153 research outputs found
Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System
This paper studies the issue of sparsity adaptive channel reconstruction in time-varying cooperative
communication networks through the amplify-and-forward transmission scheme. A new sparsity adaptive system
identification method is proposed, namely reweighted norm ( < < ) penalized least mean square(LMS)algorithm.
The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing
so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification.
Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper
bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm.
With the upper bounds, we prove that the ( < < ) norm sparsity inducing cost function is superior to the
reweighted norm. An optimal selection of for the norm problem is studied to recover various sparse channel
vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus
demonstrate that the proposed algorithm has a better convergence speed and better steady state behavior than other LMS
algorithms
Compressive Sensing for Multi-channel and Large-scale MIMO Networks
Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently.
Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels.
Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data recoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications
Compressive Sensing for Multi-channel and Large-scale MIMO Networks
Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the
channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently.
Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of
two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding
schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including
the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels.
Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data precoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation
and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications
Recommended from our members
Ultra-Wideband Relay Communication Systems
Impulse-radio ultra-wide-band (IR-UWB) signaling is a promising technique
for high-speed, short-range relay communications networks. Depending on how
the relay node retransmits the signal, there are two main relay schemes: conventional
one-directional (one-way) relay model, and bi-directional (two-way) relay
model. In bi-directional relay communications, wireless network coding (WNC),
also called physical-layer network coding (PNC), could be applied to overcome
the spectral efficiency limitation of the conventional one-way relay.
In the first part of this work, we propose asynchronous, differential, and
bidirectional decode and forward (ADBDF) and asynchronous, differential, and
bidirectional denoise and forward (ADBDNF) UWB relay methods, where the
relay node (RN) does not need to be synchronized with the end nodes (ENs). The
proposed schemes are attractive for networks in which stringent/complicated
synchronization between the RN and the ENs may not be feasible.
The second part of this work focuses on UWB channel classification. We propose
a 2-dimensional (2-D) LOS/NLOS classification scheme that uses skewness of the channel impulse/pulse response. The proposed channel classification decreases
the complexity of existing channel classification methods and can be used
in a variety of areas such as localization, relay communications, and cooperative
communications.
The final part of this work deals with compressive sensing (CS) algorithms
that employ sub-Nyquist sampling for UWB communications. We develop coarse
graining (CG) for the proposed CS sub-Nyquist sampling technique, which leads
to: (1) reduced sampling rate at the receiver, and hence reduced use of analog-to-digital
converters (ADCs) resources; and (2) low-complexity channel estimation
Ultra Wideband
Ultra wideband (UWB) has advanced and merged as a technology, and many more people are aware of the potential for this exciting technology. The current UWB field is changing rapidly with new techniques and ideas where several issues are involved in developing the systems. Among UWB system design, the UWB RF transceiver and UWB antenna are the key components. Recently, a considerable amount of researches has been devoted to the development of the UWB RF transceiver and antenna for its enabling high data transmission rates and low power consumption. Our book attempts to present current and emerging trends in-research and development of UWB systems as well as future expectations
- …