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| Zero-Forcing (ZF) und Minimum Mean-Square Error (MMSE) Equalization× | Satz von der Kanalkapazität nach Shannon× | |
|---|---|---|
| Fachgebiet | Telekommunikation | Telekommunikation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1974 | 1948 |
| Urheber≠ | Saleh Mansour and Paul Zervos | Claude Shannon |
| Typ≠ | linear equalization algorithm | fundamental theoretical bound |
| Wegweisende Quelle≠ | Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill. link ↗ | Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423. DOI ↗ |
| Aliasnamen | channel equalization, interference cancellation | channel capacity, information theory bound |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Shannon's channel capacity theorem, published in 1948, establishes the maximum rate at which information can be reliably transmitted over a noisy channel. Expressed as C = B log2(1 + S/N) for additive white Gaussian noise (AWGN), it is a fundamental bound in information theory and communications engineering. Shannon proved that reliable communication is possible at any rate below capacity, and impossible above it. This theorem underpins the design of all modern communication systems and motivates coding theory, modulation, and signal processing techniques. |
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