Joint Resource Allocation, Task Processing, and Trajectory Design for UAV-assisted Industrial IoT Users in 6G Networks

Mohammadisarab, Amir; Khalili, Ata; Nouruzi, Ali; Mokari, Nader; Arand, Bijan Abbasi; Jorswieck, Eduard A.

doi:10.1109/cscn57023.2022.10050976

Cited by 1 publication

(6 citation statements)

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“…The computation task of node u is represented by 𭟋 u , which consists of DL u bits. For processing each bit of this task, Ω u CPU cycles are needed [1], [48]. PD-NOMA allows more users to be served with linked resources compared to other multiple access (MA) techniques.…”

Section: Paper Organizationmentioning

confidence: 99%

“…g t u,b,k and p t u,b,k denote the Rayleigh fading channel gain with the reference-distance unit power gain 1.4 × 10 −4 and transmission power on the k−th subcarrier from node u to ABS b at time slot t, respectively. Consequently, the average signal-to-interference-plus-noise ratio (SINR) for node u and ABS b on the k-th subcarrier is expressed as [1], [49],…”

Section: Paper Organizationmentioning

confidence: 99%

“…The ABS decodes user messages using successive interference cancellations (SIC) [1], [50]. At the ABS, decoding is always conducted from the node with a better channel quality and SINR to the node with a worse channel quality and SINR, or else a significant amount of power will be required by the node with a worse channel quality and SINR to offset the path loss [1], [51]. In addition, inter-group interference can be characterized by…”

Section: Paper Organizationmentioning

confidence: 99%

“…An M/M/1 queuing system is a single-queue single-server queuing system. As each user can also link with one ABS (as a MEC server), M/M/1 model is appropriate to be used for the queueing delay [1], [54]. Also, the packet (task) arrival rate based on Poisson process at time slot t for ABS b is DL t b in packet per second ([pps]).…”

Section: Paper Organizationmentioning

confidence: 99%

“…Channel estimation is performed to obtain channel gains. However, it is impractical to move each UAV to all possible locations to acquire perfect CSI since such a sweeping search will consume significant power and time [1], [55]. Due to the necessity of an online algorithm for solving our formulated complex optimization problem in (16a), MARDPG is proposed to tackle this issue.…”

Section: Proposed Algorithmmentioning

confidence: 99%

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Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks

Mohammadisarab,

Nouruzi,

Khalili

et al. 2024

IEEE Open J. Commun. Soc.

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This research explores strategies to augment the connectivity among users within Hierarchical Non-Terrestrial Networks (HNTNs) dedicated to Disaster Relief Services (DRS). The primary goal is to optimize radio resources, computing capacities, and the trajectories of Unmanned Aerial Vehicles (UAVs) at each time slot, aiming to maximize the number of satisfactory connections (NSCs). UAVs function as aerial base stations (ABSs), establishing links for reduced capability (RedCap) user equipment (UE) using power domain non-orthogonal multiple access (PD-NOMA). Given the potential inoperability of terrestrial networks during disasters, swift data transmission is critical for mission-critical (MC) UEs. Therefore, end-to-end (E2E) delay is a crucial quality of service (QoS) constraint. The proposed problem is solved using a multi-agent recurrent deterministic policy gradient (MARDPG) algorithm, where the ABSs collaborate to maximize the NSCs and determine their optimal policy by interacting with the environment. Additionally, a sharing experience module (SEM) is incorporated, enabling agents to encode actions and observations using long short-term memory (LSTM), allowing each agent to utilize the historical actions and observations of other agents. To demonstrate the superiority of MARDPG, three algorithmic benchmarks and four different system models are implemented. The numerical results demonstrate the impact of various parameters, such as the number of subcarriers, users, and the maximum tolerable E2E delay on the NSCs. Furthermore, different scenarios indicate that MARDPG outperforms the benchmarks, achieving approximately a 6 percent optimality gap and a 91 percent fairness for achievable rate among users. HNTNs, DRS, UAVs, PD-NOMA, MARDPG I. INTRODUCTION A. Motivations and State-of-the-Art W ireless communication technologies have undergone significant advancements in recent years, emerging as a critical facilitator for upcoming consumer applications and services [2], [3]. The transition from the fourth-generation (4G) to the fifth generation leads to a categorizing of quality of services (QoSs), which are named ultra-reliable low-latency communications (URLLC), massive machine-type communication (mMTC), and enhanced mobile broadband

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Section: Paper Organizationmentioning

confidence: 99%

Section: Paper Organizationmentioning

confidence: 99%