We propose a new approach to reach a near maximum likelihood (ML) MIMO detection performance with a strongly reduced computational effort. This method is based on a two-stage detection. In the first detection step a Zero-Forcing (ZF) equalizer is applied followed by a subsequent decision unit. The sec-ond step is a reduced-search (RS) algorithm over the ZF solution, which will be performed in an efficient way at the different transmitted data streams. The method provides a near ML performance while it de-mands a fixed computational effort which is extremely advantageous for the Hardware implementation of the detector.
Abstract – In this paper, a best channel matrix selection scheme (BCMS) is proposed to approximate m...
Abstract—The maximum likelihood (ML) detection for multiple-input multiple-output (MIMO) system achi...
ABSTRACT This paper presents a VLSI implementation of reduced hardware-complexity and reconfigurable...
The Maximum Likelihood Detector (MLD) offers an optimal bit-error-ratio for an un-coded multiple-inp...
Abstract — For multiple-input multiple-output (MIMO) sys-tems, the optimum maximum likelihood (ML) d...
In this paper, we propose a low complexity detection scheme for MIMO systems incorporating spatial m...
In this paper, we propose a simplified Maximum Likelihood (ML) detection scheme for Multiple-Input M...
Abstract: Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often di...
Abstract—We propose a fixed-effort MIMO decoder for fre-quency selective indoor channels that are ch...
International audienceIn Spatial Multiplexing MIMO systems, many powerful non-linear detection techn...
AIn this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
Abstract — This paper proposes a suboptimal maximum like-lihood detection (MLD) algorithm for multip...
Abstract – In this paper, a best channel matrix selection scheme (BCMS) is proposed to approximate m...
Abstract—The maximum likelihood (ML) detection for multiple-input multiple-output (MIMO) system achi...
ABSTRACT This paper presents a VLSI implementation of reduced hardware-complexity and reconfigurable...
The Maximum Likelihood Detector (MLD) offers an optimal bit-error-ratio for an un-coded multiple-inp...
Abstract — For multiple-input multiple-output (MIMO) sys-tems, the optimum maximum likelihood (ML) d...
In this paper, we propose a low complexity detection scheme for MIMO systems incorporating spatial m...
In this paper, we propose a simplified Maximum Likelihood (ML) detection scheme for Multiple-Input M...
Abstract: Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often di...
Abstract—We propose a fixed-effort MIMO decoder for fre-quency selective indoor channels that are ch...
International audienceIn Spatial Multiplexing MIMO systems, many powerful non-linear detection techn...
AIn this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detec...
Abstract — This paper proposes a suboptimal maximum like-lihood detection (MLD) algorithm for multip...
Abstract – In this paper, a best channel matrix selection scheme (BCMS) is proposed to approximate m...
Abstract—The maximum likelihood (ML) detection for multiple-input multiple-output (MIMO) system achi...
ABSTRACT This paper presents a VLSI implementation of reduced hardware-complexity and reconfigurable...