Adaptive step-size algorithms for blind interference suppression in DS/CDMA systems

Krishnamurthy, Vikram; Yin, Gang; Singh, Shailendra

doi:10.1109/78.890361

Cited by 51 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system is still given by (2). To track the Markov chain, if we use a constant step size , we have an algorithm of the following form: (20) As far as the step size selection is concerned, there are three possible choices: (i) , (ii) (for example for some ), and (iii) . In the first case, the Markov chain changes very slowly.…”

Section: A Unmodeled Dynamics and Differential Inclusion Formulationmentioning

confidence: 99%

“…Development of algorithms in this paper follows the approaches of [21] and [22]. Adaptive step-size algorithms have been successfully used in spreading code optimization and adaptation in CDMA wireless networks [19], and blind interference suppression in signal processing problems [20].…”

Section: B Adaptive Step Size Algorithmsmentioning

confidence: 99%

“…Formally differentiating (20) with respect to at time leads to a recursion for . Thus we can construct algorithms that tracks the Markov chain , adaptively estimates the step size, and estimates the mean square derivative simultaneously.…”

Section: B Adaptive Step Size Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Systems With Regime Switching and Unmodeled Dynamics

Yin

Kan

Wang

et al. 2009

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-This paper is concerned with persistent identification of systems that involve deterministic unmodeled dynamics and stochastic observation disturbances, and whose unknown parameters switch values (possibly large jumps) that can be represented by a Markov chain. Two classes of problems are considered. In the first class, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains with transition rates sufficiently faster than adaptation rates of the identification algorithms. In this case, tracking real-time parameters by output observations becomes impossible and we show that an averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and can be estimated with periodic inputs and least-squares type algorithms. Upper and lower error bounds are established that explicitly show impact of unmodeled dynamics. In contrast, the second class of problems represents systems whose state transitions occur infrequently. An adaptive algorithm with variable step sizes is introduced for tracking the time-varying parameters. Convergence and error bounds are derived. Numerical results are presented to illustrate the performance of the algorithm.

show abstract

Section: A Unmodeled Dynamics and Differential Inclusion Formulationmentioning

confidence: 99%

Section: B Adaptive Step Size Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Systems With Regime Switching and Unmodeled Dynamics

Yin

Kan

Wang

et al. 2009

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…Therefore, the channel estimate can be written as which contains a constant term and a random complex Gaussian matrix of zero mean. Then, the estimate of the mutual information function can be written as (25) which is equivalent to the mutual information of a Rician flat fading MIMO channel with a nonzero mean matrix . The expressions of the mean and variance of the capacity of a noniid Rician are derived in [22] which correspond to (19) and (24), respectively.…”

Section: ) Aggressive Algorithm To Optimize the Mutual Informationmentioning

confidence: 99%

Adaptive MIMO antenna selection via discrete stochastic optimization

Berenguer

Wang

Krishnamurthy

2005

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

Abstract-Recently it has been shown that it is possible to improve the performance of multiple-input multiple-output (MIMO) systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. In the same spirit as traditional adaptive filtering algorithms, we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider scenarios of timevarying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the fast convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities.

show abstract

“…Finally, user group 3 consisted of eight MA1 users transmitting at 20 dB and one MA1 user transmitting at 30 dB. The MA1 structure was constructed according to a Markov chain z[n] with transition probability matrix We compare the SINR performances of the AS-CMA receiver, the AS-MOE receiver [3] and the AS-SA receiver 151. All the receiver lengths were chosen to be twice that of the spreading gain.…”

Section: Numerical Resultsmentioning

confidence: 99%