101 research outputs found
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
Five Facets of 6G: Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the
globe, researchers have turned their attention to the exploration of radical
next-generation solutions. At this early evolutionary stage we survey five main
research facets of this field, namely {\em Facet~1: next-generation
architectures, spectrum and services, Facet~2: next-generation networking,
Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing,
as well as Facet~5: applications of deep learning in 6G networks.} In this
paper, we have provided a critical appraisal of the literature of promising
techniques ranging from the associated architectures, networking, applications
as well as designs. We have portrayed a plethora of heterogeneous architectures
relying on cooperative hybrid networks supported by diverse access and
transmission mechanisms. The vulnerabilities of these techniques are also
addressed and carefully considered for highlighting the most of promising
future research directions. Additionally, we have listed a rich suite of
learning-driven optimization techniques. We conclude by observing the
evolutionary paradigm-shift that has taken place from pure single-component
bandwidth-efficiency, power-efficiency or delay-optimization towards
multi-component designs, as exemplified by the twin-component ultra-reliable
low-latency mode of the 5G system. We advocate a further evolutionary step
towards multi-component Pareto optimization, which requires the exploration of
the entire Pareto front of all optiomal solutions, where none of the components
of the objective function may be improved without degrading at least one of the
other components
Multidimensional Index Modulation for 5G and Beyond Wireless Networks
This study examines the flexible utilization of existing IM techniques in a
comprehensive manner to satisfy the challenging and diverse requirements of 5G
and beyond services. After spatial modulation (SM), which transmits information
bits through antenna indices, application of IM to orthogonal frequency
division multiplexing (OFDM) subcarriers has opened the door for the extension
of IM into different dimensions, such as radio frequency (RF) mirrors, time
slots, codes, and dispersion matrices. Recent studies have introduced the
concept of multidimensional IM by various combinations of one-dimensional IM
techniques to provide higher spectral efficiency (SE) and better bit error rate
(BER) performance at the expense of higher transmitter (Tx) and receiver (Rx)
complexity. Despite the ongoing research on the design of new IM techniques and
their implementation challenges, proper use of the available IM techniques to
address different requirements of 5G and beyond networks is an open research
area in the literature. For this reason, we first provide the dimensional-based
categorization of available IM domains and review the existing IM types
regarding this categorization. Then, we develop a framework that investigates
the efficient utilization of these techniques and establishes a link between
the IM schemes and 5G services, namely enhanced mobile broadband (eMBB),
massive machine-type communications (mMTC), and ultra-reliable low-latency
communication (URLLC). Additionally, this work defines key performance
indicators (KPIs) to quantify the advantages and disadvantages of IM techniques
in time, frequency, space, and code dimensions. Finally, future recommendations
are given regarding the design of flexible IM-based communication systems for
5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible
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RIS-empowered MEC for URLLC systems with digital-twin-driven architecture
This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time
Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS-aided MISO URLLC Systems
Reconfigurable intelligent surfaces (RISs) can assist the wireless systems in
providing reliable and low-latency links to realize the requirements in
Industry 4.0. In this paper, the practical phase shift optimization in a
RIS-aided ultra-reliable and low-latency communication (URLLC) system at a
factory setting is performed by applying a novel deep reinforcement learning
(DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3).
First, the system achievable rate in finite blocklength (FBL) regime is
identified for each actuator then, the problem is formulated where the
objective is to maximize the total achievable FBL rate, subject to non-linear
amplitude response and the phase shift values constraint. Since the amplitude
response equality constraint is highly non-convex and non-linear, we employ the
TD3 to tackle the problem. The considered method relies on interacting RIS with
industrial scenario by taking actions which are the phase shifts at the RIS
elements, to maximize the total FBL rate. We assess the performance loss of the
system when the RIS is non-ideal, i.e., non-linear amplitude response
with/without phase quantization and compare it with ideal RIS. The numerical
results show that optimizing phase shifts in non-ideal RIS via the considered
TD3 method is highly beneficial to improve the performance.Comment: This work has been submitted to the IEEE for possible publication.
