Fronto-Limbic Alterations in Negatively Biased Attention in Young Adults with Subthreshold Depression

Attentional bias toward negative stimuli has been observed in major depression disorders (MDDs). Imaging studies suggest the engagement of fronto-limbic regions like amygdala, anterior cingulate cortex (ACC), and lateral prefrontal cortex, is related to negatively biased attention. However, neural correlates of attentional bias for negative stimuli in individuals with subthreshold depression (SubD), that is individuals who have clinically relevant depressive symptoms but do not fulfill the criteria for MDD, remain unclear. Here, we used functional neuroimaging and the dot-probe task to elucidate the neural substrates of negatively biased attention among individuals with SubD. Behavioral results found that individuals with SubD allocated more attention toward negative stimuli relative to neutral stimuli, which were not observed among non-depressed controls (NCs). Imaging results found greater amygdala and rostral ACC activity in attentional bias toward negative stimuli among participants with SubD compared to NCs; Additionally, participants with SubD showed reduced engagement of bilateral inferior frontal gyrus compared with NCs in the attentional processing of negative stimuli. Together, these results suggest that alteration of fronto-limbic systems relative to controls, known to be related to negative detection and attentional control, is associated with negatively biased attention in individuals with SubD

Cooperative Transmission Strategy Over Users’ Mobility for Downlink Distributed Antenna Systems

Previously, a scheme in [1] is proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on SINR threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility

A Robust Secure Hybrid Analog and Digital Receive Beamforming Scheme for Efficient Interference Reduction

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Cooperative transmission strategy for downlink distributed antenna systems over time-varying channel

The channel state information (CSI) is used to optimise data transmission in time division duplex (TDD) systems, which is obtained at the time of channel estimation. The actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation in user movement environment. In this paper the impact of different user mobility on TDD downlink multiuser distributed antenna system is investigated. Based on mobility state information (MSI), an autocorrelation based feedback interval technique is proposed and updates CSI and mitigate the performance degradation imposed by the user speed and transmission delay. Cooperative clusters are formed to maximize sum rate and a channel gain based antenna selection and user clustering based on SINR threshold is applied to reduce computational complexity. Numerical results show that the proposed scheme can provide improved sum rate over the non cooperative system and no MSI knowledge. The proposed technique has good performance for wide range of speed and suitable for future wireless communication systems

Phase Optimization for Massive IRS-aided Two-way Relay Network

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We mainly focus on the phase optimization of IRS to improve the system rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable rate enhancement, which is called Max-RPS-EVD. To further achieve a higher rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the case of random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20\% rate gain over random phase. Additionally, it is also proved the optimal rate can be achieved when TW-DFR and IRS are located in the middle of S and D.Comment: 9 pages,10 figure

Mutual Coupling Calibration for Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a promising technique to greatly increase the spectral efficiency and may be adopted by the next generation mobile communication systems. Base stations (BSs) equipped with large-scale antennas can serve multiple users simultaneously by exploiting the downlink precoding in time division duplex (TDD) mode. However, channel state information (CSI) of uplink transmissions cannot be simply used for downlink precoding, because the gain mismatches of the transceiver radio frequency (RF) circuits disable the channel reciprocity. In this paper, we focus on antenna calibration for massive MIMO systems with maximal ratio transmit (MRT) precoding to solve the channel nonreciprocity problem. A new calibration method, called mutual coupling calibration, is proposed by using the effect of mutual coupling between adjacent antennas. By exploiting this method, the BS can perform the calibration without extra hardware circuit and users' involvement. We also build up the model of calibration error and derive the closed-form expressions of the ergodic sum-rates for evaluating the impact of calibration error on system performance. Simulation results verify the high calibration accuracy of the proposed method and show the significant improvement of system performance by performing antenna calibration

Neural and genetic determinants of creativity

Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r = 0.267, p = 0.0078) with an accuracy of 64.6%. In addition, the creativity–related functional connectivity network we identified in the first dataset was still significantly correlated with the creativity score in the new dataset (p<). In summary, our research demonstrates that strong top-down control versus weak bottom-up processes underlie creativity, which is modulated by competition between the glutamate and GABA neurotransmitter systems. Our work provides the first insights into both the neural and the genetic bases of creativity