Energy-Aware Resource Optimization for Improved URLLC in Multi-Hop Integrated Aerial Terrestrial Networks

• Published in: IEEE transactions on green communications and networking Vol. 8; no. 1; pp. 252 - 264
• Main Authors: Awais, Muhammad, Pervaiz, Haris, Jamshed, Muhammad Ali, Yu, Wenjuan, Ni, Qiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ABSs; Algorithms; blocklength optimization; Communications systems; Decoding; Heuristic algorithms; integrated aerial terrestrial networks; Internet of Things; Mathematical programming; Monte Carlo simulation; NOMA; Optimization; Power consumption; Quality of service; Reliability; Resource allocation; Resource management; Space-air-ground integrated networks; URLLC; Wireless networks
• ISSN: 2473-2400; 2473-2400
• DOI: 10.1109/TGCN.2023.3330018

The development of futuristic wireless infrastructure necessitates low power consumption, high reliability, and massive connectivity. One of the most promising solutions to address these requirements is the integration of aerial base station (ABS) based communication systems that employ both in the air (aerial) and on the ground (terrestrial) components. This integration enhances line of sight connections, enabling the fulfillment of escalating quality-of-service (QoS) demands. This article examines the problem of resource allocation in ABS assisted multi-hop wireless networks. We investigate a joint optimization problem that involves subcarrier (SC) assignment, power allocation, and blocklength allocation, subject to delay, reliability, and QoS constraints to improve the sum-rate under the finite blocklength (FBL) regime. We propose an approach for SC allocation and selection of cooperative ABSs based on matching theory. Subsequently, we employ an alternating optimization method to propose a novel bisection-based low-complexity adaptation (BLCA) algorithm to optimize the resource allocation policy. This algorithm includes a two-step projected gradient descent-based strategy to optimize the power allocation on each SC using dynamic and geometric programming. Furthermore, we examine flexible blocklength and power allocation use cases under the next generation of multiple access techniques. Monte-Carlo simulations validate that the proposed algorithmic solution significantly achieves a near-optimal solution while requiring 1600 times less computational cost compared to benchmarks in its counterparts.