Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
420
Dynamic nulling-and-cancelling with near-ML performance
- in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP
, 2004
"... The conventional nulling-and-cancelling (NC) detection scheme for MIMO systems uses the layerwise post-detection mean-square errors (MSEs) as reliability measures for layer sorting. These MSEs are average measures that do not depend on the received vector. In this paper, we propose the novel dynamic ..."
Abstract
-
Cited by 8 (4 self)
- Add to MetaCart
in an MMSE nulling technique that uses a simple layersorting rule with significantly improved performance. Our simulation results show that the DNC scheme can yield near-ML performance for a wide range of system sizes and signal-to-noise ratios. 1.
Efficient dipole parameter estimation in EEG systems with near-ML performance
- IEEE Trans. Biomed. Eng
"... Abstract—Source signals that have strong temporal correlation can pose a challenge for high-resolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other high-resolution methods such as mult ..."
Abstract
-
Cited by 6 (5 self)
- Add to MetaCart
subspace fitting concept, and has been shown to provide performance that is asymptotically equivalent to the di-rect ML method. Both techniques lead to a considerably simpler optimization than ML since the estimation of the source locations and dipole moments is decoupled. Examples using data from sim
A low-complexity near-ML performance achieving algorithm for large MIMO detection
- in Proc. IEEE ISIT’2008
, 2008
"... Abstract—In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The propos ..."
Abstract
-
Cited by 14 (11 self)
- Add to MetaCart
Abstract—In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems
Efficient near-ML detection for MIMO channels: The sphere-projection algorithm
- in Proc. IEEE Globecom 2003, vol. IV
, 2003
"... Abstract — It is well known that suboptimal detection schemes for MIMO spatial multiplexing systems (equalization-based as well as nulling-and-cancelling detectors) cannot exploit all of the available diversity. In this paper, we show that this inferior performance is primarily caused by poorly cond ..."
Abstract
-
Cited by 4 (2 self)
- Add to MetaCart
conditioned channel realizations. We then present the novel sphere-projection algorithm (SPA) that is robust to poorly conditioned channels. The SPA is a computationally efficient add-on to standard suboptimal detectors. Simulation results show that the SPA is able to achieve near-ML performance
Dynamic nulling-and-canceling for efficient near-ML decoding of MIMO systems
- IEEE Transactions on Signal Processing
, 2006
"... Abstract—It is known that conventional nulling-and-canceling (NC) detection for multiple-input/multiple-output (MIMO) systems cannot exploit all of the available diversity, and, thus, its performance is significantly inferior to that of maximum likelihood (ML) detection. Conventional NC employs the ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
that it is upper bounded by the error probability of conventional NC. Simulation results are presented for spatial multiplexing systems and for systems using linear dispersion codes. It is demonstrated that the DNC technique can yield near-ML performance for a wide range of system sizes and channel SNRs at a
Research Article A Near-ML Complex K-Best Decoder with Efficient Search Design for MIMO Systems
"... License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A low-complexity near-ML K-Best sphere decoder is proposed. The development of the proposed K-Best sphere decoding algorithm (SDA) involves two stages. First, a new ca ..."
Abstract
- Add to MetaCart
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A low-complexity near-ML K-Best sphere decoder is proposed. The development of the proposed K-Best sphere decoding algorithm (SDA) involves two stages. First, a new
MMSE-based lattice-reduction for near-ML detection of MIMO systems
- in ITG Workshop on Smart Antennas
"... Abstract — Recently the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In this paper, we adopt these lattice-reduction aided schemes to the MMSE criterion. We show that an obvious way to do this is suboptimum and propose an alternative method based on an ..."
Abstract
-
Cited by 14 (0 self)
- Add to MetaCart
on an extended system model. In conjunction with simple successive interference cancellation this scheme almost reaches the performance of maximum-likelihood detection. Furthermore, we demonstrate that the application of Sorted QR Decomposition (SQRD) as a initialization step can significantly reduce
A VLSI 8 × 8 MIMO Near-ML Detector with Preprocessing
"... Abstract Multiple-input multiple-output (MIMO) systems are of significant interest due to their ability to increase the capacity of wireless communications systems, but for these to be useful they must also be practical for implementation in VLSI circuits. A particularly difficult part of these syst ..."
Abstract
- Add to MetaCart
preprocessing unit, which performs the channel decomposition functions that are either omitted or performed “off-line ” in other designs. The proposed device achieves near maximum likelihood bit error rate results at 57.6 Mbps. Other novelties include a high speed sorting mechanism and power saving features.
An Efficient Quasi-Maximum Likelihood Decoding for Finite Constellations
- in Conference on Information Sciences and Systems (CISS) 2005
, 2005
"... In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N- dimensional complex space. In general, this algorithm is shown to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on SemiDefinite ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. The general algorithm built on these models has a near-ML performance with polynomial computational complexity.
An Efficient Quasi-Maximum Likelihood Decoding for Finite Constellations
- in Conference on Information Sciences and Systems (CISS) 2005
, 2005
"... In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this algorithm is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Defini ..."
Abstract
- Add to MetaCart
-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. The resulting algorithms built on these models have near-ML performances with polynomial computational complexities.
Results 1 - 10
of
420
