Effects of Channel Features on Parameters of Genetic Algorithm for MIMO Detection
スポンサーリンク
概要
- 論文の詳細を見る
Genetic algorithm (GA) is now an important tool in the field of wireless communications. For multiple-input/multiple-output (MIMO) wireless communications system employing spatial multiplexing transmission, we evaluate the effects of GA parameters value on channel parameters in fading channels. We assume transmit-correlated Rayleigh and Rician fading with realistic Laplacian power azimuth spectrum. Azimuth spread (AS) and Rician K-factor are selected according to the measurement-based WINNER II channel model for several scenarios. Herein we have shown the effects of GA parameters and channel parameters in different WINNER II scenarios (i.e., AS and K values) and rank of the deterministic components. We employ meta GA that suitably selects the population (P), generation (G) and mutation probability (pm) for the inner GA. Then we show the cumulative distribution function (CDF) obtain experimentally for the condition number C of the channel matrix H. It is found that, GA parameters depend on the channel parameters, i.e., GA parameters are the functions of the channel parameters. It is also found that for the poorer channel conditions smaller GA parameter values are required for MIMO detection. This approach will help to achieve maximum performance in practical condition for the lower numerical complexity.
- The Institute of Electronics, Information and Communication Engineersの論文
The Institute of Electronics, Information and Communication Engineers | 論文
- Compensation Effect of Quasi-Inverse Filter (QIF) on Frequency Characteristic Distortion in Wideband Systems
- Subblock Processing for Frequency-Domain Turbo Equalization under Fast Fading Environments
- Measurement-Based Performance Evaluation of Coded MIMO-OFDM Spatial Multiplexing with MMSE Spatial Filtering in an Indoor Line-of-Sight Environment
- Design of a Multiple-Input SC DC-DC Converter Realizing Long Battery Runtime
- The Influence of a Low-Level Color or Figure Adaptation on a High-Level Face Perception