Linear time and frequency domain Turbo equalization
|
|
- Stuart Daniel
- 5 years ago
- Views:
Transcription
1 Linear time and frequency domain Turbo equalization Michael Tüchler, Joachim Hagenauer Lehrstuhl für Nachrichtentechnik TU München München, Germany Abstract For coded data transmission over channels introducing inter-symbol interference, one approach for joint equalization and decoding in the receiver is Turbo Equalization. We rederive existing linear equalization algorithms applicable to Turbo Equalization for Ñ -ary signal alphabets and compare their computational complexity. Moreover, by evaluating the algorithm performance properly, we select for each iteration the most suitable of the two algorithms with lowest computational complexity and achieve at low bit error rates a performance close to that of optimal approaches for equalization, i.e., maximum a-posteriori probability symbol detection. 1. Introduction We consider a coded data transmission system, where blocks of data bits are encoded to code bits using forward error correction (FEC), which are subsequently interleaved, mapped to symbols from a Ñ -ary signal alphabet and transmitted over a channel with inter-symbol interference (ISI). The channel is modeled in discrete time with the Å finite-length impulse response filter Ò ¼ ÆÒ,, of length Å. The impulse response has energy Å ¼. The coefficients are assumed to be time-invariant and known to the receiver. The noise process is assumed to be independent and identically distributed (i.i.d.) and independent of the data. This system model is valid for many communication systems with frequency selective or multipath channels. The receiver of such a system can perform joint decoding and equalization using Turbo Equalization (Turbo Equ.), which was pioneered in [4] and enhanced in [1, 2]. However, the used trellis-based detection algorithms (soft-out Viterbi equalization (SOVE), maximum a-posteriori probability (MAP) symbol detection) become prohibitively complex for increasing Å and Ñ. In [5, 6, 9, 12], new equalization techniques based on linear filtering were applied to significantly reduce the computational complexity. Among them, we differentiate between minimum mean squared error (MMSE) linear equalization (LE) and matched filtering (MF). The LE algorithm derived in [9] was also implemented in an approximate version (APPLE). In this paper, we provide a framework to use the linear approaches given a Ñ -ary signal alphabet (LE: Section 4.1, APPLE: Section 4.2, MF: Section 5) and specify how to select the most suitable equalization algorithm for each iteration - an approach, which significantly improves the performance as shown in [9]. In all systems, a convolutional code with MAP-based decoding is used for FEC. We start with a brief system definition, explain next the general approach to derive linear algorithms applicable for Turbo Equ., derive in detail the different algorithms (LE, APPLE, and MF) in the time and, if possible, in the frequency domain, devise an adaptation criterion to switch between the algorithms, compare the computational complexity, and conclude the paper with results and final remarks. data c n Encoder Π c n Transmitter Equalizer x k L e ( c n ) ISI channel Π -1 w k L e ( c n ) z k L ( c n ) L ( c n ) Receiver Π Decoder Figure 1. Coded data transmission system. 2. System definition data estimate Consider the communication system in Figure 1 with a receiver performing Turbo Equ. Binary data is encoded using a binary convolutional code to length Ñ blocks ¼ ¼ ¼ ¼ ¼ Ñ Ì of code symbols ¼ Ò ¼. The interleaver permutes ¼ to ¼ Ñ Ì denoted as ¼ µ. The deinterleaver µ reverses the permutation µ. The modulator maps Ñ code bits Ñ, ¼ Ñ µ, to a complex symbol Ü according to the Ñ -
2 ary symbol alphabet Ë ¼ Ñ, where corresponds to the bit pattern ¼ Ñ µ, ¼. We require that Ñ ¼ ¼ and Ñ Ñ ¼. Transmitted over the channel is the sequence Ü Ü ¼ Ü Ü after the length Å prefix or guard interval Ü Å Ü Å Ü, where we assume that the transmitter knows Å. In many applications, the prescribed prefix is already part of Ü due to fixed header and tail sequences. The receiving process of the transmitted Ü is disturbed by complex-valued additive white Gaussian noise (AWGN), i.e., both the real and imaginary part of the noise samples Û is i.i.d. with pdf Ò ¼ Û Ûµ defined as Ò Ûµ ÜÔ Û µ µ Ô Û Ê Ê Thus, we have Û Û Û and Û Û. The receiver observes the sequence Þ Þ ¼ Þ Þ (the first Å symbols are neglected). Due to the prefix, the channel state at the block ends is equal and we can express Þ as Å Þ Ü µ mod Û ¼ µ ¼ In case all Ë are real, we can design a receiver using Þ only, which yields Û Û and Û ¼. Before proceeding, some frequently used notation is introduced. The matrix ¼ contains all zeros, contains all ones. Á is the identity matrix. The operator is the expectation with respect to the joint probability density function (pdf) of the Ü and Û. The covariance operator Cov Ü Ýµ equals Ü Ý À ÜÝ À, where À is the Hermitian operator. The -value operator µ, ¼, equals µ ÐÒ Ö¼, i.e., µ is the Ö log likelihood ratio (LLR). The operator Diag applied to a length Æ vector returns a Æ Æ matrix with the vector elements along the diagonal. 