An Analytical Framework for Heterogeneous Partial Feedback Design in Heterogeneous Multicell OFDMA Networks

The inherent heterogeneous structure resulting from user densities and large scale channel effects motivates heterogeneous partial feedback design in heterogeneous networks. In such emerging networks, a distributed scheduling policy which enjoys multiuser diversity as well as maintains fairness among users is favored for individual user rate enhancement and guarantees. For a system employing the cumulative distribution function based scheduling, which satisfies the two above mentioned desired features, we develop an analytical framework to investigate heterogeneous partial feedback in a general OFDMA-based heterogeneous multicell employing the best-M partial feedback strategy. Exact sum rate analysis is first carried out and closed form expressions are obtained by a novel decomposition of the probability density function of the selected user's signal-to-interference-plus-noise ratio. To draw further insight, we perform asymptotic analysis using extreme value theory to examine the effect of partial feedback on the randomness of multiuser diversity, show the asymptotic optimality of best-1 feedback, and derive an asymptotic approximation for the sum rate in order to determine the minimum required partial feedback.Comment: To appear in IEEE Trans. on Signal Processin

Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition

This paper studies joint beamforming and power control in a coordinated multicell downlink system that serves multiple users per cell to maximize the minimum weighted signal-to-interference-plus-noise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear Perron-Frobenius theory and the multicell network duality. The iterative algorithm, though operating in a distributed manner, still requires instantaneous power update within the coordinated cluster through the backhaul. The backhaul information exchange and message passing may become prohibitive with increasing number of transmit antennas and increasing number of users. In order to derive asymptotically optimal solution, random matrix theory is leveraged to design a distributed algorithm that only requires statistical information. The advantage of our approach is that there is no instantaneous power update through backhaul. Moreover, by using nonlinear Perron-Frobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on Wireless Communications are correcte

