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J M Cioffi - One of the best experts on this subject based on the ideXlab platform.
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optimized resource allocation for upstream vectored dsl systems with zero forcing generalized Decision Feedback equalizer
IEEE Journal of Selected Topics in Signal Processing, 2007Co-Authors: Chiangyu Chen, Rui Zhang, Kibeom Seong, J M CioffiAbstract:In upstream vectored DSL systems using zero-forcing generalized Decision Feedback Equalizers (ZF-GDFE), different decoding orders cause performance tradeoffs among the users. In this paper, these tradeoffs are characterized by formulating optimization problems with practical constraints. Lagrange dual decomposition and a two-step algorithm are used to solve the dual problems optimally with the computational complexity linear in the number of DMT tones. However, solving the tonal subproblem, which is shared by all the proposed optimization problems, associates with a high-complexity exhaustive search of K\ orderings, where K is the number of users. Thus, this paper proposes two low-complexity algorithms in order to find suboptimal orderings: successive ordering search (SOS) with complexity O(K4) and modified greedy search (MGA) with complexity O(K3). Numerical results show that MGA performs well enough in finding the achievable rate region. For problems related to feasibility check, SOS is suitable for closely approximating the optimal solution.
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mismatched finite complexity mmse Decision Feedback Equalizers
IEEE Transactions on Signal Processing, 1997Co-Authors: Naofal Aldhahir, J M CioffiAbstract:Closed-form expressions that quantify the degradation in the Decision-point signal-to-noise ratio of the finite-length minimum mean-square-error Decision Feedback equalizer (MMSE-DFE) due to channel and noise mismatch conditions are derived. The analysis is further extended to other receiver structures, namely, the MMSE-DFE with an adaptive feedforward filter, the MMSE linear equalizer, and the discrete multitone transceiver, all under a finite-complexity constraint. The limiting case of infinite-length filters is also analyzed. In addition, we present computer simulation results that compare the performance of the various receiver structures under study, assuming a particular channel and noise mismatch model for the high-bit-rate digital subscriber loop environment. Finally, several methods that can be used to mitigate the effects of mismatch are outlined.
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mmse Decision Feedback Equalizers and coding i equalization results
IEEE Transactions on Communications, 1995Co-Authors: J M Cioffi, G P Dudevoir, Vedat M Eyuboglu, G D ForneyAbstract:The minimum mean-squared-error Decision-Feedback equalizer (MMSE-DFE) has properties that suggest that it is a canonical equalization structure for systems that combine equalization with coded modulation. The structure and performance of the MMSE-DFE are succinctly derived using linear-estimation-theoretic principles in this first part of this two-part paper. The front-end of the MMSE-DFE, called the "mean-square whitened matched filter" (MS-WMF), is preferable in some ways to a matched filter or a whitened matched filter as a canonical receiver front end. In a coded system, the Feedback filter of the MMSE-DFE may be implemented in the transmitter using precoding. The MMSE-DFE can perform significantly better than a zero-forcing Decision-Feedback equalizer, particularly at moderate-to-low SNR's and on severe-ISI channels. The MMSE-DFE is biased. The optimum unbiased MMSE-DFE is the MMSE-DFE with the bias removed. Removing bias improves error probability, but reduces the SNR to SNR/sub MMSE-DFE,U/=SNR/sub MMSE-DFE/-1. It is shown that this SNR relationship is a particular case of a very general result and that SNR/sub MMSE-DFE,U/ gives a more realistic estimate of SNR. The results are extended to partial response equalization and to equalization with correlated inputs in an appendix. >
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mmse Decision Feedback Equalizers and coding ii coding results
