Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

Micolau, Francesc Rey; Lamarca, M.; Vazquez, G.

doi:10.1109/tsp.2004.842199

Cited by 90 publications

(68 citation statements)

References 19 publications

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“…The MBER filtering design has also been incorporated into turbo iterative detection [51][52][53]. Other applications include the MBER transmission schemes [54][55][56], the MBER rake receiver [57,58], the MBER-based optical receiver [59][60][61][62], and the power control or allocation based on the MBER criterion [63][64][65][66]. It can be seen that the introduction of the novel MBER design has opened up a new research direction and has stimulated wide interests in the communication research community.…”

Section: Introductionmentioning

confidence: 99%

Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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Adaptive filtering has been an enabling technology and has found ever-increasing applications in various state-of-the-art communication systems. Traditionally, adaptive filtering has been developed based on the Wiener or minimum mean square error (MMSE) approach, and the famous least mean square algorithm with its low computational complexity readily meets the fast real-time computational constraint of modern high-speed communication systems. For a communication system, however, it is the system's bit error rate (BER), not the mean square error (MSE), that really matters. It has been recognised that minimising the MSE criterion does not necessarily produce the minimum BER (MBER) performance. The introduction of the novel MBER design has opened up a whole new chapter in the optimisation of communication systems, and its design trade-offs have to be documented in contrast to those of the classic but actually still unexhausted MMSE and other often-used optimisation criteria. This contribution continues this theme, and we provide a generic framework for adaptive minimum error-probability filter design suitable for the employment in a variety of communication systems. Advantages and disadvantages of the adaptive minimum error-probability filter design are analysed extensively, in comparison with the classic Wiener filter design. r

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Section: Introductionmentioning

confidence: 99%

Adaptive minimum error-rate filtering design: A review

Chen

Tan

et al. 2008

Signal Processing

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“…Since , , and are independent, the MSE expression (7) can be written as (8) Because and are independent, the first term of MSE is (9) For the inner expectation, due to the fact that the distribution of is matrix-variate complex Gaussian with zero mean, the following equation holds [25]: (10) Applying (10) and the corresponding result for to (9), the first term of MSE becomes (11) Similarly, the second term of MSE in (8) can be simplified as (12) Based on (11) and (12), the MSE (8) equals to (13) where (14) (15) Notice that the matrix is the autocorrelation matrix of the receive signal at the relay.…”

Section: Problem Formulationmentioning

confidence: 99%

Robust Joint Design of Linear Relay Precoder and Destination Equalizer for Dual-Hop Amplify-and-Forward MIMO Relay Systems

Xing

2010

IEEE Trans. Signal Process.

176

205

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Abstract-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.Index Terms-Amplify-and-forward (AF), equalizer, minimum mean-square-error (MMSE), multiple-input multiple-output (MIMO), precoder, relay.

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“…Previously, MIMO spatial multiplexing systems with joint linear processing at both ends have been studied from different perspectives under the minimum total MSE design criterion subject to a total power constraint [1]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…The receiver, however, has perfect CSI. In [4], Chapter 7, and [8], channel estimation error at both ends has been taken into account, but no results have been obtained when there is channel correlation. More recently, in [9], channel estimation error as well as receive correlation information has been considered in the transceiver design.…”

Section: Introductionmentioning

confidence: 99%

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MIMO LMMSE Transceiver Design with Imperfect CSI at Both Ends

Ding

Blostein

2007

2007 IEEE International Conference on Communications

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Abstract-This paper presents a new result on minimum total mean squared error (MSE) joint transceiver design for multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at both ends and subject to a total power constraint. The channel knowledge here is the channel mean and transmit correlation information. The joint design is formulated into an optimization problem, to which the closed-form optimum solution is found. The optimum precoder consists of a linear filter, a matrix which collects effective channel eigenmodes, and a diagonal power allocation matrix. The linear filter balances the suppression of channel noise and the additional noise induced by channel estimation error. Given the precoder, the optimum decoder is simply a linear minimum MSE (LMMSE) data estimator. The performance degradations due to imperfect channel estimation and/or transmit correlation are demonstrated by simulation results. The relation between the minimum total MSE design and the maximum mutual information design is determined as well, under the above mentioned imperfect CSI.

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Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

Cited by 90 publications

References 19 publications

Adaptive minimum error-rate filtering design: A review

Adaptive minimum error-rate filtering design: A review

Robust Joint Design of Linear Relay Precoder and Destination Equalizer for Dual-Hop Amplify-and-Forward MIMO Relay Systems

MIMO LMMSE Transceiver Design with Imperfect CSI at Both Ends

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