Concurrently supporting heterogeneous services, e.g., sensing and communication (S&C), presents a significant challenge for future wireless networks due to the increasing number of connected devices, limited resources, and the complexity of integrated service provisioning. Furthermore, dynamic network conditions, along with varying heterogeneous needs from coexisting devices, further exacerbate the challenges of traditional rigid system operation, where heterogeneous network services are treated as either entirely independent or fully integrated. This rigid operation neglects the fluctuating gains and costs of the integrated heterogeneous service provisioning. To transform isolated operations into a highly integrated paradigm, this paper proposes a progressive scheme for integrated sensing and communication (ISAC). The scheme elastically adjusts the integration level based on continuously accumulated system state observations, including user demand, resource conditions, and environmental changes, to regulate resource utilization dynamically. Specifically, we present a unified Value of Service (VoS) metric, which adaptively incorporates user service experiences, resource costs, and gains from S&C coupling to guide efficient resource allocation. In addition, building on this progressive integration scheme, we develop a dynamic stage-dependent resource optimization algorithm for bandwidth allocation. Simulation results demonstrate the effectiveness of the proposed integrated framework and algorithm in optimizing resource allocation and maintaining system performance under stringent resource constraints.

The exponential increase in concurrent demands, driven by diverse intelligent applications, poses substantial challenges for emerging beyond-communication networks, such as integrated sensing and communication (ISAC). Specifically, this growing demand necessitates the development of efficient resource allocation strategies and unified performance metrics to effectively manage heterogeneous service requirements in dynamic environments. To realize 'sensing with communication' and ultimately enhance service quality, this paper proposes an adaptive resource allocation scheme aimed at maximizing integration gain. To achieve this, we introduce an integration gain evaluation metric that leverages the inherent similarities between sensing and communication (S&C) channels to quantify information sharing, thereby continuously strengthening the correlation and integration of both functions within the ISAC system. To enable unified system operation across heterogeneous services, an integration gain-guided adaptive grained search algorithm is developed for bandwidth resource allocation. Simulation results demonstrate improved performance of the proposed integration framework and resource allocation algorithm compared with other benchmarks.

Supporting rapidly growing industrial applications with complex heterogeneous service requests exacerbates the radio resource shortage, posing a perpetual challenge for future networks. Emerging beyond-communication technologies for providing concurrent services, such as integrated sensing and communication (ISAC), further complicate resource allocation due to the uneven and non-uniform distribution of heterogeneous service demands as well as the growing network conflict among concurrent services. To tackle these issues, this paper propose an opportunistic spatiotemporal resource reuse scheme that optimally improve resource utilization efficiency by leveraging the disparities in resource utilization capabilities among users across both spatial and temporal domains. To enhance the heterogeneous service provisioning for different users, a Value of Service (VoS) metric, adopted to evaluate resource allocation performance per user per unit space, is optimized through a clustering-based resource-sharing strategy. To reduce mutual interference among users, the base station utilizes a clustering process that considers each user’s physical location and service request. In each cluster, we derive an analytical solution for communication resource allocation and use a many-to-many matching-based algorithm for assigning the sensing subchannels. The numerical simulation results demonstrate enhanced resource utilization efficiency of our proposed scheme compared with other benchmarks.

The rapid convergence of wireless infrastructure and vertical applications has brought the growing needs for integrated sensing and communications. Due to competing purposes and limited radio resources, an effectively designed integrated sensing and communication (ISAC) system has to precisely adjust its resource allocation to communication and sensing. To maximize the value of service (VoS) for ISAC operation with varying concurrent demands and resource conditions, a successive resource allocation scheme for multi-user ISAC systems is proposed. Specifically, the bandwidth and power allocation are formulated as a mixed integer optimization problem by considering the varying user requirements and resource availability. To solve this problem, a deep-reinforcement learning (DRL) based adaptive resource allocation algorithm is utilized for successive ISAC operational gain maximization. Simulation results demonstrate the adaptiveness and effectiveness of the proposed resource allocation scheme under dynamic scenarios.

