776 research outputs found
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey
Intelligent reflecting surfaces (IRSs) are considered a promising technology
that can smartly reconfigure the wireless environment to enhance the
performance of future wireless networks. However, the deployment of IRSs still
faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV)
enabled wireless environments to achieve higher capacity. This paper sheds
light on the different deployment strategies for IRSs in future terrestrial and
non-terrestrial networks. Specifically, in this paper, we introduce key
theoretical concepts underlying the IRS paradigm and discuss the design aspects
related to the deployment of IRSs in 6G networks. We also explore
optimization-based IRS deployment techniques to improve system performance in
terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement
learning (RL) techniques from the deployment aspect to address the challenges
of achieving higher capacity in complex and mobile IRS-assisted UAV wireless
systems. Finally, we highlight challenges and future research directions from
the deployment aspect of IRSs for improving system performance for the future
6G network.Comment: 16 pages, 3 Figures, 7 table
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks
Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of
Things (IoT) networks in mission-critical data collection, thanks to their
flexibility and cost-effectiveness. However, challenges arise due to the UAV's
limited onboard energy and the unpredictable status updates from sensor nodes
(SNs), which impact the freshness of collected data. In this paper, we
investigate the energy-efficient and timely data collection in IoT networks
through the use of a solar-powered UAV. Each SN generates status updates at
stochastic intervals, while the UAV collects and subsequently transmits these
status updates to a central data center. Furthermore, the UAV harnesses solar
energy from the environment to maintain its energy level above a predetermined
threshold. To minimize both the average age of information (AoI) for SNs and
the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN
scheduling, and offloading strategy. Then, we formulate this problem as a
Markov decision process (MDP) and propose a meta-reinforcement learning
algorithm to enhance the generalization capability. Specifically, the
compound-action deep reinforcement learning (CADRL) algorithm is proposed to
handle the discrete decisions related to SN scheduling and the UAV's offloading
policy, as well as the continuous control of UAV flight. Moreover, we
incorporate meta-learning into CADRL to improve the adaptability of the learned
policy to new tasks. To validate the effectiveness of our proposed algorithms,
we conduct extensive simulations and demonstrate their superiority over other
baseline algorithms
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
In this paper, the problem of the trajectory design for a group of
energy-constrained drones operating in dynamic wireless network environments is
studied. In the considered model, a team of drone base stations (DBSs) is
dispatched to cooperatively serve clusters of ground users that have dynamic
and unpredictable uplink access demands. In this scenario, the DBSs must
cooperatively navigate in the considered area to maximize coverage of the
dynamic requests of the ground users. This trajectory design problem is posed
as an optimization framework whose goal is to find optimal trajectories that
maximize the fraction of users served by all DBSs. To find an optimal solution
for this non-convex optimization problem under unpredictable environments, a
value decomposition based reinforcement learning (VDRL) solution coupled with a
meta-training mechanism is proposed. This algorithm allows the DBSs to
dynamically learn their trajectories while generalizing their learning to
unseen environments. Analytical results show that, the proposed VD-RL algorithm
is guaranteed to converge to a local optimal solution of the non-convex
optimization problem. Simulation results show that, even without meta-training,
the proposed VD-RL algorithm can achieve a 53.2% improvement of the service
coverage and a 30.6% improvement in terms of the convergence speed, compared to
baseline multi-agent algorithms. Meanwhile, the use of meta-learning improves
the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must
deal with a previously unseen task
Meta-learning applications for machine-type wireless communications
Abstract. Machine Type Communication (MTC) emerged as a key enabling technology for 5G wireless networks and beyond towards the 6G networks. MTC provides two service modes. Massive MTC (mMTC) provides connectivity to a huge number of users. Ultra-Reliable Low Latency Communication (URLLC) achieves stringent reliability and latency requirements to enable industrial and interactive applications. Recently, data-driven learning-based approaches have been proposed to optimize the operation of various MTC applications and allow for obtaining the desired strict performance metrics. In our work, we propose implementing meta-learning alongside other deep-learning models in MTC applications. First, we analyze the model-agnostic meta-learning algorithm (MAML) and its convergence for regression and reinforcement learning (RL) problems. Then, we discuss uncrewed aerial vehicles (UAVs) trajectory planning as a case study in mMTC and RL, illustrating the system model and the main challenges. Hence, we propose the MAML-RL formulation to solve the UAV path learning problem. Moreover, we address the MAML-based few-pilot demodulation problem in massive IoT deployments. Finally, we extend the problem to include the interference cancellation with Non-Orthogonal Multiple Access (NOMA) as a paradigm shift towards non-orthogonal communication thanks to its potential to scale well in massive deployments. We propose a novel, data-driven, meta-learning-aided NOMA uplink model that minimizes the channel estimation overhead and does not require perfect channel knowledge. Unlike conventional deep learning successive interference cancellation (SICNet), Meta-Learning aided SIC (meta-SICNet) can share experiences across different devices, facilitating learning for new incoming devices while reducing training over- head. Our results show the superiority of MAML performance in addressing many problems compared to other deep learning schemes. The simulations also prove that MAML can successfully solve the few-pilot demodulation problem and achieve better performance in terms of symbol error rates (SERs) and convergence latency. Moreover, the analysis confirms that the proposed meta-SICNet outperforms classical SIC and conventional SICNet as it can achieve a lower SER with fewer pilots
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Wireless Powered Metaverse: Joint Task Scheduling and Trajectory Design for Multi-Devices and Multi-UAVs
To support the running of human-centric metaverse applications on mobile
devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge
Computing (WPMEC) is promising to compensate for limited computational
capabilities and energy supplies of mobile devices. The high-speed
computational processing demands and significant energy consumption of
metaverse applications require joint resource scheduling of multiple devices
and UAVs, but existing WPMEC solutions address either device or UAV scheduling
due to the complexity of combinatorial optimization. To solve the above
challenge, we propose a two-stage alternating optimization algorithm based on
multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time,
schedule computation tasks, and optimize trajectory of UAVs and mobile devices
in a wireless powered metaverse scenario. First, considering energy constraints
of both UAVs and mobile devices, we formulate an optimization problem to
maximize the computation efficiency of the system. Second, we propose a
heuristic algorithm to efficiently perform time allocation and charging
scheduling for mobile devices. Following this, we design a multi-task DRL
scheme to make charging scheduling and trajectory design decisions for UAVs.
Finally, theoretical analysis and performance results demonstrate that our
algorithm exhibits significant advantages over representative methods in terms
of convergence speed and average computation efficiency
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