889 research outputs found

    Capacity Analysis of IEEE 802.11ah WLANs for M2M Communications

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    Focusing on the increasing market of the sensors and actuators networks, the IEEE 802.11ah Task Group is currently working on the standardization of a new amendment. This new amendment will operate at the sub-1GHz band, ensure transmission ranges up to 1 Km, data rates above 100 kbps and very low power operation. With IEEE 802.11ah, the WLANs will offer a solution for applications such as smart metering, plan automation, eHealth or surveillance. Moreover, thanks to a hierarchical signalling, the IEEE 802.11ah will be able to manage a higher number of stations (STAs) and improve the 802.11 Power Saving Mechanisms. In order to support a high number of STAs, two different signalling modes are proposed, TIM and Non-TIM Offset. In this paper we present a theoretical model to predict the maximum number of STAs supported by both modes depending on the traffic load and the data rate used. Moreover, the IEEE 802.11ah performance and energy consumption for both signalling modes and for different traffic patterns and data rates is evaluated. Results show that both modes achieve similar Packet Delivery Ratio values but the energy consumed with the TIM Offset is, in average, a 11.7% lower.Comment: Multiple Access Communications 201

    Framework for Content Distribution over Wireless LANs

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    Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive technology for Intent access. Due to the low-cost of chipsets and support for high data rates, Wi-Fi has become a universal solution for ever-increasing application space which includes, video streaming, content delivery, emergency communication, vehicular communication and Internet-of-Things (IoT). Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11 standard has been amended several times over the last two decades, to incorporate the requirement of future applications. The 802.11 based Wi-Fi networks are infrastructure networks in which devices communicate through an access point. However, in 2010, Wi-Fi Alliance has released a specification to standardize direct communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi Direct after 9 years of its release is still used for very basic services (connectivity, file transfer etc.), despite the potential to support a wide range of applications. The reason behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit its performance in dense networks. These include the issues related to topology design, such as non-optimal group formation, Group Owner selection problem, clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense networks where the topology changes frequently which directly affects the network performance. The dynamic nature of such networks challenges the operators to design and make optimum planifications. In this dissertation, we propose solutions to the aforementioned problems. We contributed to the existing Wi-Fi Direct technology by enhancing the group formation process. The proposed group formation scheme is backwards-compatible and incorporates role selection based on the device's capabilities to improve network performance. Optimum clustering scheme using mixed integer programming is proposed to design efficient topologies in fixed dense networks, which improves network throughput and reduces packet loss ratio. A novel architecture using Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive networks using machine-learning algorithms to predict the network changes ahead of time and self-configuring the network

