Bandingkan kaedah

	Penyamaan Sifar-Menyebabkan dan Ralat Min-Maksimum Persegi (ZF dan MMSE)	Multiple-Input Multiple-Output (MIMO)	Teorem Kapasiti Saluran Shannon
Bidang	Telekomunikasi	Telekomunikasi	Telekomunikasi
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1974	1995	1948
Pengasas	Saleh Mansour and Paul Zervos	Telatar, Foschini, and Gans	Claude Shannon
Jenis	linear equalization algorithm	spatial multiplexing technique	fundamental theoretical bound
Sumber perintis	Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill.	Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595.	Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Alias	channel equalization, interference cancellation	spatial multiplexing, antenna diversity	channel capacity, information theory bound
Berkaitan	5	5	5
Ringkasan	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.	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.	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.