communications in a cellular network can improve the spectral efficiency by allowing the reuse of cellular resources. However, it is not easy to maintain the channel quality of the D2D links and to protect the cellular links from the D2D interferences, since the resource allocations for the cellular users will change with time due to the time-varying nature of the cellular channels. To mitigate the performance degradation of D2D links, we propose to exploit unlicensed bands as auxiliary resources when the D2D links share the uplink cellular resources. The effectiveness of the proposed scheme is verified through simulations.

• Journal of Platform Technology
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• v.10 no.3
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• pp.51-59
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• 2022

Machine learning, which has recently innovatively developed, has become an important technology that can solve various optimization problems. In this paper, we introduce the latest research papers that solve the problem of channel sharing in heterogeneous networks using machine learning, analyze the characteristics of mainstream approaches, and present a guide to future research directions. Existing studies have generally adopted Q-learning since it supports fast learning both on online and offline environment. On the contrary, conventional studies have either not considered various coexistence scenarios or lacked consideration for the location of machine learning controllers that can have a significant impact on network performance. One of the powerful ways to overcome these disadvantages is to selectively use a machine learning algorithm according to changes in network environment based on the logical network architecture for machine learning proposed by ITU.