The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

Convex decomposition of dimension-altering quantum channels

Quantum channels, which are completely positive and trace preserving mappings, can alter the dimension of a system, e.g. a quantum channel from a qubit to a qutrit. We study the convex set properties of dimension-altering quantum channels, and particularly the channel decomposition problem in terms of convex sum of extreme channels. We provide various quantum circuit representations of extreme and generalized extreme channels, which can be employed in an optimization to approximately decompose an arbitrary channel. Numerical simulations of low-dimensional channels are performed to demonstrate our channel decomposition scheme.