87 research outputs found
Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting
Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has
aroused. Specifically, UAVs can be used in cellular networks as aerial users
for delivery, surveillance, rescue search, or as an aerial base station (aBS)
for communication with ground users in remote uncovered areas or in dense
environments requiring prompt high capacity. Aiming to satisfy the high
requirements of wireless aerial networks, several multiple access techniques
have been investigated. In particular, space-division multiple access(SDMA) and
power-domain non-orthogonal multiple access (NOMA) present promising
multiplexing gains for aerial downlink and uplink. Nevertheless, these gains
are limited as they depend on the conditions of the environment. Hence, a
generalized scheme has been recently proposed, called rate-splitting multiple
access (RSMA), which is capable of achieving better spectral efficiency gains
compared to SDMA and NOMA. In this paper, we present a comprehensive survey of
key multiple access technologies adopted for aerial networks, where aBSs are
deployed to serve ground users. Since there have been only sporadic results
reported on the use of RSMA in aerial systems, we aim to extend the discussion
on this topic by modelling and analyzing the weighted sum-rate performance of a
two-user downlink network served by an RSMA-based aBS. Finally, related open
issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa
NOMA-Based UAV-Aided Networks for Emergency Communications
High spectrum efficiency (SE) requirement and massive connections are the main challenges for the fifth generation (5G) and beyond 5G (B5G) wireless networks, especially for the case when Internet of Things (IoT) devices are located in a disaster area. Non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-aided network is emerging as a promising technique to overcome the above challenges. In this paper, an emergency communications framework of NOMA-based UAV-aided networks is established, where the disasters scenarios can be divided into three broad categories that have named emergency areas, wide areas and dense areas. First, a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas. Then, a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas. Furthermore, a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas. Simulation results are provided to validate the effectiveness of the above three schemes. Finally, potential research directions and challenges are also highlighted and discussed
Resource Allocation for UAV-Assisted Industrial IoT User with Finite Blocklength
We consider a relay system empowered by an unmanned aerial vehicle (UAV) that
facilitates downlink information delivery while adhering to finite blocklength
requirements. The setup involves a remote controller transmitting information
to both a UAV and an industrial Internet of Things (IIoT) or remote device,
employing the non-orthogonal multiple access (NOMA) technique in the first
phase. Subsequently, the UAV decodes and forwards this information to the
remote device in the second phase. Our primary objective is to minimize the
decoding error probability (DEP) at the remote device, which is influenced by
the DEP at the UAV. To achieve this goal, we optimize the blocklength,
transmission power, and location of the UAV. However, the underlying problem is
highly non-convex and generally intractable to be solved directly. To overcome
this challenge, we adopt an alternative optimization (AO) approach and
decompose the original problem into three sub-problems. This approach leads to
a sub-optimal solution, which effectively mitigates the non-convexity issue. In
our simulations, we compare the performance of our proposed algorithm with
baseline schemes. The results reveal that the proposed framework outperforms
the baseline schemes, demonstrating its superiority in achieving lower DEP at
the remote device. Furthermore, the simulation results illustrate the rapid
convergence of our proposed algorithm, indicating its efficiency and
effectiveness in solving the optimization problem.Comment: This paper is accepted by IEEE VTC 2023-Fall, Hong Kong, Chin
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
Cooperative Communications for 5G Wireless Networks and Beyond.
Cooperative communication is an appealing technique stemming from the information-theoretic notion of cooperative diversity and having gradually evolved into a mainstream design paradigm in 4G LTE-Advanced. By exploiting cooperation among multiple transmission/reception nodes the technique reveals tremendous benefits, both in theoretical research and practical deployment, to enhance network performance in terms of throughput, reliability, latency, and network coverage. Due to these desirable attributes, it is expected that the technique will continue to be utilized in the coming generations of wireless networks. However, a major challenge to enable cooperative communication in the context of 5G and beyond is the advent of new wireless technologies and network architectures. The emergence of these technologies, on the one hand, enables a plethora of new applications. On the other hand, they entail new network operational constraints, which hinders the cooperation among network nodes. To address this challenge, it is crucial to study cooperative communication in specific network scenarios where these new technologies are employed to gain new insights their impact on in-network cooperation. Motivated by this, the thesis studies the role of cooperative communication in two futuristic network scenarios, encompassing two novel wireless technologies, namely non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs)
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
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
A High Altitude Platform Station (HAPS) is a network node that operates in
the stratosphere at an of altitude around 20 km and is instrumental for
providing communication services. Precipitated by technological innovations in
the areas of autonomous avionics, array antennas, solar panel efficiency
levels, and battery energy densities, and fueled by flourishing industry
ecosystems, the HAPS has emerged as an indispensable component of
next-generations of wireless networks. In this article, we provide a vision and
framework for the HAPS networks of the future supported by a comprehensive and
state-of-the-art literature review. We highlight the unrealized potential of
HAPS systems and elaborate on their unique ability to serve metropolitan areas.
The latest advancements and promising technologies in the HAPS energy and
payload systems are discussed. The integration of the emerging Reconfigurable
Smart Surface (RSS) technology in the communications payload of HAPS systems
for providing a cost-effective deployment is proposed. A detailed overview of
the radio resource management in HAPS systems is presented along with
synergistic physical layer techniques, including Faster-Than-Nyquist (FTN)
signaling. Numerous aspects of handoff management in HAPS systems are
described. The notable contributions of Artificial Intelligence (AI) in HAPS,
including machine learning in the design, topology management, handoff, and
resource allocation aspects are emphasized. The extensive overview of the
literature we provide is crucial for substantiating our vision that depicts the
expected deployment opportunities and challenges in the next 10 years
(next-generation networks), as well as in the subsequent 10 years
(next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial
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