Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the spectrum in a wireless network, while the motion planning is seldom considered, as a wireless device itself may not decide where to go. In this article, joint communication–motion planning (JCMP) is proposed to boost the capability of NRS by exploiting both the adaptive communications and mobility control of autonomous robots. Specifically, we propose a JCMP framework for NRS, which aims at jointly exploiting the degree-of-freedom in mobility, space, time, frequency and power dimensions from both the motion and communication components. Afterward, we design and evaluate JCMP in a conventional and a CR-relay-assisted robot system, which shows the capability of JCMP in improving the performance of NRS. Finally, we summarize the proposed JCMP-enabled NRS framework and provide a series of future research directions.

This paper investigates the multi-channel access and rendezvous problem in unmanned aerial vehicle (UAV) relaying system in the absence of pre-allocated control channel. Both the geographical sensing range and the spectrum sensing bandwidth of each UAV are limited due to onboard payload constraints, hence it becomes challenging to design effective and efficient channel rendezvous mechanisms. To address the challenge, this paper first observes and analyzes the effects of UAV relaying network topology and geographical sensing range on the rendezvous, and it is found that a joint exploitation of motion and frequency control is essential to achieve efficient rendezvous. Based on the important insight, this paper formulates the UAV relaying rendezvous problem and proposes a novel joint space-frequency rendezvous (JSFR) method for multi-UAV networks, incorporating with distributed reinforcement learning techniques. The simulation results show that the JSFR method may significantly improve the effectiveness and efficiency of rendezvous in UAV relaying networks, in terms of rendezvous probabilities and convergence rates.

Unmanned Aerial Vehicle (UAV) swarms are key enablers for cooperative intelligent tasks in Internet of Everything (IoE) environments, like environmental monitoring and disaster response. Meanwhile, Digital Twin (DT) technology is rapidly advancing in academia and industry due to its potential to design, test, and optimize evolving IoE networks. While DT-empowered UAV swarms provide a compelling solution for complex tasks, challenges remain in jointly designing swarm control and service provision strategies, especially in dynamic IoE environments. To harness the potential of UAV swarm intelligence, we propose a DT-empowered cluster formation technique employing the Over-the-Air Computation (AirComp) transmission scheme for energy-efficient IoE data aggregation, where UAV and IoE device clusters are dynamically constructed and adjusted. Furthermore, we consider the limited power resources by maximizing energy efficiency, which leads to a joint cluster formation, multi-UAV trajectory design, and power allocation problem under the constraint of AirComp signal distortion. To efficiently solve this mixed-integer nonlinear fractional programming problem, we develop a low-complexity iterative algorithm based on the block coordinate descent method. Simulation results show that the proposed DT-empowered AirComp cluster formation technique achieves up to 42% higher energy efficiency and 34% greater throughput than the baselines, validating its potential for efficient data aggregation tasks.

Motivated by the potential applications of unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) in urban communication networks, this article constructs a two-hop communication system assisted by mobile UAVs and tunable IRS. The system comprises five different topologies spanning from a remote ground robot (RGR) to a ground base station (GBS). Unlike idealized models, this study considers non-perfect phase compensation at the IRS, modeled as a Gaussian-distributed phase error. We derive the closed-form expressions for the outage probability (OP) at the GBS. Simulation results verify the correctness of our theoretical derivations. Based on the derived OP, we employ a joint communication-motion planning (JCMP) method to demonstrate the rationality of considering phase compensation errors through numerical simulation.