A critical component for various interactive visual Internet of Things (IoT) applications is to reconstruct 3-D scenes from RGB images, i.e., 3-D visualization. When multiple cameras are involved, the visualization outcome mainly depends on the quality of input images, which carry correlated and complementary visual information from different camera perspectives. One main challenge to improve visualization performance is how to efficiently coordinate multiple cameras under complex environmental conditions. To overcome this challenge, we propose a situation-aware multicamera collaboration scheme based on the maximization of correlated information among different inputs. First, the information gain of a single camera is modeled by quantifying the effect of view direction, resolution and signal-to-noise ratio (SNR) on image quality. A spherical Gaussian is then designed to model the mutual information among neighboring viewpoints and further calculate the total correlated information of the camera group by considering their information redundancy and complementarity. An adaptive coarse-to-fine algorithm is proposed to maximize the correlated information, which achieves effective decision making of optimal multicamera collaboration strategy, including cameras’ location, direction, and focal length configurations. Simulation and realistic experiments demonstrate the accuracy of the correlated information model and the efficacy of the scheme to improve reconstruction quality.

The proliferation of wireless-enabled industrial applications highlights the growing importance of Integrated Sensing and Communication (ISAC) for concurrent provisioning of environment sensing and data transmission capabilities. However, the resource-hungry nature of sensing processes, coupled with competing demands from coexisting users, poses the fundamental challenge of effective and fair resource allocation in multi-user ISAC systems. To address this challenge, we propose a value of service (VoS)-oriented resource allocation scheme for concurrent heterogeneous service provisioning in a multi-user collaborative ISAC system. Specifically, a performance indicator VoS is utilized to guide system-wide effective resource allocation while guaranteeing fairness among all ISAC users. Specifically, we formulate the multi-user resource allocation problem as a bargaining game-based model and tackle it with an iterative algorithm to attain the Nash equilibrium. In each iteration, the allocation of power and bandwidth resources is optimized by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions, service demands, and channel states. The results demonstrate the superiority of the proposed scheme over non-collaborative alternatives and the other two benchmark schemes.

The rapid proliferation of smart devices and Internet of Things (IoT) applications have brought significantly increased demands for concurrent sensing, localization and communication services. To achieve multiple functions concurrently, new unified wireless systems including integrated localization and communication (ILAC) and integrated sensing and communication (ISAC) are facing the fundamental challenge of integrative resource allocation among coexisting functions and services. In addressing this challenge, an ILAC system based on the efficient allocation of the common hardware and radio resource pool for localization and communication is proposed. A novel concept, termed Value of Service (VoS), is coined to maximize the unified performance of ILAC system for diverse service provisioning including localization accuracy and communication data rate. Furthermore, the bandwidth and temporal resource allocation problem is formulated for ILAC to maximize its VoS. Specifically, the problem is treated as a mixed-integer nonlinear problem solved by an iterative joint resource allocation (JRA) strategy. In each iteration, the resource allocation is decomposed into two steps. Firstly, the bandwidth resource is optimized with a Kelly mechanism-based continuous allocation method followed by discretization. Secondly, the temporal resource is assigned with the aid of an adaptive particle swarm optimization (PSO)-based approach. Simulation results demonstrate the significant superiority of our proposed VoS evaluation metric and JRA method in ILAC system under limited resources.

Accurately localizing a moving target (MT) assisted with 5G in indoor environment could enable a wide variety of new applications. However, an MT in 3-dimensional space is usually considered as a rigid body with six degrees of freedom for industrial applications. Furthermore, the radio-based localization suffers from the non-line-of-sight (NLOS) condition in indoor scenes due to the uncertain environments, which proves to be a main source of location error. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signals, a novel rigid body localization and environment sensing scheme is proposed in this paper. The angle of arrivals (AOAs) derived from 5G channel estimation combined with singular value decomposition (SVD) method is adopted to achieve rigid body position and orientation estimation. Also, we propose a reflection point estimation method by leveraging a hierarchical iterative maximum likelihood-DCS-SOMP (HIML-DCS-SOMP) algorithm to extract the angular information of the single-bounce specular reflections. Simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and sketch the environment information in indoor scene.