IEEE 802.11 기반 Enterprise 무선 LAN을 위한 자원 관리 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 전화숙.IEEE 802.11이 무선 LAN (wireless local area network, WLAN)의 실질적인 표준이 됨에 따라 수 많은 엑세스 포인트(access points, APs)가 배치되었고, 그 결과 WLAN 밀집 환경이 조성되었다. 이러한 환경에서는, 이웃한 AP들에 동일한 채널을 할당하는 문제를 피할 수 없으며, 이는 해당 AP들이 같은 채널을 공유하게 하고 그로 인한 간섭을 야기한다. 간섭으로 인한 성능 저하를 줄이기 위해 채널 할당(channelization) 기법이 중요하다. 또한, 한 조직이 특정 지역에 밀집 배치된 AP들을 관리한다면 특정 사용자를 서비스할 수 있는 AP가 여럿일 수 있다. 이 경우, 사용자 접속(user association, UA) 기법이 준정적(quasi-static) 환경과 차량 환경 모두에서 네트워크 성능에 큰 영향을 미친다. 본 논문에서는 밀집 배치된 WLAN 환경에서 와이파이(WiFi) 성능 향상을 위해 채널 할당 기법을 제안한다. 먼저, 제안한 기법에서는 각각의 AP에 채널을 할당하기 위해 간섭 그래프(interference graph)를 이용하며 채널 결합(channel bonding)을 고려한다. 다음으로, 주어진 채널 결합 결과를 기반으로 해당 AP가 동적 채널 결합을 지원하는지 여부에 따라 주 채널(primary channel)을 결정한다. 한편, 준정적 환경과 차량 환경에서의 UA 문제는 다소 차이가 있다. 따라서 본 논문에서는 각각의 환경에 따라 서로 다른 UA 기법을 설계하였다. 준정적 환경에서의 UA 기법은 멀티캐스트 전송, 다중 사용자 MIMO (multi-user multiple input multiple output), 그리고 AP 수면과 같은 다양한 기술과 함께 AP간의 부하 분산(load balancing)과 에너지 절약을 고려한다. 제안하는 기법에서 UA 문제는 다목적함수 최적화 문제로 정식화하였고 그 해를 구하였다. 차량 환경에서의 UA 기법은 핸드오버(handover, HO) 스케줄 문제로 귀결된다. 본 논문에서는 도로의 지형을 고려하여 사용자가 접속할 AP를 결정하는 HO 스케줄 기법을 제안한다. 사용자는 단지 다음 AP로 연결을 맺을 시기만 결정하면 되기 때문에, 차량 환경에서의 매우 빠르고 효율적인 HO 기법을 구현할 수 있다. 이를 위해, 그래프 모델링 기법(graph modeling technique)을 활용하여 도로를 따라 배치된 AP사이의 관계를 표현한다. 현실적인 시나리오를 위해 직선 구간, 우회 구간, 교차로, 그리고 유턴 구간 등을 포함하는 복잡한 도로 구조를 고려한다. 도로 구조를 고려하여 각 사용자의 이동 경로를 예측하고, 그에 기반하여 각 사용자 별 HO의 목적 AP 집합을 선택한다. 제안하는 HO 스케줄 기법의 설계 목적은 HO 지연 시간의 합을 최소화하고 각 AP에서 해당 채널을 사용하려는 사용자 수를 줄이면서 WiFi 연결 시간을 최대화하는 것이다. 마지막으로, 본 논문에서는 준정적 환경에서 제안한 채널 할당 기법과 UA 기법의 현실성을 증명하기 위한 시험대(testbed)를 구성하였다. 또한, 광범위한 시뮬레이션을 통해 준정적 환경과 차량 환경에서 제안한 기법들과 기존의 기법들의 성능을 비교하였다.As the IEEE 802.11 (WiFi) becomes the defacto global standard for wireless local area network (WLAN), a huge number of WiFi access points (APs) are deployed. This condition leads to a densely deployed WLANs. In such environment, the conflicting channel allocation between the neighboring access points (APs) is unavoidable, which causes the channel sharing and interference between APs. Thus, the channel allocation (channelization) scheme has a critical role to tackle this issue. In addition, when densely-deployed APs covering a certain area are managed by a single organization, there can exist multiple candidate APs for serving a user. In this case, the user association (UA), i.e., the selection of serving AP, holds a key role in the network performance both in quasi-static and vehicular environments. To improve the performance of WiFi in a densely deployed WLANs environment, we propose a channelization scheme. The proposed channelization scheme utilizes the interference graph to assign the channel for each AP and considers channel bonding. Then, given the channel bonding assignment, the primary channel location for each AP is determined by observing whether the AP supports the static or dynamic channel bonding. Meanwhile, the UA problem in the quasi-static and vehicular environments are slightly different. Thus, we devise UA schemes both for quasi-static and vehicular environments. The UA schemes for quasi-static environment takes account the load balancing among APs and energy saving, considering various techniques for performance improvement, such as multicast transmission, multi-user MIMO, and AP sleeping, together. Then, we formulate the problem into a multi-objective optimization and get the solution as the UA scheme. On the other hand, the UA scheme in the vehicular environment is realized through handover (HO) scheduling