Transport Capacity Optimization for Resource Allocation in Tera-IoT Networks

TL;DR

This paper introduces an adaptive resource allocation strategy for Tera-IoT networks that jointly optimizes subwindow, transmit power, and transmission distance to maximize transport capacity, considering THz-specific losses.

Contribution

It formulates a novel optimization problem incorporating rate, distance, and THz band characteristics, and proposes an effective two-stage solution for large-scale Tera-IoT networks.

Findings

Our method outperforms benchmark strategies in numerical evaluations.

Joint optimization of power, subwindow, and distance enhances transport capacity.

The approach effectively handles high path loss in THz bands.

Abstract

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multi-device terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporate both rate and transmission distance into the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of the rate-distance products over all users. This metric has been widely adopted in large-scale ad hoc networks, and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.