6 research outputs found
A framework for joint design of pilot sequence and linear precoder
Most performance measures of pilot-assisted multiple-input multiple-output systems are functions of the linear precoder and the pilot sequence. A framework for the optimization of these two parameters is proposed, based on a matrix-valued generalization of the concept of effective signal-to-noise ratio (SNR) introduced in the famous work by Hassibi and Hochwald. Our framework aims to extend the work of Hassibi and Hochwald by allowing for transmit-side fading correlations, and by considering a class of utility functions of said effective SNR matrix, most notably including the well-known capacity lower bound used by Hassibi and Hochwald. We tackle the joint optimization problem by recasting the optimization of the precoder (resp. pilot sequence) subject to a fixed pilot sequence (resp. precoder) into a convex problem. Furthermore, we prove that joint optimality requires that the eigenbases of the precoder and pilot sequence be both aligned along the eigenbasis of the channel correlation matrix. We finally describe how to wrap all studied subproblems into an iteration that converges to a local optimum of the joint optimization.Peer ReviewedPostprint (author's final draft
Unified Joint Matrix-Monotonic Optimization of MIMO Training Sequences and Transceivers
Channel estimation and transmission constitute the most fundamental
functional modules of multiple-input multiple-output (MIMO) communication
systems. The underlying key tasks corresponding to these modules are training
sequence optimization and transceiver optimization. Hence, we jointly optimize
the linear transmit precoder and the training sequence of MIMO systems using
the metrics of their effective mutual information (MI), effective mean squared
error (MSE), effective weighted MI, effective weighted MSE, as well as their
effective generic Schur-convex and Schur-concave functions. Both statistical
channel state information (CSI) and estimated CSI are considered at the
transmitter in the joint optimization. A unified framework termed as joint
matrix-monotonic optimization is proposed. Based on this, the optimal precoder
matrix and training matrix structures can be derived for both CSI scenarios.
Then, based on the optimal matrix structures, our linear transceivers and their
training sequences can be jointly optimized. Compared to state-of-the-art
benchmark algorithms, the proposed algorithms visualize the bold explicit
relationships between the attainable system performance of our linear
transceivers conceived and their training sequences, leading to implementation
ready recipes. Finally, several numerical results are provided, which
corroborate our theoretical results and demonstrate the compelling benefits of
our proposed pilot-aided MIMO solutions.Comment: 29 pages, 7 figure
A framework for joint design of pilot sequence and linear precoder
Most performance measures of pilot-assisted multiple-input multiple-output systems are functions of the linear precoder and the pilot sequence. A framework for the optimization of these two parameters is proposed, based on a matrix-valued generalization of the concept of effective signal-to-noise ratio (SNR) introduced in the famous work by Hassibi and Hochwald. Our framework aims to extend the work of Hassibi and Hochwald by allowing for transmit-side fading correlations, and by considering a class of utility functions of said effective SNR matrix, most notably including the well-known capacity lower bound used by Hassibi and Hochwald. We tackle the joint optimization problem by recasting the optimization of the precoder (resp. pilot sequence) subject to a fixed pilot sequence (resp. precoder) into a convex problem. Furthermore, we prove that joint optimality requires that the eigenbases of the precoder and pilot sequence be both aligned along the eigenbasis of the channel correlation matrix. We finally describe how to wrap all studied subproblems into an iteration that converges to a local optimum of the joint optimization.Peer Reviewe