28 research outputs found
A worst-case robust MMSE transceiver design for nonregenerative MIMO relaying
Transceiver designs have been a key issue in guaranteeing the performance of multiple-input multiple-output (MIMO) relay systems, which are, however, often subject to imperfect channel state information (CSI). In this paper, we aim to design a robust MIMO transceiver for nonregenerative MIMO relay systems against imperfect CSI from a worst-case robust perspective. Specifically, we formulate the robust transceiver design, under the minimum mean-squared error (MMSE) criterion, as a minimax problem. Then, by decomposing the minimax problem into two subproblems with respect to the relay precoder and destination equalizer, respectively, we show that the optimal solution to each subproblem has a favorable channel-diagonalizing structure under some mild conditions. Based on this finding, we transform the two complex-matrix subproblems into their equivalent scalar forms, both of which are proven to be convex and can be efficiently solved by our proposed methods. We further propose an alternating algorithm to jointly optimize the precoder and equalizer that only requires scalar operations. Finally, the effectiveness of the proposed robust design is verified by simulation results
Robust MMSE beamforming for multiantenna relay networks
In this paper, we propose a robust minimum mean square error (MMSE) based beamforming
technique for multiantenna relay broadcast channels, where a multi-antenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot and it then forwards the received signal to different users based on amplify and forward protocol. We propose a robust beamforming technique for sum-power
minimization problem with imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and Nemirovski
Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint non-convexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting Nemirovski Lemma, and an iterative algorithm is developed by
alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results have been provided to validate the convergence of the proposed algorithm
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
Hybrid Transceiver Optimization for Multi-Hop Communications
Multi-hop communication with the aid of large-scale antenna arrays will play
a vital role in future emergence communication systems. In this paper, we
investigate amplify-and-forward based and multiple-input multiple-output
assisted multi-hop communication, in which all nodes employ hybrid
transceivers. Moreover, channel errors are taken into account in our hybrid
transceiver design. Based on the matrix-monotonic optimization framework, the
optimal structures of the robust hybrid transceivers are derived. By utilizing
these optimal structures, the optimizations of analog transceivers and digital
transceivers can be separated without loss of optimality. This fact greatly
simplifies the joint optimization of analog and digital transceivers. Since the
optimization of analog transceivers under unit-modulus constraints is
non-convex, a projection type algorithm is proposed for analog transceiver
optimization to overcome this difficulty. Based on the derived analog
transceivers, the optimal digital transceivers can then be derived using
matrix-monotonic optimization. Numeral results obtained demonstrate the
performance advantages of the proposed hybrid transceiver designs over other
existing solutions.Comment: 32 pages, 6 figures. This manuscript has been submitted to IEEE
Journal on Selected Areas in Communications (special issue on Multiple
Antenna Technologies for Beyond 5G
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 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
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