3. Linear algorithms for Turbo equalization We present here the general framework to rederive LE, APPLE, and MF for a Ñ -ary signal constellation using the results in [9]. At first, the statistics Ü Ü and Ú Cov Ü Ü µ of the symbols Ü are computed using the a- priori information Ò µ provided by the decoder: Ü Ú Ë ÖÜ Ë ÖÜ Ñ Ë ¼ Ö Ñ Ü (1) The equalizer assumes the Ò to be independent (which is locally achieved using interleaving) such that ÖÜ is the product of Ñ terms Ö Ñ, which are determined using Ò µ, Ò Ñ. From the independence assumption follows Cov Ü Ü ¼ µ ¼, ¼, too. Filtering Ü with Ò gives Þ Þ Å ¼ Ü µ mod ¼ µ which is subtracted from the received symbols Þ. This difference is filtered using a length Æ linear FIR filter with possibly time-varying coefficients Æ Æ Æ, (Æ Æ Æ ). The output of this filter are the estimates Ü. The equalizer output LLRs Ò µ, Ò ¼ Ñ µ, are the extrinsic information (a-posteriori minus a-priori information) about Ò given the channel observations: Ò µ ÔÓ Ø Ò µ Ò µ ÐÒ Ö Ò ¼Ü Ö Ò Ü Ò µ ÐÒ Ô Ü Ò ¼µ Ô Ü Ò µ It is shown in [8] that this decomposition of the a-posteriori LLR ÔÓ Ø Ò µ yields the best performance in the more general problem of linear MMSE estimation using a-priori information. We must satisfy that Ò µ and hence also Ü is not a function of Ò µ [8]. This is achieved by extending the approach in [8], which is to remove the influence of all Ñ µ, ¼ Ñ µ, on Ü and to replace it with the influence of Ñ µ ¼,. We assume that Ü exhibits a complex Gaussian distribution Ô Ü Ü Üµ, Ü, conditioned on Ü, ¼ Ñ µ: Ü Ü Cov Ü Ü Ü µ Ô Ü Ü Üµ ÜÔ Ü µ In case all Ë are real, Ô Ü Ü Üµ can be a single Gaussian pdf, i.e., Ô Ü Ü Üµ Ò Üµ, Ü Ê. By averaging over all Ô Ü Ü Üµ with Ò ¼ or Ò, respectively, Ò µ is computed as Ò µ ÐÒ Ô Ü Ü Ü µ ÖÜ Ò ¼ ËÒ¼ Ô Ü Ü Ü µ ÖÜ Ò ËÒ where Ò Ñ and ¼ Ñ µ. This simplifies to ± Ü (2) É Ñ ÜÔ ± µ м Ö Ñ Ð Ð ËÒ¼ Ð Ò µ ÐÒ É Ñ ÜÔ ± µ м Ö Ñ Ð Ð ËÒ Ð
3 using the fact that, as shown later, does not depend on. We assume that no additional a-priori information besides that of the decoder is available. Thus, in the first iteration we have Ò É µ ¼, Ò, and Ò µ can be computed without Ñ the terms Ö Ñ Ð Ð. м Ð 4. Turbo equalization using MMSE linear equalization 4.1. Exact implementation This approach was derived in [8] and applied to Turbo Equ. in [9]. The design rule to obtain the is to minimize the MMSE cost function Ü Ü. In general, the estimate Ü is computed from Þ Þ Æµ mod Þ Æ µ mod Þ Æµ mod Ì a length Æ vector of received symbols, as follows (Appendix A in [8]): Þ Þ Æµ mod Þ Æµ mod Ì Ú DiagÚ Æµ mod Ú Å Æµ mod À ¼ Æ ¼ Æ Å µ Ì Û Á Æ ÀÎ À À µ Ü Ü Ú À Þ Þ µ (in general) where À is the Æ Æ Å µ channel convolution matrix À ¼ Å ¼ ¼ ¼ ¼ Å ¼ ¼... ¼ ¼ ¼ Å Due to the mod operation we circularly equalize on a tail-biting block Þ, which is possible using the prefix in the transmitter. However, Ü depends on Ñ µ, ¼ Ñ µ, over Ü and Ú. As mentioned, we set Ñ µ ¼,, yielding Ü ¼, and Ú and recompute and Ü by replacing Ü and Ú with ¼ and : ¼ Û Á Æ À Î À À Ú µ À µ Ü ¼ À Þ Þ Ü ¼µ µ We can express ¼ as scaled version of using the matrix inversion lemma and Û Á Æ ÀÎ À À : ¼ Ú µ À µ Ú µ À µ In [8], a recursive algorithm was derived to compute from with a number of operations only proportional to Æ and Å. With the final expression for the estimates: Ü Ú µ À µ À Þ Þ Ü µ we can compute the statistics and : À Þ Ü Þ Ü µ À À Cov Þ Þ Ü µ À Ú À µ where Ú µ À. The derivation to obtain Ò µ is completed by finding an expression for ± : ± À Ú À µµ À Þ Þ Ü µ À For the case that Ò µ ¼, Ò, we have Ü Þ ¼ and Ú,, yielding a time-invariant Æ : Æ Û Á Æ ÀÀ À µ ± À Æ À Æ µµ À Æ Þ À Æ where Æ stands for No A-priori information Approximate implementation The costly computation of the vector for each is neglected by simply using the vector Æ despite the presence of non-zero a-priori information Ò µ ¼ [8]. The estimates Ü are now given by Ü À Æ Þ Þ Ü µ However, computing the statistics and becomes more difficult: À Æ Þ Ü Þ Ü µ À Æ À Æ Û Á Æ À Î À À Ú À µ Æ In [8], was approximated by a crude time average. Here, the average is over all ¼ (any can be selected) corresponding to each Ü, ¼ µ: ¼ ¼ Û À Æ Æ ¼ The exponents ± are given by Ú À Æ À ÀÀ À µ Æ ± Ü À Æ (3) for general Ò µ Ê. For Ò µ ¼, Ò, yielding Ü ¼ and Ú,, we have especially À Æ À Æ µ and Ü À Æ Þ. 5. Turbo equalization using matched filtering This approach was first introduced in [4] and modified yielding better results in [9]. The estimator filter coefficients are set to yield a matched filter to the ISI channel
4 response Ò. The algorithm in [9] to compute the estimate Ü is used without adaptation to a Ñ -ary signal alphabet: Å Ð Ð Å µ Å µ м Å Ü Ü ¼ Þ µ mod Þ µ mod µ Û Ú Å Ð Å Ú Ðµ mod Ð The exponents ± for general Ò µê are given by ± Ü (4) Å For Ò µ ¼, Ò, we have especially Ü ¼ Þ and Û Å Ð Å Ð. 6. Algorithm adaptation The APPLE and the MF approach derived in Sections 4.2 and 5 require a number of operations per received symbol increasing only linearly with Æ or Å, which is much better than LE (Æ ) or MAP equalization ( ÑÅ ). However, both APPLE and MF suffer a significant performance loss compared to, e.g., LE. In [9], a scheme was proposed to properly switch between APPLE or MF depending on Ò µ, which is unfortunately based on empirical performance evaluation requiring the transmitted data. We propose a novel approach using the signal-to-noise ratio (SNR) of Ü to decide, prior to equalization, which algorithm to use: SNR Ü Ü Cov Ü Ü Ü µ à where Ã Æ À for APPLE and à for MF. We suggest that the algorithm yielding the highest average SNR Ñ Ñ ¼ ¼ à à ¼ ¼ should be used. The average variance is computed as in Section 4.2 yielding the lower bounds APPLE: MF: Ú Ú Ã ¼ à ¼ ¼ ¼ ¼ À Æ À Æ Û À Æ Æ Ú À Æ ÀÀÀ À µ Æ Ã Û Ú Å Ð Å Ð µ on. Thus, the receiver uses the algorithm with largest à for equalization. The bound is tight whenever ¼ is constant in, e.g., for Ú ¼ (symbols Ü are known to the receiver) and Ú (no a-priori information). The average SNR and its lower bound à are monotonically decreasing in Ú ¼ [8]. The maximum is using MF for Û Ú ¼, which is the SNR of an AWGN channel with noise variance Û. We thus expect the Turbo Equ. system performance to be below than that of coded data transmission over the equivalent AWGN channel, since the equalizer is at most able to provide the same SNR of the estimates Ü. We will not consider LE as alternative algorithm due to the much larger computational effort. For MAP equalization, an analysis using is not possible. We rely here, if applicable at all, on the EXIT charts introduced in [9, 11]. 