A Markov Model for MIMO Channel Condition Number with Application to Dual-Mode Antenna Selection

Kuo, Ping-Heng; Smith, Peter J.; Garth, L.M.

doi:10.1109/vetecs.2007.108

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…Therefore, it is possible to define log K k = instead of k , which can be approximated by a gamma random variable [10]. Based on the preceding discussion in the section, SM is selected if K is less than or equal to the threshold 2 min, 2 min,…”

Section: B Markov Model Of the Channel Condition Numbermentioning

confidence: 99%

“…Here, the derivation of the LCR for K is epitomized, however a more complete version is offered in [10]. Because the distribution of K can be very accurately approximated by a gamma variable, the LCR formula for the gamma process is given by:…”

Section: B Markov Model Of the Channel Condition Numbermentioning

confidence: 99%

“…Substituting (14) into (12), the LCR can be computed. And then, according to (10) and (11), the transition probabilities can be calculated. Finally, it is interested to analyze average probabilities of mode selection error, which is given by 6 10 Monte Carlo samples are generated.…”

Section: B Markov Model Of the Channel Condition Numbermentioning

confidence: 99%

See 2 more Smart Citations

Adaptive switching based on Markov model for multiuser MIMO-OFDM systems

Tang

Jing

Zhang

2013

2013 22nd Wireless and Optical Communication Conference

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MIMO technology combined with OFDM has been used widely in LTE-Advanced (LTE-A) system. An adaptive switching algorithm based on a two-state Markov model using the condition number is proposed to perform the switching between spatial diversity and spatial multiplexing. Furthermore, the level crossing rates (LCR) of condition number is defined to calculate the transition probabilities. The simulation results indicate that the proposed algorithm could improve the performance of the bit error rate (BER) in multiuser MIMO-OFDM systems. Based on that, the transition probabilities and the average probability of mode selection error are used to investigate in different switching threshold.

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Section: B Markov Model Of the Channel Condition Numbermentioning

confidence: 99%

Section: B Markov Model Of the Channel Condition Numbermentioning

confidence: 99%

See 1 more Smart Citation

Adaptive switching based on Markov model for multiuser MIMO-OFDM systems

Tang

Jing

Zhang

2013

2013 22nd Wireless and Optical Communication Conference

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“…However, his analysis was limited to the case of (2 × 2) central Wishart matrices with unit variance where the generalized work of Ratnarajah et al [9] accounted for matrices of random size and with arbitrary variance. An interesting approach to model the temporal transition probabilities of the condition number using a finite-state Markov process can be found in [10]; more importantly, it was shown that the CDF of the logarithm of the condition number can be approximated particularly well via a gamma variable. Yet, all the above cited papers ( [8]- [10]) were limited to Rayleighfading MIMO channels while the extension to Ricean-fading channels remains an open problem.…”

mentioning

confidence: 99%

“…An interesting approach to model the temporal transition probabilities of the condition number using a finite-state Markov process can be found in [10]; more importantly, it was shown that the CDF of the logarithm of the condition number can be approximated particularly well via a gamma variable. Yet, all the above cited papers ( [8]- [10]) were limited to Rayleighfading MIMO channels while the extension to Ricean-fading channels remains an open problem. On this basis, in the present study we explore the statistics of the condition number of a dual non-central Wishart matrix and introduce closedform formulae for its PDF and CDF as weighted sums of polynomials.…”

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confidence: 99%

On analytical derivations of the condition number distributions of dual non-central Wishart matrices

Matthaiou

Laurenson

Wang

2009

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we explore the statistical characterization of Multiple-Input Multiple-Output (MIMO) channel correlation matrices with the main focus being on their condition number statistics. More specifically, novel expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the MIMO condition number. Contrary to the majority of related studies, where only the common case of Rayleigh fading was considered, our investigation is extended to account for the generalized case of Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link. The overall analysis is based on the principles of random matrix theory and particularly of dual complex noncentral Wishart matrices; the latter represent a practical class of MIMO systems, namely dual-branch systems which are equipped with two transmit and receive antenna elements. All the derived formulae are validated through extensive simulations with the attained accuracy being remarkably good.

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Limited Feedback Beamforming Over Temporally-Correlated Channels

Huang

Heath

Andrews

2009

IEEE Trans. Signal Process.

116

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Feedback of quantized channel state information (CSI), called limited feedback, enables transmit beamforming in multiple-input-multiple-output (MIMO) wireless systems with a small amount of overhead. Due to its efficiency, beamforming with limited feedback has been adopted in several wireless communication standards. Prior work on limited feedback commonly adopts the block fading channel model where temporal correlation in wireless channels is neglected. This paper considers temporallycorrelated channels and designs single-user transmit beamforming with limited feedback. Analytical results concerning CSI feedback are derived by modeling quantized CSI as a first-order finite-state Markov chain. These results include the source bit rate generated by time-varying quantized CSI, the required bit rate for a CSI feedback channel, and the effect of feedback delay. In particular, based on the theory of Markov chain convergence rate, feedback delay is proved to reduce the throughput gain due to CSI feedback at least exponentially. Furthermore, an algorithm is proposed for CSI feedback compression in time. Combining the results in this work leads to a new method for designing limited feedback beamforming as demonstrated by a design example. I. INTRODUCTIONFor a multiple-input-multiple-out (MIMO) communication system, transmit beamforming alleviates the negative effect of channel fading by exploiting spatial diversity, and thereby increases system throughput.Typically, transmit beamforming requires feedback of channel state information (CSI) from a receiver to a transmitter. Such CSI feedback can potentially incur excessive overhead due to the multiplicity of channel coefficients. This motivates designing highly-efficient CSI quantization algorithms based on communication measures such as information capacity. These algorithms for both beamforming and other MIMO techniques form an active field called limited feedback (see e.g. [1] and the references therein). This paper focuses on limited feedback beamforming systems over temporally correlated channels. It addresses a set of open issues including the information rate inherent in CSI, the required bit rate for a CSI feedback channel, the effect of feedback delay on throughput, and feedback CSI compression.

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A Markov Model for MIMO Channel Condition Number with Application to Dual-Mode Antenna Selection

Cited by 5 publications

References 9 publications

Adaptive switching based on Markov model for multiuser MIMO-OFDM systems

Adaptive switching based on Markov model for multiuser MIMO-OFDM systems

On analytical derivations of the condition number distributions of dual non-central Wishart matrices

Limited Feedback Beamforming Over Temporally-Correlated Channels

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