Toward Facilitating Power Efficient URLLC Systems in UAV Networks Under Jittering

Ranjha, Ali; Javed, Muhammad Awais; Piran, Md. Jalil; Asif, Muhammad; Hussien, Mostafa; Zeadally, Sherali; Frnda, Jaroslav

doi:10.1109/tce.2023.3305550

Cited by 6 publications

(2 citation statements)

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“…It jointly optimized the time, the bandwidth, and the UAV position to maximize the transmission rate of the backward link under the constraints of URLLC requirements in the forward link. By employing age of information (AoI) as a new system metric, the author in [24] proposed an energyefficient resource allocation scheme to support UAV-assisted URLLC communication systems under the blocklength and imperfect channel conditions constraints.…”

Section: Introductionmentioning

confidence: 99%

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Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Li,

Zhang,

et al. 2024

Int J Comput Intell Syst

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Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

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Section: Introductionmentioning

confidence: 99%

“…Train the neural network based on loss function in (24) to optimize the parameter θ, s t n ← s t+1 n , a t n ← a t+1 n , t ← t + 1. 9: end for 10: end for 11: Obtain the power value p m and blocklength value l m over each agent and compute the rate performance.…”

mentioning

confidence: 99%