Last edited by Sajar
Tuesday, August 4, 2020 | History

2 edition of Low-complexity parallel structures for symbol-by-symbol detection. found in the catalog.

Low-complexity parallel structures for symbol-by-symbol detection.

Javan A.* Erfanian

Low-complexity parallel structures for symbol-by-symbol detection.

by Javan A.* Erfanian

  • 119 Want to read
  • 24 Currently reading

Published .
Written in English


The Physical Object
Pagination87 leaves
Number of Pages87
ID Numbers
Open LibraryOL14751832M

  In LTE orthogonal space frequency block codes (OSTBC) are used that allow simple receiver structures ⇒Symbol by symbol detection rather than vector detection. The main attractive feature of STBC is the quaternionic structure (see Appendix for more discussions on quaternions) of the spatio-temporal channel matrix. detection algorithms. In the Bayesian setting, the change-point is modeled as a geometrically distributed random variable. For this case, the average detection delay (ADD) and the probability of false alarm (PFA) are used as performance metrics. We propose a novel algorithm, namely the parallel-sum algorithm, for the purpose of change detection.

Abstract: The paper presents an efficient parallel carrier synchronization algorithm suitable for high speed demodulator system and easy to implement on FPGA platform. The parallel algorithm combines the phase frequency detector (PFD) algorithm of the fast capture and the phase detector (PD) algorithm of the stable by: 1.   A low-complexity multiple-input multiple-output (MIMO) subspace detection algorithm is proposed. It is based on decomposing a MIMO channel into multiple subsets of decoupled streams that can be detected separately. The new scheme employs triangular decomposition followed by elementary matrix operations to transform the channel into a generalized elementary matrix whose structure Cited by:

Parallel Soft Spherical Detection for Coded MIMO Systems: /ch This Chapter briefly evaluates different multiple-input multiple-output (MIMO) detection techniques in the literature as the candidates for the nextCited by: 1.   The simple low-complexity detection (SLCD) and adaptive simple low-complexity detection (ASLCD) were proposed in [17]. In [18, 19], the M-algorithm to maximum likelihood (MML) detector with prioritized tree-search structure was presented.


Share this book
You might also like
problem child in school

problem child in school

The Word of God for the people of God

The Word of God for the people of God

The Years Best Science Fiction

The Years Best Science Fiction

San Gabriel foothills greenbelt study

San Gabriel foothills greenbelt study

Breast cancer

Breast cancer

city of Gloucester

city of Gloucester

Canadas trial courts

Canadas trial courts

Britain in the Pacific Islands.

Britain in the Pacific Islands.

Depositions in matrimonial cases

Depositions in matrimonial cases

Problem-oriented literature searching

Problem-oriented literature searching

Harappan potteries

Harappan potteries

CD-recordable bible

CD-recordable bible

relationship between body concept and motor skill development among pre-school children.

relationship between body concept and motor skill development among pre-school children.

Authors Guide to Literary Agents

Authors Guide to Literary Agents

Low-complexity parallel structures for symbol-by-symbol detection by Javan A.* Erfanian Download PDF EPUB FB2

A fully-parallel structure is developed, and through systematic reformulations of the algorithm, the computational requirements are reduced considerably. In addition, the problems associated with a large dynamic range such as overflow (or underflow) are (practically) removed.

DOI: /TCOMM Corpus ID: Reduced complexity symbol detectors with parallel structure for ISI channels @article{ErfanianReducedCS, title={Reduced complexity symbol detectors with parallel structure for ISI channels}, author={Javan Erfanian and Subbarayan Pasupathy and P.

Glenn Gulak}, journal={IEEE Trans. Communications}, year={}, volume={42}. Title: Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication Authors: Yuting Fang, Adam Noel, Nan Yang, Andrew W. Eckford, Rodney A. Kennedy (Submitted on 9 Jan (v1), last revised 22 Jan (this version, v2))Cited by: 1.

This Letter proposes a new symbol detection scheme for an uplink wide-banded cyclic prefixed filter bank multiple access system. The feature of the proposed scheme is that it eliminates the analysis filter bank processing that is commonly employed in the existing schemes. By leveraging a particular interleaving structure, the large matrix inversion operation is decomposed into parallel Cited by: 1.

A Very Low Complexity Successive Symbol-by-Symbol Sequence Estimator for Faster-than-Nyquist Signaling Ebrahim Bedeer, Mohamed Hossam Ahmed, and Halim Yanikomeroglu Abstract In this paper, we investigate the sequence estimation problem of binary and quadrature phase shiftAuthor: Ebrahim Bedeer, Mohamed Hossam Ahmed, Halim Yanikomeroglu.

