CHARACTERIZATION OF HOTSPOT COVERAGE PLAN IN 2.4/ 5GHZ FREQUENCY BAND (NNAMDI AZIKIWE UNIVERSITY, NIGERIA, AS A CASE STUDY)

Research and tertiary institutions today uses wireless connectivity owing to the benefits of mobility flow-awarecommunication and flexibility advantages generally. In this case, mobility computing involving the use of smartdevices, laptops, wifi-desktops, etc, largely depends on a deployed hotspot infrastructure. In particular, the physicalposition of the mobile system (and hence of the user) and the hotspot infrastructure design layout are fundamentalconsiderations for service efficiency. While previous works have focused on user position estimation, signal strengthquality and network QoS, this work leverages the contemporary challenges of network connectivity in tertiaryinstitutions in Nigeria with respect to optimal coverage and cost minimization. Using Nnamdi Azikiwe University-Unizik, Awka as testbed, we carried out a study on hotspot/WLAN IEEE 802.11 deployments while devising a costeffective coverage plan in 2,4/5GHz frequency band. A mathematical model on cost optimization for WLANHotpot project processes was developed using Linear programming, the installation procedure, coverage plan basedon specifications of the deployment hardware, and data security were covered in this work. Consequently, from themodel, we argue that with careful selection of optimization criteria in the deployment, an efficient design cost plan,and QoS, could eliminate possible trade-offs in the deployment contexts by over 95%.Keywords: Mobility, Flow-aware, Hotspot, Infrastructure, Optimization, Design, Minimizatio

Flexible Investment Decisions in the Telecommunications Industry: Case Applications using Real Options

The telecommunications sector is one of the most innovative, high-growth, capitalintensive yet volatile sector of the economy. This research addresses critical concerns of how, when, and why an enterprise or a service provider should undertake new investments. The study investigates the power of flexibility in investment decision making process, by applying the real options methodology. Five case applications are studied: a) investment decisions in next generation wireless networks; b) investing in integrated wireless networks; c) migration to wireless broadband internet services; d) valuing deployment of Wi-Fi networks in enterprise markets; and e) valuing Hosted VOIP services for enterprise markets. The case studies are analyzed both qualitatively and quantitatively

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

ACUTA Journal of Telecommunications in Higher Education

In This Issue 5G\u27s Promise: 1,000 x Capacity, 1,000 x Challenges Higher-Speed WLANs About to Emerge State of the Residential Network 2013 LTE: The Next Wave of Wireless Evolution The 10 Most Costly Pitfalls of DAS Deployment and How to Avoid Them DAS on Campus: Solutions for Wireless Service Decision Criteria for Selecting a Wireless lntrusion Prevention System lnstitutional Excellence Award President\u27s Message From the CE

Optimizing multiuser MIMO for access point cooperation in dense wireless networks

As the usage of wireless devices continues to grow rapidly in popularity, wireless networks that were once designed to support a few laptops must now host a much wider range of equipments, including smart phones, tablets, and wearable devices, that often run bandwidth-hungry applications. Improvements in wireless local access network (WLAN) technology are expected to help accommodate the huge traffic demands. In particular, advanced multicell Multiple-Input Multiple-Output (MIMO) techniques, involving the cooperation of APs and multiuser MIMO processing techniques, can be used to satisfy the increasing demands from users in high-density environments. The objective of this thesis is to address the fundamental problems for multiuser MIMO with AP cooperation in dense wireless network settings. First, for a very common multiuser MIMO linear precoding technique, block diagonalization, a novel pairing-and-binary-tree based user selection algorithm is proposed. Second, without the zero-forcing constraint on the multiuser MIMO transmission, a general weighted sum rate maximization problem is formulated for coordinated APs. A scalable algorithm that performs a combined optimization procedure is proposed to determine the user selection and MIMO weights. Third, we study the fair and high-throughput scheduling problem by formally specifying an optimization problem. Two algorithms are proposed to solve the problem using either alternating optimization or a two-stage procedure. Fourth, with the coexistence of both stationary and mobile users, different scheduling strategies are suggested for different user types. The provided theoretical analysis and simulation results in this thesis lay out the foundation for the realization of the clustered WLAN networks with AP cooperation.Ph.D

