Downlink Resource Management in Femtocell Networks
- 주제(키워드) Femtocell Networks
- 발행기관 아주대학교
- 발행년도 2012
- 학위수여년월 2012. 8
- 학위명 석사
- 학과 및 전공 일반대학원 컴퓨터공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000012708
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
An increasing demand for high data rate services has been seen since a couple of decades. A recent idea introduced for increasing the performance and coverage of broadband wireless access (BWA) networks is femtocell networks. These are short range wireless access devices capable of providing better indoor coverage and quality. However once they are deployed in conjunction with the macrocell infrastructure, the application of smart resource allocation mechanism in resulting environment becomes more challenging. This motivates researchers to design and develop novel algorithms that guarantee high quality of service (QoS) at the end user under limited spectrum resources. It means to allocate power, subcarrier, and other resources optimally to minimize interference and outage probability, while maximizing spectral efficiency. Meanwhile designed algorithms should have less complexity so that they can run efficiently while occupying minimum hardware resources. The following research venture proposes some approaches and shows their real significance for improved resource allocation among femtocells. Initially a step towards achieving self-configurability in femtocell networks has been taken through which they can follow a decision making mechanism for adjusting their transmission power and coverage area to enhance end users capacity. An adaptive downlink (DL) coverage management algorithm is proposed and evaluated using simulation results which illustrate improved coverage advantages. Furthermore we have shown a road map to extend the resulting algorithm for adaptive mode selection by femtocell, through which a femtocell can itself choose its appropriate mode to optimize the performance of overall two-tier macro-femtocell network. Our solution illustrates how self-configurable characteristics can be incorporated in femtocells. Hence theycan get better resource allocation and enhanced coverage advantages which eventually lead to better results in terms of signal to interference plus noise ratio (SINR), spectral efficiency and the outage probability of end users. Next an iterative joint subcarrier and power allocation strategy is described in femtocell network named as distributed joint resource allocation (DJRA) scheme. Our proposed approach iteratively updates power such that all parameters achieve their ideal values in the given environment thus yielding optimal capacity results for each individual femtocell. Finally, this dissertation provides excellent solutions for key challenges in the practical deployment of femtocells by proposing adaptive and decentralized interference mitigation techniques which can be easily used in future.
more목차
1. An Introduction to Femtocell Networks 7
1.1 Overview. 7
1.2 Cognitive Femtocell Network. 7
1.2.1 Femtocell operation modes. 8
1.2.2 Main features of femtocells. 9
1.3 Research Motivation 9
1.4 Thesis Outline 11
1.4.1 Adaptive Downlink Coverage Management for Hybrid Macro/Femtocell Networks. 11
1.4.2 Joint Resource Allocation in OFDMA-based Cognitive Femtocell Networks. 12
2. Adaptive Coverage Management in Macro-femtocell Networks 13
2.1 Introduction 13
2.2 System Model 14
2.3 Coverage Management for Throughput Enhancement and Interference Avoidance. 17
2.3.1 Motivation. 17
2.3.2 Adaptive Downlink Coverage Management. 19
2.4 Simulation Results. 20
2.4.1 Parameters for Simulation. 20
2.4.2 Results. 22
2.5 Adaptive Mode Selection by fBS 24
2.5.1 Description 26
2.5.2 Benefits. 27
2.6 Analysis of Simulation Results 27
2.7 Conclusion and Comments 30
3. Joint Resource Allocation in OFDMA-based Cognitive Femtocell Networks (CFNs). 32
3.1 Introduction. 32
3.2 System Model. 33
3.2.1 Assumptions and notations. 33
5
3.2.2 Problem formulation and preliminary setup procedure 34
3.3 Distributed Joint Resource Allocation (DJRA) Scheme. 35
3.3.1 Optimal Slot Allocation 35
3.3.2 Optimal Power Allocation 36
3.3.3 Distributed Joint Resource Allocation (DJRA) Algorithm 38
3.4 Simulation Results 39
3.5 Conclusion and Future Work 43
REFERENCES 44
