수신 상관정보를 이용한 상향링크 셀룰러 시스템에서의 사용자 스케줄링에 관한 연구

In this paper, we consider multi-user scheduling to maximize the ergodic capacity near the cell boundary in the uplink of cellular systems. The base station (BS) determines a user pair that can minimize the interference from other cells by exploiting the receive correlation matrices of users from adjacent BSs. The performance of the proposed scheme is verified by computer simulation. Simulation results show that the proposed multi-cell scheduling significantly increase the ergodic capacity near the cell boundary compared to conventional random user scheduling, particularly in highly correlated channel environments

상향링크 셀룰러 시스템에서 채널 상관 기반의 협력 사용자 스케줄링 기법

In this paper, we consider multi-user scheduling to avoid other cell interference (OCI) in the uplink of cellular systems. The base station (BS) determines a user group that can minimize the interference from other cells by exploiting the spatial correlation matrix of users from adjacent BSs. The proposed scheme is applicable to multi-input multi-output (MIMO) as well as single-input multi-output (SIMO) environments by applying an eigen-beamforming technique, enabling the use of flexible antenna structures at the transmitter. Simulation results show that the proposed multi-cell scheduling significantly increase the ergodic capacity by avoiding the OCI compared to conventional scheduling schemes, particularly in high mobility and highly correlated channel environments.Seoul R&BD Progra

Generalized multivariate analysis of variance - A unified framework for signal processing in correlated noise

Generalized multivariate analysis of variance (GMANOVA) and related reduced-rank regression are general statistical models that comprise versions of regression, canonical correlation, and profile analyses as well as analysis of variance (ANOVA) and covariance in univariate and multivariate settings. It is a powerful and, yet, not very well-known tool. We develop a unified framework for explaining, analyzing, and extending signal processing methods based on GMANOVA. We show the applicability of this framework to a number of detection and estimation problems in signal processing and communications and provide new and simple ways to derive numerous existing algorithms. Many of the methods were originally derived from scratch , without knowledge of their close relationship with the GMANOVA model. We explicitly show this relationship and present new insights and guidelines for generalizing these methods. Our results could inspire applications of the general framework of GMANOVA to new problems in signal processing. We present such an application to flaw detection in nondestructive evaluation (NDE) of materials. A promising area for future growth is image processing