Approaching the MIMO Capacity with a Low-Rate Feedback Channel in V-BLAST

Chung, Seong Taek; Lozano, Angel; Huang, Howard; Sutivong, A.; Cioffi, J.M.

doi:10.1155/s1110865704312035

Cited by 37 publications

(29 citation statements)

References 18 publications

(26 reference statements)

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“…The unquantized rate is the sum of unquantized bit assignments , where is computed according to (3). For the quantized rate , predictive quantization of bit loading is applied assuming the transmitter knows only the quantized bit loading is predicted using (14) and estimated using (15). The curve " ( known)" is the quantized transmission rate assuming is known to the transmitter so that the bit loading is predicted with the knowledge of in (8) and the quantizer is adapted according to (10).…”

Section: Simulation Examplesmentioning

confidence: 99%

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Variable-Rate Transmission for MIMO Time-Correlated Channels With Limited Feedback

Lin

2014

IEEE Trans. Signal Process.

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Abstract-In this paper we consider variable-rate transmission for time-correlated MIMO (multi-input multi-output) channels with limited feedback. The number of bits loaded on each subchannel of the MIMO system is dynamically assigned according to the current channel condition and fed back to the transmitter. As the channel is time-correlated, bit loading is a vector signal that is also time-correlated. We propose to feedback bit loading using predictive coding, which is known to be a powerful quantization technique when the underlying signal is correlated in time. Assuming the channel is a first-order Gauss-Markov random process, we derive the predictor for the bit loading to be coded and analyze the corresponding prediction error variance when the channel is varying slowly. By exploiting the prediction error variance, we adapt the quantizer of the prediction error to have a smaller quantization error. Furthermore we show that the prediction error variance is proportional to a term that depends only on the time-correlation coefficient. This leads to the conclusion that, a codebook that is designed for a particular time correlation coefficient can be easily modified to a codebook for a different correlation coefficient without redesign. Simulations are presented to demonstrate that the proposed predictive coding can achieve a very good approximation of the desired transmission rate with a very low feedback rate.Index Terms-MIMO, limited feedback, quantization of bit loading, variable rate transmission.

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Section: Simulation Examplesmentioning

confidence: 99%

“…Feedback of quantized precoder, power allocation and/or bit allocation is considered in [12], [13]. Successive quantization of power and bit allocation is proposed in [14]. An efficient algorithm for per antenna power and rate control is developed in [15].…”

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confidence: 99%

Variable-Rate Transmission for MIMO Time-Correlated Channels With Limited Feedback

Lin

2014

IEEE Trans. Signal Process.

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“…In the following sections, we will first discuss the problem of minimizing the transmitted power subject to a specified total bit rate and a specified error probability in each sub-stream. Assume the components of are zero-mean uncorrelated processes representing independent data streams with power so that the input covariance is (4) We assume the data stream is a -bit QAM constellation. From (3), since the error vector at the input of the decision device is where is zero-mean Gaussian, the error components are zero-mean Gaussian with variance…”

Section: Formulating the Power Minimization Problemmentioning

confidence: 99%

“…Equation (15) is called the optimum bit loading formula. 4 For any fixed precoder , receiver , and specified probabilities of error , the bit allocation that minimizes the transmitted power is given by (15). With this , the quantities are computed from (7) where is as in (5).…”

Section: Optimal Bit-loaded Dfe Transceiversmentioning

confidence: 99%

“…Third, for the case of linear transceivers, the joint optimization of the precoder, the (linear) equalizer, and bit allocation has been studied in [17] (under ZF constraint), and in [21] (without ZF constraint). Fourth, if the precoding matrix is restricted to be a diagonal matrix where only power loading applies, the optimization problem of rate and power allocation for the systems with DFE receiver has been discussed in [4]. If no perfect channel state information is present (only channel statistics known at the transmitter), the optimization of power and rate allocation for the system with DFE receiver was addressed in [24].…”

Section: Introductionmentioning

confidence: 99%

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MIMO Transceivers With Decision Feedback and Bit Loading: Theory and Optimization

Weng

Chen

Vaidyanathan

2010

IEEE Trans. Signal Process.

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Abstract-This paper considers MIMO transceivers with linear precoders and decision feedback equalizers (DFEs), with bit allocation at the transmitter. Zero-forcing (ZF) is assumed. Considered first is the minimization of transmitted power, for a given total bit rate and a specified set of error probabilities for the symbol streams. The precoder and DFE matrices are optimized jointly with bit allocation. It is shown that the generalized triangular decomposition (GTD) introduced by Jiang, Li, and Hager offers an optimal family of solutions. The optimal linear transceiver (which has a linear equalizer rather than a DFE) with optimal bit allocation is a member of this family. This shows formally that, under optimal bit allocation, linear and DFE transceivers achieve the same minimum power. The DFE transceiver using the geometric mean decomposition (GMD) is another member of this optimal family, and is such that optimal bit allocation yields identical bits for all symbol streams-no bit allocation is necessary-when the specified error probabilities are identical for all streams. The QR-based system used in VBLAST is yet another member of the optimal family and is particularly well-suited when limited feedback is allowed from receiver to transmitter. Two other optimization problems are then considered: a) minimization of power for specified set of bit rates and error probabilities (the QoS problem), and b) maximization of bit rate for fixed set of error probabilities and power. It is shown in both cases that the GTD yields an optimal family of solutions.

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Performance analysis for relay networks with hierarchical support vector machines

Liu

Feng

Chang

2011

Int J Communication

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SUMMARY In this paper, we consider a cooperative relay scheme for a mobile network with MIMO technology. The channel capacity for two well‐known relaying schemes are investigated: analogue relaying (amplify and forward) and digital relaying (decode and forward) from a mobile device to the base station through a relay node. In order to further increase the channel capacity, we propose an efficient hierarchical procedure based on support vector machine, namely hierarchical support vector machines (HSVM), to estimate the wireless networks condition approximately and design two ways (matched filter and minimum mean square error filter) of increasing the channel capacity according to the estimated wireless network condition. The proposed HSVM can estimate the wireless networks condition in much shorter time compared with the traditional minimum mean square error scheme without incurring much estimation error, which is spatial, useful for delay sensitive communication. For digital relaying, the effect of imperfect channel decode is also addressed. Our numerical results demonstrate the reduction of estimation complexity by adopting HSVM and the significant improvement of network capacity by applying the matched filter weight at relay nodes according to the network estimation. Copyright © 2011 John Wiley & Sons, Ltd.

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Approaching the MIMO Capacity with a Low-Rate Feedback Channel in V-BLAST

Cited by 37 publications

References 18 publications

Variable-Rate Transmission for MIMO Time-Correlated Channels With Limited Feedback

Variable-Rate Transmission for MIMO Time-Correlated Channels With Limited Feedback

MIMO Transceivers With Decision Feedback and Bit Loading: Theory and Optimization

Performance analysis for relay networks with hierarchical support vector machines

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