New Methods of Efficient Base Station Control for Green Wireless Communications

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 이병기.This dissertation reports a study on developing new methods of efficient base station (BS) control for green wireless communications. The BS control schemes may be classified into three different types depending on the time scale — hours based, minutes based, and milli-seconds based. Specifically, hours basis pertains to determining which BSs to switch on or offminutes basis pertains to user equipment (UE) associationand milli-seconds basis pertains to UE scheduling and radio resource allocation. For system model, the dissertation considers two different models — heterogeneous networks composed of cellular networks and wireless local area networks (WLANs), and cellular networks adopting orthogonal frequency division multiple access (OFDMA) with carrier aggregation (CA). By combining each system model with a pertinent BS control scheme, the dissertation presents three new methods for green wireless communications: 1) BS switching on/off and UE association in heterogeneous networks, 2) optimal radio resource allocation in heterogeneous networks, and 3) energy efficient UE scheduling for CA in OFDMA based cellular networks. The first part of the dissertation presents an algorithm that performs BS switchingon/off and UE association jointly in heterogeneous networks composed of cellular networks and WLANs. It first formulates a general problem which minimizes the total cost function which is designed to balance the energy consumption of overall network and the revenue of cellular networks. Given that the time scale for determining the set of active BSs is much larger than that for UE association, the problem may be decomposed into a UE association algorithm and a BS switching on/off algorithm, and then an optimal UE association policy may be devised for the UE association problem. Since BS switching-on/off problem is a challenging combinatorial problem, two heuristic algorithms are proposed based on the total cost function and the density of access points of WLANs within the coverage of each BS, respectively. According to simulations, the two heuristic algorithms turn out to considerably reduce energy consumption when compared with the case where all the BSs are always turned on. The second part of the dissertation presents an energy-per-bit minimized radioresource allocation scheme in heterogeneous networks equipped with multi-homing capability which connects to different wireless interfaces simultaneously. Specifically, an optimization problem is formulated for the objective of minimizing the energy-per-bit which takes a form of nonlinear fractional programming. Then, a parametric optimization problem is derived out of that fractional programming and the original problem is solved by using a double-loop iteration method. In each iteration, the optimal resource allocation policy is derived by applying Lagrangian duality and an efficient dual update method. In addition, suboptimal resource allocation algorithms are developed by using the properties of the optimal resource allocation policy. Simulation results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency. The third part of the dissertation presents an energy efficient scheduling algorithm for CA in OFDMA based wireless networks. In support of this, the energy efficiency is newly defined as the ratio of the time-averaged downlink data rate and the time-averaged power consumption of the UE, which is important especially for battery-constrained UEs. Then, a component carrier and resource block allocation problem is formulated such that the proportional fairness of the energy efficiency is guaranteed. Since it is very complicated to determine the optimal solution, a low complexity energy-efficient scheduling algorithm is developed, which approaches the optimal algorithm. Simulation results demonstrate that the proposed scheduling scheme performs close to the optimal scheme and outperforms the existing scheduling schemes for CA.Abstract i List of Figures viii List of Tables x 1 Introduction 1 2 A Joint Algorithm for Base Station Operation and User Association in Heterogeneous Networks 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 UE Association Algorithm . . . . . . . . . . . . . . . . . . . . . . 14 2.5 BS Switching-on/off Algorithm . . . . . . . . . . . . . . . . . . . . 17 2.5.1 Cost Function Based (CFB) Algorithm . . . . . . . . . . . 19 2.5.2 AP Density Based (ADB) Algorithm . . . . . . . . . . . . 19 2.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Energy-per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 System Model and Problem Formulation . . . . . . . . . . . . . . . 30 3.3 Parametric Approach to Fractional Programming . . . . . . . . . . 36 3.3.1 Parametric Approach . . . . . . . . . . . . . . . . . . . . . 37 3.3.2 Double-Loop Iteration to Determine Optimal θ . . . . . . . 38 3.4 Optimal Resource Allocation Algorithm . . . . . . . . . . . . . . . 39 3.4.1 Optimal Allocation of Subcarrier and Power . . . . . . . . . 41 3.4.2 Optimal Allocation of Time Fraction . . . . . . . . . . . . . 44 3.4.3 Lagrangian Multipliers Update Algorithm . . . . . . . . . . 48 3.5 Design of Suboptimal Algorithms . . . . . . . . . . . . . . . . . . 51 3.5.1 Time-Fraction Allocation First (TAF) Algorithm . . . . . . 51 3.5.2 Normalized Time-Fraction Allocation (NTA) Algorithm . . 53 3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4 Energy Efficient Scheduling for Carrier Aggregation in OFDMA Based Wireless Networks 68 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3 Energy Efficiency Proportional Fairness (EEPF) Scheduling . . . . 74 4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 78 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Conclusion 87 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . 91 References 93Docto