    IEEE 802.11 ๊ธฐ๋ฐ˜ Enterprise ๋ฌด์„  LAN์„ ์œ„ํ•œ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์ „ํ™”์ˆ™.IEEE 802.11์ด ๋ฌด์„  LAN (wireless local area network, WLAN)์˜ ์‹ค์งˆ์ ์ธ ํ‘œ์ค€์ด ๋จ์— ๋”ฐ๋ผ ์ˆ˜ ๋งŽ์€ ์—‘์„ธ์Šค ํฌ์ธํŠธ(access points, APs)๊ฐ€ ๋ฐฐ์น˜๋˜์—ˆ๊ณ , ๊ทธ ๊ฒฐ๊ณผ WLAN ๋ฐ€์ง‘ ํ™˜๊ฒฝ์ด ์กฐ์„ฑ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ™˜๊ฒฝ์—์„œ๋Š”, ์ด์›ƒํ•œ AP๋“ค์— ๋™์ผํ•œ ์ฑ„๋„์„ ํ• ๋‹นํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ”ผํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, ์ด๋Š” ํ•ด๋‹น AP๋“ค์ด ๊ฐ™์€ ์ฑ„๋„์„ ๊ณต์œ ํ•˜๊ฒŒ ํ•˜๊ณ  ๊ทธ๋กœ ์ธํ•œ ๊ฐ„์„ญ์„ ์•ผ๊ธฐํ•œ๋‹ค. ๊ฐ„์„ญ์œผ๋กœ ์ธํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ฑ„๋„ ํ• ๋‹น(channelization) ๊ธฐ๋ฒ•์ด ์ค‘์š”ํ•˜๋‹ค. ๋˜ํ•œ, ํ•œ ์กฐ์ง์ด ํŠน์ • ์ง€์—ญ์— ๋ฐ€์ง‘ ๋ฐฐ์น˜๋œ AP๋“ค์„ ๊ด€๋ฆฌํ•œ๋‹ค๋ฉด ํŠน์ • ์‚ฌ์šฉ์ž๋ฅผ ์„œ๋น„์Šคํ•  ์ˆ˜ ์žˆ๋Š” AP๊ฐ€ ์—ฌ๋Ÿฟ์ผ ์ˆ˜ ์žˆ๋‹ค. ์ด ๊ฒฝ์šฐ, ์‚ฌ์šฉ์ž ์ ‘์†(user association, UA) ๊ธฐ๋ฒ•์ด ์ค€์ •์ (quasi-static) ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ ๋ชจ๋‘์—์„œ ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฐ€์ง‘ ๋ฐฐ์น˜๋œ WLAN ํ™˜๊ฒฝ์—์„œ ์™€์ดํŒŒ์ด(WiFi) ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด ์ฑ„๋„ ํ• ๋‹น ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์—์„œ๋Š” ๊ฐ๊ฐ์˜ AP์— ์ฑ„๋„์„ ํ• ๋‹นํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ„์„ญ ๊ทธ๋ž˜ํ”„(interference graph)๋ฅผ ์ด์šฉํ•˜๋ฉฐ ์ฑ„๋„ ๊ฒฐํ•ฉ(channel bonding)์„ ๊ณ ๋ คํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์ฃผ์–ด์ง„ ์ฑ„๋„ ๊ฒฐํ•ฉ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•ด๋‹น AP๊ฐ€ ๋™์  ์ฑ„๋„ ๊ฒฐํ•ฉ์„ ์ง€์›ํ•˜๋Š”์ง€ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ์ฃผ ์ฑ„๋„(primary channel)์„ ๊ฒฐ์ •ํ•œ๋‹ค. ํ•œํŽธ, ์ค€์ •์  ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ UA ๋ฌธ์ œ๋Š” ๋‹ค์†Œ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ๊ฐ์˜ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ค๋ฅธ UA ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ค€์ •์  ํ™˜๊ฒฝ์—์„œ์˜ UA ๊ธฐ๋ฒ•์€ ๋ฉ€ํ‹ฐ์บ์ŠคํŠธ ์ „์†ก, ๋‹ค์ค‘ ์‚ฌ์šฉ์ž MIMO (multi-user multiple input multiple output), ๊ทธ๋ฆฌ๊ณ  AP ์ˆ˜๋ฉด๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ ๊ณผ ํ•จ๊ป˜ AP๊ฐ„์˜ ๋ถ€ํ•˜ ๋ถ„์‚ฐ(load balancing)๊ณผ ์—๋„ˆ์ง€ ์ ˆ์•ฝ์„ ๊ณ ๋ คํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์—์„œ UA ๋ฌธ์ œ๋Š” ๋‹ค๋ชฉ์ ํ•จ์ˆ˜ ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์ •์‹ํ™”ํ•˜์˜€๊ณ  ๊ทธ ํ•ด๋ฅผ ๊ตฌํ•˜์˜€๋‹ค. ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ UA ๊ธฐ๋ฒ•์€ ํ•ธ๋“œ์˜ค๋ฒ„(handover, HO) ์Šค์ผ€์ค„ ๋ฌธ์ œ๋กœ ๊ท€๊ฒฐ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋„๋กœ์˜ ์ง€ํ˜•์„ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž๊ฐ€ ์ ‘์†ํ•  AP๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” HO ์Šค์ผ€์ค„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž๋Š” ๋‹จ์ง€ ๋‹ค์Œ AP๋กœ ์—ฐ๊ฒฐ์„ ๋งบ์„ ์‹œ๊ธฐ๋งŒ ๊ฒฐ์ •ํ•˜๋ฉด ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ์˜ ๋งค์šฐ ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์ธ HO ๊ธฐ๋ฒ•์„ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ๊ทธ๋ž˜ํ”„ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•(graph modeling technique)์„ ํ™œ์šฉํ•˜์—ฌ ๋„๋กœ๋ฅผ ๋”ฐ๋ผ ๋ฐฐ์น˜๋œ AP์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ํ‘œํ˜„ํ•œ๋‹ค. ํ˜„์‹ค์ ์ธ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์œ„ํ•ด ์ง์„  ๊ตฌ๊ฐ„, ์šฐํšŒ ๊ตฌ๊ฐ„, ๊ต์ฐจ๋กœ, ๊ทธ๋ฆฌ๊ณ  ์œ ํ„ด ๊ตฌ๊ฐ„ ๋“ฑ์„ ํฌํ•จํ•˜๋Š” ๋ณต์žกํ•œ ๋„๋กœ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•œ๋‹ค. ๋„๋กœ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฐ ์‚ฌ์šฉ์ž์˜ ์ด๋™ ๊ฒฝ๋กœ๋ฅผ ์˜ˆ์ธกํ•˜๊ณ , ๊ทธ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ฐ ์‚ฌ์šฉ์ž ๋ณ„ HO์˜ ๋ชฉ์  AP ์ง‘ํ•ฉ์„ ์„ ํƒํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” HO ์Šค์ผ€์ค„ ๊ธฐ๋ฒ•์˜ ์„ค๊ณ„ ๋ชฉ์ ์€ HO ์ง€์—ฐ ์‹œ๊ฐ„์˜ ํ•ฉ์„ ์ตœ์†Œํ™”ํ•˜๊ณ  ๊ฐ AP์—์„œ ํ•ด๋‹น ์ฑ„๋„์„ ์‚ฌ์šฉํ•˜๋ ค๋Š” ์‚ฌ์šฉ์ž ์ˆ˜๋ฅผ ์ค„์ด๋ฉด์„œ WiFi ์—ฐ๊ฒฐ ์‹œ๊ฐ„์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ค€์ •์  ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆํ•œ ์ฑ„๋„ ํ• ๋‹น ๊ธฐ๋ฒ•๊ณผ UA ๊ธฐ๋ฒ•์˜ ํ˜„์‹ค์„ฑ์„ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•œ ์‹œํ—˜๋Œ€(testbed)๋ฅผ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ด‘๋ฒ”์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ค€์ •์  ํ™˜๊ฒฝ๊ณผ ์ฐจ๋Ÿ‰ ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•๋“ค๊ณผ ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค์˜ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์˜€๋‹ค.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

