Nonlinear self-interference (SI) cancellation is crucial for eliminating the impact of the transmitter-side nonlinearity on the comprehensive SI cancellation performance in full-duplex radios. However, in the propagation environment with rich reflection paths of signals, the number of the delayed taps required for time-domain nonlinear SI cancellation booms exponentially with the increase of the multi-path number, leading to unacceptable complexity. In this paper, a novel frequency-domain nonlinear SI canceller based on successive interference cancellation (SIC) technique is proposed for orthogonal frequency division multiplexing (OFDM) modulated full-duplex systems subjected to both transmitter nonlinearity and receiver nonlinearity. The proposed frequency-domain nonlinear SIC (FD-NSIC) cancels nonlinear components one by one via separately estimating the channel frequency response suffered by each order nonlinear component, which significantly relaxes the computational cost. Besides, to avoid the influence of the strong correlation between nonlinear components, orthogonalization is operated before SIC to white basis functions, which accelerates the convergence speed of the proposed FD-NSIC. Simulations are explicitly performed, showing that compared with the conventional frequency-domain nonlinear canceller based on recursive least squares (RLS) channel estimation, the proposed FD-NSIC is capable of canceling the SI to the noise floor with only 52% computational cost under 50 dB interference-to-noise ratio.

A fundamental challenge in remote sensing-based forest fire detection lies in accurately discerning fire characteristics on various scales against the backdrop of intricate and heterogeneous forest landscapes. In response to this challenge, we propose a dual-path network (DPMNet) with multidimensional feature interaction for real time remote sensing forest fire detection. Initially, a dual-path backbone network is designed, integrating coarse-grained and fine-grained parallel pathways, working in tandem to capture both global visual features and nuanced local texture details. Subsequently, we develop the Multidimensional Interactive Feature Pyramid Network (MiFPN), a novel structure that amalgamates information streams from varied levels through a three-branch structure and engenders profound fusion and dynamic interaction of features across multiple scales. Thereafter, the Context-Enriched Adaptive Fusion Module (CEAFM) is proposed, which emerges to meticulously blend macroscopic visual elements harvested via coarse-grained conduits, employing a multi-faceted pathway strategy to bolster the model’s overarching comprehension and precision in forest fire detection. Finally, the Enhanced Contextual Pooling Bottleneck (ECPB) is put forward, an integration that augments the model’s spatial perception and contextual acumen through the incorporation of dilated convolution and global pooling techniques. Extensive experiments are conducted on the remote sensing forest fire dataset in order to confirm the efficacy of DPMNet. The experimental results demonstrate that our DPMNet achieves satisfactory performance in terms of real-time performance as well as accuracy and provides an effective solution for real-time detection of remote sensing forest fires based on UAVs.

In physical layer security, imperfect time synchronization results in random mismatch between the received cooperative jamming (CJ) and the reconstructed CJ, leading to performance degradation of CJ cancellation at the authorized receiver. In this paper, the CJ cancellation performance with imperfect time synchronization is analyzed and evaluated by jamming cancellation ratio (JCR). Then, by assuming that the timing error is uniformly distributed within the synchronization accuracy, the expectation and cumulative distribution function (CDF) of JCR are provided. Experimental results confirm the theoretical analysis that imperfect time synchronization causes dramatic performance degradation of CJ cancellation, especially for high jamming-to-noise ratio (JNR).

Ambient backscatter (AB) communication enables the backscatter devices to modulate and transmit data by reflecting surrounding digital video broadcasting (DVB) signals, and has emerged as a promising solution to connect low-power and small-sized devices in Internet of Things (IoT). Unfortunately, the AB receiver may suffer from the strong direct-link interference (DLI) transmitted from the ambient DVB station, making it difficult to decode the backscattered signal (BS) directly. In this paper, to tackle this issue, we propose a DLI cancellation method for AB communications over ambient DVB signals. First, a general signal processing operation for DLI cancellation at the AB receiver is designed, where neither special design on waveform nor cooperation between the ambient DVB station and the backscatter transceiver is required. Then, by minimizing the residual reception power after DLI cancellation, a three-layer parameter searching algorithm is proposed to separate the BS from the ambient DVB signal, which is of low implementation complexity. Simulation results show that when the DLI-to-BS ratio (ISR) is greater than 30 dB, the proposed DLI cancellation method can effectively suppress the DLI at the AB receiver, and the demodulation and capacity performance of the ambient DVB transmission and the backscatter transmission are barely deteriorated, as compared with the scenarios of original DVB transmission without BS and pure backscatter transmission without DLI. In addition, there is a trade-off between the convergence time and the interference cancellation ratio (ICR) performance of the proposed DLI cancellation scheme.

The artificial noise (AN) shielded frequency-hopping (FH) architecture can effectively resist wiretapping and electromagnetic interference, but its wide hopping bandwidth will raise a non-negligible in-phase and quadrature (IQ) imbalance at transceiver oscillators, yielding significant signal distortions during IQ mixing. Considering this, first, signal distortions raised by transceiver IQ imbalances are modelled in this study, and their characteristics are mathematically investigated as well. Next, the signal to distortion-plus-noise ratio (SDNR) is extracted to measure the quality of the obtained signal after AN cancellation, and the secrecy capacity is subsequently derived to evaluate system secrecy under multiple wiretappers. Then, based on the performance evaluations, the transmitting power ratio of AN to information signals is optimized to counteract the secrecy degradation raised by transceiver IQ imbalances. Simulations show that both the AN cancellation and system secrecy performance will decline sharply as the amplitude or phase imbalance deteriorates. Besides, compared with the case where the power ratio of AN to information signals is 2, the average secrecy enhancements of an equal power scheme, a power scheme ignoring IQ imbalances, and our proposed power scheme approximate 8.4%, 9.66%, and 11.05% for a fixed phase imbalance, and approximate 10.88%, 12.61%, and 14.26% for a fixed amplitude imbalance, respectively.

Full-duplex integrated sensing and communication (ISAC) in vehicle-to-everything (V2X) networks is emerging for next-generation wireless communication. However, the self-interference (SI) problem is one of the key challenges for communication and sensing systems to share the same frequency band. In this paper, we propose a novel nonlinear SI cancellation (NSIC) for V2X networks considering the beam tracking introduced by vehicle movement. The proposed scheme can be performed with or without the channel state information (CSI) of SI, where the reconstruction coefficients are obtained by projecting from the path coefficients or updating the previous reconstruction coefficients, respectively. Simulation results demonstrate that the proposed NSIC obtains stable cancellation performance in beam tracking, and can reach the performance of the conventional NSIC under fixed beam pointing.