Real-world noise often exhibits complex non-Gaussian characteristics, frequently manifesting as heavy-tailed distributions contaminated by outliers. The effectiveness of filtering algorithms can vary significantly across these diverse noise types. To this end, this letter presents an adaptive filtering algorithm based on the Student's-t mixture model (SMM). The proposed approach systematically employs SMM to model the noise distribution, optimizes the mixture model's hyper-parameters via the Expectation-Maximization (EM) algorithm, and ultimately derives the filter by maximizing the log-likelihood function of the parameterized SMM. Leveraging the inherent heavy-tailed properties of SMM, the algorithm demonstrates strong robustness against extreme outliers and is suitable for a wide range of noise environments. Simulation results confirm its superior performance under various noise disturbances.

Millimeter-wave (mmWave) radar has been deemed a key enabling technology for Internet of Things (IoT) applications and offers robust and non-invasive sensing capabilities of intelligent systems. However, conventional Fourier transform (FT)-based range estimation methods suffer from limited resolution, which can restrict performance, especially in dynamic IoT scenarios. To address this, we propose a fractional Fourier transform (FrFT)-based range estimation scheme that achieves higher resolution and accuracy. The proposed method incorporates a recursive clutter removal mechanism to enhance signal quality and employs a random forest regressor to dynamically determine the optimal FrFT angle parameter α for improved performance. Experimental results demonstrate that our proposed new FrFT-based approach achieves a mean absolute percentage error (MAPE) of 16.67%, which significantly outperforms the conventional FT-based method with a MAPE of 37.13%. Furthermore, we validate the effectiveness of our new approach by presenting its application to human height measurement to showcase its potential for real-world applications.

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, perfect CSI is usually required to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. In our simulation, the proposed Capacity-Netbased unsupervised learning without channel estimation obtains performance similar to that of an iterative optimization algorithm and the unsupervised learning that is trained by perfect CSI in terms of achievable rates.

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio.

Impulse noise (IN) can seriously degrade the performance of conventional active noise control (ANC) algorithms. Here, we present a novel feedforward ANC algorithm based on an information-theoretic learning framework with the data-reuse scheme of affine-projection-based algorithms. Inspired by the maximum correntropy criterion (MCC), the proposed algorithm is referred to as the modified filtered-x affine-projection-like MCC (MFxAPLMCC). Furthermore, we developed an objective function to maximize the correntropy between the system's desired vectors and the secondary path's output vectors to enhance robustness. Moreover, linear approximation reduces computational complexity, and the optimal step size is derived mathematically to accelerate convergence and increase the noise reduction ratio. The MFxAPLMCC algorithm was thoroughly evaluated in terms of stability and computational complexity; numerical simulations were used to confirm the effectiveness in terms of average noise reduction. The efficiency of our method was verified using three types of input: (1) symmetric alpha-stable (S$\alpha$S) IN, (2) a mixture of sinusoidal and IN, and (3) in-vehicle engine acceleration noise. We also verified the tracking capability of the adaptive algorithm for a case in which the primary path changes abruptly. Furthermore, the real measured acoustic path for ANC in-ear headphone development was used to validate the proposed method in real environments. The proposed algorithm significantly outperformed comparative ANC algorithms in convergence rate and noise reduction ratio. We also confirmed that the theoretical bound for stable step size coincides with the numerical results. The parameter sensitivity of the MFxAPLMCC was analyzed as well.

The method is proposed to detect human body with frequency modulation continuous wave (FMCW) and millimeter-wave (mmWave) radar. Human detection is the first step before performing vital sign monitoring, which is one of the most important applications for mmWave radar. In this Abstract, we exploit the range profile (RP) variation within a certain distance to detect human. This requires less computational storage spaces than conventional approaches. First, we perform clutter removal processing to weak ranging signals, and then calculate the standard deviation of each chirp to identify whether there is human in radar’s field of view (Fov). The experimental results confirmed that the accuracy our proposed method is higher than 97% even if the received ranging signals are weak.