7. Frequency domain implementation In [12], the APPLE and MF algorithm were implemented in the frequency domain. The adaptation to Ñ -ary signal alphabets is straightforward. Table 1 depicts this implementation (the DFT operator is the Discrete Fourier Transform). ÁÒÔÙØ - Þ ¼ Þ Ì, ¼ µ Ñ µ Ì, Ò, and Û, ÁÒØÐÞØÓÒ ¼ Ì DFT Þ ¼ Þ Ì À ¼ À Ì DFT ¼ Å ¼ ŵ Ì ¼ À Û À ÖÓÖ ØÓ ÕÙÐÞØÓÒ - compute: Ü ¼ Ü Ì and Ú ¼ Ú Ì, - decide: use APPLE or MF by comparing Ã, ÕÙÐÞØÓÒ ¼ Ì DFT Ü ¼ Ü Ì APPLE: À À Û À Û À µ, MF: À À µ, Ü ¼ Ü Ì DFT ¼ Ì Ø ØÓ ÕÙÐÞØÓÒ - compute: ¼ µ Ñ µ Ì. Table 1. Frequency domain equalization. 8. Complexity comparison In this section, the computational complexity of MAP equalization, LE, APPLE, and MF is compared. We assume that the statistics Ü and Ú are available for all and skip the computation to obtain Ò µ including and (both mappings Ü Ú Ò µ and Ò µ Ü strongly depend on Ë). Any overhead due to initialization (one-time computations for all iterations), e.g., to compute Å for APPLE or À,,, is neglected. Table 2 gives the number of real multiplications and additions per iteration required to equalize symbols Þ yielding estimates Ü. The DFT is carried out using a radix-2 FFT requiring roughly ÐÓ µ real multiplications and ÐÓ µ real additions for Ð, Ð, [10]. For the complexity of LE see [8]. For MAP equalization, we considered only the computation of all s and the «, recursion [7].
5 approach domain real multiplications real additions MF time ¼Å µ ¼Å µ MF frequency ÐÓ µ ÐÓ µ APPLE time Æ Å µ Æ Å µ APPLE frequency ÐÓ µ ÐÓ µ LE - Æ Å ¼Å Æ µ Æ Å ¼Æ Å µ MAP equ. - ÑÅ Ñ Ñ Å µ µ ÑÅ Ñ µ Ñ Å µ µ Table 2. Computational complexity of equalization per iteration per block. 9. Results and Conclusions We tested the bit error rate performance (simulation of at least ¼¼¼ data bit errors) of a Turbo Equ.-based receiver. Data is encoded (code generator µ µ) to length ¼ blocks of code symbols Ò including S-random (S=30) interleaving [3]. The Ò are modu- µ ¼¼ ¼ ¼ Ü ß Ô ß Ô ß Ô ß Ô Ü Ô ØÒ µ ØÒ µ ßµ Ú Ü ß Ô µ µ Ô ¼ ¼ Ü µ Ô ¼ ¼ Ü Table 3. QPSK modulation. lated to Ü according to Table 3 (includes also Eqs. (1) and (2)). The time-invariant channel impulse response is Ò ¼ ÆÒ ¼ ÆÒ ¼ ÆÒ ¼ ÆÒ ¼ ÆÒ. The system SNR is Æ ¼. The filter parameters for LE and APPLE are Æ and Æ. Figure Û 2 depicts the BER results after iterations: MAP equalization and LE perform best followed by switched APPLE/MF. Using APPLE and MF alone is not satisfactory. Similar results were obtained for unknown Ò (including training) and/or fading coefficients. In conclusion, the novel switched APPLE/MF approach yields a comfortable gain to one-time MAP equalization and decoding. We think, that this and the LE algorithm are most suitable for low coplexity Turbo Equ. using higher order signal alphabets, e.g., PSK in the the Enhanced Data rates for GSM evolution (EDGE) standard. Part of on-going work is an accurate performance analysis and the implementation of channel parameter estimation into the iterative algorithm. References [1] A. Anastasopoulos and K. Chugg. Iterative equalization/decoding for TCM for frequency-selective fading channels. Record on the 31th Asilomar Conf. on Signals, Systems & Computers, 1: , November [2] G. Bauch and V. Franz. A comparison of soft-in/soft-out algorithms for turbo detection. Proceedings on the Intern. Conf. on Telecomm. (ICT 98), pages , June BER no channel APPLE LE SIC mixed LE/SIC MAP one time MAP equ E b /N 0 in db Figure 2. BER performance comparison. [3] C. Heegard and S. Wicker. Turbo Coding. Kluwer Academic Publishing, Boston, [4] A. Glavieux, C. Laot, and J. Labat. Turbo equalization over a frequency selective channel. Intern. Symposium on Turbo codes & related topics, pages , Sep [5] C. Douillard et al. Iterative correction of intersymbol interference: Turbo equalization. European Trans. on Telecomm., 6(5): , Sep-Oct [6] D. Raphaeli and A. Saguy. Linear equalizers for turbo equalization: A new optimization criterion for determining the equalizer taps. Proc. of the 2nd Intern. Symp. on Turbo Codes & Related Topics, pages , Sep [7] L.R. Bahl et al. Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory, 20: , March [8] M. Tüchler, A. Singer, and R. Kötter. Minimum mean squared error (MMSE) equalization using priors. submitted to IEEE Transactions on Signal Processing, [9] M. Tüchler, R. Kötter, and A. Singer. Turbo equalization: principles and new results. submitted to IEEE Trans. on Communications, [10] J. Proakis and D. Manolakis. Digital Signal Processing, 3rd Ed. Prentice Hall, Upper Saddle River, New Jersey, [11] S. ten Brink. Convergence of iterative decoding. Electronic Letters, 35(10): , May [12] M. Tüchler and J. Hagenauer. Turbo equalization using frequency domain equalizers. Proc. of the Allerton Conference, Monticello, IL, U.S.A., October 2000.
