Integrated sensing, communication, and computation (ISCC) network has been recognized as a key enabler to realize the vision of Internet-of-Things. In this paper, we explore the resource allocation problem in ISCC networks, where the task execution workflow consists of multiple dependent processes, i.e., wireless sensing, signal processing, data delivery, and data processing. To this end, a tandem-parallel queuing model is first proposed to characterize the end-to-end (E2E) task execution process. Given the model, the E2E delay upper bound is derived according to the stochastic network calculus theory. Based on the analytical results, the joint allocation problem of the sensing, communication, and computation (SCC) resources is formulated to minimize the E2E delay while satisfying the constraints of network resources, tolerable delay, and sensing mutual information, etc. Further, this non-convex optimization problem is parameterized to enable a learning-based optimization approach. Next, we design the unsupervised learning (UL) framework based on multilevel decomposition architecture (MDA) and residual network (RN) to accelerate training speed and ensure effective primal-dual learning. Numerical results demonstrate that the proposed UL-MDA-RN framework is superior to existing baselines with excellent convergence efficiency and lower achieved E2E delay. In addition, our results analyze the impacts of the network parameters on the E2E delay performance to guide the design of appropriate SCC resource provisioning patterns.

The stochastic geometry-based modeling and analysis of large-scale Integrated Sensing and Communication (ISAC) networks are vital for providing useful ISAC design insights. One important ISAC network characteristic is the sensing and communication (S&C) spatial correlations since the communication users (CUs) are more interested in the sensing target (STs) around them and the base stations prefer to utilize one ISAC signal to serve the CUs and STs close to each other to enable effective S&C coverage. However, most existing works focused on ISAC systems where the locations of CUs and STs are assumed to be independent. This paper bridges this gap by proposing an analytical framework for the ISAC networks where the unified ISAC waveform is modeled with limited main lobe beamwidth and the CUs and STs served by one signal are assumed to be correlatively distributed in spatial domain. Given the model, we first derive some prerequisite auxiliary quantities (i.e., the probability that an ST is served by the main lobe or side lobe, the link distance distribution, etc.) to analyze the network characteristics. Further, the communication/sensing coverage probability, as well as the joint and conditional ISAC coverage probability are analyzed to provide the comprehensive analysis results. Combining the theoretical analysis and simulation results, it verifies the accuracy of the analytical framework and quantifies the impact of the network parameters and spatial correlations on the S&C performance. Moreover, our results reveal how the sensing performance and communication performance are mutually restricted to describe the tradeoffs of S&C performance.

This paper investigates a cooperative integrated sensing and communication (ISAC) system, where multiple base stations (BSs) employ coordinated transmit beamforming to communicate with their respective users and jointly sense a set of extended targets (ETs). Different from prior cooperative ISAC works considering point targets (PTs), the visible scatterers on the same extended target (ET) and the radar cross section (RCS) of the same scatterers observed by multiple BSs are considered to be different. Given the model, we first derive the Cramér-Rao bound (CRB) for the BSs to cooperatively estimate the ET’s parameters, thus quantifying the cooperative sensing gains. Next, based on the derived CRB, we formulate a joint node selection and coordinated transmit beamforming design problem with the object of minimizing the average trace of sensing CRB, while satisfying the minimum communication rate constraint, the maximum transmit power constraint, and the node selection constraints. To solve this non-convex optimization problem, we first utilize the block coordinate descent (BCD) method to decompose it into node selection sub-problem and beamforming design sub-problem. Next, the continuous relaxation and linear programming (LP) approach are employed to handle the node selection sub-problem, and a decentralized augmented Lagrangian manifold optimization algorithm is developed to solve the beamforming sub-problem with reduced computation complexity. Numerical simulations demonstrate that the proposed design outperforms benchmark designs with larger CRB-rate region. Moreover, our results show the impacts of the ET’s state and the number of network nodes on network performance to enable valuable ISAC beamforming design insights.

