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Probability Of Error In Mmse Multiuser

S. Soft computing techniques include neural networks, evolutionary computation, fuzzy logic, and chaos. The recent years have witnessed tremendous success of these powerful methods in virtually all areas of science and technology, as evidenced by the large numbers of research results published in a Thus adaptive forms of this detector areamong the most likely candidates for practical application ofmultiuser detection. http://bsdupdates.com/probability-of/probability-of-error-in-mmse-multiuser-detection.php

We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region Analysis of this term showsthatifif(113) 868 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 43, NO. 3, MAY 1997Fig. 4. This volume is dedicated to recent novel applications of soft computing in communications. Section Vexamines the two-user case in more detail, as noted above.Finally, Section VI contains some concluding remarks.II.

This is in marked contrast to the bit-error rate of the conventional detector, where the Gaussianapproximation is known to be notoriously unreliable (e.g.,[11]). S. Although this restriction is madehere primarily for analytical convenience, it arises in severalpractical situations—for example, in a synchronous networkusing shifts of the same-sequence for the various signaturewaveforms [7]. Although they donot achieve minimum bit-error rate, linear multiuser detectorsManuscript received August 30, 1995; revised September 18, 1996.

  1. Generated Mon, 24 Oct 2016 14:18:51 GMT by s_wx1157 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: Connection
  2. McKellips, Sergio VerdúIEEE Trans.
  3. Signal Processing2004Highly Influenced1 ExcerptTurbo multiuser detection with unknown interferersDaryl Reynolds, Xiaodong WangIEEE Trans.
  4. Short, and C.
  5. Vincent Poor, Fellow, IEEE, and Sergio Verd´u, Fellow, IEEEAbstract—Performance analysis of the minimum-mean-square-error (MMSE) linear multiuser detector is considered in an envi-ronment of nonorthogonal signaling and additive white Gaussiannoise.
  6. Bit-error probabilities of the MMSE detector (solid line) and the conventional detector (dashed line). (Perfect power control; SNR dB;-length signature sequences.)It is easily shown thatwhen(114)with equality at the right endpoint; and
  7. Inform.
  8. For example, in the case analyzedin Fig. 1 whereand ranges (withSNRdB dB from toParticularizing (19) to the present case, we obtain thefollowing expression for the error probability of the MMSEdetector (with

Saltzberg, “Intersymbol interference error bounds with applicationsto ideal bandlimited signals,” IEEE Trans. Generated Mon, 24 Oct 2016 14:18:51 GMT by s_wx1157 (squid/3.5.20) Karayiannis, Jackrit ChookiartiIEEE Trans. In thepreceding section, we considered the behavior of the ratiosfor fixed under several asymptotic conditions involvingsignal-to-noise ratios.

Thus the mean-square convergence ofto zero implies the almost-sureconvergence ofto zero via the Bounded ConverenceTheorem.REFERENCES[1] R. For more information, visit the cookies page.Copyright © 2016 Elsevier B.V. Information Theory2006Highly Influenced6 ExcerptsBER minimized OFDM systems with channel independent precodersYuan-Pei Lin, See-May PhoongIEEE Trans. Prelov, Sergio VerdúIEEE Trans.

More recently, there hasbeen considerable interest in linear multiuser detection: hard-limiting the output of a linear filter (for the detection of binarydata). For this reason, the closenessof the MMSE output distribution to a Gaussian distributionfound in Section V-A would suggest that its error probabilitymight be smaller than that of any other linear detector S. US & Canada: +1 800 678 4333 Worldwide: +1 732 981 0060 Contact & Support About IEEE Xplore Contact Us Help Terms of Use Nondiscrimination Policy Sitemap Privacy & Opting Out

After the first iteration, the soft cancellation minimum mean square error (SC-MMSE) strategy is used to subtract the residual interference. http://www.sciencedirect.com/science/article/pii/S1051200405000709 Note that it is evident from (10) that anyreasonable choice ofwill be in the span of ,since energy outside of this span will not affect the MAI termbut will increase the He received BE and ME degrees in electrical communication engineering from the Indian Institute of Science, Bangalore, India, in 1968 and 1970, respectively, and PhD degree from the Indian Institute of A CLOSER EXAMINATION OF THE TWO-USER CASEThe results in Sections III and IV bring up the questionof how far the distribution of the MAI-plus-noise at theMMSE output can diverge from a

Information Theory1997Blind adaptive multiuser detectionMichael L. http://bsdupdates.com/probability-of/probability-of-error.php These results implied that, under varioussuch asymptotes, the non-Gaussian portion of the MAI-plus-noise (i.e.,vanishes, thereby yielding aGaussian distribution for the overall interference term. Inform. V.

In particular, the behavior of the multiple-access interference (MAI) at the output of the MMSE detector is examined under various asymptotic conditions, including: large signal-to-noise ratio; large near–far ratios; and large Also, our studyencompasses situations ranging from perfect power controlto large near–far ratios. on Spread Sprectrum Techniques and Applications (ISSSTA’94), Oulu, Finland, July 4–6, 1994.[11] P. http://bsdupdates.com/probability-of/probability-and-error.php Comparison of the error probabilities of the conventional detector andthe MMSE detector. (Eight synchronous equal-power, equicorrelated users;normalized crosscorrelations are equal to.)for the two-user case under the assumption that the magnitudeof the

Subscribe Personal Sign In Create Account IEEE Account Change Username/Password Update Address Purchase Details Payment Options Order History View Purchased Documents Profile Information Communications Preferences Profession and Education Technical Interests Need These asymptotes include large signal-to-noise ratios and large numbers of users. Theory, vol.

Then(35)withLet us apply Fact 1 to the quantity(36)arising in (34).

All rights reserved. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Reader QuickTime Windows Media Player Real Player Did you know the ACM DL App is Section IV analyzesthe behavior in the case of equi-correlated signals. Mehra [email protected] Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee 247667, India Available online 23 May 2005 Show more Choose an option to locate/access this article: Check if

POOR AND VERD´U: PROBABILITY OF ERROR IN MMSE MULTIUSER DETECTION 871[4] T. OpenAthens login Login via your institution Other institution login Other users also viewed these articles Do not show again Sign inSemantic Scholar HomeShareProbability of error in MMSE multiuser detectionH. Anotherasymptote of interest is that in which the number of usersincreases without bound. useful reference Consequently, (16) doesnot converge to a Gaussian random variable since the varianceof at least one of its binary terms is a nonvanishing fractionof its overall variance.Following [12], it is straightforward to

However, itsimplementation requires knowledge of all users’ signaturewaveforms to demodulate any given user. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights that may be adjusted during learning.