Nearest Kronecker Product Decomposition Based Subband Adaptive Filter: Algorithms and Applications

Recently, the nearest Kronecker product (NKP) decomposition-based normalized least mean square (NLMS-NKP) algorithm has demonstrated superior convergence performance compared to the conventional NLMS algorithm. However, its convergence rate exhibits significant degradation when processing highly correlated input signals. To address this problem, we propose a type-I NKP-based normalized subband adaptive filter (NSAF) algorithm, namely NSAF-NKP-I. Nevertheless, this algorithm incurs substantially higher computational overhead than the NLMS-NKP algorithm. Remarkably, our enhanced type-II NKP-based NSAF (NSAF-NKP-II) algorithm achieves equivalent convergence performance while substantially reducing computational complexity. Furthermore, to enhance robustness against impulsive noise interference, we develop two robust variants: the maximum correntropy criterion-based robust NSAF-NKP (RNSAF-NKP-MCC) and logarithmic criterion-based robust NSAF-NKP (RNSAF-NKP-LC) algorithms. Additionally, detailed analyses of computational complexity, step-size range, and theoretical steady-state performance are provided for theproposed algorithms. To enhance the practicability of the NSAF-NKP-II algorithm in complex nonlinear environments, we further devise two nonlinear implementations: the trigonometric functional link network-based NKP-NSAF (TFLN-NSAF-NKP) and Volterra series expansion-based NKP-NSAF (Volterra-NKP-NSAF) algorithms. In active noise control (ANC) systems, we further propose the filtered-x NSAF-NKP-II (NKP-FxNSAF) algorithm. Simulation experiments in echo cancellation, sparse system identification, nonlinear processing, and ANC scenarios are conducted to validate the superiority of the proposed algorithms over existing state-of-the-art counterparts.

Spectral-Efficient LoRa with Low Complexity Detection

In this paper, we propose a spectral-efficient LoRa (SE-LoRa) modulation scheme with a low complexity successive interference cancellation (SIC)-based detector. The proposed communication scheme significantly improves the spectral efficiency of LoRa modulation, while achieving an acceptable error performance compared to conventional LoRa modulation, especially in higher spreading factor (SF) settings. We derive the joint maximum likelihood (ML) detection rule for the SE-LoRa transmission scheme that turns out to be of high computational complexity. To overcome this issue, and by exploiting the frequency-domain characteristics of the dechirped SE-LoRa signal, we propose a low complexity SIC-based detector with a computation complexity at the order of conventional LoRa detection. By computer simulations, we show that the proposed SE-LoRa with low complexity SIC-based detector can improve the spectral efficiency of LoRa modulation up to $445.45\%$, $1011.11\%$, and $1071.88\%$ for SF values of $7$, $9$, and $11$, respectively, while maintaining the error performance within less than $3$ dB of conventional LoRa at symbol error rate (SER) of $10^{-3}$ in Rician channel conditions.

Low-complexity spectral shaping method for OFDM signals with dynamically adaptive emission mask

Orthogonal frequency division multiplexing (OFDM) signals with rectangular pulses exhibit low spectral confinement. Shaping their power spectral density (PSD) is imperative in the increasingly overcrowded spectrum to benefit from the cognitive radio (CR) paradigm. However, since the available spectrum is non-contiguous and its occupancy changes with time, the spectral shaping solution has to be dynamically adapted. This work proposes a framework that allows using a reduced set of preoptimized pulses to shape the spectrum of OFDM signals, irrespective of its spectral width and location, by means of simple transformations. The employed pulses combine active interference cancellation (AIC) and adaptive symbol transition (AST) terms in a transparent way to the receiver. They can be easily adapted online by the communication device to changes in the location or width of the transmission band, which contrasts with existing methods of the same type that require solving NP-hard optimization problems.

Pinching Antenna-aided NOMA Systems with Internal Eavesdropping

As a novel member of flexible antennas, the pinching antenna (PA) is realized by integrating small dielectric particles on a waveguide, offering unique regulatory capabilities on constructing line-of-sight (LoS) links and enhancing transceiver channels, reducing path loss and signal blockage. Meanwhile, non-orthogonal multiple access (NOMA) has become a potential technology of next-generation communications due to its remarkable advantages in spectrum efficiency and user access capability. The integration of PA and NOMA enables synergistic leveraging of PA's channel regulation capability and NOMA's multi-user multiplexing advantage, forming a complementary technical framework to deliver high-performance communication solutions. However, the use of successive interference cancellation (SIC) introduces significant security risks to power-domain NOMA systems when internal eavesdropping is present. To this end, this paper investigates the physical layer security of a PA-aided NOMA system where a nearby user is considered as an internal eavesdropper. We enhance the security of the NOMA system through optimizing the radiated power of PAs and analyze the secrecy performance by deriving the closed-form expressions for the secrecy outage probability (SOP). Furthermore, we extend the characterization of PA flexibility beyond deployment and scale adjustment to include flexible regulation of PA coupling length. Based on two conventional PA power models, i.e., the equal power model and the proportional power model, we propose a flexible power strategy to achieve secure transmission. The results highlight the potential of the PA-aided NOMA system in mitigating internal eavesdropping risks, and provide an effective strategy for optimizing power allocation and cell range of user activity.