Tiheiden Wi-Fi verkkojen optimointi Markov-ketjumallien ja simuloidun jäähdytyksen avulla

Currently, the demand for wireless communication capacity is rising rapidly due to challenging applications such as video streaming and the emerging Internet of things. In meeting these ambitious requirements, the most important factor is predicted to be network densification, which refers to increasing the geographical density of simultaneously communicating devices. A natural choice for implementing dense networks is the wireless local area network technology Wi-Fi, characterized by being cheap and easy to deploy. Network density aggravates the harmful effects of interference and causes scarcity of free transmission bandwidth. To counter this, dense networks need radio resource management algorithms. This thesis presents a Wi-Fi radio resource management algorithm, which jointly optimizes access point channels, user association and transmission power. It estimates future throughput using a continuous time Markov chain based model, and finds solutions maximizing this estimate via a discrete search metaheuristic called simulated annealing. The algorithm is validated through a wide range of simulations where for instance network density is varied. The algorithm is found to be highly versatile, yielding good performance in all scenarios. Moreover, the general design approach places few restrictions on further algorithm improvement and extension. Markov chain modeling, although accurate in an idealized setting, turns out to be inaccurate with real-world Wi-Fi, with a simpler model offering similar accuracy but lighter computational load.Nykyisin vaatimukset langattoman tiedonsiirron kapasiteetille ovat voimakkaassa kasvussa johtuen haastavista sovelluksista kuten videon suoratoistosta ja tulossa olevasta esineiden Internetistä. Näiden vaatimusten täyttämiseksi tärkein keino on langattomien tiedonsiirtoverkkojen tihentäminen, mikä tarkoittaa yht’aikaa samalla maantieteellisellä alueella kommunikoivien laitteiden määrän kasvattamista. Luonnollinen valinta tiheiden verkkojen toteuttamiseen on langattomien lähiverkkojen teknologia Wi-Fi, jonka etuja ovat edullisuus ja asennuksen helppous. Langattoman verkon tiheys lisää haitallista interferenssiä ja aikaansaa pulaa vapaista lähetystaajuuksista. Näiden ongelmien ratkaisemiseksi tarvitaan radioresurssien hallinta-algoritmeja. Tässä työssä suunnitellaan Wi-Fiä varten radioresurssien hallinta-algoritmi, joka optimoi samanaikaisesti tukiasemien kanavia, käyttäjien allokaatiota tukiasemille sekä lähetystehoja. Se estimoi tulevia tiedonsiirtonopeuksia jatkuvan ajan Markov-ketjuihin pohjautuvan mallin avulla ja löytää tämän estimaatin maksimoivia ratkaisuja hyödyntämällä diskreettiä hakumenetelmää nimeltä simuloitu jäähdytys. Algoritmi validoidaan käyttäen monipuolista joukkoa simulaatioita, jossa vaihtelee esimerkiksi verkon tiheys. Algoritmi osoittautuu erittäin monipuoliseksi, sillä sen suorituskyky on hyvä kaikissa simulaatioskenaarioissa. Käytetyn lähestymistavan etuna on myös se, että se asettaa varsin vähän rajoituksia algoritmin jatkokehitykselle. Markov-ketjumallit osoittautuvat todellisen Wi-Fin tapauksessa epätarkoiksi, vaikka ne idealisoidussa ympäristössä ovatkin tarkkoja. Käy ilmi, että yksinkertaisemmalla mallilla saadaan vastaava tarkkuus, mutta laskentatehoa tarvitaan vähemmän