Task-Oriented and Semantic-Aware Heterogeneous Networks for Artificial Intelligence of Things: Performance Analysis and Optimization

Abstract

We propose a novel task-oriented and semantic-aware heterogeneous networks (TOSA-HetNets) framework for multitype Artificial Intelligence of Things (AIoT) devices with various requirements, where the dense edge servers with different transmission capabilities, computing resources, and power consumption are divided into different layers to provide on-demand collaboration for AIoT devices located in accessible areas. Moreover, we propose a device–edge collaboration intelligent tasks inference scheme between edge servers and AIoT devices in TOSA-HetNets, it includes AIoT devices performing semantic features extraction and uploading the corresponding semantic features to the associated edge servers, multiple layers of edge servers collaborating with AIoT devices to execute the intelligent tasks and transmit the intelligent task results back to AIoT devices. To investigate the performance of TOSA-HetNets in supporting device–edge collaboration intelligent tasks inference, we adopt stochastic geometry to obtain the closed-form expressions of average task success probability, power consumption, and network throughput in the downlink transmission. Furthermore, we define a metric of average achievable task back-transmission energy efficiency (TBT-EE) to measure the information bit of successfully transmitted correct intelligent task results with unit power consumption, which is a function of average task success probability, average network throughput on the unit area, and the total power consumption. Meanwhile, we maximize the average achievable TBT-EE by optimizing the density of edge servers and the average semantic compression ratio. Simulation results verify the correctness of the obtained closed-form expressions and show that the edge servers’ density and average semantic compression ratio have different influences on the performance of TOSA-HetNets.