Resource Allocation and Task Scheduling for Integrated Sensing and Communications

Abstract

Integrated Sensing and Communications (ISAC) has emerged as a promising paradigm for future Sixth-Generation (6G) wireless networks. In this paradigm, wireless networks can have both Sensing and Communication (SAC) capabilities using shared network resources. ISAC not only enables the provisioning of SAC services but also has the potential to enhance their performance: end-user devices can offload sensing data collection tasks to Access Points (APs) co-located with edge servers and upload raw or preprocessed sensing data to powerful edge servers for high-performance processing; meanwhile, APs can leverage contextual information about communication tasks obtained through sensing, such as the visibility of virtual content in mobile Augmented Reality (AR) streaming, to enhance communication efficiency. The interesting issue is to efficiently utilize network resources to optimize SAC service performance in the presence of high and spatiotemporally varying service demands. However, the main technical challenges are: 1) how network resource allocation and SAC task scheduling are proactively determined to enable efficient coordination between APs and mobile end-user devices for achieving satisfactory service performance; 2) how an end-user device adaptively offloads computation-intensive Deep Neural Network (DNN)-based sensing tasks to an edge server to optimize task performance, under dynamic task arrival, task processing, and server workload statuses; and 3) how an AP efficiently acquires contextual information about communication tasks through sensing individual mobile AR users and dynamic environment for resource-efficient mobile AR streaming.

In this thesis, we develop efficient resource management schemes for ISAC, including resource allocation and task scheduling, to address the above three technical challenges. First, we investigate proactive resource management for ISAC, determining the reservation of radio and computing resources, the active probability of mobile devices for communications, and the partitioning of sensing regions. To cope with the non-stationary spatial distributions of mobile devices and sensing targets, which can result in the drift in modeling the distributions and ineffective resource management decisions, we construct Digital Twins (DTs) of the network slices for individual SAC services. In each DT, a drift-adaptive DNN-empowered statistical model and an emulation function are developed for the spatial distributions in the corresponding slice, which facilitates closed-form decision-making and efficient validation of a resource management decision, respectively. Numerical results demonstrate that the proposed scheme can significantly enhance service satisfaction ratios and reduce resource consumption compared to benchmark schemes. Second, we investigate task offloading for DNN-based sensing data processing. Particularly, we consider that an end-user device stochastically generates and adaptively offloads DNN-based sensing data processing tasks to an AP co-located with an edge server. To adapt to the dynamic on-device and edge server workload status, leveraging the multi-layer and multi-exit architecture of the considered DNNs, an offloading decision for each sensing task is made on whether and when to stop on-device task processing and offload the task to the edge server to complete the processing. Two DTs are constructed to evaluate all potential offloading decisions for each sensing task, which provides augmented training data for a machine learning-assisted decision-making algorithm, and to estimate the task processing status at the device, which avoids frequently fetching the status information from the device and thus reduces the signaling overhead. Simulation results demonstrate the outstanding performance of the proposed task offloading scheme in terms of balancing sensing result accuracy, delay, and energy consumption. Third, we propose an efficient resource allocation scheme in sensing-assisted mobile AR streaming. In specific, we consider that the position and surrounding environment of an AR user can be captured via sensing to extract contextual information for AR streaming, i.e., the visibility of virtual content. The goal is to minimize the overall radio resource consumption for delivering virtual content visible to the AR user by properly determining the radio resource allocation for user positioning and environment mapping. To this end, we first develop a mathematical model to estimate the content visibility uncertainty and the content delivery resource consumption. We then generate a reference resource allocation decision that guides a deep reinforcement learning-based decision process to efficiently adapt to non-stationary user and environment dynamics. Trace-driven simulations demonstrate that the proposed scheme significantly reduces radio resource consumption for delivering virtual content visible to an individual AR user, compared to benchmark schemes.

In summary, we have proposed efficient resource management schemes for ISAC that optimize SAC service performance with efficient resource utilization and practical operational complexity. The research results from the thesis provide valuable insights into the design of scalable and adaptive ISAC systems that seamlessly unify sensing, communications, and intelligence in the future 6G.