무선 중계 네트워크에서 신호대잡음비의 누적분포함수 기반 중계기 선택 기법의 성능 분석

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이재홍.무선 중계 기술은 차세대 무선통신 시스템에서 요구되는 높은 서비스 품질 및 데이터 전송률 달성을 위해 고려되고 있는 대표적인 기술 중 하나이다. 무선 중계 기술이 갖고 있는 다양한 장점으로 인해 현재까지 IEEE 802.16j 및 3GPP LTE-Advanced 등의 무선통신 시스템 표준에 반영되기도 하였다. 실질적으로 두 노드 사이 채널의 통계적 특성은 그들의 위치에 따라 달라지기 때문에 각 채널들의 통계적 특성은 서로 동일하지 않다. 각 채널들의 통계적 특성이 동일하지 않을 때, 무선 중계 기술에서 가장 유용한 기법 중 하나인 중계기 선택 기법은 특정 중계기들이 더 자주 선택되는 등의 공정성 문제를 유발시킬 수 있다. 특히, 이 문제는 제한된 배터리를 가진 중계기들로 구성된 네트워크에서 네트워크의 수명을 줄이게 하는 요인이 될 수 있다. 따라서 이러한 네트워크에서는 사용자들의 통신 신뢰도 뿐만 아니라, 중계기에서의 선택 공정성도 함께 고려할 필요가 있다. 본 논문에서는 무선 중계 네트워크에서 사용자들의 통신 신뢰도와 중계기 간의 선택 공정성을 함께 고려하기 위해 수신 신호대잡음비의 누적분포함수를 기반으로 하는 새로운 중계기 선택 기법을 제안한다. 주요한 연구 결과는 다음과 같다. 먼저, 나카가미-m 페이딩 채널 환경을 가진 일방향 중계 네트워크를 위한 프로액티브(proactive) 및 리액티브(reactive) 방식의 수신 신호대잡음비 누적분포함수 기반 중계기 선택 기법을 제안한다. 각각의 중계기 선택 기법을 위해 중계기 선택 확률을 유도하여 제안된 각 중계기 선택 기법들의 평균 중계기 공정성을 분석한다. 또한 각 선택 기법에 대한 불능 확률을 수식으로 유도하고, 유도한 불능 확률을 점근적 표현으로 나타내어 각 기법들이 얻을 수 있는 다이버시티 차수를 분석한다. 모의실험을 통해 얻어진 평균 중계기 공정성과 불능 확률이 유도한 평균 중계기 공정성 및 불능 확률 값과 일치함을 확인한다. 그리고 제안된 기법이 중계기들 사이에 공정성을 완벽하게 보장하고 네트워크 수명을 증가시키며, 다이버시티 차수가 중계기의 수와 페이딩 파라미터 m 값에 따라 달라짐을 확인한다. 둘째, 나카가미-m 페이딩 채널 환경을 가진 양방향 중계 네트워크를 위한 프로액티브 및 리액티브 방식의 수신 신호대잡음비 누적분포함수 기반 중계기 선택 기법을 제안한다. 제안된 프로액티브 방식의 중계기 선택 기법에 대해서는 정확한 중계기 선택 확률의 유도를 통해 평균 중계기 공정성을 분석한다. 제안된 리액티브 방식의 중계기 선택 기법에 대해서는 중계기 선택 확률의 적분 및 근사 표현을 유도하여 평균 중계기 공정성을 분석한다. 또한 각 선택 기법에 대한 불능 확률을 수식으로 유도하고, 유도한 불능 확률을 점근적 표현으로 나타내어 각 기법들이 얻을 수 있는 다이버시티 차수를 분석한다. 모의실험을 통해 얻어진 평균 중계기 공정성과 불능 확률이 유도한 평균 중계기 공정성 및 불능 확률 값과 일치함을 확인한다. 그리고 제안된 기법이 중계기들 사이에 공정성을 완벽하게 보장하고 네트워크 수명을 증가시키며, 다이버시티 차수가 중계기의 수와 페이딩 파라미터 m 값에 따라 달라짐을 확인한다.Wireless relay technology is one of the most promising technologies for the future communication systems which provide coverage extension and better quality of service (QoS) such as higher data rate and lower outage probability with few excessive network loads. Due to its advantages, it has been adopted in wireless standards such as IEEE 802.16j and 3GPP LTE-Advanced. In practice, since statistics of the channel between any two nodes vary depending on their locations, they are not identical which means that channels can experience different fading. When statistics of the channel are not identical, relay selection, which is one of the most useful techniques for wireless relay technology, can cause fairness problem that particular relays are selected more frequently than other relays. Especially, this problem can cause reduction of lifetime in the network with multiple relays having limited battery power. In this network, it is needed to focus on selection fairness for relays as well as reliability at end-users. In this dissertation, to focus on both selection fairness for relays and reliability at end-users, we propose novel relay selection schemes based on cumulative distribution functions (CDFs) of signal-to-noise ratios (SNRs) in wireless relay networks. The dissertation consists of two main results. First, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for one-way relay networks over Nakagami-m fading channels. If a relay is selected before the source transmission, it is called as proactive relay selection. Otherwise, if a relay is selected after the source transmission, it is called as reactive relay selection. For both the proactive and the reactive relay selection schemes, we analyze average relay fairness by deriving relay selection probability. For the proactive relay selection scheme, we obtain diversity order by deriving the integral and asymptotic expressions for outage probability. Also, for the reactive relay selection scheme, we obtain diversity order by deriving the exact closed-form and asymptotic expressions for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters. Second, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for two-way relay networks over Nakagami-m fading channels. For the proactive relay selection scheme, we analyze average relay fairness by deriving relay selection probability. Also, we analyze diversity order by deriving the integral and asymptotic expressions for outage probability. For the reactive relay selection scheme, we analyze average relay fairness by deriving the integral and asymptotic expressions for relay selection probability. Also, we obtain diversity order by deriving the asymptotic expression for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters.Contents Abstract i 1 Introduction 1 1.1 Background and Related Work . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Wireless Relay Technology . . . . . . . . . . . . . . . . . . . . 3 1.2 Outline of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Relay Selection Based on CDFs of SNRs for One-Way Relay Networks 14 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.1 Proactive CDF-Based Relay Selection . . . . . . . . . . . . . 19 2.1.2 Reactive CDF-Based Relay Selection . . . . . . . . . . . . . . 20 2.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 22 2.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 22 2.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 27 2.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 34 2.3.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 34 2.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 36 2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 39 2.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 53 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3 Relay Selection Based on CDFs of SNRs for Two-Way Relay Networks 66 3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.1.1 Proactive CDF-based Relay Selection . . . . . . . . . . . . . . 68 3.1.2 Reactive CDF-based Relay Selection . . . . . . . . . . . . . . 72 3.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 73 3.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 73 3.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 74 3.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 82 3.3.1 Average Relay Fairness Anlaysis . . . . . . . . . . . . . . . . . 82 3.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 86 3.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 89 3.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 100 3.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 105 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4 Conclusion 116 4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.2 Possible Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 4.2.1 Device-to-Device (D2D) Communications . . . . . . . . . . . 118 4.2.2 Low Power Body Sensor Networks . . . . . . . . . . . . . . . 120 4.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Bibliography 122 Korean Abstract 139Docto