High-Precision Channel Estimation for Sub-Noise Self-Interference Cancellation

Self-interference cancellation plays a crucial role in achieving reliable full-duplex communications. In general, it is essential to cancel the self-interference signal below the thermal noise level, which necessitates accurate reconstruction of the self-interference signal. In this paper, we propose a high-precision channel estimation method specifically designed for sub-noise self-interference cancellation. Exploiting the fact that all transmitted symbols are known to their respective receivers, our method utilizes all transmitted symbols for self-interference channel estimation. Through analytical derivations and numerical simulations, we validate the effectiveness of the proposed method. The results demonstrate the superior performance of our approach in achieving sub-noise self-interference cancellation

Security-Oriented Polar Coding Based on Channel-Gain-Mapped Frozen Bits

In this paper, a novel design named security-oriented polar coding (SOPC) is proposed to enhance the physical layer security (PLS), where the active pattern of frozen bits in a transmission is determined by random channel gain of the legitimate link. Since the channel gain value is not exchanged between the legitimate transmitter and the desired receiver, eavesdroppers cannot ascertain the frozen bit pattern engaged in the legitimate polar coding. When the signal-to-noise ratio (SNR) is low, eavesdroppers are unable to appropriately decode the confidential information delivered over the legitimate link. As the SNR increases, eavesdroppers may have chance to sort out the correct frozen bit pattern through a brute-force search. However, this chance is significantly reduced by our SOPC. We design the SOPC for both single-input-single-output single-antenna eavesdropper (SISOSE) and multiple-input-multiple-output multi-antenna eavesdropper (MIMOME) channels. Its PLS functioning is assessed in terms of the error rate difference between the legitimate receiver and the eavesdropper. Illustrative simulation results substantiate that the SOPC design guarantees degraded decoding performance at eavesdroppers, for both SISOSE and MIMOME channels, even in the presence of a powerful eavesdropper possessing infinite computational resources

Biochar for supercapacitor electrodes: Mechanisms in aqueous electrolytes

The utilization of biomass materials that contain abundant carbon–oxygen/nitrogen functional groups as precursors for the synthesis of carbon materials presents a promising approach for energy storage and conversion applications. Porous carbon materials derived from biomass are commonly employed as electric‐double‐layer capacitors in aqueous electrolytes. However, there is a lack of detailed discussion and clarification regarding the kinetics analysis and energy storage mechanisms associated with these materials. This study focuses on the modification of starch powders through the KOH activation process, resulting in the production of porous carbon with tunable nitrogen/oxygen functional groups. The kinetics and energy storage mechanism of this particular material in both acid and alkaline aqueous electrolytes are investigated using in situ attenuated total reflectance‐infrared in a three‐electrode configuration

Permutation-Based Transmissions in Finite Blocklength Regime: Efficient and Effective Resource Utilisation

In this paper, the concept of permutation-based transmissions is developed at the transport layer of short-packet communications, where the application-layer data is divided into two parts: one is conveyed by the permutation of various lengths in a group of packets, and the other is encapsulated into these various-length packets. The former part, referred to as permutation-conveyed data unit (PCDU), improves the goodput and reduces the latency. From a finite blocklength perspective, the maximal payload rate of permutation-based transmissions is formulated, based on which the goodput and latency are derived and obtained in analytical forms. Moreover, the spectral efficiency and energy efficiency are analysed. Illustrative numerical results on the performance and resource utilisation comparisons not only substantiate the advantages of our permutation-based transmissions over conventional transport-layer encapsulation schemes, but also provide useful tools and specifications for the PCDU design in short-packet communications

Movable Antenna-Enabled Co-Frequency Co-Time Full-Duplex Wireless Communication

Movable antenna (MA) provides an innovative way to arrange antennas that can contribute to improved signal quality and more effective interference management. This method is especially beneficial for co-frequency co-time full-duplex (CCFD) wireless communication, which struggles with self-interference (SI) that usually overpowers the desired incoming signals. By dynamically repositioning transmit/receive antennas, we can mitigate the SI and enhance the reception of incoming signals. Thus, this paper proposes a novel MA-enabled point-to-point CCFD system and formulates the minimum achievable rate of two CCFD terminals. To maximize the minimum achievable rate and determine the near-optimal positions of the MAs, we introduce a solution based on projected particle swarm optimization (PPSO), which can circumvent common suboptimal positioning issues. Moreover, numerical results reveal that the PPSO method leads to a better performance compared to the conventional alternating position optimization (APO). The results also demonstrate that an MA-enabled CCFD system outperforms the one using fixed-position antennas (FPAs).Comment: This paper has been submitted to IEEE Wireless Communications Letter

Closed-Loop Unsupervised Representation Disentanglement with β\beta-VAE Distillation and Diffusion Probabilistic Feedback

Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unresolved core issues: (i) heavy reliance on label annotation and synthetic data -- causing poor generalization on natural scenarios; (ii) heuristic/hand-craft disentangling constraints make it hard to adaptively achieve an optimal training trade-off; (iii) lacking reasonable evaluation metric, especially for the real label-free data. To address these challenges, we propose a \textbf{C}losed-\textbf{L}oop unsupervised representation \textbf{Dis}entanglement approach dubbed \textbf{CL-Dis}. Specifically, we use diffusion-based autoencoder (Diff-AE) as a backbone while resorting to $\beta$-VAE as a co-pilot to extract semantically disentangled representations. The strong generation ability of diffusion model and the good disentanglement ability of VAE model are complementary. To strengthen disentangling, VAE-latent distillation and diffusion-wise feedback are interconnected in a closed-loop system for a further mutual promotion. Then, a self-supervised \textbf{Navigation} strategy is introduced to identify interpretable semantic directions in the disentangled latent space. Finally, a new metric based on content tracking is designed to evaluate the disentanglement effect. Experiments demonstrate the superiority of CL-Dis on applications like real image manipulation and visual analysis

DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

Traditional neural objection detection methods use multi-scale features that allow multiple detectors to perform detecting tasks independently and in parallel. At the same time, with the handling of the prior box, the algorithm's ability to deal with scale invariance is enhanced. However, too many prior boxes and independent detectors will increase the computational redundancy of the detection algorithm. In this study, we introduce Dubox, a new one-stage approach that detects the objects without prior box. Working with multi-scale features, the designed dual scale residual unit makes dual scale detectors no longer run independently. The second scale detector learns the residual of the first. Dubox has enhanced the capacity of heuristic-guided that can further enable the first scale detector to maximize the detection of small targets and the second to detect objects that cannot be identified by the first one. Besides, for each scale detector, with the new classification-regression progressive strapped loss makes our process not based on prior boxes. Integrating these strategies, our detection algorithm has achieved excellent performance in terms of speed and accuracy. Extensive experiments on the VOC, COCO object detection benchmark have confirmed the effectiveness of this algorithm