Lattice-reduction-aided detectors for MIMO communication systems

By H. Yao, Gregory W. Wornell. Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE, 2002

535 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/94b687bd784c054d022a29f496db1b9d2142d74b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Giuseppe Caire
2. Hesham El Gamal
3. Lajos Hanzo
4. Mohamed Oussama Damen
5. Arul D. Murugan
6. Shaoshi Yang
7. Robert Schober
8. Wolfgang H. Gerstacker

In Electrical and Electronic Engineering

1. Giuseppe Caire
2. Lajos Hanzo
3. Hesham El Gamal
4. Shaoshi Yang
5. Mohamed Oussama Damen
6. Arul D. Murugan
7. Robert Schober
8. Zabih Ghassemlooy

In Signal Processing

1. Hesham El Gamal
2. Giuseppe Caire
3. Lajos Hanzo
4. Shaoshi Yang
5. Arul D. Murugan
6. Mohamed Oussama Damen

Citations in the corpus

• Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, methodology): “For example, the LR technique was used in conjunction with traditional linear ZF and nonlinear ZF-SIC detectors in [83], as well as with linear MMSE and nonlinear MMSE-SIC detectors in [85], [465], both achieving a substantial performance gain with little additional computational complexity.”
• Multiple-antenna techniques for wireless communications - a comprehensive literature survey (2009, background): “An interesting approach to improve the performance of the BLAST scheme was presented in [28].”
• AirSync: Enabling Distributed Multiuser MIMO With Full Spatial Multiplexing (2012, methodology): “ performance when allowing for exible user selection [9,38]. In the future we plan to experiment with more sophisticated precoding techniques, such as regularized ZF [22], lattice reduction precoding [37] and modied Tomlinson-Harashima precoding [5], which can be regarded as a viable and very ecient”
• A unified framework for tree search decoding: rediscovering the sequential decoder (2005, methodology): “We also report the performance of the Yao–Wornell and Windpassinger–Fischer (YWWF) decoder which has the same complexity as the MMSE-DFE Babai decoder [48], [49].”
• Experimental Demonstration of 50-Mb/s Visible Light Communications Using 4 $\,\times\,$ 4 MIMO (2014, methodology): “Other standard MIMO techniques such as the sphere decoding with maximum likelihood detector [14] as well as the lattice-reduction-aided detection [15] are available but are also not considered here.”
• Complex Lattice Reduction Algorithm for Low-Complexity Full-Diversity MIMO Detection (2009, background): “However, since the traditional lattice formulation is only directly applicable to a real-valued channel matrix, most conventional lattice-based detectors were derived based on the real-valued equivalent matrix of the complex-valued channel matrix.”
• Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction (2004, unknown): “Recently, lattice-reduction (LR) has been proposed in order to transform the system model into an equivalent one with better conditioned channel matrix prior to low-complexity linear or SIC detection [3], [4].”