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Abstract: This paper shows a new algorithm to improve the performance of IQ demodulators and frequency converters exhibiting gain and phase imbalances between their branches. This algorithm does not require any input calibration signal and is independent of the input signal level. It exploits the spectral coherence (SC) concept using a monobit kernel to achieve optimization targets with minimum time to convergence, low computational load, and a wide range of input levels. Its effectiveness is shown through a low-IF receiver that improves its image rejection ratio (IRR) from 30 dB to 60 dB. [Copyright &y& Elsevier]

Abstract: This paper presents new algorithms that exploit uncorrelation between adjacent data samples produced by IFFT in ADSL systems. These algorithms update time-domain equalizer (TEQ) coefficients by adjacent lag autocorrelation minimization (ALAM) to shorten the channel. ALAM is an addition to the family of several existing autocorrelation-based algorithms that can achieve similar or better performance at lower complexity compared to other algorithms. The complexity reduction is achieved by designing a cost function without the sum-square, and further utilizing symmetrical TEQ property to reduce the complexity of the adaptation of TEQ to half of other existing algorithms. Furthermore, to avoid the limitations of lower bitrate after reaching the maximum bitrate and high complexity, an adaptive TEQ using equal-taps constraint (ETC) is introduced to maximize the bitrate along with achieving the lowest complexity. In addition, a simplified approach is used to calculate the expectation to reduce the computational complexity for ALAM and the other existing autocorrelation-based algorithms. Simulation results demonstrate that the proposed algorithms and modified existing algorithms achieve similar or better performance figures with reduced complexities. [Copyright &y& Elsevier]

Abstract: Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity. [Copyright &y& Elsevier]

Abstract: Square Contour Algorithm (SCA) is a relatively new blind equalization algorithm which reduces the dispersion between the output of the equalizer and a square contour. In this paper we develop Frequency Domain Square Contour Algorithm (FD-SCA) which is based on frequency domain implementation of block based SCA. Simulation results reveal that FD-SCA exhibits the same performance as that of existing time domain SCA with significantly low computational complexity. The performance of the FD-SCA in terms of convergence rate is further enhanced using normalization in frequency bins. The complete hardware architecture for the proposed FD-SCA based on ASM design flow along with implementation results is also presented. [Copyright &y& Elsevier]