1,193 research outputs found
Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems
In this work, decision feedback (DF) detection algorithms based on multiple
processing branches for multi-input multi-output (MIMO) spatial multiplexing
systems are proposed. The proposed detector employs multiple cancellation
branches with receive filters that are obtained from a common matrix inverse
and achieves a performance close to the maximum likelihood detector (MLD).
Constrained minimum mean-squared error (MMSE) receive filters designed with
constraints on the shape and magnitude of the feedback filters for the
multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive
implementation of the proposed MB-MMSE-DF detector is developed along with a
recursive least squares-type algorithm for estimating the parameters of the
receive filters when the channel is time-varying. A soft-output version of the
MB-MMSE-DF detector is also proposed as a component of an iterative detection
and decoding receiver structure. A computational complexity analysis shows that
the MB-MMSE-DF detector does not require a significant additional complexity
over the conventional MMSE-DF detector, whereas a diversity analysis discusses
the diversity order achieved by the MB-MMSE-DF detector. Simulation results
show that the MB-MMSE-DF detector achieves a performance superior to existing
suboptimal detectors and close to the MLD, while requiring significantly lower
complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications,
201
Low-Complexity Lattice Reduction Aided Schnorr Euchner Sphere Decoder Detection Schemes with MMSE and SIC Pre-processing for MIMO Wireless Communication Systems
© 2021, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00045The LRAD-MMSE-SIC-SE-SD (Lattice Reduction Aided Detection - Minimum Mean Squared Error-Successive Interference Cancellation - Schnorr Euchner - Sphere Decoder) detection scheme that introduces a trade-off between performance and computational complexity is proposed for Multiple-Input Multiple-Output (MIMO) in this paper. The Lenstra-Lenstra-Lovász (LLL) algorithm is employed to orthogonalise the channel matrix by transforming the signal space of the received signal into an equivalent reduced signal space. A novel Lattice Reduction aided SE-SD probing for the Closest Lattice Point in the transformed reduced signal space is hereby proposed. Correspondingly, the computational complexity of the proposed LRAD-MMSE-SIC-SE-SD detection scheme is independent of the constellation size while it is polynomial with reference to the number of antennas, and signal-to-noise-ratio (SNR). Performance results of the detection scheme indicate that SD complexity is significantly reduced at only marginal performance penalty
Mimo Detection
The use of digital wireless communication systems has become more
and more common during recent years. A multiple-input-multiple-output
(MIMO) system techniques can be implemented to enhance the capacity of
a wireless link. We have investigated the performances of MIMO detectors
: Linear detectors(ZF detector, MMSE detector), SIC(Successive Interfer-
ence Cancellation) signal detectors, Maximum Likelihood detector, Sphere
decoding. In SIC signal detection we use MMSE weight matrix
Dual-lattice ordering and partial lattice reduction for SIC-based MIMO detection
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we propose low-complexity lattice detection algorithms for successive interference cancelation (SIC) in multi-input multi-output (MIMO) communications. First, we present a dual-lattice view of the vertical Bell Labs Layered Space-Time (V-BLAST) detection. We show that V-BLAST ordering is equivalent to applying sorted QR decomposition to the dual basis, or equivalently, applying sorted Cholesky decomposition to the associated Gram matrix. This new view results in lower detection complexity and allows simultaneous ordering and detection. Second, we propose a partial reduction algorithm that only performs lattice reduction for the last several, weak substreams, whose implementation is also facilitated by the dual-lattice view. By tuning the block size of the partial reduction (hence the complexity), it can achieve a variable diversity order, hence offering a graceful tradeoff between performance and complexity for SIC-based MIMO detection. Numerical results are presented to compare the computational costs and to verify the achieved diversity order
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