IEEE Transactions on Communications, 1995Co-Authors: J M Cioffi, G P Dudevoir, M V Eyuboglu, G D ForneyAbstract:For pt.I see ibid., vol.43, no.10, p.2582 (1995). The minimum-mean-squared-error Decision-Feedback equalizer (MMSE-DFE) has properties that suggest that it is a canonical equalization structure in systems that combine equalization with coded modulation. With a given symbol rate 1/T and transmit spectrum, the output signal-to-noise ratio SNR/sub MMSE-DFE,U/ of a MMSE-DFE with an unbiased Decision rule is a single parameter that characterizes the channel for coding purposes. Indeed, the transmit spectrum that maximizes SNR/sub MMSE-DFE,U/ is the capacity-achieving (water-pouring) spectrum, and the capacity C(T) (in bits per two dimensions) is given by C(T)=log/sub 2/[1+SNR/sub MMSE-DFE,U/] regardless of the channel characteristics. The performance of a coded system with a MMSE-DFE equalization structure may be accurately estimated using the gain of the coding scheme at a given Pr(E). This performance is shown to be approximately the same as that of a multicarrier system using the same transmit spectrum and similar coding; such systems are known to be able to approach capacity arbitrarily closely. The MMSE-DFE can perform significantly better than a zero-forcing Decision-Feedback equalizer, particularly at moderate-to-low SNR's and on severe-ISI channels. Simulation results indicate that performance of the MMSE-DFE is surprisingly insensitive to transmit spectral shaping, as long as the transmit spectrum exceeds the capacity-achieving band, but that there is an optimal symbol rate that should (approximately) be used. >
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mmse Decision Feedback Equalizers
1995Co-Authors: Naofal Aldhahir, J M CioffiAbstract:This paper extends a number of results on the infinite-length minimum-mean-square-error Decision Feedback equalizer (MMSE-DFE) reported in (SI to the finite-length case. Cholesky factorization and displacement structure theory are demonstrated to be two powerful analytical tools for analyzing the finite-length MMSE-DFE. Our objective throughout the paper is to establish finite-length analogs of the well-known infinite-length MMSE-DFE results. Similarities and differences between the two cases are examined and delineated. Finally, convergence of our derived finite-length results to their well-established infinite-length counterparts is shown.
Naofal Aldhahir - One of the best experts on this subject based on the ideXlab platform.
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design and analysis of sparsifying dictionaries for fir mimo Equalizers
arXiv: Information Theory, 2017Co-Authors: Abubakr O Alabbasi, Ridha Hamila, Waheed U Bajwa, Naofal AldhahirAbstract:In this paper, we propose a general framework that transforms the problems of designing sparse finite-impulseresponse linear Equalizers and non-linear Decision-Feedback Equalizers, for multiple antenna systems, into the problem of sparsestapproximation of a vector in different dictionaries. In addition, we investigate several choices of the sparsifying dictionaries under this framework. Furthermore, the worst-case coherences of these dictionaries, which determine their sparsifying effectiveness, are analytically and/or numerically evaluated. Moreover, we show how to reduce the computational complexity of the designed sparse equalizer filters by exploiting the asymptotic equivalence of Toeplitz and circulant matrices. Finally, the superiority of our proposed framework over conventional methods is demonstrated through numerical experiments.
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a new design framework for sparse fir mimo Equalizers
IEEE Transactions on Communications, 2011Co-Authors: Ahmad Gomaa, Naofal AldhahirAbstract:In this paper, we propose a new framework for the design of sparse finite impulse response (FIR) Equalizers. We start by formulating greedy and convex-optimization-based solutions for sparse FIR linear equalizer tap vectors given a maximum allowable loss in the Decision-point signal-to-noise ratio. Then, we extend our formulation to Decision Feedback Equalizers and multiple-antenna systems. This is followed by further generalization to the channel shortening setup which is important for communication systems operating over broadband channels with long channel impulse responses. We propose a novel approach to design a sparse target impulse response. Finally, as an application of current practical interest, we consider self far-end crosstalk cancellation on vectored very high-speed digital subscriber line systems for cellular backhaul networks.