Frequency-domain adaptive echo cancellers are a conventional approach for cancelling the line echoes in wire-line full-duplex data transmission. However, abrupt changes in sampling instant at the receiver side can cause significant signal-to-noise ratio (SNR) degradation since the equivalent echo path is changed simultaneously. This paper proposes a digital compensation method to mitigate such SNR degradation by predicting the coefficients of echo cancellers (EC) while sampling instants are being changed. We mathematically derive the proposed prediction algorithm and numerically verify that our method is insensitive to the nonlinearity of the phase interpolator in the timing recovery loop. As an example, in a 10GBASE-T system, the simulation results show that our method could reduce SNR degradation by up to 2.25 dB. Without compensation, the adaptive EC would require about 2.4 × 105symbol times to recover such degradation.

In this paper, we propose an interference detectable adaptive notch filter (ANF) for GPS receivers. The proposed ANF can estimate the existence of continuous wave interference (CWI) and its power, by exploiting the statistic value within an adaptive second-order infinite impulse response (IIR) filter. Moreover, our ANF is modulized, which allows more modules can be included to deal with multiple CWIs. We also design an adaptation algorithm for each ANF module and prove that the ANF module can adaptively notch the strongest CWI at its input, and this merit enables us to notch the primary CWIs with a limited number of ANF modules. Simulation results show that the adaption of the ANF modules converges in four iterations, and the signal to interference plus noise ratio (SINR) improvements can reach 21 dB.

Timing recovery loops with low loop delay are desirable. This paper presents receiver architectures with asynchronous equalization to reduce the loop delay. We propose a new asynchronous delayed least-mean-square (AD-LMS) adaptation algorithm together with an interaction-free loop to eliminate interaction between timing and equalization loops. In addition, a timing recovery scheme to reduce the timing jitter is developed. The proposed architecture can apply to 10GBASE-T systems. Simulation results show that the conventional approach suffers from the loop-interaction and the proposed method can eliminate this issue. Moreover, our approach has high phase margin, low gain peaking, and low jitter properties.

For 10GBASE-T systems, variation in a multiple-input-multiple-output (MIMO) channel degrades the decision-point signal-to-noise ratio (DP-SNR) owing to imperfect pre-equalisation in the Tomlinson–Harashima precoding (THP) at the transmitter sides and catastrophic error propagation in far-end crosstalk (FEXT) cancellation at the receiver sides. Moreover, by using fixed THP coefficients during data transmission, as specified in the 10GBASE-T standard, and the non-linearity of THP pose challenges in the design of adaptive receivers. The authors propose an adaptive two-stage equalisation and FEXT cancellation (TS-EFC) architecture without updating the THP coefficients to combat channel variation at both the transmitter and receiver sides. In the first stage, we propose a new non-decision-directed FEXT canceller at the transmitter side using a joint training architecture to avoid error propagation. In the second stage, we devise an adaptive MIMO equaliser together with a novel pre-processing unit at the receiver side to combat channel variation. The pre-processing unit can eliminate the non-linearity issue by estimating both effective data sequences and precoded channel inputs. In addition, we develop a block least mean square algorithm that exploits the properties of two-dimensional modulated symbols for updating coefficients of the adaptive MIMO equaliser. Simulation results show that our TS-EFC architecture is robust against channel variation and significantly improves the DP-SNR. It eliminates the error propagation and also achieves faster convergence rates during the adaptation process.

Extraction of correct timing error information is very important for high-speed digital transmission systems. In this letter, a novel effective-data-sequence-based timing error detector (EDS-TED) is presented for a baseband transmission system using nonlinear Tomlinson-Harashima precoding (THP), such as 10GBASE-T. The key idea of our TED is to minimize the mean square error between the received and desired EDS, rather than between the transmitted data signals. This formulation can exploit the autocorrelation between the EDS signals and extract the timing information embedded in the received signal. Moreover, the proposed architecture can lead to a simple and feasible circuit implementation. Simulation results show that a timing loop based on the proposed EDS-TED achieves a superior performance over traditional TED in terms of the peak-to-peak jitter and the TED gain.