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A general and flexible technique is proposed for sampling the complex envelope of a bandpass signal by using a nonuniformly interleaved two-channel analog-to-digital converter (TIADC). The signal is sampled directly without any demodulation operation, but the two sampling channels are not uniformly interleaved, since some TIADC timing offsets are rendered forbidden. The proposed complex envelope sampling scheme requires the implementation of two digital FIR filters and a discrete-time modulator. Quadrature sampling, a special case that assumes certain parameter relations, is investigated as an approximation technique. Computer simulations are presented and illustrate a −57dB mean-square error (MSE) can be achieved via quadrature approximation for sufficiently large filter orders. [ABSTRACT FROM AUTHOR]

Switching noise generated by digital circuits can degrade analog circuit performance in mixed-signal integrated circuits (ICs). In an analog-to-digital converter (ADC), one major source of switching noise is digital output drivers. Traditional methods for mitigating this problem mostly have been to isolate the analog and digital circuits to minimize digital noise coupling into sensitive analog nodes. This paper presents two fully digital and adaptive algorithms, which find and cancel errors due to switching noise coupling at the output of an ADC without using noise sensors. To demonstrate the operation of these algorithms, a 12-bit, 40-MS/s pipelined ADC has been designed and fabricated in 0.18-µm CMOS process. The system consists of an ADC with its own output drivers, and eight other independent digital output drivers (noise buffers) that can be programmed to produce four different kinds of switching noise. The switching noise cancellation (SNC) algorithm estimates noise parameters and stores them in look-up tables. At the ADC output, the effects of switching noise are digitally removed to recover the input samples. Test results show that the ADC achieves a signal-to-noise-and-distortion-ratio (SNDR) of 64.9 dB with all of the noise-generating buffers off. With the noise buffers on, the worst case SNDR before and after SNC is 51.9 dB and 63.7 dB, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, we consider robust optimization of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay precoders in presence of deterministic imperfect channel state information (CSI), when the CSI uncertainty lies in a norm bounded region. Two widely used performance metrics, mutual information (MI) and mean square error (MSE), are adopted as design objectives. According to the philosophy of worst-case robustness, the robust optimization problems with respect to maximizing the worst-case MI and minimizing the worst-case MSE are formulated as maximin and minimax problems, respectively. Due to the fact that these two problems do not have a concave-convex or convex-concave structure, we cannot rely on the conventional saddle point theory to find the robust solutions. Nevertheless, by exploiting majorization theory, we show that the formulated maximin and minimax problems both admit saddle points. We further analytically characterize the saddle points, and provide closed-form solutions to robust relay precoder designs. Interestingly, we find that, under both MI and MSE metrics, the robust relay optimization leads to a channel-diagonalizing structure, meaning that eigenmode transmission is optimal from the worst-case robustness perspective. The proposed robust designs can improve the spectral efficiency and reliability of AF MIMO relaying against CSI uncertainties at the similar cost of computational complexity as the existing non-robust schemes. [ABSTRACT FROM AUTHOR]

Two approaches to calibrate a pipelined analog-to-digital converter (ADC) and adapt an equalizer in a baseband receiver are proposed. Adaptive digital correction is used to calibrate the weights applied to the ADC output decisions so that the mean square error across the slicer in the receiver is minimized. Simulation results are presented to demonstrate the joint calibration of a pipelined ADC and adaptation of a channel equalizer. [ABSTRACT FROM AUTHOR]

This paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities. The minimax problem admits a saddle point which is characterized. The robust test applies a nonlinear transformation which flattens the nominal likelihood ratio in the vicinity of one. Results are illustrated by considering the transmission of binary data in the presence of additive noise. [ABSTRACT FROM AUTHOR]

