Reconfigurable Intelligent Surface-Empowered Self-Interference Cancellation for 6G Full-Duplex MIMO Communication Systems

With the advent of sixth-generation (6G) wireless communication networks, it requires substantially increasing wireless traffic and extending serving coverage. Reconfigurable intelligent surface (RIS) is widely considered as a promising technique which is capable of improving the system data rate, energy efficiency and coverage extension as well as the benefit of low power consumption. Moreover, full-duplex (FD) transmission provides simultaneous transmit and received signals, which theoretically enhances twice spectrum efficiency. However, the self-interference (SI) in FD is a challenging task requiring complex and high-overhead cancellation, which can be resolved by configuring appropriate phase of RIS elements. This paper has proposed an RIS-empowered full-duplex self-interference cancellation (RFSC) scheme to alleviate the severe SI in an RIS-FD system. We consider the SI minimization of RIS-FD uplink (UL) while guaranteeing quality-of-service (QoS) of UL users. The closed-form solution is theoretically derived by exploiting Lagrangian method under different numbers of RIS elements and receiving antennas. Simulation results reveal that the proposed RFSC scheme outperforms the scenario without RIS deployment in terms of higher signal-to-interference-plus-noise ratio (SINR). Due to effective interference mitigation, the proposed RFSC can achieve the highest SINR compared to other existing schemes in open literatures

Credit Scoring Based on Hybrid Data Mining Classification

The credit scoring has been regarded as a critical topic. This study proposed four approaches combining with the NN (Neural Network) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different approaches combined with NN classifier were constructed by selecting features. NN classifier combines with conventional statistical LDA, Decision tree, Rough set and F-score approaches as features preprocessing step to optimize feature space by removing both irrelevant and redundant features. The procedure of the proposed algorithm is described first and then evaluated by their performances. The results are compared in combination with NN classifier and nonparametric Wilcoxon signed rank test will be held to show if there has any significant difference between these approaches. Our results suggest that hybrid credit scoring models are robust and effective in finding optimal subsets and the compound procedure is a promising method to the fields of data mining

CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks

With proliferation of fifth generation (5G) new radio (NR) technology, it is expected to meet the requirement of diverse traffic demands. We have designed a coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for 5G-NR networks to improve the network performance in terms of throughput and latency. We have proposed a CoMP enhanced joint subcarrier and power allocation (CESP) scheme which aims at maximizing sum rate under the considerations of transmit power limitation and guaranteed quality-of-service (QoS) including throughput and latency restrictions. By employing difference of two concave functions (D.C.) approximation and abstract Lagrangian duality method, we theoretically transform the original non-convex nonlinear problem into a solvable maximization problem. Moreover, the convergence of our proposed CESP algorithm with D.C. approximation is analytically derived with proofs, and is further validated via numerical results. Simulation results demonstrated that our proposed CESP algorithm outperforms the conventional non-CoMP and single numerology mechanisms along with other existing benchmarks in terms of lower latency and higher throughput under the scenarios of uniform and edge users

Robust Active and Passive Beamforming for RIS-Assisted Full-Duplex Systems under Imperfect CSI

The sixth-generation (6G) wireless technology recognizes the potential of reconfigurable intelligent surfaces (RIS) as an effective technique for intelligently manipulating channel paths through reflection to serve desired users. Full-duplex (FD) systems, enabling simultaneous transmission and reception from a base station (BS), offer the theoretical advantage of doubled spectrum efficiency. However, the presence of strong self-interference (SI) in FD systems significantly degrades performance, which can be mitigated by leveraging the capabilities of RIS. Moreover, accurately obtaining channel state information (CSI) from RIS poses a critical challenge. Our objective is to maximize downlink (DL) user data rates while ensuring quality-of-service (QoS) for uplink (UL) users under imperfect CSI from reflected channels. To address this, we introduce the robust active BS and passive RIS beamforming (RAPB) scheme for RIS-FD, accounting for both SI and imperfect CSI. RAPB incorporates distributionally robust design, conditional value-at-risk (CVaR), and penalty convex-concave programming (PCCP) techniques. Additionally, RAPB extends to active and passive beamforming (APB) with perfect channel estimation. Simulation results demonstrate the UL/DL rate improvements achieved considering various levels of imperfect CSI. The proposed RAPB/APB schemes validate their effectiveness across different RIS deployment and RIS/BS configurations. Benefited from robust beamforming, RAPB outperforms existing methods in terms of non-robustness, deployment without RIS, conventional successive convex approximation, and half-duplex systems

CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI

In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a non-line-of-sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human Presence Detection), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that apply machine learning, non-learning based methods, as well as non-CSI based features in open literature. CRONOS achieves the highest presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS scenarios

Conformational change of the AcrR regulator reveals a possible mechanism of induction

The Escherichia coli AcrR multidrug-binding protein represses transcription of acrAB and is induced by many structurally unrelated cytotoxic compounds. The crystal structure of AcrR in space group P2221 has been reported previously. This P2221 structure has provided direct information about the multidrug-binding site and important residues for drug recognition. Here, a crystal structure of this regulator in space group P31 is presented. Comparison of the two AcrR structures reveals possible mechanisms of ligand binding and AcrR regulation

Involvement of F-Actin in Chaperonin-Containing t-Complex 1 Beta Regulating Mouse Mesangial Cell Functions in a Glucose-Induction Cell Model

The aim of this study is to investigate the role of chaperonin-containing t-complex polypeptide 1 beta (CCT2) in the regulation of mouse mesangial cell (mMC) contraction, proliferation, and migration with filamentous/globular-(F/G-) actin ratio under high glucose induction. A low CCT2 mMC model induced by treatment of small interference RNA was established. Groups with and without low CCT2 induction examined in normal and high (H) glucose conditions revealed the following major results: (1) low CCT2 or H glucose showed the ability to attenuate F/G-actin ratio; (2) groups with low F/G-actin ratio all showed less cell contraction; (3) suppression of CCT2 may reduce the proliferation and migration which were originally induced by H glucose. In conclusion, CCT2 can be used as a specific regulator for mMC contraction, proliferation, and migration affected by glucose, which mechanism may involve the alteration of F-actin, particularly for cell contraction

Attention-based Learning for Sleep Apnea and Limb Movement Detection using Wi-Fi CSI Signals

Wi-Fi channel state information (CSI) has become a promising solution for non-invasive breathing and body motion monitoring during sleep. Sleep disorders of apnea and periodic limb movement disorder (PLMD) are often unconscious and fatal. The existing researches detect abnormal sleep disorders in impractically controlled environments. Moreover, it leads to compelling challenges to classify complex macro- and micro-scales of sleep movements as well as entangled similar waveforms of cases of apnea and PLMD. In this paper, we propose the attention-based learning for sleep apnea and limb movement detection (ALESAL) system that can jointly detect sleep apnea and PLMD under different sleep postures across a variety of patients. ALESAL contains antenna-pair and time attention mechanisms for mitigating the impact of modest antenna pairs and emphasizing the duration of interest, respectively. Performance results show that our proposed ALESAL system can achieve a weighted F1-score of 84.33, outperforming the other existing non-attention based methods of support vector machine and deep multilayer perceptron

Doping and temperature dependence of electron spectrum and quasiparticle dispersion in doped bilayer cuprates

Within the t-t'-J model, the electron spectrum and quasiparticle dispersion in doped bilayer cuprates in the normal state are discussed by considering the bilayer interaction. It is shown that the bilayer interaction splits the electron spectrum of doped bilayer cuprates into the bonding and antibonding components around the $(\pi,0)$ point. The differentiation between the bonding and antibonding components is essential, which leads to two main flat bands around the $(\pi,0)$ point below the Fermi energy. In analogy to the doped single layer cuprates, the lowest energy states in doped bilayer cuprates are located at the $(\pi/2,\pi/2)$ point. Our results also show that the striking behavior of the electronic structure in doped bilayer cuprates is intriguingly related to the bilayer interaction together with strong coupling between the electron quasiparticles and collective magnetic excitations.Comment: 9 pages, 4 figures, updated references, added figures and discussions, accepted for publication in Phys. Rev.

3510-V 390-m Omega . cm(2) 4H-SiC Lateral JFET on a Semi-Insulating Substrate

The performance of high-voltage 4H-SiC lateral JFETs on a semi-insulating substrate is reported in this letter. The design of the voltage-supporting layers is based on the charge compensation of p- and n-type epilayers. The best measured breakdown voltage is 3510 V, which, to the authors\u27 knowledge, is the highest value ever reported for SiC lateral switching devices. The R-on of this device is 390 m Omega . cm(2), in which 61% is due to the drift-region resistance. The BV2/R-on is 32 MW/cm(2), which is typical among other reported SiC lateral devices