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	Modello di propagazione Ray Tracing	Multiple-Input Multiple-Output (MIMO)	Equalizzazione Zero-Forcing e Minimum Mean-Square Error
Campo	Telecomunicazioni	Telecomunicazioni	Telecomunicazioni
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	1993	1995	1974
Ideatore	Maciel, Bertoni, and Xia	Telatar, Foschini, and Gans	Saleh Mansour and Paul Zervos
Tipo	deterministic propagation algorithm	spatial multiplexing technique	linear equalization algorithm
Fonte seminale	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.
Alias	deterministic propagation, site-specific modeling	spatial multiplexing, antenna diversity	channel equalization, interference cancellation
Correlati	4	5	5
Sintesi	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.