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mismatched finite complexity mmse Decision Feedback Equalizers
IEEE Transactions on Signal Processing, 1997Co-Authors: Naofal Aldhahir, J M CioffiAbstract:Closed-form expressions that quantify the degradation in the Decision-point signal-to-noise ratio of the finite-length minimum mean-square-error Decision Feedback equalizer (MMSE-DFE) due to channel and noise mismatch conditions are derived. The analysis is further extended to other receiver structures, namely, the MMSE-DFE with an adaptive feedforward filter, the MMSE linear equalizer, and the discrete multitone transceiver, all under a finite-complexity constraint. The limiting case of infinite-length filters is also analyzed. In addition, we present computer simulation results that compare the performance of the various receiver structures under study, assuming a particular channel and noise mismatch model for the high-bit-rate digital subscriber loop environment. Finally, several methods that can be used to mitigate the effects of mismatch are outlined.
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mmse Decision Feedback Equalizers
1995Co-Authors: Naofal Aldhahir, J M CioffiAbstract:This paper extends a number of results on the infinite-length minimum-mean-square-error Decision Feedback equalizer (MMSE-DFE) reported in (SI to the finite-length case. Cholesky factorization and displacement structure theory are demonstrated to be two powerful analytical tools for analyzing the finite-length MMSE-DFE. Our objective throughout the paper is to establish finite-length analogs of the well-known infinite-length MMSE-DFE results. Similarities and differences between the two cases are examined and delineated. Finally, convergence of our derived finite-length results to their well-established infinite-length counterparts is shown.
Sumei Sun - One of the best experts on this subject based on the ideXlab platform.
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Signal Detection for Large MIMO Systems using Block-iterative Generalized Decision Feedback Equalizers (BI-GDFE)
2016Co-Authors: Ying-chang Liang, Sumei SunAbstract:Abstract — This paper studies the problem of signal detection for generic MIMO channels with large signal dimensions. We propose a block-iterative generalized Decision Feedback equal-ization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-Decision correlation (IDC), a measure for the reliability of the earlier-made Decisions, we design the feed-forward Equalizers (FFEs) and Feedback Equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each iteration. We propose an IDC determination method to achieve fast and guaranteed convergence for the proposed receiver. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound for large MIMO channels with high SNR. I
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block iterative generalized Decision Feedback Equalizers for large mimo systems algorithm design and asymptotic performance analysis
IEEE Transactions on Signal Processing, 2006Co-Authors: Ying-chang Liang, Sumei SunAbstract:This paper studies the problem of signal detection for multiple-input multiple-output (MIMO) channels with large signal dimensions. We propose a block-iterative generalized Decision Feedback equalization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-Decision correlation, a measure for the reliability of the earlier-made Decisions, we design the feed-forward Equalizers (FFEs) and Feedback Equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each of the iterations. Novel implementations are also introduced to simplify the complexity of the receiver, which requires only one-tap filters for FFE and FBE. The proposed receiver also works when the signal dimension is greater than the observation dimension. The asymptotic performance of the proposed receiver is analyzed and its convergence has been confirmed through numerical evaluations for various parameters. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound (MFB) for large random MIMO channels when the received SNR is high enough.
Ying-chang Liang - One of the best experts on this subject based on the ideXlab platform.