Reduced-Complexity Multiple-Symbol Detection for Free-Space Optical Communications Conference Paper in IEEE Transactions on Communications 57(4) - December with 8 Reads. A low-complexity symbol-level differential detection scheme for IEEE O-QPSK signals Conference Paper October with 16 Reads How we measure 'reads'.

The ISI is another problem in WBASN algorithm. The authors in[10] [12] use Square Root Raised Cosine (SRRC) filter and Symbol by Symbol (SBS) demodulator respectively to overcome the problem of.

Low Complexity MIMO Detection introduces the principle of MIMO systems and signal detection via MIMO channels. This book systematically introduces the symbol detection in MIMO systems. Keyboard shortcuts: Enter series and parallel symbols.

For this symbol: Type this + Shift and + || Shift and | (Look for the key with the \ symbol.) To view these keyboard shortcuts as you work To print this Help topic Terms of. Corresponding to these three Gaussian approximations, three low-complexity iterative parallel soft interference cancellation [5, 11] algorithms, namely, joint Gaussian detector (JGD), grouped joint Gaussian detector (GJGD), and graph-based iterative Gaussian detector (GIGD), will be described by utilizing factor graphs [12, 13] as a general Cited by: We propose a symbol-by-symbol (SBS) detector for Gaussian minimum shift keying (GMSK) signals in body area networks (BAN).

Our detector exploits the orthogonality between adjacent GMSK-modulated symbols. By dividing the received signals into inphase-/quadrature-channels and odd-/even-streams, the strongest inter-symbol interference (ISI) from the adjacent symbols is removed.

Abstract: Time selectivity of multipath channels introduces significant inter-carrier interference (ICI) in OFDM systems demanding high levels of mobility and capacity. In this paper, a piecewise channel approximation is presented for improving the structure of the frequency-domain channel gain matrix, leading to a low-complexity iterative equalization scheme for OFDM by: Low-Complexity PTS Methods Using Dominant Time-Domain Samples for MFTN Signaling on the low complexity symbolby- symbol detector.

Specifically, we optimize the time-frequency spacing under the. Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and.

In Fig. 5, Fig. 6, Fig. 7, Fig. 8, the comparison of the optimal and suboptimal estimates and the corresponding MSEs show us that performance of the SF is quite similar to the optimal one. Conclusion.

In this paper, we have designed a new SF for linear continuous-discrete dynamic systems with uncertainties. This filter represents a linear combination of local Kalman filters with weights Cited by: 1. In this paragraph, we compare the computational cost of the suggested variations on the NKF-equalizer and of the optimal MAP symbol-by-symbol equalizer (MAPSE) presented complexity of the MAPSE is summarized in Table the NKF-based equalizer, assuming that symbols are i.i.d (∀ i, Q i is a constant), many equations in the NKF-algorithm are the by: 4.

Book Overview. Altmetric Badge. Chapter 1 PPS: A Low-Latency and Low-Complexity Switching Architecture Based on Packet Prefetch and Arbitration Prediction Altmetric Badge. Chapter 2 SWR: A Novel NN-Based Model for Android Malware Detection Over Task Kernel Structures Altmetric Badge.

Chapter 34 Moving Target Defense Against Injection Attacks. On the other hand, a low complexity symbol-by-symbol detector which directly deals with the samples at the matched-filter output has been proposed in. By modeling the ISI and ICI introduced by time-frequency packing as additive Gaussian noise, an attractive BER and spectral efficiency performance can be also obtained by the : Siming Peng, Aijun Liu, Xinhai Tong, Ke Wang, Giulio Colavolpe.

In This Chapter. In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to will explain how to choose between data structures like hash-tables, arrays, dynamic arrays and sets implemented by hash-tables or.

Low-Complexity Sequential Search A sequence of K frequency coefficients vectors, R 0, R 1.R K- 1, is obtained by FD ADC consecutively with the segment duration of T P. The proposed searcher recovers accurate symbol timing by searching R 0, R 1.R K-1 of k received preambles sequentially.eral flat channels, enabling low complexity equalization and symbol-by-symbol detection.

However, OFDM is very sen-sitive to Doppler distortion (Doppler scaling in particular) which destroys subcarrier orthogonality and introduces se-vere inter-carrier interference (ICI).

Recent work has focused on two major approaches for.IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 67, NO. 12, J Online Change-Point Detection of Linear Regression Models Jun Geng, Member, IEEE, Bingwen Zhang, Laure.