mechanism. Specifically, we propose a HO scheduling scheme running on a server, which determines the AP to which a user will be handed over, considering the road topology. Since a user only needs to decide when to initiate the connection to the next AP, a very fast and efficient HO in the vehicular environment can be realized. For this purpose, we utilize the graph modeling technique to map the relation between APs within the road. We consider a practical scenario where the structure of the road is complex, which includes straight, curve, intersection, and u-turn area. Then, the set of target APs for HO are selected for each user moving on a particular road based-on its moving path which is predicted considering the road topology. The design objective of the proposed HO scheduling is to maximize the connection time on WiFi while minimizing the total HO latency and reducing the number of users which contend for the channel within an AP. Finally, we develop a WLAN testbed to demonstrate the practicality and feasibility of the proposed channelization and UA scheme in a quasi-static environment. Furthermore, through extensive simulations, we compare the performance of the proposed schemes with the existing schemes both in quasi-static and vehicular environments.1 Introduction 1.1 Background and Motivation 1.2 Related Works 1.3 Research Scope and Proposed Schemes 1.3.1 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding 1.3.2 User Association for Load Balancing and Energy Saving in Enterprise WLAN 1.3.3 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks 1.4 Organization 2 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding 2.1 System Model 2.2 Channel Sharing and Bonding 2.2.1 Interference between APs 2.2.2 Channel Sharing 2.2.3 Channel Bonding 2.3 Channelization Scheme 2.3.1 Building Interference Graph 2.3.2 Channel Allocation 2.3.3 Primary Channel Selection 2.4 Implementation 3 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs 3.1 System Model 3.1.1 IEEE 802.11 ESS-based Enterprise WLAN 3.1.2 Downlink Achievable Rate for MU-MIMO Groups 3.1.3 Candidate MU-MIMO Groups 3.2 User Association Problem 3.2.1 Factors of UA Objective 3.2.2 Problem Formulation 3.3 User Association Scheme 3.3.1 Equivalent Linear Problem 3.3.2 Solution Algorithm 3.3.3 Computational Complexity (Execution Time) 3.4 Implementation 4 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks 4.1 System Model 4.2 Graph-Based Modeling 4.2.1 Division of Road Portion into Road Segments 4.2.2 Relation between PoAs on a Road Segment 4.2.3 Directed Graph Representation 4.3 Handover Scheduling Problem 4.3.1 Problem Formulation 4.3.2 Weight of Edge 4.3.3 HO Scheduling Algorithm 4.4 Handover Scheduling Operation 4.4.1 HO Schedule Delivery 4.4.2 HO Triggering and Execution 4.4.3 Communication Overhead 5 Performance Evaluation 5.1 CentralizedChannelizationSchemeforWirelessLANsExploitingChannel Bonding 5.1.1 Experiment Settings 5.1.2 Comparison Schemes 5.1.3 Preliminary Experiment for Building Interference Graph 5.1.4 Experiment Results 5.2 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs 5.2.1 Performance Metrics 5.2.2 Experiment Settings 5.2.3 Experiment Results 5.2.4 Simulation Settings 5.2.5 Comparison Schemes 5.2.6 Simulation Results 5.2.7 Simulation for MU-MIMO System 5.3 A Graph-BasedHandover Scheduling for Heterogenous Vehicular Networks 5.3.1 Performance Metrics 5.3.2 Simulation Settings 5.3.3 Simulation Results 6 Conculsion Bibliography AcknowledgementsDocto

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