Performance of Soft Iterative Channel Estimation in Turbo Equalization
Performance of Soft Iterative Channel Estimation in Turbo Equalization M. Tüchler Ý, R. Otnes Þ, and A. Schmidbauer Ý Ý Institute for Communications Engineering, Munich University of Technology, Arcisstr.
More informationLinear Turbo Equalization for Parallel ISI Channels
860 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 51, NO. 6, JUNE 2003 Linear Turbo Equalization for Parallel ISI Channels Jill Nelson, Student Member, IEEE, Andrew Singer, Member, IEEE, and Ralf Koetter,
More informationSNR Estimation in Nakagami Fading with Diversity for Turbo Decoding
SNR Estimation in Nakagami Fading with Diversity for Turbo Decoding A. Ramesh, A. Chockalingam Ý and L. B. Milstein Þ Wireless and Broadband Communications Synopsys (India) Pvt. Ltd., Bangalore 560095,
More informationStudy of Turbo Coded OFDM over Fading Channel
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 3, Issue 2 (August 2012), PP. 54-58 Study of Turbo Coded OFDM over Fading Channel
More informationIterative Detection and Channel Estimation for MC-CDMA
Iterative Detection and Estimation for MC-CDMA Thomas Zemen Siemens Austria, PSE PRO RCD Erdbergerlände 26 A-1031 Vienna, Austria E-mail: thomaszemen@siemenscom Joachim Wehinger, Christoph Mecklenbräuker
More informationCONVENTIONAL single-carrier (SC) modulations have
16 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 55, NO. 1, JANUARY 2007 A Turbo FDE Technique for Reduced-CP SC-Based Block Transmission Systems António Gusmão, Member, IEEE, Paulo Torres, Member, IEEE, Rui
More informationPerformance of Nonuniform M-ary QAM Constellation on Nonlinear Channels
Performance of Nonuniform M-ary QAM Constellation on Nonlinear Channels Nghia H. Ngo, S. Adrian Barbulescu and Steven S. Pietrobon Abstract This paper investigates the effects of the distribution of a
More informationLayered Frequency-Domain Turbo Equalization for Single Carrier Broadband MIMO Systems
Layered Frequency-Domain Turbo Equalization for Single Carrier Broadband MIMO Systems Jian Zhang, Yahong Rosa Zheng, and Jingxian Wu Dept of Electrical & Computer Eng, Missouri University of Science &
More informationTurbo Equalization: An Overview Michael Tüchler and Andrew C. Singer, Fellow, IEEE
920 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 57, NO 2, FEBRUARY 2011 Turbo Equalization: An Overview Michael Tüchler Andrew C Singer, Fellow, IEEE Dedicated to the memory of Ralf Koetter (1963 2009)
More informationSimulation Performance of MMSE Iterative Equalization with Soft Boolean Value Propagation
Simulation Performance of MMSE Iterative Equalization with Soft Boolean Value Propagation Aravindh Krishnamoorthy, Leela Srikar Muppirisetty, Ravi Jandial ST-Ericsson (India) Private Limited http://www.stericsson.com
More informationTHE idea behind constellation shaping is that signals with
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 52, NO. 3, MARCH 2004 341 Transactions Letters Constellation Shaping for Pragmatic Turbo-Coded Modulation With High Spectral Efficiency Dan Raphaeli, Senior Member,
More informationPERFORMANCE ANALYSIS AND DESIGN OF STBC S FOR FADING ISI CHANNELS
PEFOMANCE ANALYSIS AND DESIGN OF STBC S FO FADING ISI CHANNELS obert Schober, Wolfgang H. Gerstacker, and Lutz H. J. Lampe Department of Electrical and Computer Engineering, University of Toronto e-mail:
More informationDegrees of Freedom in Adaptive Modulation: A Unified View
Degrees of Freedom in Adaptive Modulation: A Unified View Seong Taek Chung and Andrea Goldsmith Stanford University Wireless System Laboratory David Packard Building Stanford, CA, U.S.A. taek,andrea @systems.stanford.edu
More informationA rate one half code for approaching the Shannon limit by 0.1dB
100 A rate one half code for approaching the Shannon limit by 0.1dB (IEE Electronics Letters, vol. 36, no. 15, pp. 1293 1294, July 2000) Stephan ten Brink S. ten Brink is with the Institute of Telecommunications,
More informationOptimal Power Allocation over Fading Channels with Stringent Delay Constraints
1 Optimal Power Allocation over Fading Channels with Stringent Delay Constraints Xiangheng Liu Andrea Goldsmith Dept. of Electrical Engineering, Stanford University Email: liuxh,andrea@wsl.stanford.edu
More informationRemoving Error Floor for Bit Interleaved Coded Modulation MIMO Transmission with Iterative Detection
Removing Error Floor for Bit Interleaved Coded Modulation MIMO Transmission with Iterative Detection Alexander Boronka, Nabil Sven Muhammad and Joachim Speidel Institute of Telecommunications, University
More informationIterative Multiuser Joint Decoding: Optimal Power Allocation and Low-Complexity Implementation
Iterative Multiuser Joint Decoding: Optimal Power Allocation and Low-Complexity Implementation Giuseppe Caire, Ralf Müller Ý and Toshiyuki Tanaka Þ March 12, 2003 Institut Eurecom, 2229 Route des Crétes,
More informationITERATIVE processing in communication systems according
1918 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 6, NOVEMBER 2004 Iterative Channel Estimation for Turbo Equalization of Time-Varying Frequency-Selective Channels Roald Otnes, Member, IEEE,
More informationA low cost soft mapper for turbo equalization with high order modulation
University of Wollongong Research Online Faculty of Engineering and Information Sciences - Papers: Part A Faculty of Engineering and Information Sciences 2012 A low cost soft mapper for turbo equalization
More informationA Capacity Achieving and Low Complexity Multilevel Coding Scheme for ISI Channels
A Capacity Achieving and Low Complexity Multilevel Coding Scheme for ISI Channels arxiv:cs/0511036v1 [cs.it] 8 Nov 2005 Mei Chen, Teng Li and Oliver M. Collins Dept. of Electrical Engineering University