This paper considers a robust mean-square-error (MSE) equalizer design problem for multiple-input multiple-output (MIMO) communication systems with imperfect channel and noise information at the receiver. When the channel state information (CSI) and the noise covariance are known exactly at the receiver, a minimum-mean-square-error (MMSE) equalizer can be employed to estimate the transmitted signal. However, in actual systems, it is necessary to take into account channel and noise estimation errors. We consider here a worst-case equalizer design problem where the goal is to find the equalizer minimizing the equalization MSE for the least favorable channel model within a neighborhood of the estimated model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and estimated channel models. Lagrangian optimization is used to convert this min-max problem into a convex min-min problem over a convex domain, which is solved by interchanging the minimization order. The robust MSE equalizer and associated least favorable channel model can then be obtained by solving numerically a scalar convex minimization problem. Simulation results are presented to demonstrate the MSE and bit error rate (BER) performance of robust equalizers when applied to the least favorable channel model. [ABSTRACT FROM AUTHOR]

A blind maximum-likelihood equalization algorithm is described and its convergence behavior is analyzed. Since the algorithm employs the Viterbi algorithm (VA) to execute the expectation step of the expectation-maximization (EM) iteration, we call it the expectation-maximization Viterbi algorithm (EMVA). An EMVA-based blind channel-acquisition technique which achieves a high global convergence probability is developed. The performance of the method is evaluated via numerical simulations under static and fading channel conditions. [ABSTRACT FROM AUTHOR]

A blind maximum likelihood equalization method is proposed for frequency selective fast fading Ricean channels. This method employs the expectation-maximization Viterbi algorithm (EMVA) developed in [1] for blind channel estimation and signal detection. Since the Viterbi algorithm (VA) is used to execute the E-phase of an expectation-maximization (EM) iteration, it requires that the observed sequence can be modelled as a finite-state hidden Markov process. We develop a hidden Markov model for frequency selective fast fading Ricean channels, so that the observed process can be viewed as the noisy output of a finite state machine (FSM), to which the VA is applicable. The EMVA is then employed to obtain a blind maximum likelihood estimate of the specular part of the channel and, for one special case, of a noise parameter measuring the total power of the additive and multiplicative channel noise components. Simulation results are presented which show that the EMVA achieves an accurate estimate of the channel specular part and has an error rate performance close to that of the maximum likelihood detector based on true parameters for the given FSM model. [ABSTRACT FROM AUTHOR]

This paper considers a worst-case precoder design problem for multiple-input multiple-output (MIMO) wireless communication systems with imperfect channel knowledge at the transmitter. When the MIMO channel is a full-row rank matrix, which arises generically when the number nr of transmit antennas is greater or equal to the number nR of receive antennas, and when channel state information is known perfectly at the transmitter, the channel can be equalized exactly by employing a precoder equal to the channel pseudo-inverse. However, in actual systems, it is necessary to take into account the channel estimation error. We consider here a worst-case precoder design problem where the goal is to find the precoder minimizing the equalization mean-square error for the least favorable channel located in a ball centered about the estimated channel. Lagrangian optimization is used to convert this min-max problem into a min-max convex minimization problem over a convex domain which can be solved in closed form. The robust precoder and associated least favorable channel have an intuitive interpretation since the least-favorable channel zeroes out the weakest subchannels, and the robust precoder implements a pseudo-inverse only for the remaining subchannels. [ABSTRACT FROM AUTHOR]

Given a nominal statistical model, we consider the minimax estimation problem consisting of finding the best least-squares estimator for the least favorable statistical model within a neighborhood of the nominal model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and nominal models. For a Gaussian nominal model and a finite observations interval, or for a stationary Gaussian process over an infinite interval, the usual noncausal Wiener filter remains optimal. However, the worst case performance of the filter is affected by the size of the neighborhood representing the model uncertainty. On the other hand, standard causal least-squares estimators are not optimal, and a characterization is provided for the causal estimator and the corresponding least favorable model. The causal estimator takes the form of a risk-sensitive estimator with an appropriately selected risk sensitivity coefficient. [ABSTRACT FROM AUTHOR]