Integrated sensing and communication (ISAC) is a promising technique for future wireless applications that demand both high-quality communication and precise sensing. Characterizing the fundamental tradeoff between sensing and communication (S&C) is essential for guiding cost-effective ISAC designs. However, the exact performance boundary characterizing the tradeoff between the communication rate and the sensing estimation accuracy remains unknown. To tackle this issue, this paper studies the exact Pareto boundary of the Cramér-Rao bound (CRB) and communication rate performance region, as well as the optimal input signal distributions that achieve this boundary. First, the explicit analytical expression of the expected CRB is derived with arbitrary random inputs, which quantify the accuracy of sensing estimation. It is proved that the bound-achieving distribution, which maximizes communication rate while ensuring CRB and power constants, is both timeindependent and circular symmetric. Leveraging these properties, a modified Blahut-Arimoto algorithm is developed based on functional analysis to numerically determine the optimal distribution on the CRB-rate boundary and correspondingly characterize the exact CRB-rate region. Numerical results demonstrate that ISAC systems enable a flexible CRB-rate tradeoff by adjusting the input signal distribution, outperforming separated S&C design benchmark. It is revealed that the communication benefits from greater signal amplitude randomness, while sensing accuracy improves with higher instantaneous power.

This paper investigates an integrated sensing and communication (ISAC) system with finite constellations. Both the problems of performance loss caused by discrete inputs and the conflicting requirements on symbol distribution of sensing and communication (S&C) functionalities are considered. Towards this, a waveform design based on the probabilistic shaping (PS) scheme is proposed, where a base station sends unified signals to estimate a sensing target and communicate with a user simultaneously. The analytical closed-form expressions of the sensing estimation rate (SER) and communication ergodic rate for PS-based ISAC are derived to evaluate the performance of the S&C functionalities, which are found to be mutually influenced by both the probability mass function (PMF) and power scaling factor. Furthermore, the optimal PMF and scaling factor are obtained by the alternative optimization algorithm to maximize SER while preserving communication capability. Simulation results validate the efficiency of our proposed PSbased waveform compared with those of schemes based on other input distributions. Additionally, the performance limit of ISAC systems with finite constellations is numerically obtained, which characterizes the tradeoffs between S&C functionalities.

The modeling and analysis of large-scale stochastic MEC systems are of great significance in providing useful design guidelines for practical MEC networks. In most prior works, the users generally adopt the same strategy to select appropriate MEC access points (MAPs), where the fact that the available mobile computing services are different among the randomly distributed users is ignored. To this end, this paper proposes a user-centric cooperative offloading scheme to enable more flexible and efficient user task offloading. Specifically, each user can be served by one or two MAPs based on both the communication performance and computing performance. To evaluate the performance gains acquired from the proposed task offloading scheme, we first derive the service mode assignment probability, link distance distribution, and interference intensity to capture the network characteristics. Further, the moment and the meta distribution of the task transmission performance are analyzed. Based on the above results, we focus on the distribution of the computation workload to evaluate the edge computing service capability. From the analytical and simulation results, it is demonstrated that compared with the non-cooperative offloading scheme, the proposed task offloading scheme not only achieves more reliable and fair task offloading but also increases the computing service capacity.

Integrated sensing and communication (ISAC) has been recognized as a key technology of the next-generation wireless networks. This paper focuses on the ISAC system with discrete signal inputs. Compared with the ISAC systems with continuous inputs in most prior works, it can provide useful insights for ISAC networks with digital modulation at the expense of compensating for the performance gap caused by the finite constellations. To tackle the above problem, a unified analytical framework is proposed to analyze and optimize the ISAC systems with finite constellations in this paper. Based on the analytical framework, both the communication ergodic rate and sensing estimation rate considering non-uniform constellations are derived to characterize the sensing and communication (S&C) performance and quantify the performance gap caused by discrete signals in ISAC networks. To compensate for the performance gap, the probabilistic shaping (PS)-based waveform design is formulated as a multi-objective optimization problem by jointly optimizing the discrete symbol distribution and the power scaling factor for achieving the Pareto-optimal S&C performance. Given the non-convexity of the above problem, an alternating algorithm is proposed to obtain the PS-based waveform. Moreover, the properties of the sensing-optimal and communication-optimal PS-based waveform are analyzed. It is proved that the sensing function prefers amplitude-deterministic signals, and the probabilities of outermost constellations are equal if they are distributed in a circularly symmetric manner. It is indicated that communication functionality requires quasi-Gaussian distribution, which is consistent with the results of our proposed algorithm. Numerical results characterize the tradeoff between S&C and validate the superiority of the proposed PS scheme in comparison with other input distributions.