Fully Asynchronous Unsourced Random Access over Fading Channels

We examine unsourced random access in a fully asynchronous setup, where active users transmit their data without restriction on the start time over a fading channel. In the proposed scheme, the transmitted signal consists of a pilot sequence and a polar codeword, with the polar codeword distributed across the data part of the packet in an on-off pattern. The receiver uses a double sliding-window decoder, where the inner window employs iterative decoding with joint timing and pilot detection, channel estimation, single-user decoding, and successive interference cancellation to recover the message bits, while the outer window enhances interference cancellation. The numerical results indicate that the proposed scheme exhibits only a slight performance loss compared to the synchronous benchmark while being more applicable in practice.

Neural Network-Assisted RIS Weight Optimization for Spatial Nulling in Distorted Reflector Antenna Systems

Reconfigurable intelligent surfaces (RIS) have recently been proposed as an effective means for spatial interference suppression in large reflector antenna systems. Existing RIS weight optimization algorithms typically rely on accurate theoretical radiation models. However, in practice, distortions on the reflector antenna may cause mismatches between the theoretical and true antenna patterns, leading to degraded interference cancellation performance when these weights are directly applied. In this report, a residual learning network-assisted simulated annealing (ResNet-SA) framework is proposed to address this mismatch without requiring explicit knowledge of the distorted electric field. By learning the residual difference between the theoretical and true antenna gains, a neural network (NN) is embedded in a heuristic optimization algorithm to find the optimal weight vector. Simulation results demonstrate that the proposed approach achieves improved null depth in the true radiation pattern as compared with conventional methods that optimize weights based solely on the theoretical model, validating the effectiveness of the ResNet-SA algorithm for reflector antenna systems with approximate knowledge of the pattern.

On the Limits of Coherent Time-Domain Cancellation of Radio Frequency Interference

In many sensing (viz., radio astronomy) and radar applications, the received signal of interest (SOI) exhibits a significantly wider bandwidth or weaker power than the interference signal, rendering it indistinguishable from the background noise. Such scenarios arise frequently in applications such as passive radar, cognitive radio, low-probability-of-intercept (LPI) radar, and planetary radar for radio astronomy, where canceling the radio frequency interference (RFI) is critical for uncovering the SOI. In this work, we examine the Demodulation-Remodulation (Demod-Remod) based interference cancellation framework for the RFI. This approach demodulates the unknown interference, creates a noise-free interference replica, and coherently subtracts it from the received signal. To evaluate the performance limits, we employ the performance metric termed \textit{interference rejection ratio} (IRR), which quantifies the interference canceled. We derive the analytical expressions of IRR as a function of the optimal estimation variances of the signal parameters. Simulation results confirm the accuracy of the analytical expression for both single-carrier and multi-carrier interference signals and demonstrate that the method can substantially suppress the interference at a sufficient interference-to-noise ratio (INR), enabling enhanced detection and extraction of the SOI. We further extend the analysis to the scenario where the SOI is above the noise floor, and confirm the validity of the theoretical IRR expression in this scenario. Lastly, we compare the Demod-Remod technique to other time-domain cancellation methods. The result of the comparison identifies the conditions under which each method is preferred, offering practical guidelines for interference mitigation under different scenarios.

RSMA-Assited and Transceiver-Coordinated ICI Management for MIMO-OFDM System

High-mobility scenarios are becoming increasingly critical in next-generation communication systems. While multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) stands as a prominent technology, its performance in such scenarios is fundamentally limited by Doppler-induced inter-carrier interference (ICI). Rate splitting multiple access (RSMA), recognized as a key multiple access technique for future communications, demonstrates superior interference management capabilities that we leverage to address this challenge. In specific, we propose a novel RSMA-assisted and transceiver-coordinated transmission scheme for ICI management in MIMO-OFDM system: (1) At the receiver side, we develop a hybrid successive interference cancellation (SIC) architecture with dynamic subcarrier clustering, which enables parallel intra-cluster and serial inter-cluster processing to balance complexity and performance. (2) At the transmitter~side, we design a matched hybrid precoding through formulated sum-rate maximization, solved via our proposed augmented boundary-compressed particle swarm optimization (ABC-PSO) algorithm for analog phase optimization and weighted minimum mean-square error (WMMSE)-based digital precoding iteration. Simulation results show that our scheme brings effective ICI suppression and enhanced system capacity with controlled complexity.

Decision Feedback-Aided Known-Interference Cancellation

Known-interference cancellation (KIC) in combination with cooperative jamming can be used to provide covertness and security to wireless communications at the physical layer. However, since the signal of interest (SI) of a wireless communication system acts as estimation noise, i.e., interference, to KIC, the SI limits the extent to which the known interference (KI) can be canceled and that in turn limits the throughput of the wireless communication system that is being hidden or secured. In this letter, we analyze a decision feedback-aided known-interference cancellation (DF-KIC) structure in which both the KI and SI are canceled iteratively and successively. Measurement results demonstrate that introducing decision feedback to KIC improves its KI cancellation capability and hence increases the wireless communication system's useful throughput, albeit at the expense of a higher computational load.

Interference Mitigation Recommender System using U-Net Autoencoders

Building on the previous work on interference mitigation, this paper introduces a modular recommender system that automatically selects the most effective interference mitigation strategy based on the interference characteristics present in the received signal. The system integrates three key stages: an SPS classifier module, a SIR predictor, and a bank of specialized U-Net autoencoders designed for different interference conditions. The classification block identifies the parameters required for cancellation. The recommender then directs the signal to the appropriate mitigation model, optionally incorporating SIR-based decisions for scenarios where successive interference cancellation may be advantageous. Experiments conducted across diverse SIR levels and modulation environments show that the recommender strategy improves robustness and reduces BER compared to using any single mitigation method alone. The results demonstrate the potential of adaptive, model-selective architectures to enhance interference resilience in dynamic communication environments.