More informationInterference Mitigation in MIMO Interference Channel via Successive Single-User Soft Decoding
Interference Mitigation in MIMO Interference Channel via Successive Single-User Soft Decoding Jungwon Lee, Hyukjoon Kwon, Inyup Kang Mobile Solutions Lab, Samsung US R&D Center 491 Directors Pl, San Diego,
More informationSNR Estimation in Nakagami-m Fading With Diversity Combining and Its Application to Turbo Decoding
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 50, NO. 11, NOVEMBER 2002 1719 SNR Estimation in Nakagami-m Fading With Diversity Combining Its Application to Turbo Decoding A. Ramesh, A. Chockalingam, Laurence
More informationLow complexity iterative receiver for Linear Precoded OFDM
Low complexity iterative receiver for Linear Precoded OFDM P.-J. Bouvet, M. Hélard, Member, IEEE, and V. Le Nir France Telecom R&D 4 rue du Clos Courtel, 3551 Cesson-Sévigné, France Email: {pierrejean.bouvet,maryline.helard}@francetelecom.com
More informationTHE computational complexity of optimum equalization of
214 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 53, NO. 2, FEBRUARY 2005 BAD: Bidirectional Arbitrated Decision-Feedback Equalization J. K. Nelson, Student Member, IEEE, A. C. Singer, Member, IEEE, U. Madhow,
More informationOn the performance of Turbo Codes over UWB channels at low SNR
On the performance of Turbo Codes over UWB channels at low SNR Ranjan Bose Department of Electrical Engineering, IIT Delhi, Hauz Khas, New Delhi, 110016, INDIA Abstract - In this paper we propose the use
More informationPerformance comparison of convolutional and block turbo codes
Performance comparison of convolutional and block turbo codes K. Ramasamy 1a), Mohammad Umar Siddiqi 2, Mohamad Yusoff Alias 1, and A. Arunagiri 1 1 Faculty of Engineering, Multimedia University, 63100,
More information_ MAPequalizer _ 1: COD-MAPdecoder. : Interleaver. Deinterleaver. L(u)
Iterative Equalization and Decoding in Mobile Communications Systems Gerhard Bauch, Houman Khorram and Joachim Hagenauer Department of Communications Engineering (LNT) Technical University of Munich e-mail:
More informationINTERSYMBOL interference (ISI) is a significant obstacle
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 53, NO. 1, JANUARY 2005 5 Tomlinson Harashima Precoding With Partial Channel Knowledge Athanasios P. Liavas, Member, IEEE Abstract We consider minimum mean-square
More informationJoint Iterative Equalization, Demapping, and Decoding with a Soft Interference Canceler
COST 289 meeting, Hamburg/Germany, July 3-4, 23 Joint Iterative Equalization, Demapping, and Decoding with a Soft Interference Canceler Markus A. Dangl, Werner G. Teich, Jürgen Lindner University of Ulm,
More informationBlind Iterative Channel Identification and Equalization
Blind Iterative Channel Identification and Equalization R. R. Lopes and J. R. Barry School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 333-5 Abstract We propose an
More informationTotally Blind APP Channel Estimation with Higher Order Modulation Schemes
Totally Blind APP Channel Estimation with Higher Order Modulation Schemes Frieder Sanzi Institute of Telecommunications, University of Stuttgart Pfaffenwaldring 47, D-7569 Stuttgart, Germany Email: sanzi@inue.uni-stuttgart.de
More informationPerformance Analysis of Maximum Likelihood Detection in a MIMO Antenna System
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 50, NO. 2, FEBRUARY 2002 187 Performance Analysis of Maximum Likelihood Detection in a MIMO Antenna System Xu Zhu Ross D. Murch, Senior Member, IEEE Abstract In
More informationMIMO Iterative Receiver with Bit Per Bit Interference Cancellation
MIMO Iterative Receiver with Bit Per Bit Interference Cancellation Laurent Boher, Maryline Hélard and Rodrigue Rabineau France Telecom R&D Division, 4 rue du Clos Courtel, 3552 Cesson-Sévigné Cedex, France
More informationOFDM Code Division Multiplexing with Unequal Error Protection and Flexible Data Rate Adaptation
OFDM Code Division Multiplexing with Unequal Error Protection and Flexible Data Rate Adaptation Stefan Kaiser German Aerospace Center (DLR) Institute of Communications and Navigation 834 Wessling, Germany
More informationPERFORMANCE ANALYSIS OF IDMA SCHEME USING DIFFERENT CODING TECHNIQUES WITH RECEIVER DIVERSITY USING RANDOM INTERLEAVER
1008 PERFORMANCE ANALYSIS OF IDMA SCHEME USING DIFFERENT CODING TECHNIQUES WITH RECEIVER DIVERSITY USING RANDOM INTERLEAVER Shweta Bajpai 1, D.K.Srivastava 2 1,2 Department of Electronics & Communication
More informationIDMA Technology and Comparison survey of Interleavers
International Journal of Scientific and Research Publications, Volume 3, Issue 9, September 2013 1 IDMA Technology and Comparison survey of Interleavers Neelam Kumari 1, A.K.Singh 2 1 (Department of Electronics
More informationA Simple Space-Frequency Coding Scheme with Cyclic Delay Diversity for OFDM
A Simple Space-Frequency Coding Scheme with Cyclic Delay Diversity for A Huebner, F Schuehlein, and M Bossert E Costa and H Haas University of Ulm Department of elecommunications and Applied Information
More informationIterative Detection and Decoding with PIC Algorithm for MIMO-OFDM Systems
, 2009, 5, 351-356 doi:10.4236/ijcns.2009.25038 Published Online August 2009 (http://www.scirp.org/journal/ijcns/). Iterative Detection and Decoding with PIC Algorithm for MIMO-OFDM Systems Zhongpeng WANG
More informationImpact of Linear Prediction Coefficients on Totally Blind APP Channel Estimation
Impact of Linear Prediction Coefficients on Totally Blind APP Channel Estimation Marc C. Necker, Frieder Sanzi 2 Institute of Communication Networks and Computer Engineering, University of Stuttgart, Pfaffenwaldring
More informationIMPERIAL COLLEGE of SCIENCE, TECHNOLOGY and MEDICINE, DEPARTMENT of ELECTRICAL and ELECTRONIC ENGINEERING.
