Neural networks

In a discrete multitone receiver, a time-domain equalizer (TEQ) reduces the intersymbol interference (ISI) by shortening the effective duration of the channel impulse response. Current TEQ design methods such as the minimum mean-squared error (MMSE), maximum shortening SNR (MSSNR), and maximum geometric SNR (MGSNR) do not directly maximize bit rate. We develop two TEQ design methods to maximize the bit rate. First, we partition an equalized multicarrier channel into its equivalent signal, noise, and ISI paths to develop a new subchannel SNR definition. Then, we derive a nonlinear function of TEQ taps that measures the bit rate, which the proposed maximum bit rate (MBR) method optimizes. We also propose a minimum-ISI method that generalizes the MSSNR method by weighting the ISI in the frequency domain to obtain higher performance. The minimum-ISI method is amenable to real-time implementation on a fixed-point digital signal processor. Based on simulations using eight different carrier-serving-area loop channels, (1) the proposed methods yield higher bit rates than MMSE, MGSNR, and MSSNR methods; (2) the proposed methods give three-tap TEQs with higher bit rates than 17-tap MMSE, MGSNR, and MSSNR TEQs; (3) the proposed MBR method achieves the channel capacity (as computed by the matched filter bound using the proposed subchannel SNR model) with a five-tap TEQ; and (4) the proposed minimum-ISI method achieves the bit rate of the optimal MBR method.

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Equalization for discrete multitone transceivers to maximize bit rate

This paper proposes a low computational cost method for the near-field narrowband source localization problem, which does not require multidimensional search or high-order statistics. The proposed method is based on the second-order statistics (SOS) of the outputs of a uniform linear array (ULA). More precisely, the range and angle parameters are estimated through a weighted linear prediction (LP) algorithm applied to a properly chosen array output correlation sequence. Detailed performance analysis and derivation of the optimal weightings are provided. Simulation results are finally presented to validate the theoretical analysis results and to assess the performance of the proposed method.

A weighted linear prediction method for near-field source localization

Passive source localization is one of the issues in array signal processing fields. In some practical applications, the signals received by an array are the mixture of near-field and far-field sources, such as speaker localization using microphone arrays and guidance (homing) systems. To localize mixed near-field and far-field sources, this paper develops a two-stage MUSIC algorithm using cumulant. The key points of this paper are: (i) in the first stage, this paper derives one special cumulant matrix, in which the virtual ?steering vector? is the function of the common electric angle in both near-field and far-field signal models so that source direction-of-arrival (DOA) (near-field or far-field one) can be obtained from this electric angle using the conventional high-resolution MUSIC algorithm; (ii) in the second stage, this paper derives another particular cumulant matrix, in which the virtual ?steering matrix? has full column rank no matter whether the received signals are multiple near-field sources or multiple far-field ones or their mixture. What is more important, the virtual ?steering vector? can be separated into two parts, in which the first one is the function of the common electric angle in both signal models, whereas the second part is the function of the electric angle that exists only in near-field signal model. Furthermore, by substituting the common electric angle estimated in the first stage into one special Hermitian matrix formed from another MUSIC spectral function, the range of near-field sources can be obtained from the eigenvector of the Hermitian matrix. The resultant algorithm avoids two- dimensional search and pairing parameters; in addition, it avoids the estimation failure problem and alleviates aperture loss. Simulation results are presented to validate the performance of the proposed method.

Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm

This letter exploits the cyclic prefix to create a blind adaptive globally convergent channel-shortening algorithm, with a complexity like least mean squares. The cost function is related to that of the shortening signal-to-noise solution of Melsa et al. (see IEEE Trans. Commun., vol.44, p.1662-72, Dec. 1996), and simulations are provided to demonstrate the performance of the algorithm.

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A blind adaptive TEQ for multicarrier systems

Locating and tracking a speaker in real time using microphone arrays is important in many applications such as hands-free video conferencing, speech processing in large rooms, and acoustic echo cancellation. A speaker can be moving from the far field to the near field of the array, or vice versa. Many neural-network-based localization techniques exist, but they are applicable to either far-field or near-field sources, and are computationally intensive for real-time speaker localization applications because of the wide-band nature of the speech. We propose a unified neural-network-based source localization technique, which is simultaneously applicable to wide-band and narrow-band signal sources that are in the far field or near field of a microphone array. The technique exploits a multilayer perceptron feedforward neural network structure and forms the feature vectors by computing the normalized instantaneous cross-power spectrum samples between adjacent pairs of sensors. Simulation results indicate that our technique is able to locate a source with an absolute error of less than 3.5/spl deg/ at a signal-to-noise ratio of 20 dB and a sampling rate of 8000 Hz at each sensor.

A unified neural-network-based speaker localization technique

We propose an optimum channel shortening method for discrete multitone (DMT) transceivers. The proposed method shortens a given channel to a desired length while maximizing the number of bits transmitted on a DMT symbol. The key to the optimum solution is the definition of the SNR in a subchannel using the equivalent signal, noise, and ISI paths in the system. Our simulation results show that the proposed method outperforms the best existing method with a 18% increase in the bit rate. We show that the maximum shortening SNR method is a special case of the proposed method and both methods are nearly equivalent when the input energy distribution is constant over all subchannels.

Optimum channel shortening for discrete multitone transceivers

Commonly used time domain equalizer (TEQ) design methods have been recently unified as an optimization problem involving an objective function in the form of a Rayleigh quotient. The direct generalized eigenvalue solution relies on matrix decompositions. To reduce implementation complexity, we propose an iterative refinement approach in which the TEQ length starts at two taps and increases by one tap at each iteration. Each iteration involves matrix-vector multiplications and vector additions with 2 × 2 matrices and two-element vectors. At each iteration, the optimization of the objective function either improves or the approach terminates. The iterative refinement approach provides a range of communication performance versus implementation complexity tradeoffs for any TEQ method that fits the Rayleigh quotient framework. We apply the proposed approach to three such TEQ design methods: maximum shortening signal-to-noise ratio, minimum intersymbol interference, and minimum delay spread.

/pdf/iterative-refinement-methods-for-time-domain-equalizer-3ptff6btlm.pdf

Iterative refinement methods for time-domain equalizer design

A technique includes receiving a radio block from a transmission channel. The radio block includes first data and second data. The technique includes evaluating the first data to obtain a first evaluation result, evaluating the second data to obtain a second evaluation result and determining whether the radio block is a partial block based on the first and second evaluation results.

Guner Arslan

Papers

Equalization for discrete multitone transceivers to maximize bit rate

A unified neural-network-based speaker localization technique

Optimum channel shortening for discrete multitone transceivers

Iterative refinement methods for time-domain equalizer design

Partial radio block detection