On maximum-likelihood detection and the search for the closest lattice point

By Mohamed Oussama Damen, Hesham El Gamal, Giuseppe Caire. IEEE Transactions on Information Theory, 2003

1,391 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e938e1ffa36fd7ad0ef97386f2e65fe7ef8e68b7

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. Lajos Hanzo
2. John S. Thompson
3. Giuseppe Caire
4. Luis G. Barbero
5. Shaoshi Yang
6. Hesham El Gamal
7. Mohamed Oussama Damen
8. Arul D. Murugan

In Electrical and Electronic Engineering

1. Lajos Hanzo
2. Giuseppe Caire
3. John S. Thompson
4. Shaoshi Yang
5. Luis G. Barbero
6. Nihar Jindal
7. Hesham El Gamal
8. Robert W. Heath

Citations in the corpus

• Algorithm and implementation of the K-best sphere decoding for MIMO detection (2006, background): “is commonly preprocessed in various practical MIMO detectors [23], [24].”
• Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, methodology): “Several strategies have been proposed to circumvent this predicament, such as the “pseudo-inverse” based linear detection [180], [239], the group detection [513]–[518], the generalized SD detector [56], [57], [519]–[526], the modified non-decorrelated PDA detection [112], [116], [121], [130], the mo”
• Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback (2007, methodology): “These codes can be optimally decoded by using a sphere decoder [59], [60] and achieve the performance promised by the above analysis.”
• VLSI implementation of MIMO detection using the sphere decoding algorithm (2005, methodology): “It can easily be shown [13] that with (4) where is the zero-forcing (or unconstrained ML) solution 1 .”
• MIMO Transceiver Design via Majorization Theory (2007, background): “Interestingly, due to the recent developments on efficient implementation of ML decoding such as sphere decoding [34] and semidefinite relaxation [95], it is now feasible to consider the use of an ML receiver in some cases.”
• Fixing the Complexity of the Sphere Decoder for MIMO Detection (2008, methodology): “the performance of the SD is the same as that of the MLD, if, at least, one vector Hs is found inside the hypersphere [8].”
• Channel Estimation for OFDM (2014, background): “The LSD can be considered as the soft output version of the sphere detector introduced in [159] and [160].”
• Lattice coding and decoding achieve the optimal diversity-multiplexing tradeoff of MIMO channels (2004, methodology): “decoding algorithms using number theoretic tools [ 9 ].”
• A unified framework for tree search decoding: rediscovering the sequential decoder (2005, methodology): “5 Consequently, the algorithm finds the closest point in a shorter time [4].”