IMPERIAL COLLEGE of SCIENCE, TECHNOLOGY and MEDICINE, DEPARTMENT of ELECTRICAL and ELECTRONIC ENGINEERING. COMPACT LECTURE NOTES on COMMUNICATION THEORY. Prof. Athanassios Manikas, version Spring 22 Digital
More informationIN RECENT years, inspired by the development of turbo
796 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL 52, NO 3, MARCH 2004 A RAKE-Based Iterative Receiver Space-Time Block-Coded Multipath CDMA Sudharman K Jayaweera, Member, IEEE, H Vincent Poor, Fellow, IEEE
More informationNear-Optimal Low Complexity MLSE Equalization
Near-Optimal Low Complexity MLSE Equalization Abstract An iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer (detector) with hard outputs, that has a computational complexity quadratic in
More informationPerformance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels
Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels Abstract A Orthogonal Frequency Division Multiplexing (OFDM) scheme offers high spectral efficiency and better resistance to
More informationNotes 15: Concatenated Codes, Turbo Codes and Iterative Processing
16.548 Notes 15: Concatenated Codes, Turbo Codes and Iterative Processing Outline! Introduction " Pushing the Bounds on Channel Capacity " Theory of Iterative Decoding " Recursive Convolutional Coding
More informationDepartment of Electronic Engineering FINAL YEAR PROJECT REPORT
Department of Electronic Engineering FINAL YEAR PROJECT REPORT BEngECE-2009/10-- Student Name: CHEUNG Yik Juen Student ID: Supervisor: Prof.
More informationGMP based channel estimation for single carrier transmissions over doubly selective channels
University of Wollongong Research Online Faculty of Engineering and Information Sciences - Papers: Part A Faculty of Engineering and Information Sciences 2010 GMP based channel estimation for single carrier
More informationSYSTEM-LEVEL PERFORMANCE EVALUATION OF MMSE MIMO TURBO EQUALIZATION TECHNIQUES USING MEASUREMENT DATA
4th European Signal Processing Conference (EUSIPCO 26), Florence, Italy, September 4-8, 26, copyright by EURASIP SYSTEM-LEVEL PERFORMANCE EVALUATION OF MMSE TURBO EQUALIZATION TECHNIQUES USING MEASUREMENT
More informationLayered Space-Time Codes
6 Layered Space-Time Codes 6.1 Introduction Space-time trellis codes have a potential drawback that the maximum likelihood decoder complexity grows exponentially with the number of bits per symbol, thus
More informationIterative Decoding for MIMO Channels via. Modified Sphere Decoding
Iterative Decoding for MIMO Channels via Modified Sphere Decoding H. Vikalo, B. Hassibi, and T. Kailath Abstract In recent years, soft iterative decoding techniques have been shown to greatly improve the
More informationDetection and Estimation of Signals in Noise. Dr. Robert Schober Department of Electrical and Computer Engineering University of British Columbia
Detection and Estimation of Signals in Noise Dr. Robert Schober Department of Electrical and Computer Engineering University of British Columbia Vancouver, August 24, 2010 2 Contents 1 Basic Elements
More informationESTIMATION OF CARRIER-FREQUENCY OFFSET AND FREQUENCY-SELECTIVE CHANNELS IN MIMO OFDM SYSTEMS USING A COMMON TRAINING SIGNAL
ESTIMATION OF CARRIER-FREQUENCY OFFSET AND FREQUENCY-SELECTIVE CHANNELS IN MIMO OFDM SYSTEMS USING A COMMON TRAINING SIGNAL Hlaing Minn, Member, IEEE and Naofal Al-Dhahir, Senior Member, IEEE Department
More informationPerformance analysis of MISO-OFDM & MIMO-OFDM Systems
Performance analysis of MISO-OFDM & MIMO-OFDM Systems Kavitha K V N #1, Abhishek Jaiswal *2, Sibaram Khara #3 1-2 School of Electronics Engineering, VIT University Vellore, Tamil Nadu, India 3 Galgotias
More informationPerformance of Combined Error Correction and Error Detection for very Short Block Length Codes
Performance of Combined Error Correction and Error Detection for very Short Block Length Codes Matthias Breuninger and Joachim Speidel Institute of Telecommunications, University of Stuttgart Pfaffenwaldring
More informationComparison Between Serial and Parallel Concatenated Channel Coding Schemes Using Continuous Phase Modulation over AWGN and Fading Channels
Comparison Between Serial and Parallel Concatenated Channel Coding Schemes Using Continuous Phase Modulation over AWGN and Fading Channels Abstract Manjeet Singh (ms308@eng.cam.ac.uk) - presenter Ian J.
