Reinforcement learning based joint trajectory design and resource allocation for RIS-aided UAV multicast networks

Ji, Pengshuo; Jia, Jie; Chen, Jian; Guo, Liang; Du, An; Wang, Xingwei

doi:10.1016/j.comnet.2023.109697

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…The researcher in [172] presented beamforming control and trajectory design algorithm based on as multi-pass deep Q-network. In this algorithm, the UAV serves as an agent responsible for periodically observing the state of the UAV multicast network and taking actions to adapt to the dynamic environment.…”

Section: ) Optimizing Uav Trajectory Using MLmentioning

confidence: 99%

A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and Challenges

Banafaa,

Pepeoğlu,

Shayea

et al. 2024

IEEE Access

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The rapid advancement of fifth-generation (5G)-and-beyond networks coupled with unmanned aerial vehicles (UAVs) has opened up exciting possibilities for diverse applications and cutting-edge technologies, revolutionizing the way connections, communications, and innovations unfold in the digital age. This paper presents a comprehensive survey of the deployment scenarios, applications, emerging technologies, regulatory aspects, research trends, and challenges associated with the use of UAVs in 5G-and-beyond networks. It begins with a succinct background and motivation, followed by a systematic UAV classification and a review of relevant works. The survey covers UAV deployment scenarios, including single and multiple UAV configurations. The categorization of UAV applications in 5G is presented, along with investigations into emerging technologies for enhancing UAV communications. Regulatory considerations encompassing flight guidelines, spectrum allocation, privacy, and safety are discussed. Moreover, light is shed on the latest research trends and open challenges in the field, with promising directions for future investigations identified, concluding with a summary of key findings and contributions. This survey serves as a valuable resource for researchers, practitioners, and policymakers in the UAV and communication domains. Additionally, it offers a comprehensive foundation for informed decision-making, fostering collaboration, and driving advancements in UAV and communication technologies to address the evolving needs of our interconnected world.INDEX TERMS Drone, 5G, unmanned aerial vehicles, wireless systems.

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Section: ) Optimizing Uav Trajectory Using MLmentioning

confidence: 99%

A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and Challenges

Banafaa,

Pepeoğlu,

Shayea

et al. 2024

IEEE Access

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show abstract

“…In [81], the authors maximize the sum rate of the UAV-enabled multi-cast network by jointly designing the UAV movement, re-configurable intelligent surface (RIS) reflection matrix, and beam-forming design from the UAV to users based on a multi-pass deep Q Network (BT-MP-DQN). In the proposed model, the UAV is the agent and beam-forming control and trajectory design are considered system actions.…”

Section: Ai For Resource Allocation In Uav Networkmentioning

confidence: 99%

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Ali Shah,

Fernando,

Kashef

2023

Preprint

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The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

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“…Ji et al [32] proposed the beamforming control and trajectory design algorithm based on a multi-pass deep Q-network (BT-MP-DQN). The UAV plays the role of the agent in this model, periodically monitoring the state of the multicast network and adapting to dynamic environmental changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems

El-Gayar,

Ajour

2023

Electronics

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Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within a single cell, where users are scattered randomly and rely on relays for dependability. This paper presents a model for improving energy efficiency, content distribution, latency, and transmission speeds in communication systems using NOMA and OMA systems within a single cell. Additionally, this paper also proposes a caching strategy using unmanned aerial vehicles (UAVs) as aerial base stations for ground users. These UAVs distribute cached content to minimize the overall latency of content demands from ground users while modifying their positions. We carried out simulations using various cache capacities and user counts linked to their respective UAVs. Furthermore, we evaluated OMA and NOMA in terms of the achievable rate and energy efficiency. The proposed model has achieved noteworthy enhancement across various scenarios including different sum rates, numbers of mobility users, diverse cache sizes, and amounts of power allocation.

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Reinforcement learning based joint trajectory design and resource allocation for RIS-aided UAV multicast networks

Cited by 15 publications

References 32 publications

A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and Challenges

A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and Challenges

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems

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