Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многовходовая многовыходная система (MIMO)× | Нулевое принуждение и минимальная среднеквадратическая ошибка выравнивания× | |
|---|---|---|
| Область | Телекоммуникации | Телекоммуникации |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1995 | 1974 |
| Автор метода≠ | Telatar, Foschini, and Gans | Saleh Mansour and Paul Zervos |
| Тип≠ | spatial multiplexing technique | linear equalization algorithm |
| Основополагающий источник≠ | Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595. DOI ↗ | Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill. link ↗ |
| Другие названия | spatial multiplexing, antenna diversity | channel equalization, interference cancellation |
| Связанные | 5 | 5 |
| Сводка≠ | MIMO is a technique that uses multiple transmit and receive antennas to significantly increase channel capacity and reliability. Pioneered theoretically by Telatar (1999) and Foschini & Gans (1998), MIMO exploits multipath propagation—typically a liability in wireless—as an asset by creating independent spatial channels. It is now fundamental to all modern wireless systems including LTE, WiFi-6, and 5G, where it provides both capacity gains through spatial multiplexing and robustness through diversity. | Zero-Forcing (ZF) and Minimum Mean-Square Error (MMSE) equalization are fundamental linear receiver algorithms for combating intersymbol interference in dispersive channels. Developed in the context of data transmission theory, these methods form the basis of modern channel equalization in wireless and wired systems. While ZF aggressively cancels interference, MMSE balances interference suppression with noise enhancement, making it the optimal linear solution under Gaussian noise. |
| ScholarGateНабор данных ↗ |
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