Intelligent Surface Empowered Sensing and Communication: A Novel Mutual Assistance Design

Integrated sensing and communication (ISAC) is a promising paradigm to provide both sensing and communication (S&C) services in vehicular networks. However, the power of echo signals reflected from vehicles may be too weak to be used for future precise positioning, due to the practically small radar cross section of vehicles with random reflection/scattering coefficient. To tackle this issue, we propose a novel mutual assistance scheme for intelligent surface-mounted vehicles, where S&C are innovatively designed to assist each other for achieving an efficient win-win integration, i.e., sensing-assisted phase shift design and communication-assisted high-precision sensing. Specifically, we first derive closed-form expressions of the echo power and achievable rate under uncertain angle information. Then, the communication rate is maximized while satisfying sensing requirements, which is proved to be a monotonic optimization problem on time allocation. Furthermore, we unveil the feasible condition of the problem and propose a polyblock-based optimal algorithm. Simulation results validate that the performance trade-off bound of S&C is significantly enlarged by the novel design exploiting mutual assistance in intelligent surface-aided vehicular networks.Comment: 5 pages, 5 figures, submitted to IEEE for possible publication

Sensing-Assisted Communication in Vehicular Networks with Intelligent Surface

The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet high-throughput and low-latency communication as well as high-resolution localization requirements in vehicular networks. However, considering the limited transmit power of the road site units (RSUs) and the relatively small radar cross section (RCS) of vehicles with random reflection coefficients, the power of echo signals may be too weak to be utilized for effective target detection and tracking. Moreover, high-frequency signals usually suffer from large fading loss when penetrating vehicles, which seriously degrades the quality of communication services inside the vehicles. To handle this issue, we propose a novel sensing-assisted communication mechanism by employing an intelligent omni-surface (IOS) on the surface of vehicles to enhance both sensing and communication (S&C) performance. To this end, we first propose a two-stage ISAC protocol, including the joint S&C stage and the communication-only stage, to fulfill more efficient communication performance improvements benefited from sensing. The achievable communication rate maximization problem is formulated by jointly optimizing the transmit beamforming, the IOS phase shifts, and the duration of the joint S&C stage. However, solving this ISAC optimization problem is highly non-trivial since inaccurate estimation and measurement information renders the achievable rate lack of closed-form expression. To handle this issue, we first derive a closed-form expression of the achievable rate under uncertain location information, and then unveil a sufficient and necessary condition for the existence of the joint S&C stage to offer useful insights for practical system design. Moreover, two typical scenarios including interference-limited and noise-limited cases are analyzed.Comment: IEEE Transactions on Vehicular Technology, 2023. arXiv admin note: text overlap with arXiv:2211.0420

Cooperative Cellular Localization with Intelligent Reflecting Surface: Design, Analysis and Optimization

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cram\'er-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem ...Comment: 14 pages, This work has been submitted to IEEE for possible publicatio

Network-Level Integrated Sensing and Communication: Interference Management and BS Coordination Using Stochastic Geometry

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for the case of the ASE maximization tends to employ all spacial resources towards multiplexing and diversity gain, without interference nulling. By contrast, for the sensing objectives, resource allocation tends to eliminate certain interference especially when the antenna resources are sufficient, because the inter-cell interference becomes a more dominant factor affecting sensing performance. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.Comment: 13 pages, 12 figures. This work has been submitted to the IEEE for possible publicatio

Duration-adaptive Video Highlight Pre-caching for Vehicular Communication Network

Video traffic in vehicular communication networks (VCNs) faces exponential growth. However, different segments of most videos reveal various attractiveness for viewers, and the pre-caching decision is greatly affected by the dynamic service duration that edge nodes can provide services for mobile vehicles driving along a road. In this paper, we propose an efficient video highlight pre-caching scheme in the vehicular communication network, adapting to the service duration. Specifically, a highlight entropy model is devised with the consideration of the segments' popularity and continuity between segments within a period of time, based on which, an optimization problem of video highlight pre-caching is formulated. As this problem is non-convex and lacks a closed-form expression of the objective function, we decouple multiple variables by deriving candidate highlight segmentations of videos through wavelet transform, which can significantly reduce the complexity of highlight pre-caching. Then the problem is solved iteratively by a highlight-direction trimming algorithm, which is proven to be locally optimal. Simulation results based on real-world video datasets demonstrate significant improvement in highlight entropy and jitter compared to benchmark schemes

Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication

Unmanned aerial vehicle (UAV) is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a penalty-based algorithm to efficiently solve the considered problem. The optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints and propose an efficient algorithm for solving the problem with location constraints.Comment: 32 pages, This work has been submitted to the IEEE for possible publicatio

A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE'' tasks. Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.Comment: 5 pages, 3 figures, 5 table

Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.Comment: 5 pages, 3 figures, 3 table

Activation of aldehyde dehydrogenase-2 improves ischemic random skin flap survival in rats

ObjectiveRandom skin flaps have many applications in plastic and reconstructive surgeries. However, distal flap necrosis restricts wider clinical utility. Mitophagy, a vital form of autophagy for damaged mitochondria, is excessively activated in flap ischemia/reperfusion (I/R) injury, thus inducing cell death. Aldehyde dehydrogenase-2 (ALDH2), an allosteric tetrameric enzyme, plays an important role in regulating mitophagy. We explored whether ALDH2 activated by N-(1,3-benzodioxol-5-ylmethyl)-2,6-dichlorobenzamide (Alda-1) could reduce the risk of ischemic random skin flap necrosis, and the possible mechanism of action.MethodsModified McFarlane flap models were established in 36 male Sprague-Dawley rats assigned randomly to three groups: a low-dose Alda-1 group (10 mg/kg/day), a high-dose Alda-1 group (20 mg/kg/day) and a control group. The percentage surviving skin flap area, neutrophil density and microvessel density (MVD) were evaluated on day 7. Oxidative stress was quantitated by measuring the superoxide dismutase (SOD) and malondialdehyde (MDA) levels. Blood perfusion and skin flap angiogenesis were assessed via laser Doppler flow imaging and lead oxide-gelatin angiography, respectively. The expression levels of inflammatory cytokines (IL-1β, IL-6, and TNF-α), vascular endothelial growth factor (VEGF), ALDH2, PTEN-induced kinase 1 (PINK1), and E3 ubiquitin ligase (Parkin) were immunohistochemically detected. Indicators of mitophagy such as Beclin-1, p62, and microtubule-associated protein light chain 3 (LC3) were evaluated by immunofluorescence.ResultsAlda-1 significantly enhanced the survival area of random skin flaps. The SOD activity increased and the MDA level decreased, suggesting that Alda-1 reduced oxidative stress. ALDH2 was upregulated, and mitophagy-related proteins (PINK1, Parkin, Beclin-1, p62, and LC3) were downregulated, indicating that ALDH2 inhibited mitophagy through the PINK1/Parkin signaling pathway. Treatment with Alda-1 reduced neutrophil infiltration and expressions of inflammatory cytokines. Alda-1 significantly upregulated VEGF expression, increased the MVD, promoted angiogenesis, and enhanced blood perfusion.ConclusionALDH2 activation can effectively enhance random skin flap viability via inhibiting PINK1/Parkin-dependent mitophagy. Moreover, enhancement of ALDH2 activity also exerts anti-inflammatory and angiogenic properties

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length