Compared to satellite imagery, ground-based cameras capture cloud data (ground-to-sky data) with higher temporal and spatial resolutions, providing more detailed cloud information. However, the spectral information available in ground-to-sky data is limited. Therefore, extracting features with strong discrimination from optical remote sensing images (ORSIs) is challenging. Currently, deep learning-based cloud detection methods face two main challenges. Firstly, although Convolutional Neural Networks (CNNs) effectively extract high-frequency (HF) components from images through convolutions, they struggle to capture low-frequency (LF) components, which are capable of representing global features and target structures. Secondly, in ORSIs, the spectral characteristics of thin clouds and the sky are similar, making it difficult to distinguish cloud regions from the background. To address these challenges, we propose a network consisting of two main modules: the Mixer Module (MM) and the Cloud Aware Attention Module (CAAM). The MM comprises a HF and a LF components extraction branch. The HF branch extracts local textures through max-pooling and parallel convolution operations. The LF branch captures long-range dependency by decomposing a large kernel convolution. It leverages the advantages of both convolution and self-attention to effectively capture global features. In addition, we introduce the CAAM, which quantifies images into histograms to separate clouds from the background and enhances the perception of clouds using attention mechanism. We conducted experiments using both daytime and nighttime cloud image data from the SWINySeg dataset with mIoU reaching 88.93% and OA reaching 93.97%. The results demonstrate that our proposed method achieves promising performance compared to state-of-the-art cloud detection methods.

The stochastic geometry-based modeling and analysis of large-scale mobile edge computing (MEC) networks are vital for the effective configuration of MEC networks. In this paper, we develop a meta-analytical framework for MEC-enabled heterogeneous networks with the communication-computation-aware (CCA) user association mechanism. Compared with the communication-based user association mechanisms in most existing works, the CCA user association mechanism can capture the impacts of network computation capability on the association process between the user and MEC access point, at the expense of dealing with the more complex coupling of communication and computing. Given the need for interference characterization, we first derive the essential prerequisite quantities (i.e., per-tier association probability, link distance distribution, interferer process intensity, etc.) to represent the computation-dependent interference model. Further, the moment and the meta distribution of the task success offloading probability are derived, based on which we investigate the task execution latency performance, including the communication latency, local computing latency, and edge computing latency. By theoretical analysis and simulation results, it is demonstrated that the proposed analytical framework can provide accurate fine-grained network information for the MEC-enabled HetNets. Moreover, we elaborate on the impacts of the edge computation capability on the network performance and reveal important tradeoffs of the performance metrics.

Coded caching is a technique that promises significant reductions in network traffic by exploiting the multicast opportunities among multiple cache-enabled users. Most works in this area investigate the fundamental performance limits of coded caching from an information-theoretic perspective. In this paper, from a practical perspective, we focus on the design and analysis of the coded caching scheme in large-scale stochastic networks with dynamic traffic. Aiming at the challenges of the large number of users, dynamic packet requests, and distance-dependent interference, the packet caching scheme and packet delivery scheme are jointly designed to ensure the availability of the coded caching gain. To characterize the coded caching performance, the queue dynamic of the packet requests and channel state are first analyzed. Then by combining the successive group decoding theory and stochastic geometry approach, the network interference is characterized and the successful transmission probability of the multicast coded signal is further derived. Moreover, the coded caching scheme is optimized to minimize the average delay of the packet request, which is proved to ensure service fairness and reduce the complexities of network performance analysis. The analytical and numerical results provide useful insights for the provisioning and planning of the coded caching network.

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

Mobile edge computing (MEC) is an emerging architecture to enable variety of innovative applications and services with ultra low latency at the resource-limited mobile devices. In this paper, we investigate how the communication resources and the computing resources, including mobile users and MEC servers, interact with each other in multi-cell MEC-enabled stochastic wireless networks. To this end, the MEC-enabled network model including mobile users with limited storage capacity and computing capabilities is considered, which is characterized in random node distribution, dynamic traffic, orthogonal frequency division multiple access and task retransmission mechanism. Based on the model, the two-dimensional discrete Markov chain is employed to characterize the task execution process. We derive the stationary distribution of the buffer length and outage probability by combining the queuing theory and stochastic geometry, based on which the radio access network throughput is calculated to measure the network performance. Extensive simulations have been conducted to verify the effectiveness of the proposed offloading strategy and to provide valuable insight.