Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

MIMO Detection

In course

EE-543: Advanced wireless receivers

Students extend their knowledge on wireless communication systems to spread-spectrum communication and to multi-antenna systems. They also learn about the basic information theoretic concepts, about channel coding, and bit-interleaved coded modulation. ...

Description

This lecture covers MIMO receivers in advanced wireless communications, focusing on system models, linear receivers (MMSE & ZF), successive interference cancellation, and performance analysis. It explores spatial multiplexing, optimum MIMO detection, graphical interpretations, and Voronoi regions. The lecture delves into complexity analysis, zero forcing detectors, linear zero forcing detectors, and performance comparisons of MIMO receivers. It also discusses MMSE receivers, V-BLAST ordering, and efficient SIC with QR decomposition. The lecture concludes with a detailed examination of SIC performance, ordered SIC, and the architecture for MIMO detection.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Official source

Ontological neighbourhood

Electrical engineering

Telecommunications: Wireless communication

Mathematics

Algebra: Linear algebra

Related concepts (32)

MIMO

In radio, multiple-input and multiple-output (MIMO) (ˈmaɪmoʊ,_ˈmiːmoʊ) is a method for multiplying the capacity of a radio link using multiple transmission and receiving antennas to exploit multipath propagation. MIMO has become an essential element of wireless communication standards including IEEE 802.11n (Wi-Fi 4), IEEE 802.11ac (Wi-Fi 5), HSPA+ (3G), WiMAX, and Long Term Evolution (LTE). More recently, MIMO has been applied to power-line communication for three-wire installations as part of the ITU G.

Precoding

Precoding is a generalization of beamforming to support multi-stream (or multi-layer) transmission in multi-antenna wireless communications. In conventional single-stream beamforming, the same signal is emitted from each of the transmit antennas with appropriate weighting (phase and gain) such that the signal power is maximized at the receiver output. When the receiver has multiple antennas, single-stream beamforming cannot simultaneously maximize the signal level at all of the receive antennas.

QR decomposition

In linear algebra, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm. Any real square matrix A may be decomposed as where Q is an orthogonal matrix (its columns are orthogonal unit vectors meaning ) and R is an upper triangular matrix (also called right triangular matrix).

Singular value decomposition

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any matrix. It is related to the polar decomposition. Specifically, the singular value decomposition of an complex matrix M is a factorization of the form where U is an complex unitary matrix, is an rectangular diagonal matrix with non-negative real numbers on the diagonal, V is an complex unitary matrix, and is the conjugate transpose of V.

Schur decomposition

In the mathematical discipline of linear algebra, the Schur decomposition or Schur triangulation, named after Issai Schur, is a matrix decomposition. It allows one to write an arbitrary complex square matrix as unitarily equivalent to an upper triangular matrix whose diagonal elements are the eigenvalues of the original matrix. The Schur decomposition reads as follows: if A is an n × n square matrix with complex entries, then A can be expressed as where Q is a unitary matrix (so that its inverse Q−1 is also the conjugate transpose Q* of Q), and U is an upper triangular matrix, which is called a Schur form of A.

Related lectures (30)

QR Factorization: Least Squares System Resolution MOOC: Linear Algebra (Part 3)

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Diagonalization Techniques: Jacobi Method

Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.

Matrix Decompositions: LU, Cholesky, QR, Eigendecomposition

Explores matrix decompositions, algorithms, computational complexity, and predator-prey interactions in numerical linear algebra.

Latent Factor Analysis: Movie Genre Classification

Explores latent factor analysis for movie genre classification based on male versus female leads.

Singular Value Decomposition