Intelligent Resource Optimization in Integrated Aerial Terrestrial Networks
Creators: name: Awais, Muhammad
Publisher: name: Lancaster University
Year: 2025
Subjects: Future Wireless (5G/6G)
esource Optimization
ntegrated Aerial Terrestrial Networks
5G &amp; Beyond Green Communication
Machine Learning
Intelligent Communication Techniques
Energy-Efficient Green Communications
Resource Allocation

description: Developing sixth generation (6G) wireless communication necessitates
 low-power consumption, high-reliability, and massive-connectivity. One of
 the most promising solutions to address these requirements is aerial base
 station (ABS) based communication systems that employ both in-the-air
 (aerial) and on-the-ground (terrestrial) components. ABSs enhance line of
 sight (LoS) connections, fulfilling escalating quality of service (QoS)
 demands. Nevertheless, integrating aerial and terrestrial networks into
 future three dimensional (3D) networks introduces emerging requirements
 for resource allocation and new functional challenges, such as latency,
 reliability, energy consumption, and QoS. Motivated by the above
 observations, this thesis investigates the challenges of intelligent
 resource optimization in integrated aerial terrestrial networks. 
 
 
 An integrated aerial and terrestrial network is initially examined to
 design a bisection-based low-complexity adaptation (BLCA) algorithm for
 optimal resource allocation. A joint optimization problem that involves
 sub-carrier (SC) assignment, blocklength, and power allocation (PA)
 subject to delay, reliability, and QoS constraints is investigated to
 enhance system performance in a finite blocklength (FBL) regime. The
 proposed solution includes sub-carrier allocation based on matching
 theory, optimal blocklength allocation using the bisection algorithm, and
 a two-step projected gradient descent power distribution by optimizing the
 power budget on each sub-carrier. Case studies on flexible blocklength and
 PA are also examined under the next generation of multiple access
 techniques.
 
 
 The second part integrates digital twin (DT) technology with mobile edge
 computing (MEC) to facilitate mobile offloading in an integrated
 aerial-terrestrial network. An advanced bisection sampling-based
 stochastic solution enhancement (BSSE) algorithm is specifically tailored
 to jointly optimize transmit power, central processing unit (CPU)
 frequency, and the task offloading policy to minimize the system's
 energy-time cost against benchmarks. The proposed solution includes a
 one-climb policy to narrow the search space, a closed-form solution for
 calculating the optimal CPU frequency and transmit power for given
 offloading decisions, and an inequality condition formulated to manage
 dependent tasks efficiently. The scalability of the proposed scheme is
 also analyzed.
 
 
 In the final part, machine learning techniques are adopted to improve the
 system performance in an integrated aerial-terrestrial wireless network.
 The proposed solution employs unsupervised learning techniques for the
 grouping of internet of things smart devices (ISDs), Q-learning (a type of
 reinforcement learning) for the intelligent ABS placement, and deep
 learning for power allocation. A closed-form expression is also derived
 for PA among multiplexed devices based on their QoS requirements.
 Numerical results indicate that the proposed scheme significantly
 outperforms existing benchmark schemes.
 
 
 This thesis presents valuable insights into innovative, sustainable, and
 energy-efficient resource optimization in integrated aerial-terrestrial
 future-generation networks, setting the stage for further advancements in
 resource allocation to enhance reliability, QoS, and latency.