In a discrete multitone receiver, a time-domain equalizer (TEQ) reduces intersymbol interference (ISI) by shortening the effective duration of the channel impulse response. Current TEQ design methods such as minimum mean-squared error (MMSE), maximum shortening SNR (MSSNR), and maximum geometric SNR (MGSNR) do not directly maximize bit rate. In this paper, we develop two TEQ design methods to maximize 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 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.

The minimum intersymbol interference min-ISI method yields time-domain equalizer TEQ designs for discrete multitone DMT modulation transceivers that are close to channel capacity. For eight standard ADSL channels, the min-ISI design method r e aches within 1 of the matched lter bound at the TEQ output. However, the min-ISI method relies several computationally expensive matrix multiplications. In this paper, we develop low-complexity algorithms for these mulitplications to allow for real-time implementation of the min-ISI method on programmable digital signal processors.

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.

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 de nition 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 o v er all subchannels.

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 degrees at a signal-to-noise ratio of 20 dB and a sampling rate of 8000 Hz at each sensor.