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Signal Detection for Large MIMO Systems using Block-iterative Generalized Decision Feedback Equalizers (BI-GDFE)
2016Co-Authors: Ying-chang Liang, Sumei SunAbstract:Abstract — This paper studies the problem of signal detection for generic MIMO channels with large signal dimensions. We propose a block-iterative generalized Decision Feedback equal-ization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-Decision correlation (IDC), a measure for the reliability of the earlier-made Decisions, we design the feed-forward Equalizers (FFEs) and Feedback Equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each iteration. We propose an IDC determination method to achieve fast and guaranteed convergence for the proposed receiver. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound for large MIMO channels with high SNR. I
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Feasibility of Transmit Diversity for IS-136 TDMA Systems
2014Co-Authors: Ying-chang Liang, K. Ray J. LiuAbstract:Abstract- In this paper, we investigate the performance of transmit diversity technique operating in IS-136 TDMA systems where the Doppler frequency can be as high as 184Hz. Transmit diversity transforms a pat fading channel into a frequency selective fading channel, thus we use finite length Decision Feedback Equalizers to estimate the desired signal at the receiver. The channel parameters are trained via the training symbols and tracked using Decision-directed symbols. It is shown that systems with transmit diversity can provide a significant performance improvement over systems without transmit diversity for Rayleigh pat fading channels when the Doppler frequency is less than 40 Hz; however for systems with high Doppler frequency, transmit diversity alone cannot totally mitigate the fast fading effect since the effects of the channel estimation errors may cancel the benefit of the transmit diversity. I
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Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications, 2008Co-Authors: Lan Zhang, Ying-chang Liang, Yan XinAbstract:A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based Decision Feedback Equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.
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block iterative generalized Decision Feedback Equalizers for large mimo systems algorithm design and asymptotic performance analysis
IEEE Transactions on Signal Processing, 2006Co-Authors: Ying-chang Liang, Sumei SunAbstract:This paper studies the problem of signal detection for multiple-input multiple-output (MIMO) channels with large signal dimensions. We propose a block-iterative generalized Decision Feedback equalization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-Decision correlation, a measure for the reliability of the earlier-made Decisions, we design the feed-forward Equalizers (FFEs) and Feedback Equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each of the iterations. Novel implementations are also introduced to simplify the complexity of the receiver, which requires only one-tap filters for FFE and FBE. The proposed receiver also works when the signal dimension is greater than the observation dimension. The asymptotic performance of the proposed receiver is analyzed and its convergence has been confirmed through numerical evaluations for various parameters. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound (MFB) for large random MIMO channels when the received SNR is high enough.
John M. Cioffi - One of the best experts on this subject based on the ideXlab platform.
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Optimized transmission for upstream vectored DSL systems using zero-forcing generalized Decision Feedback Equalizers
2015Co-Authors: Chiangyu Chen, Rui Zhang, Kibeom Seong, John M. CioffiAbstract:Abstract — In upstream vectored DSL transmission, the far-end crosstalk (FEXT) can be completely cancelled by using zero-forcing generalized Decision-Feedback Equalizers (ZF-GDFE). When the spatially correlated alien crosstalk is present, the achievable data rates of DSL lines with ZF-GDFE depend on their decoding orders at each DMT tone. Given a weighted sum-rate maximization problem, the optimal orderings for all DMT tones can be found by the Lagrange dual decomposition method. However, the computational complexity of such approach grows with the factorial of the number of users, which makes the optimal search infeasible with a large number of vectored lines. This paper presents a modified greedy algorithm (MGA) that performs close to the optimal search of decoding orders. The complexity of MGA is only proportional to the cube of the number of users, which is the same as it of QR decomposition. With a significant reduction of complexity, MGA is a promising technique for practical DSL systems. I
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MMSE Decision-Feedback Equalizers: finite-length results
IEEE Transactions on Information Theory, 1995Co-Authors: Naofal Al-dhahir, John M. CioffiAbstract:This paper extends a number of results on the infinite-length minimum-mean-square-error Decision Feedback equalizer (MMSE-DFE) reported by Cioffi, Dudevoir, Eyuboglu and Forney (see IEEE Trans. Commun., 1995) to the finite-length case. Cholesky factorization and displacement structure theory are demonstrated to be two powerful analytical tools for analyzing the finite-length MMSE-DFE. Our objective throughout the paper is to establish finite-length analogs of the well-known infinite-length MMSE-DFE results. Similarities and differences between the two cases are examined and delineated. Finally, convergence of our derived finite-length results to their well-established infinite-length counterparts is shown. >
