Maximum likelihood source separation for finite impulse response multiple input-multiple output channels in the presence of additive noise

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

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Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

10.36227/techrxiv.15054321.v2 ◽

2021 ◽

Author(s):

Pasquale Di Viesti ◽

ALESSANDRO DAVOLI ◽

Giorgio Guerzoni ◽

Giorgio Matteo Vitetta

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Multiple Input Multiple Output ◽

Likelihood Estimation ◽

Approximate Maximum Likelihood ◽

Radar Systems ◽

Multiple Input ◽

Input Multiple Output ◽

Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

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Joint maximum-likelihood estimation of frequency offset and channel coefficients in multiple-input multiple-output orthogonal frequency-division multiplexing systems with timing ambiguity

IET Communications ◽

10.1049/iet-com.2010.0505 ◽

2011 ◽

Vol 5 (14) ◽

pp. 1964-1970 ◽

Cited By ~ 7

Author(s):

S. Salari ◽

M. Heydarzadeh

Keyword(s):

Maximum Likelihood ◽

Orthogonal Frequency Division Multiplexing ◽

Multiple Input Multiple Output ◽

Likelihood Estimation ◽

Frequency Division Multiplexing ◽

Frequency Division ◽

Joint Maximum Likelihood Estimation ◽

Multiple Input ◽

Input Multiple Output ◽

Multiplexing Systems

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MIMO Detectors Under Correlated Channels

Semina Ciências Exatas e Tecnológicas ◽

10.5433/1679-0375.2016v37n1p3 ◽

2016 ◽

Vol 37 (1) ◽

pp. 3

Author(s):

Bruno Felipe Costa ◽

Alex Miyamoto Mussi ◽

Taufik Abrão

Keyword(s):

Maximum Likelihood ◽

Interference Cancellation ◽

Mean Squared Error ◽

Multiple Input Multiple Output ◽

Successive Interference Cancellation ◽

Correlated Channels ◽

Minimum Mean Squared Error ◽

Squared Error ◽

Multiple Input ◽

Input Multiple Output

Este artigo analisa o desempenho de detectores com múltiplas antenas transmissoras e múltiplas antenas receptoras (MIMO – multiple-input multiple-output) em canais com desvanecimento correlacionados. Dois esquemas de detecção MIMO denominados erro quadrático médio mínimo (MMSE – minimum mean squared error) – com ou sem a etapa de cancelamento de interferência sucessiva ordenado (OSIC – ordered successive interference cancellation) – e técnica de redução treliça (LR – lattice reduction) são analisados e comparados com o limite de detecção de máxima verossimilhança (ML – maximum likelihood) em cenários específicos de interesse: (a) com incremento da eficiência espectral através do aumento do número de antenas. (b) quando há aumento nos índices de correlação de desvanecimento do canal. Neste contexto, o desempenho do detector ótimo ML-MIMO é utilizado como referência visando caracterizar o comportamento da taxa de erro de bit (BER) destes detectores MIMO e quão próximo esses estão do desempenho ML-MIMO.

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Partition-Based Hybrid MIMO Decoding Schemes with Combined Depth- and Breath-First Search

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2652 ◽

2013 ◽

Vol 284-287 ◽

pp. 2652-2656

Author(s):

Jong In Park ◽

Young Po Lee ◽

Seok Ho Yoon

Keyword(s):

Computational Complexity ◽

Maximum Likelihood ◽

Multiple Input Multiple Output ◽

Search Algorithms ◽

Search Method ◽

Breadth First Search ◽

Error Performance ◽

Search Methods ◽

Multiple Input ◽

Input Multiple Output

In this paper, we propose a novel maximum likelihood (ML) decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, possibly exploiting the advantages of both the depth- and breadth-first search methods in an organized way. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.

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