494 research outputs found
Robust MMSE Precoding Strategy for Multiuser MIMO Relay Systems with Switched Relaying and Side Information
In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.
Robust Lattice Alignment for K-user MIMO Interference Channels with Imperfect Channel Knowledge
In this paper, we consider a robust lattice alignment design for K-user
quasi-static MIMO interference channels with imperfect channel knowledge. With
random Gaussian inputs, the conventional interference alignment (IA) method has
the feasibility problem when the channel is quasi-static. On the other hand,
structured lattices can create structured interference as opposed to the random
interference caused by random Gaussian symbols. The structured interference
space can be exploited to transmit the desired signals over the gaps. However,
the existing alignment methods on the lattice codes for quasi-static channels
either require infinite SNR or symmetric interference channel coefficients.
Furthermore, perfect channel state information (CSI) is required for these
alignment methods, which is difficult to achieve in practice. In this paper, we
propose a robust lattice alignment method for quasi-static MIMO interference
channels with imperfect CSI at all SNR regimes, and a two-stage decoding
algorithm to decode the desired signal from the structured interference space.
We derive the achievable data rate based on the proposed robust lattice
alignment method, where the design of the precoders, decorrelators, scaling
coefficients and interference quantization coefficients is jointly formulated
as a mixed integer and continuous optimization problem. The effect of imperfect
CSI is also accommodated in the optimization formulation, and hence the derived
solution is robust to imperfect CSI. We also design a low complex iterative
optimization algorithm for our robust lattice alignment method by using the
existing iterative IA algorithm that was designed for the conventional IA
method. Numerical results verify the advantages of the proposed robust lattice
alignment method
Robust Beamforming for Amplify-and-Forward MIMO Relay Systems Based on Quadratic Matrix Programming
In this paper, robust transceiver design based on minimum-mean-square-error
(MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is
investigated. The channel estimation errors are modeled as Gaussian random
variables, and then the effect are incorporated into the robust transceiver
based on the Bayesian framework. An iterative algorithm is proposed to jointly
design the precoder at the source, the forward matrix at the relay and the
equalizer at the destination, and the joint design problem can be efficiently
solved by quadratic matrix programming (QMP).Comment: Proceedings of IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP'2010), U.S.
MmWave Amplify-and-Forward MIMO Relay Networks with Hybrid Precoding/Combining Design
In this paper, we consider the amplify-and-forward relay networks in mmWave
systems and propose a hybrid precoder/combiner design approach. The phase-only
RF precoding/combining matrices are first designed to support multi-stream
transmission, where we compensate the phase for the eigenmodes of the channel.
Then, the baseband precoders/combiners are performed to achieve the maximum
mutual information. Based on the data processing inequality for the mutual
information, we first jointly design the baseband source and relay nodes to
maximize the mutual information before the destination baseband receiver. The
proposed low-complexity iterative algorithm for the source and relay nodes is
based on the equivalence between mutual information maximization and the
weighted MMSE. After we obtain the optimal precoder and combiner for the source
and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to
keep the mutual information unchanged, thus obtaining the optimal mutual
information for the whole relay system. Simulation results show that our
algorithm achieves better performance with lower complexity compared with other
algorithms in the literature. In addition, we also propose a robust joint
transceiver design for imperfect channel state information
Robust joint design of linear relay precoder and destination equalizer for dual-hop amplify-and-forward MIMO relay systems
This paper addresses the problem of robust linear relay precoder and destination equalizer design for a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, two robust design algorithms are proposed to minimize the mean-square error (MSE) of the output signal at the destination. One is an iterative algorithm with its convergence proved analytically. The other is an approximated closed-form solution with much lower complexity than the iterative algorithm. Although the closed-form solution involves a minor relaxation for the general case, when the column covariance matrix of the channel estimation error at the second hop is proportional to identity matrix, no relaxation is needed and the proposed closed-form solution is the optimal solution. Simulation results show that the proposed algorithms reduce the sensitivity of the AF MIMO relay systems to channel estimation errors, and perform better than the algorithm using estimated channels only. Furthermore, the closed-form solution provides a comparable performance to that of the iterative algorithm. © 2006 IEEE.published_or_final_versio
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