The fractionally spaced vector constant modulus algorithm

Haun, M.A.; Jones, Douglas L.

doi:10.1109/icassp.1999.761241

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Its global convergence has not been established [9,15]. If multiple signals are present, depending on its initialization, VCMA may converge to any of the transmitted signals, usually to the ones that have the strongest power [18].…”

Section: Composite Criterionmentioning

confidence: 98%

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Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria

Abrudan¹,

Koivunen²

2007

Wireless Pers Commun

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In this paper we address the problem of blind recovery of multiple OFDM data streams in a Multiple-Input Multiple-Output (MIMO) system. We propose an equalization algorithm which is based on a combined criterion designed to cancel both intersymbol interference (ISI) and co-channel interference (CCI). ISI is minimized by using a modified Vector Constant Modulus criterion while CCI is minimized by a decorrelation criterion. We establish conditions for the existence of the stable minima corresponding to the zero forcing receiver which performs the joint blind equalization and the co-channel signal cancellation. The local convergence properties of the algorithm are proved under the assumption that the balance parameter weighting the two criteria is set appropriately. We also provide the optimal value for this parameter. Reliable performance is achieved with relatively fast convergence and small steady-state error. The implementation of the blind equalizer requires lowcomputational cost, without any matrix inversions or other expensive operations.

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Section: Composite Criterionmentioning

confidence: 98%

“…The global convergence of VCMA has not been established so far, not even in the fractionally spaced case [9]. The local convergence properties of VCMA have been investigated in [15].…”

Section: Introductionmentioning

confidence: 98%

Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria

Abrudan¹,

Koivunen²

2007

Wireless Pers Commun

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“…[1], [5] give intuitive considerations emphasizing the ability of VCM to equalize data with Gaussian source distributions.…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Vector-CM stable equilibrium analysis

Touzni

Tong

Casas

et al. 2000

IEEE Signal Process. Lett.

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The vector-constant modulus (VCM) criterion is an extension of the constant modulus (CM) criterion [2] introduced recently for equalization of channels involving Gaussian sources [1], [5]. In this letter, we analyze the behavior of VCM for arbitrary source distributions and combined channel-receiver impulse responses of finite dimension. We begin by pointing out the difference between the VCM and CM cost functions and showing that the VCM criterion can be expressed as a composite criterion combining the CM cost function and a penalty term involving cross-correlations of the equalizer output. We continue by providing conditions for noise-free channels, under which VCM admits stable minima corresponding to zero-forcing (ZF) channel receivers. We find that for sub-Gaussian sources, the VCM and CM criteria share the same global minima. For Gaussian and super-Gaussian sources, however, it appears that only ZF receivers corresponding to input/output transmission delays at the extremes of the range of possible delays are truly stable equilibria of VCM.

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“…However, it is often desired to use an equalizer with taps spaced at a fraction of the data symbol period T, or sampled at a multiple of the symbol rate. This configuration gives the extra degrees of freedom to perform additional filtering operations such as matched filtering and adjustment of sampling phase [HAJ99]. Such a scheme is called fractional spaced equalization (FSE).…”

Section: Fractional Spaced Equalizer (Fse)mentioning

confidence: 99%

A New Method to Prepare Polycrystalline Meta-Thioboric Acid, (HBS2)3

Karthikeyan¹,

Martindale²,

Martin³

2001

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Public reporting burden for the collection of information is estimated to average 1 hour per response. including the tine for reviewig instructions, searching existing data sources, gathering and maintaining the data needed, and completing and revieWng the collection of information. Send comments regard ingthis burden estimate or any other aspect of this collection of information, including asuggestions for reclucrngth is burden to Washington Headquarters Serces a Drectorate for Information Operations and Reports. 121 This study presents a hierarchical classification approach to the classification of digital modulation schemes of types [2,4,8]-PSK, [2,4,64,256]-QAM in low SNR levels and multipath propagation channel conditions. A hierarchical tree-based classification approach is selected as it leads to a relatively simple overall scheme with few parameters needed to differentiate between the various modulation types. Back-propagation neural network units are adopted at each tree node because they offer the flexibility needed to cope with varying propagation environments, as is the case in real-world communications. The selection of robust and well-defined higher-order statistics-based class features is considered and a small number of cumulants and moments chosen to differentiate between all various types of modulation types, except for specific M-QAM types. Simulations show that M-QAM types may be so affected by multipath and fading that higher-order statistic parameters become of very limited use. While being part of the hierarchical procedure, the identification of specific M-QAM types is conducted via equalization algorithms. Extensive simulations show overall classification performances to be strongly affected by the amount of multipath distortion and noise in the transmission channels. Results also show a much higher sensitivity of high-order M-QAM types to fading and multipath propagation distortions than other modulation types. This study first investigates the selection of robust and well-defined higher-order statistics-based class features, and next designs a classification procedure which is applied under low SNR levels, realistic fading and "real-world" type multipath propagation channel conditions.The hierarchical tree-based classification approach selected in the study leads to a relatively simple overall scheme with few parameters needed to differentiate between the various modulation types under consideration. Back-propagation neural network units are adopted at each tree node because they offer the flexibility needed to cope with varying propagation environments, as is the case in real-world communications.The selection of higher-order statistics parameters as class features for the neural network classification units is shown to be effective and robust for all classification schemes, except when differentiating between the various MQAM types considered.Simulations show that M-QAM types may be so affected by multipath and fading that xi higher-order statistic parameters become of very...

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The fractionally spaced vector constant modulus algorithm

Cited by 5 publications

References 12 publications

Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria

Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria

Vector-CM stable equilibrium analysis

A New Method to Prepare Polycrystalline Meta-Thioboric Acid, (HBS2)3

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