Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead

Sensing-as-a-service is anticipated to be the core feature of 6G perceptive mobile networks (PMN), where high-precision real-time sensing will become an inherent capability rather than being an auxiliary function as before. With the proliferation of wireless connected devices, resource allocation in terms of the users' specific quality-of-service (QoS) requirements plays a pivotal role to enhance the interference management ability and resource utilization efficiency. In this article, we comprehensively introduce the concept of sensing service in PMN, including the types of tasks, the distinctions/advantages compared to conventional networks, and the definitions of sensing QoS. Subsequently, we provide a unified RA framework in sensing-centric PMN and elaborate on the unique challenges. Furthermore, we present a typical case study named "communication-assisted sensing" and evaluate the performance trade-off between sensing and communication procedure. Finally, we shed light on several open problems and opportunities deserving further investigation in the future

Sensing With Random Signals

Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. This paper analyzes the sensing performance via random ISAC signals over a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the {optimized} sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations.Comment: 6 pages, 5 figures, submitted to ICASSP 202

Communication-Assisted Sensing in 6G Networks

The exploration of coordination gain achieved through the synergy of sensing and communication (S&C) functions plays a vital role in improving the performance of integrated sensing and communication systems. This paper focuses on the optimal waveform design for communication-assisted sensing (CAS) systems within the context of 6G perceptive networks. In the CAS process, the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the pertinent information to end-users. In our research, we establish a CAS framework grounded in the principles of rate-distortion theory and the source-channel separation theorem (SCT) in lossy data transmission. This framework provides a comprehensive understanding of the interplay between distortion, coding rate, and channel capacity. The purpose of waveform design is to minimize the sensing distortion at the user end while adhering to the SCT and power budget constraints. In the context of target response matrix estimation, we propose two distinct waveform strategies: the separated S&C and dual-functional waveform schemes. In the former strategy, we develop a simple one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the latter scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations

Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks

The integrated sensing and communication (ISAC) technique has the potential to achieve coordination gain by exploiting the mutual assistance between sensing and communication (S&C) functions. While the sensing-assisted communications (SAC) technology has been extensively studied for high-mobility scenarios, the communication-assisted sensing (CAS) counterpart remains widely unexplored. This paper presents a waveform design framework for CAS in 6G perceptive networks, aiming to attain an optimal sensing quality of service (QoS) at the user after the target's parameters successively ``pass-through'' the S$\&$C channels. In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget. The first scheme reveals a power allocation trade-off, while the latter presents a water-filling trade-off. Numerical results demonstrate the effectiveness of the proposed algorithms, where the dual-functional scheme exhibits approximately 12% performance gain compared to its separated waveform design counterpart

Does Full Waveform Inversion Benefit from Big Data?

This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI). While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural datasets published recently. Particularly, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K data pairs in total. Our experiments demonstrate that larger datasets lead to better performance and generalization of deep learning models for FWI. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement

EFWI\mathbf{\mathbb{E}^{FWI}}: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/Comment: 20 pages, 11 figure

Associations of vitamin D-related single nucleotide polymorphisms with post-stroke depression among ischemic stroke population

ObjectiveTo investigate the relationship between single nucleotide polymorphisms (SNPs) related to vitamin D (VitD) metabolism and post-stroke depression (PSD) in patients with ischemic stroke.MethodsA total of 210 patients with ischemic stroke were enrolled at the Department of Neurology in Xiangya Hospital, Central South University, from July 2019 to August 2021. SNPs in the VitD metabolic pathway (VDR, CYP2R1, CYP24A1, and CYP27B1) were genotyped using the SNPscan™ multiplex SNP typing kit. Demographic and clinical data were collected using a standardized questionnaire. Multiple genetic models including dominant, recessive, and over-dominant models were utilized to analyze the associations between SNPs and PSD.ResultsIn the dominant, recessive, and over-dominant models, no significant association was observed between the selected SNPs in the CYP24A1 and CYP2R1 genes and PSD. However, univariate and multivariate logistic regression analysis revealed that the CYP27B1 rs10877012 G/G genotype was associated with a decreased risk of PSD (OR: 0.41, 95% CI: 0.18–0.92, p = 0.030 and OR: 0.42, 95% CI: 0.18–0.98, p = 0.040, respectively). Furthermore, haplotype association analysis indicated that rs11568820-rs1544410-rs2228570-rs7975232-rs731236 CCGAA haplotype in the VDR gene was associated with a reduced risk of PSD (OR: 0.14, 95% CI: 0.03–0.65, p = 0.010), whereas no significant association was observed between haplotypes in the CYP2R1 and CYP24A1 genes and PSD.ConclusionOur findings suggest that the polymorphisms of VitD metabolic pathway genes VDR and CYP27B1 may be associated with PSD in patients with ischemic stroke