More informationAdaptive communications techniques for the underwater acoustic channel
Adaptive communications techniques for the underwater acoustic channel James A. Ritcey Department of Electrical Engineering, Box 352500 University of Washington, Seattle, WA 98195 Tel: (206) 543-4702,
More informationLow complexity iterative receiver for linear precoded MIMO systems
Low complexity iterative receiver for linear precoded MIMO systems Pierre-Jean Bouvet, Maryline Hélard, Member, IEEE, Vincent Le Nir France Telecom R&D 4 rue du Clos Courtel 35512 Césson-Sévigné France
More informationAn Alamouti-based Hybrid-ARQ Scheme for MIMO Systems
An Alamouti-based Hybrid-ARQ Scheme MIMO Systems Kodzovi Acolatse Center Communication and Signal Processing Research Department, New Jersey Institute of Technology University Heights, Newark, NJ 07102
More informationCombined Phase Compensation and Power Allocation Scheme for OFDM Systems
Combined Phase Compensation and Power Allocation Scheme for OFDM Systems Wladimir Bocquet France Telecom R&D Tokyo 3--3 Shinjuku, 60-0022 Tokyo, Japan Email: bocquet@francetelecom.co.jp Kazunori Hayashi
More informationLow complexity iterative receiver for Non-Orthogonal Space-Time Block Code with channel coding
Low complexity iterative receiver for Non-Orthogonal Space-Time Block Code with channel coding Pierre-Jean Bouvet, Maryline Hélard, Member, IEEE, Vincent Le Nir France Telecom R&D 4 rue du Clos Courtel
More informationMULTI-USER DETECTION TECHNIQUES FOR POTENTIAL 3GPP LONG TERM EVOLUTION (LTE) SCHEMES
MULTI-USER DETECTION TECHNIQUES FOR POTENTIAL 3GPP LONG TERM EVOLUTION (LTE) SCHEMES Qinghua Guo, Xiaojun Yuan and Li Ping Department of Electronic Engineering, City University of Hong Kong, Hong Kong
More informationSPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS
SPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS RASHMI SABNUAM GUPTA 1 & KANDARPA KUMAR SARMA 2 1 Department of Electronics and Communication Engineering, Tezpur University-784028,
More informationEFFECTIVE CHANNEL CODING OF SERIALLY CONCATENATED ENCODERS AND CPM OVER AWGN AND RICIAN CHANNELS
EFFECTIVE CHANNEL CODING OF SERIALLY CONCATENATED ENCODERS AND CPM OVER AWGN AND RICIAN CHANNELS Manjeet Singh (ms308@eng.cam.ac.uk) Ian J. Wassell (ijw24@eng.cam.ac.uk) Laboratory for Communications Engineering
More informationPower Efficiency of LDPC Codes under Hard and Soft Decision QAM Modulated OFDM
Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 5 (2014), pp. 463-468 Research India Publications http://www.ripublication.com/aeee.htm Power Efficiency of LDPC Codes under
More informationIN AN MIMO communication system, multiple transmission
3390 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL 55, NO 7, JULY 2007 Precoded FIR and Redundant V-BLAST Systems for Frequency-Selective MIMO Channels Chun-yang Chen, Student Member, IEEE, and P P Vaidyanathan,
More informationTRANSMIT diversity has emerged in the last decade as an
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 5, SEPTEMBER 2004 1369 Performance of Alamouti Transmit Diversity Over Time-Varying Rayleigh-Fading Channels Antony Vielmon, Ye (Geoffrey) Li,
More informationDifferentially-Encoded Turbo Coded Modulation with APP Channel Estimation
Differentially-Encoded Turbo Coded Modulation with APP Channel Estimation Sheryl Howard Dept of Electrical Engineering University of Utah Salt Lake City, UT 842 email: s-howard@eeutahedu Christian Schlegel
More informationOn the Simulation of Oscillator Phase Noise
On the Simulation of Oscillator Phase Noise Workshop at Chair of Communications Theory, May 2008 Christian Müller Communications Laboratory Department of Electrical Engineering and Information Technology
More informationImproved Modulation Classification using a Factor-Graph-based Iterative Receiver
Improved Modulation Classification using a Factor-Graph-based Iterative Receiver Daniel Jakubisin and R. Michael Buehrer Mobile and Portable Radio Research Group MPRG), Wireless@VT, Virginia Tech, Blacksburg,
More informationContents Chapter 1: Introduction... 2
Contents Chapter 1: Introduction... 2 1.1 Objectives... 2 1.2 Introduction... 2 Chapter 2: Principles of turbo coding... 4 2.1 The turbo encoder... 4 2.1.1 Recursive Systematic Convolutional Codes... 4
More informationProbability of Error Calculation of OFDM Systems With Frequency Offset
1884 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 49, NO. 11, NOVEMBER 2001 Probability of Error Calculation of OFDM Systems With Frequency Offset K. Sathananthan and C. Tellambura Abstract Orthogonal frequency-division
More informationMAP Equalization of Space-Time Coded Signals over Frequency Selective Channels Invited Paper
MAP Equalization of SpaceTime Coded Signals over Frequency Selective Channels Invited Paper Gerhard auch, Ayman.F. Naguib, and Nambi Seshadri Institute for Communication Engineering LNT Information Sciences
More informationBEING wideband, chaotic signals are well suited for
680 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 51, NO. 12, DECEMBER 2004 Performance of Differential Chaos-Shift-Keying Digital Communication Systems Over a Multipath Fading Channel
More informationFREQUENCY DOMAIN POWER ADAPTATION SCHEME FOR MULTI-CARRIER SYSTEMS
The 7th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 06) FREQUENCY DOMAIN POWER ADAPTATION SCHEME FOR MULTI-CARRIER SYSTEMS Wladimir Bocquet, Kazunori
More informationChapter 9. Digital Communication Through Band-Limited Channels. Muris Sarajlic
Chapter 9 Digital Communication Through Band-Limited Channels Muris Sarajlic Band limited channels (9.1) Analysis in previous chapters considered the channel bandwidth to be unbounded All physical channels
More informationImplementation of Different Interleaving Techniques for Performance Evaluation of CDMA System
Implementation of Different Interleaving Techniques for Performance Evaluation of CDMA System Anshu Aggarwal 1 and Vikas Mittal 2 1 Anshu Aggarwal is student of M.Tech. in the Department of Electronics
More informationMULTICARRIER communication systems are promising
1658 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 52, NO. 10, OCTOBER 2004 Transmit Power Allocation for BER Performance Improvement in Multicarrier Systems Chang Soon Park, Student Member, IEEE, and Kwang
More informationChapter 3 Convolutional Codes and Trellis Coded Modulation
Chapter 3 Convolutional Codes and Trellis Coded Modulation 3. Encoder Structure and Trellis Representation 3. Systematic Convolutional Codes 3.3 Viterbi Decoding Algorithm 3.4 BCJR Decoding Algorithm 3.5
More informationLow Complexity Decoding of Bit-Interleaved Coded Modulation for M-ary QAM
