Starling Flocks-Inspired Resource Allocation for ISAC-Aided Green Ad Hoc Networks

Efficient and intelligent environment awareness is one of the key aspects for enabling future green networks, especially for wireless ad hoc networks considering the limited resources, while poor environment awareness may lead to unbearable data transmission delay based on terrible routing strategies, which is unacceptable in some ad hoc networks. However, the sensing of communication environment and the performance of data transmission are tightly coupled with each other in physical reality. Therefore, traditional separate analyses of sensing and transmission cannot meet the low-latency and energy-efficient demand of future green ad hoc networks. In this article, we propose an environment awareness integrated sensing and communication (ISAC) network association architecture considering both the reward and cost of nodes' sensing range and transmission strategy. In addition, the resource allocation optimization problem of the proposed ISAC architecture is formulated relying on the graph theory, which is efficiently solved by a novel low-complexity algorithm. Simulation results show that our proposed ISAC network association architecture is beneficial in terms of achieving lower transmission delay in comparison to the traditional methods which separately optimize the sensing and transmission process. Besides, a near-optimal latency performance is obtained with the aid of our optimization method with low-complexity.

Automated summary

This article proposes an environment awareness integrated sensing and communication (ISAC) network association architecture to overcome the low-latency and energy-efficient demand of future green ad hoc networks. The resource allocation optimization problem of the ISAC architecture is efficiently solved using the graph theory and a novel low-complexity algorithm. Simulation results demonstrate that the proposed ISAC network association architecture can achieve lower transmission delay compared to traditional methods, and near-optimal latency performance can be obtained with the optimization method.