    Infrastructure dependent wireless multicast - the effect of spatial diversity and error correction

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    The use of multiple Access Points (APs) with one AP placed at the middle of a coverage area and the remaining placed at the edge may reduce the Packet Error Rate (PER) experienced by a group of multicast receivers. This paper shows that Spatial Diversity can augment the channel quality experienced especially by those nodes which are located farther from the Master AP, i.e. the AP at the middle, however this study also demonstrates the need for error correction scheme. The aim of this analysis is to propose a means of enhancing the infrastructure end of an IEEE 802.11n Wireless Local Area Network (WLAN), such that multicast data can be delivered reliably in order to guarantee that the received video has an adequate Peak Signal to Noise Ratio (PSNR), but with the constraint that the Medium Access Control (MAC) and the Physical (PHY) layer of the receivers are not modified, hence a legacy IEEE 802.11n node may join the multicast group and experience good Quality of Service.peer-reviewe

    Analysis and Performance Evaluation of IEEE 802.11 WLAN

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    With fast deployment of wireless local area networks VoIP over IEEE 802.11 wireless local area network (WLAN) is growing very fast and is providing a cost effective alternative for voice communications. WLANs were initially set up to handle bursty nonreal time type of data traffic. Therefore, the wireless access protocols initially defined are not suitable for voice traffic. Subsequently, updates in the standard have been made to provision for QoS requirements of data, especially the real time traffic of the type voice and video. Despite these updates, however, transmitting voice traffic over WLAN does not utilize the available bandwidth (BW) efficiently, and the number of simultaneous calls supported in practice is significantly lower than what the BW figures would suggest. Several modifications have been proposed to improve the call capacity, and recently isochronous coordination function (ICF) was introduced to mitigate the problem of low call capacity. The proposed modified ICF which further improves the performance in terms of the call capacity. The proposed scheme uses multiplexing and multicasting in the downlink to substantially increase the call capacity
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