15 research outputs found
Maximum mutual information design for amplify-and-forward multi-hop MIMO relaying systems under channel uncertainties
Conference Theme: PHY and FundamentalsIn this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables. © 2012 IEEE.published_or_final_versionThe 2012 IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, 1-4 April 2012. In IEEE Wireless Communications and Networking Conference Proceedings, 2012, p. 781-78
Transceiver Design for Dual-Hop Non-regenerative MIMO-OFDM Relay Systems Under Channel Uncertainties
In this paper, linear transceiver design for dual-hop non-regenerative
(amplify-and-forward (AF)) MIMO-OFDM systems under channel estimation errors is
investigated. Second order moments of channel estimation errors in the two hops
are first deduced. Then based on the Bayesian framework, joint design of linear
forwarding matrix at the relay and equalizer at the destination under channel
estimation errors is proposed to minimize the total mean-square-error (MSE) of
the output signal at the destination. The optimal designs for both correlated
and uncorrelated channel estimation errors are considered. The relationship
with existing algorithms is also disclosed. Moreover, this design is extended
to the joint design involving source precoder design. Simulation results show
that the proposed design outperforms the design based on estimated channel
state information only.Comment: 30 pages, 6 figures, IEEE Transactions on Signal Processing, The
Final Versio
Robust Tomlinson-Harashima precoding for non-regenerative multi-antenna relaying systems
Conference Theme: PHY and FundamentalsIn this paper, we consider the robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems. THP is adopted at the source to mitigate the spatial inter-symbol interference and then a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. Based on the elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the optimization problem is greatly simplified and can be efficiently solved. Finally, the performance advantage of the proposed robust design is assessed by simulation results. © 2012 IEEE.published_or_final_versionThe 2012 IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, 1-4 April 2012. In IEEE Wireless Communications and Networking Conference Proceedings, 2012, p. 753-75
Robust Transceiver Design for AF MIMO Relay Systems with Column Correlations
In this paper, we investigate the robust transceiver design for dual-hop
amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel
estimation errors. Aiming at maximizing the mutual information under imperfect
channel state information (CSI), source precoder at source and forwarding
matrix at the relay are jointly optimized. Using some elegant attributes of
matrix-monotone functions, the structures of the optimal solutions are derived
first. Then based on the derived structure an iterative waterfilling solution
is proposed. Several existing algorithms are shown to be special cases of the
proposed solution. Finally, the effectiveness of the proposed robust design is
demonstrated by simulation results.Comment: 6 Pages, 1 Figur
A General Robust Linear Transceiver Design for Multi-Hop Amplify-and-Forward MIMO Relaying Systems
In this paper, linear transceiver design for multi-hop amplify-and-forward
(AF) multiple-input multiple-out (MIMO) relaying systems with Gaussian
distributed channel estimation errors is investigated. Commonly used
transceiver design criteria including weighted mean-square-error (MSE)
minimization, capacity maximization, worst-MSE/MAX-MSE minimization and
weighted sum-rate maximization, are considered and unified into a single
matrix-variate optimization problem. A general robust design algorithm is
proposed to solve the unified problem. Specifically, by exploiting majorization
theory and properties of matrix-variate functions, the optimal structure of the
robust transceiver is derived when either the covariance matrix of channel
estimation errors seen from the transmitter side or the corresponding
covariance matrix seen from the receiver side is proportional to an identity
matrix. Based on the optimal structure, the original transceiver design
problems are reduced to much simpler problems with only scalar variables whose
solutions are readily obtained by iterative water-filling algorithm. A number
of existing transceiver design algorithms are found to be special cases of the
proposed solution. The differences between our work and the existing related
work are also discussed in detail. The performance advantages of the proposed
robust designs are demonstrated by simulation results.Comment: 30 pages, 7 figures, Accepted by IEEE Transactions on Signal
Processin
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
A Tutorial on the Optimization of Amplify-and-Forward MIMO Relay Systems
The remarkable promise of multiple-input multiple-output (MIMO) wireless channels has motivated an intense research activity to characterize the theoretical and practical issues associated with the design of transmit (source) and receive (destination) processing matrices under different operating conditions. This activity was primarily focused on point-to-point (single-hop) communications but more recently there has been an extensive work on two-hop or multi-hop settings in which single or multiple relays are used to deliver the information from the source to the destination. The aim of this tutorial is to provide an up-to-date overview of the fundamental results and practical implementation issues of designing amplify-and-forward MIMO relay systems
Robust Transceiver with Tomlinson-Harashima Precoding for Amplify-and-Forward MIMO Relaying Systems
In this paper, robust transceiver design with Tomlinson-Harashima precoding
(THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output
(MIMO) relaying systems is investigated. At source node, THP is adopted to
mitigate the spatial intersymbol interference. However, due to its nonlinear
nature, THP is very sensitive to channel estimation errors. In order to reduce
the effects of channel estimation errors, a joint Bayesian robust design of THP
at source, linear forwarding matrices at relays and linear equalizer at
destination is proposed. With novel applications of elegant characteristics of
multiplicative convexity and matrix-monotone functions, the optimal structure
of the nonlinear transceiver is first derived. Based on the derived structure,
the transceiver design problem reduces to a much simpler one with only scalar
variables which can be efficiently solved. Finally, the performance advantage
of the proposed robust design over non-robust design is demonstrated by
simulation results.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Theories and Methods for Advanced Wireless Relays The final version and
several typos have been correcte
Matrix-Monotonic Optimization for MIMO Systems
For MIMO systems, due to the deployment of multiple antennas at both the
transmitter and the receiver, the design variables e.g., precoders, equalizers,
training sequences, etc. are usually matrices. It is well known that matrix
operations are usually more complicated compared to their vector counterparts.
In order to overcome the high complexity resulting from matrix variables, in
this paper we investigate a class of elegant multi-objective optimization
problems, namely matrix-monotonic optimization problems (MMOPs). In our work,
various representative MIMO optimization problems are unified into a framework
of matrix-monotonic optimization, which includes linear transceiver design,
nonlinear transceiver design, training sequence design, radar waveform
optimization, the corresponding robust design and so on as its special cases.
Then exploiting the framework of matrix-monotonic optimization the optimal
structures of the considered matrix variables can be derived first. Based on
the optimal structure, the matrix-variate optimization problems can be greatly
simplified into the ones with only vector variables. In particular, the
dimension of the new vector variable is equal to the minimum number of columns
and rows of the original matrix variable. Finally, we also extend our work to
some more general cases with multiple matrix variables.Comment: 37 Pages, 5 figures, IEEE Transactions on Signal Processing, Final
Versio