Dynamic residual deep learning with photoelectrically regulated neurons for immunological classification

Dynamic deep learning is considered to simulate the nonlinear memory process of the human brain during long-term potentiation and long-term depression. Here, we propose a photoelectrically modulated synaptic transistor based on MXenes that adjusts the nonlinearity and asymmetry by mixing controllable pulses. According to the advantage of residual deep learning, the rule of dynamic learning is thus elaborately developed to improve the accuracy of a highly homologous database (colorimetric enzyme-linked immunosorbent assay [c-ELISA]) from 80.9% to 87.2% and realize the fast convergence. Besides, mixed stimulation also remarkably shortens the iterative update time to 11.6 s as a result of the photoelectric effect accelerating the relaxation of ion migration. Finally, we extend the dynamic learning strategy to long short-term memory (LSTM) and standard datasets (Cifar10 and Cifar100), which well proves the strong robustness of dynamic learning. This work paves the way toward potential synaptic bionic retina for computer-aided detection in immunology

The role of indoleamine 2,3-dioxygenase 1 in early-onset post-stroke depression

BackgroundThe immune-inflammatory response has been widely considered to be involved in the pathogenesis of post-stroke depression (PSD), but there is ambiguity about the mechanism underlying such association.MethodsAccording to Diagnostic and Statistical Manual of Mental Disorders (5th edition), depressive symptoms were assessed at 2 weeks after stroke onset. 15 single nucleotide polymorphisms (SNPs) in genes of indoleamine 2,3-dioxygenase (IDO, including IDO1 and IDO2) and its inducers (including pro-inflammatory cytokines interferon [IFN]-γ, tumor necrosis factor [TNF]-α, interleukin [IL]-1β, IL-2 and IL-6) were genotyped using SNPscan™ technology, and serum IDO1 levels were detected by double-antibody sandwich enzyme-linked immune-sorbent assay.ResultsFifty-nine patients (31.72%) were diagnosed with depression at 2 weeks after stroke onset (early-onset PSD). The IDO1 rs9657182 T/T genotype was independently associated with early-onset PSD (adjusted odds ratio [OR] = 3.008, 95% confidence interval [CI] 1.157-7.822, p = 0.024) and the frequency of rs9657182 T allele was significantly higher in patients with PSD than that in patients with non-PSD (χ2 = 4.355, p = 0.037), but these results did not reach the Bonferroni significance threshold (p > 0.003). Serum IDO1 levels were also independently linked to early-onset PSD (adjusted OR = 1.071, 95% CI 1.002-1.145, p = 0.044) and patients with PSD had higher serum IDO1 levels than patients with non-PSD in the presence of the rs9657182 T allele but not homozygous C allele (t = -2.046, p = 0.043). Stroke patients with the TNF-α rs361525 G/G genotype had higher serum IDO1 levels compared to those with the G/A genotype (Z = -2.451, p = 0.014).ConclusionsOur findings provided evidence that IDO1 gene polymorphisms and protein levels were involved in the development of early-onset PSD and TNF-α polymorphism was associated with IDO1 levels, supporting that IDO1 which underlie strongly regulation by cytokines may be a specific pathway for the involvement of immune-inflammatory mechanism in the pathophysiology of PSD

Measuring the Differences of Public Health Service Facilities and Their Influencing Factors

The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply of public medical service facilities. First aid data with location information and patient preference information can reflect the ability of each hospital and the health inequities in cities. Determining which factors affect the measured differences in public medical service facilities and how to alter these factors will help researchers formulate targeted policies to solve the current resource-balance situation of the Ministry of Public Health. In this study, we propose a method to measure the differences in influence among hospitals based on actual medical behavior and use geographically weighted regression (GWR) to analyze the spatial correlations among the location, medical equipment, medical ability, and influencing factors of each hospital. The results show that Wuhan presents obvious health inequality, with the high-grade hospitals having spatial agglomeration in the city-center area, while the number and quality of hospitals in the peripheral areas are lower than those in the central area; thus, the hospitals in these peripheral areas need to be further improved. The method used in this study can measure differences in the influence of public medical service facilities, and the results are consistent with the measured differences at hospital level. Hospital influence is not only related to the equipment and medical ability of each hospital but is also affected by location factors. This method illustrates the necessity of conducting more empirical research on the public medical service supply to provide a scientific basis for formulating targeted policies from a new perspective