Methoden vergleichen

	Ray Tracing Propagation Model	Multiple-Input Multiple-Output (MIMO)	Zero-Forcing (ZF) und Minimum Mean-Square Error (MMSE) Equalization
Fachgebiet	Telekommunikation	Telekommunikation	Telekommunikation
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	1993	1995	1974
Urheber	Maciel, Bertoni, and Xia	Telatar, Foschini, and Gans	Saleh Mansour and Paul Zervos
Typ	deterministic propagation algorithm	spatial multiplexing technique	linear equalization algorithm
Wegweisende Quelle	Maciel, T. F., Bertoni, H. L., & Xia, H. H. (1993). Unified approach to prediction of propagation over buildings for all ranges of frequencies. IEEE Transactions on Vehicular Technology, 42(1), 41-45.	Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595.	Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill.
Aliasnamen	deterministic propagation, site-specific modeling	spatial multiplexing, antenna diversity	channel equalization, interference cancellation
Verwandt	4	5	5
Zusammenfassung	Ray tracing is a deterministic propagation modeling technique for predicting electromagnetic field strength at specific locations. Instead of empirical formulas (like Okumura-Hata), ray tracing traces paths of electromagnetic energy as it reflects, diffracts, and scatters off buildings and terrain. With accurate 3D geometry and material properties, ray tracing predicts site-specific path loss, multipath delay profiles, and angle of arrival, making it ideal for detailed coverage planning, interference analysis, and system design. Ray tracing is now standard in professional cellular planning tools.	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.