132 research outputs found
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
Reinforcement Learning Empowered Unmanned Aerial Vehicle Assisted Internet of Things Networks
This thesis aims towards performance enhancement for unmanned aerial vehicles (UAVs) assisted internet of things network (IoT). In this realm, novel reinforcement learning (RL) frameworks have been proposed for solving intricate joint optimisation scenarios. These scenarios include, uplink, downlink and combined. The multi-access technique utilised is non-orthogonal multiple access (NOMA), as key enabler in this regime. The outcomes of this research entail, enhancement in key performance metrics, such as sum-rate, energy efficiency and consequent reduction in outage. For the scenarios involving uplink transmissions by IoT devices, adaptive and tandem rein forcement learning frameworks have been developed. The aim is to maximise capacity over fixed UAV trajectory. The adaptive framework is utilised in a scenario wherein channel suitability is ascertained for uplink transmissions utilising a fixed clustering regime in NOMA. Tandem framework is utilised in a scenario wherein multiple-channel resource suitability is ascertained along with, power allocation, dynamic clustering and IoT node associations to NOMA clusters and channels. In scenarios involving downlink transmission to IoT devices, an ensemble RL (ERL) frame work is proposed for sum-rate enhancement over fixed UAV trajectory. For dynamic UAV trajec tory, hybrid decision framework (HDF) is proposed for energy efficiency optimisation. Downlink transmission power and bandwidth is managed for NOMA transmissions over fixed and dynamic UAV trajectories, facilitating IoT networks. In UAV enabled relaying scenario, for control system plants and their respective remotely deployed sensors, a head start reinforcement learning framework based on deep learning is de veloped and implemented. NOMA is invoked, in both uplink and downlink transmissions for IoT network. Dynamic NOMA clustering, power management and nodes association along with UAV height control is jointly managed. The primary aim is the, enhancement of net sum-rate and its subsequent manifestation in facilitating the IoT assisted use case. The simulation results relating to aforesaid scenarios indicate, enhanced sum-rate, energy efficiency and reduced outage for UAV-assisted IoT networks. The proposed RL frameworks surpass in performance in comparison to existing frameworks as benchmarks for the same sce narios. The simulation platforms utilised are MATLAB and Python, for network modeling, RL framework design and validation
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning
This study introduces a resource allocation framework for integrated
satellite-terrestrial networks to address these challenges. The framework
leverages local cache pool deployments and non-orthogonal multiple access
(NOMA) to reduce time delays and improve energy efficiency. Our proposed
approach utilizes a multi-agent enabled deep deterministic policy gradient
algorithm (MADDPG) to optimize user association, cache design, and transmission
power control, resulting in enhanced energy efficiency. The approach comprises
two phases: User Association and Power Control, where users are treated as
agents, and Cache Optimization, where the satellite (Bs) is considered the
agent. Through extensive simulations, we demonstrate that our approach
surpasses conventional single-agent deep reinforcement learning algorithms in
addressing cache design and resource allocation challenges in integrated
terrestrial-satellite networks. Specifically, our proposed approach achieves
significantly higher energy efficiency and reduced time delays compared to
existing methods.Comment: 16, 1
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
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Energy-efficient non-orthogonal multiple access for wireless communication system
Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed
A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research
Intelligent-Reflecting-Surface-Assisted UAV Communications for 6G Networks
In 6th-Generation (6G) mobile networks, Intelligent Reflective Surfaces
(IRSs) and Unmanned Aerial Vehicles (UAVs) have emerged as promising
technologies to address the coverage difficulties and resource constraints
faced by terrestrial networks. UAVs, with their mobility and low costs, offer
diverse connectivity options for mobile users and a novel deployment paradigm
for 6G networks. However, the limited battery capacity of UAVs, dynamic and
unpredictable channel environments, and communication resource constraints
result in poor performance of traditional UAV-based networks. IRSs can not only
reconstruct the wireless environment in a unique way, but also achieve wireless
network relay in a cost-effective manner. Hence, it receives significant
attention as a promising solution to solve the above challenges. In this
article, we conduct a comprehensive survey on IRS-assisted UAV communications
for 6G networks. First, primary issues, key technologies, and application
scenarios of IRS-assisted UAV communications for 6G networks are introduced.
Then, we put forward specific solutions to the issues of IRS-assisted UAV
communications. Finally, we discuss some open issues and future research
directions to guide researchers in related fields
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