On Steepest Descent Adaptation: A Novel Batch Implementation of Blind Equalization Algorithms

Han, Huy-Dung; Ding, Zhi; Hu, Junqiang; Qian, Dayou

doi:10.1109/glocom.2010.5684129

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…In fact, the equalization objective does not depend on the costs. The difference among the costs with the choice of parameters are perhaps the convergence of the gradient descent implementations as confirmed by many works [17], [22], [32], [33]. Similar results can be observed in MIMO channel.…”

Section: A Siso Channelsupporting

confidence: 67%

“…. The CMA cost can be represented as a function of the equalizer coefficients and the channel output statistics [22]. Since the formulation here deals with single source, the source index i is omitted.…”

Section: A Cma For Single Source Recoverymentioning

confidence: 99%

“…Since, (23) is a restrictive version of (22), we have τ C ≤ τ D . Nevertheless, simulation tests in [29] showed that the solutions of (22) and (23) are nearly identical for arbitrary polynomial cost functions f (u). In the next subsection, we will show that they are actually identical in the case of CMA cost.…”

Section: A General Formulationmentioning

confidence: 99%

See 2 more Smart Citations

A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery

Han

Ding

Muhammad

2016

IEEE Trans. Veh. Technol.

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In this work, we investigate an efficient numerical approach for solving higher order statistical methods for blind and semi-blind signal recovery from non-ideal channels. We develop numerical algorithms based on convex optimization relaxation for minimization of higher order statistical cost functions. The new formulation through convex relaxation overcomes the local convergence problem of existing gradient descent based algorithms and applies to several well-known cost functions for effective blind signal recovery including blind equalization and blind source separation in both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. We also propose a fourth order pilot based cost function that benefits from this approach. The simulation results demonstrate that our approach is suitable for short-length packet data transmission using only a few pilot symbols. Keywords: Blind signal recovery, semi/blind channel equalization, convex optimization, semidefinite programming, rank 1 approximation. 4 Let J b,i denote a blind cost function, for examples, CMA, SWA, or MED, to recover the source i, the cost function for multiple blind source recovery (BSR) is [23], [24] J bsr = NT i=1 J b,i + λ cr · NT i =j Huy-Dung Han Huy-Dung Han received B.S. in

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Section: A Siso Channelsupporting

confidence: 67%

Section: A Cma For Single Source Recoverymentioning

confidence: 99%

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A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery

Han

Ding

Muhammad

2016

IEEE Trans. Veh. Technol.

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“…On the other hand, the batch processing method of CMA [11] calculate the stochastic gradient directly from a block of channel output samples and achieve a much more accurate estimation of the gradient. Furthermore, it doesn't have to refilter the input of equalizer in each iteration.…”

Section: B Open-loop Batch Methods Of Constant Modulusmentioning

confidence: 99%

“…Due to its simplicity, constant modulus algorithm (CMA) [10] is the most commonly used algorithm in blind equalization from practical implementation point of view. Compared with the stochastic gradient descent realization of CMA (SGD-CMA), the open-loop batch approach using the 4-th order cumulants of the received signal represents faster convergence speed and better performance [11].…”

Section: Introductionmentioning

confidence: 99%

Joint Batch Implementation of Blind Equalization and Timing Recovery

Yang¹,

Wang²,

Wu³

2013

JCM

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In conventional design of a digital receiver, the timing recovery system and the equalizer are considered separately. Actually, the two processes are coupled and interacted. The paper proposes a batch scheme for jointly performing blind equalization and timing recovery, that embeds the timing recovery process inside the batch blind equalization algorithm. The proposed scheme adds the timing offset of sampling and interpolation filter in the objective function of constant modulus algorithm, iteratively updates the timing offset and equalizer coefficients in batch approach, thus achieves equalization, symbol timing error detector and timing recovery jointly. In this way, the timing recovery and the equalization processes can be coordinated. Simulation results show the performance of equalization and timing recovery. 

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Novel batch algorithm of joint blind equalization, carrier phase recovery and timing recovery for short bursts

Yang

Wang

2013

2013 IEEE International Conference of IEEE Region 10 (TENCON 2013)

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On Steepest Descent Adaptation: A Novel Batch Implementation of Blind Equalization Algorithms

Cited by 4 publications

References 16 publications

A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery

A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery

Joint Batch Implementation of Blind Equalization and Timing Recovery

Novel batch algorithm of joint blind equalization, carrier phase recovery and timing recovery for short bursts

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