Currently, Large Language Model (LLM), as a typical Generative AI (GAI) implementation, has been deployed to lead to artificial general intelligence for numerous applications. Numbers of LLM releases, like ChatGPT, Llama, Gemini, ChatGLM, can online interact with users to give instructions against different kinds of queries. Moreover, LLMs can process multi-model data, like text, image, voice, video, to support the workings of robots, expert systems, and operation control systems. Due to its merits, LLM in theory can help the operation of Unmanned Aerial Vehicle (UAV) communication networks, where each UAV is constrained by its size, weight and power (SWAP) to provide limited data collection functions in the air. In this paper, we apply LLM to online control the operation of UAVs by optimizing the trajectory of UAV , to lead to UAV energy efficiency. We firstly provide well established air-ground wireless communication model within UAV and ground terminals (GTs) to provide API library to LLM to help its closed-loop reasoning. Further, we establish Chain-of-Thought (CoT) and generate optimizing instructions to support UAV communication via prompt engineering. In the end, such LLM en-powered UAV communication network will be validated via experiments. The numerical results validate that LLM can enable UAV communication networks to more adaptive to complicated environments to work with less energy consumption and higher energy efficiency.

In this article, we investigate multiple unmanned aerial vehicles (UAVs)-enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time-constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated, considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer nonconvex problem, we decompose the problem into two subproblems: 1) UAV trajectory and 2) hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed the MACO-based algorithm, is given by leveraging the MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

To increase frequency efficiency in cellular communication networks, this paper describes a cell coloring based distributed frequency allocation approach (C-DFA) for all kinds of cellular networks. C-DFA has high computational efficiency and is simpler to realize than other distributed approaches. Building on C-DFA, a distributed dynamic fractional frequency allocation (DDFFA) algorithm is designed for IEEE 802.16j supported Relay Based Cellular Networks (RBCN). It is shown that: a) C-DFA can better realize frequency efficiency and network resilience compared to centralized traditional distributed frequency allocation approach. b) DDFFA can significantly increase frequency efficiency to provide high capacity and throughput to the RBCN though at the cost of extra computation and BS-BS communication. The evaluations and analysis are based on moderate and high user congestion RBCN scenarios.

Relay Based Cellular Networks (RBCNs) are a key development in cellular networking technology. However, because of ever increasing demand and base station failure, RBCNs still suffer from user congestion and low resilience problems. This paper proposes two competing solutions: dynamic frequency allocation and antenna tilting to those problems. Firstly a new dynamic fractional frequency allocation algorithm and a heuristic antenna tilting algorithm are designed. The comparative benefits of each algorithm are investigated. Secondly, the additional benefits of applying the two algorithms sequentially or iteratively are evaluated. The benefits of iteratively integrating the two algorithms are more interesting. Such integration solution allows the two algorithms to be applied cooperatively. The evaluations are based on high demand scenarios and base station failure scenarios. The results show that for the high demand scenarios the new dynamic fractional frequency allocation algorithm is very powerful, and the advantage of antenna tilting is not large though present. However, for the BS failure case there is a marked additional benefit in antenna tilting. The integrated solution achieves significantly more benefit than simple sequential application of the two algorithms.

Cellular networks provide a range of communication services and are the principal wireless technology for domestic and business users. However, in cellular networks, an accident or a storm or a malicious action may cause a base station to fail and so be unable to provide radio coverage to sections of users. Such base station failures also might result in user congestion to nearby operational base stations. Delay Tolerate Network (DTN) is used here to mitigate these problems. DTN enables user devices to self-organize to connect to functional base stations or other users through multiple hops. This paper augments DTN routing using geographic routing and Ant Colony Optimization (ACO) methods to enhance user to base station connection and network load balancing. Three DTN routing protocols: Epidemic, Binary Spray and Wait, and PRoPHET are considered in this paper. Compared to the geographic method described, the ACO does not require users and base stations to provide their geographic location, which is more appropriate to the context in which such an approach might be applied.

This paper illustrates an approach to improving the user perceived QoS in heterogeneous networks through the use of a two layered optimization. The top layer views the problem of prediction of the network that would be chosen by a user where the criteria are offered bit rate, price, mobility support and reputation. At the second level, conditional on the strategies chosen by the users, the network operator hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR and optimizes the network coverage to support users who would otherwise not be serviced adequately. This forms an iterative cycle until a solution that optimizes the user satisfaction subject to the adjustments that the network operator can make to mitigate the binding constraints, is found. The process is illustrated through a simple example using a Wi-Fi and a WiMAX network.