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

Energy-Efficient System Design for Future Wireless Communications

The exponential growth of wireless data traffic has caused a significant increase in the power consumption of wireless communications systems due to the higher complexity of the transceiver structures required to establish the communication links. For this reason, in this Thesis we propose and characterize technologies for improving the energy efficiency of multiple-antenna wireless communications. This Thesis firstly focuses on energy-efficient transmission schemes and commences by introducing a scheme for alleviating the power loss experienced by the Tomlinson-Harashima precoder, by aligning the interference of a number of users with the symbols to transmit. Subsequently, a strategy for improving the performance of space shift keying transmission via symbol pre-scaling is presented. This scheme re-formulates complex optimization problems via semidefinite relaxation to yield problem formulations that can be efficiently solved. In a similar line, this Thesis designs a signal detection scheme based on compressive sensing to improve the energy efficiency of spatial modulation systems in multiple access channels. The proposed technique relies on exploiting the particular structure and sparsity that spatial modulation systems inherently possess to enhance performance. This Thesis also presents research carried out with the aim of reducing the hardware complexity and associated power consumption of large scale multiple-antenna base stations. In this context, the employment of incomplete channel state information is proposed to achieve the above-mentioned objective in correlated communication channels. The candidate’s work developed in Bell Labs is also presented, where the feasibility of simplified hardware architectures for massive antenna systems is assessed with real channel measurements. Moreover, a strategy for reducing the hardware complexity of antenna selection schemes by simplifying the design of the switching procedure is also analyzed. Overall, extensive theoretical and simulation results support the improved energy efficiency and complexity of the proposed schemes, towards green wireless communications systems