방법 비교

	다중 입출력 (MIMO)	오쿠무라-하타 경로 손실 예측 모델	제로 포싱 및 최소 평균 제곱 오차 등화
분야	통신공학	통신공학	통신공학
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1995	1968	1974
창시자	Telatar, Foschini, and Gans	Masahiro Okumura and Masahiro Hata	Saleh Mansour and Paul Zervos
유형	spatial multiplexing technique	empirical path loss model	linear equalization algorithm
원전	Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595.	Okumura, Y., Ohmori, E., Kawano, T., & Fukuda, K. (1968). Field strength and its variability in VHF and UHF land mobile radio service. Review of the Electrical Communication Laboratory, 16(9-10), 825-873.	Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill.
별칭	spatial multiplexing, antenna diversity	path loss model, propagation prediction	channel equalization, interference cancellation
관련	5	4	5
요약	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.	The Okumura-Hata model is an empirical propagation model for predicting path loss in mobile radio systems. Developed by Okumura (1968) and mathematically formalized by Hata (1980), it is one of the most widely used models for cellular network planning. The model predicts median path loss as a function of frequency, distance, and antenna heights, with environment-specific correction factors. Despite its age, the Okumura-Hata model remains a standard in 2G/3G planning and is often used as a baseline for more sophisticated models.	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.