Low Complexity Decoding of Bit-Interleaved Coded Modulation for M-ary QAM Enis Aay and Ender Ayanoglu Center for Pervasive Communications and Computing Department of Electrical Engineering and Computer
More informationBER and PER estimation based on Soft Output decoding
9th International OFDM-Workshop 24, Dresden BER and PER estimation based on Soft Output decoding Emilio Calvanese Strinati, Sébastien Simoens and Joseph Boutros Email: {strinati,simoens}@crm.mot.com, boutros@enst.fr
More informationPrinciples and Experiments of Communications
1 Principles and Experiments of Communications Weiyao Lin Dept. of Electronic Engineering Shanghai Jiao Tong University Textbook: Chapter 11 Lecture 06: Multicarrier modulation and OFDM Multicarrier Modulation
More informationDEGRADED broadcast channels were first studied by
4296 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 54, NO 9, SEPTEMBER 2008 Optimal Transmission Strategy Explicit Capacity Region for Broadcast Z Channels Bike Xie, Student Member, IEEE, Miguel Griot,
More informationIN A TYPICAL indoor wireless environment, a transmitted
126 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 48, NO. 1, JANUARY 1999 Adaptive Channel Equalization for Wireless Personal Communications Weihua Zhuang, Member, IEEE Abstract In this paper, a new
More informationECE 6640 Digital Communications
ECE 6640 Digital Communications Dr. Bradley J. Bazuin Assistant Professor Department of Electrical and Computer Engineering College of Engineering and Applied Sciences Chapter 8 8. Channel Coding: Part
More informationTCM-coded OFDM assisted by ANN in Wireless Channels
1 Aradhana Misra & 2 Kandarpa Kumar Sarma Dept. of Electronics and Communication Technology Gauhati University Guwahati-781014. Assam, India Email: aradhana66@yahoo.co.in, kandarpaks@gmail.com Abstract
More informationPerformance of Channel Coded Noncoherent Systems: Modulation Choice, Information Rate, and Markov Chain Monte Carlo Detection
Performance of Channel Coded Noncoherent Systems: Modulation Choice, Information Rate, and Markov Chain Monte Carlo Detection Rong-Rong Chen, Member, IEEE, Ronghui Peng, Student Member, IEEE 1 Abstract
More informationSingle-Carrier Space Time Block-Coded Transmissions Over Frequency-Selective Fading Channels
164 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO. 1, JANUARY 2003 Single-Carrier Space Time Block-Coded Transmissions Over Frequency-Selective Fading Channels Shengli Zhou, Member, IEEE, and Georgios
More informationConvolutional Coding Using Booth Algorithm For Application in Wireless Communication
Available online at www.interscience.in Convolutional Coding Using Booth Algorithm For Application in Wireless Communication Sishir Kalita, Parismita Gogoi & Kandarpa Kumar Sarma Department of Electronics
More informationLab 3.0. Pulse Shaping and Rayleigh Channel. Faculty of Information Engineering & Technology. The Communications Department
Faculty of Information Engineering & Technology The Communications Department Course: Advanced Communication Lab [COMM 1005] Lab 3.0 Pulse Shaping and Rayleigh Channel 1 TABLE OF CONTENTS 2 Summary...
More informationIMPROVED CHANNEL ESTIMATION FOR OFDM BASED WLAN SYSTEMS. G.V.Rangaraj M.R.Raghavendra K.Giridhar
IMPROVED CHANNEL ESTIMATION FOR OFDM BASED WLAN SYSTEMS GVRangaraj MRRaghavendra KGiridhar Telecommunication and Networking TeNeT) Group Department of Electrical Engineering Indian Institute of Technology
More informationSPACE TIME coding for multiple transmit antennas has attracted
486 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 3, MARCH 2004 An Orthogonal Space Time Coded CPM System With Fast Decoding for Two Transmit Antennas Genyuan Wang Xiang-Gen Xia, Senior Member,
More informationMaximum Likelihood Detection of Low Rate Repeat Codes in Frequency Hopped Systems
MP130218 MITRE Product Sponsor: AF MOIE Dept. No.: E53A Contract No.:FA8721-13-C-0001 Project No.: 03137700-BA The views, opinions and/or findings contained in this report are those of The MITRE Corporation
More informationVOL. 3, NO.11 Nov, 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.
Effect of Fading Correlation on the Performance of Spatial Multiplexed MIMO systems with circular antennas M. A. Mangoud Department of Electrical and Electronics Engineering, University of Bahrain P. O.
More informationGeneralized 8-PSK for Totally Blind Channel Estimation in OFDM
Generalized 8-PSK for Totally Blind Channel Estimation in OFDM Marc C. Necker Institute of Communication Networks and Computer Engineering, University of Stuttgart Pfaffenwaldring 47, D-70569 Stuttgart,
More informationPerformance Evaluation of different α value for OFDM System
Performance Evaluation of different α value for OFDM System Dr. K.Elangovan Dept. of Computer Science & Engineering Bharathidasan University richirappalli Abstract: Orthogonal Frequency Division Multiplexing
More informationRecent Progress in Mobile Transmission
Recent Progress in Mobile Transmission Joachim Hagenauer Institute for Communications Engineering () Munich University of Technology (TUM) D-80290 München, Germany State University of Telecommunications
More informationMIMO-BICM WITH IMPERFECT CHANNEL STATE INFORMATION: EXIT CHART ANALYSIS AND LDPC CODE OPTIMIZATION
MIMO-BICM WITH IMPERFECT CHANNEL STATE INFORMATION: EXIT CHART ANALYSIS AND LDPC CODE OPTIMIZATION Clemens Novak, Gottfried Lechner, and Gerald Matz Institut für Nachrichtentechnik und Hochfrequenztechnik,
More informationSISO MMSE-PIC detector in MIMO-OFDM systems
Vol. 3, Issue. 5, Sep - Oct. 2013 pp-2840-2847 ISSN: 2249-6645 SISO MMSE-PIC detector in MIMO-OFDM systems A. Bensaad 1, Z. Bensaad 2, B. Soudini 3, A. Beloufa 4 1234 Applied Materials Laboratory, Centre
More informationNear-Optimal Low Complexity MLSE Equalization
Near-Optimal Low Complexity MLSE Equalization HC Myburgh and Jan C Olivier Department of Electrical, Electronic and Computer Engineering, University of Pretoria RSA Tel: +27-12-420-2060, Fax +27 12 362-5000
More informationPerformance Analysis of Iterative Receiver in 3GPP/LTE DL MIMO OFDMA System
Performance Analysis of Iterative Receiver in 3GPP/LTE DL A System Laurent Boher, Rodolphe Legouable and Rodrigue Rabineau Orange Labs, 4 rue du Clos Courtel, 35512 Cesson-Sévigné Cedex, France Email:
More informationUTA EE5362 PhD Diagnosis Exam (Spring 2012) Communications
EE536 Spring 013 PhD Diagnosis Exam ID: UTA EE536 PhD Diagnosis Exam (Spring 01) Communications Instructions: Verify that your exam contains 11 pages (including the cover sheet). Some space is provided
More information