Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection a...Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.展开更多
Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typical...Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typically possible. These deployment architectures can have significant impacts on system reliability. However, existing reliability estimation approaches are typically limited to certain classes or exclusively concentrate on software reliability, neglecting the influence of hardware resources, software deployment and architectural styles. The selection of an appropriate architectural style has a significant impact on system reliability of the target system. Therefore, we propose a novel software architecture （SA） based reliability estimation model incorporating software deployment and architectural style. On the basis of two architectural styles, we design influence factors and present a new approach to calculate system reliability. Experimental results show that influence factors provide an accurate and simple method of reflecting architectural styles and software deployment on system reliability. It is important for considering the influence of other architectural styles on system reliability in large scale deployment environment.展开更多
文摘Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.
文摘Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typically possible. These deployment architectures can have significant impacts on system reliability. However, existing reliability estimation approaches are typically limited to certain classes or exclusively concentrate on software reliability, neglecting the influence of hardware resources, software deployment and architectural styles. The selection of an appropriate architectural style has a significant impact on system reliability of the target system. Therefore, we propose a novel software architecture （SA） based reliability estimation model incorporating software deployment and architectural style. On the basis of two architectural styles, we design influence factors and present a new approach to calculate system reliability. Experimental results show that influence factors provide an accurate and simple method of reflecting architectural styles and software deployment on system reliability. It is important for considering the influence of other architectural styles on system reliability in large scale deployment environment.