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Spectral and Energy Efficiency Maximization of MISO STAR-RIS-assisted URLLC Systems
This paper proposes a general optimization framework to improve the spectral
and energy efficiency (EE) of ultra-reliable low-latency communication (URLLC)
simultaneous-transfer-and-receive (STAR) reconfigurable intelligent surface
(RIS)-assisted interference-limited systems with finite block length (FBL).
This framework can solve a large variety of optimization problems in which the
objective and/or constraints are linear functions of the rates and/or EE of
users. Additionally, the framework can be applied to any interference-limited
system with treating interference as noise as the decoding strategy at
receivers. We consider a multi-cell broadcast channel as an example and show
how this framework can be specialized to solve the minimum-weighted rate,
weighted sum rate, global EE and weighted EE of the system. We make realistic
assumptions regarding the (STAR-)RIS by considering three different feasibility
sets for the components of either regular RIS or STAR-RIS. Our results show
that RIS can substantially increase the spectral and EE of URLLC systems if the
reflecting coefficients are properly optimized. Moreover, we consider three
different transmission strategies for STAR-RIS as energy splitting (ES), mode
switching (MS), and time switching (TS). We show that STAR-RIS can outperform a
regular RIS when the regular RIS cannot cover all the users. Furthermore, it is
shown that the ES scheme outperforms the MS and TS schemes.Comment: Accepted at IEEE ACCES
Heterogeneous Traffic Multiplexing in Next Generation Cellular Networks
The vision shaping the upcoming sixth-generation (6G) wireless cellular networks has recently gained considerable attention from researchers in academia and industry. 6G networks are expected to fulfill the limitations of the fifth-generation (5G) networks and support a wide range of new applications and services beyond those supported by 5G, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC) and massive machine-type communications (mMTC). Further, these emerging networks are thus mandated to support new emerging applications that concurrently demand multiple quality of service (QoS) requirements of data rate, reliability, latency, and connectivity. Due to the fundamental trade-off of such extremely diverse QoS requirements, the coexistence of these emerging applications has been identified as a major challenge in 6G networks and their predecessors. This dissertation aims at addressing the coexistence problem, specifically URLLC and eMBB traffic, by developing spectrally efficient multiplexing and scheduling solutions.
By considering different key enabling technologies, this dissertation provides unique research contributions to the coexistence problem that led to effective designs. In particular, coupling URLLC and eMBB through the Third Generation Partnership Project (3GPP) superposition/puncturing scheme naturally arises as a promising option due to the latter's tolerance in terms of latency and reliability. Moreover, reconfigurable intelligent surface (RIS) has been proposed as a potential low-cost and energy-efficient technology that can control the wireless propagation environment providing endless benefits in supporting coexisting 6G services.
Regarding the superposition scheme, this thesis investigates the joint scheduling of eMBB and URLLC traffic while minimizing the eMBB rate loss, considering URLLC reliability and the eMBB QoS. In the context of puncturing, this thesis studied the interplay between the RIS configuration, URLLC reliability and eMBB rate by proposing proactive RIS configurations to guarantee the URLLC latency requirements. Although simulation results demonstrate that adopting the proposed scheme can further boost eMBB and URLLC traffic performance, the computational complexity of optimizing the RIS phase shifts is challenging. To this end, this thesis proposes two low-complexity methods for optimizing the RIS phase shift matrix. The first solution proposes reducing the number of optimization variables configuring the RIS to the number of users. The second algorithm is based on a closed-form expression for the RIS phase shift matrix. Finally, a new puncturing strategy is proposed to mitigate the impact on the eMBB transmission. The key idea of the proposed scheme is to puncture the eMBB data that has maximum symbol similarities with the URLLC leading to reducing the contaminated eMBB symbols. We study the performance of the proposed schemes in terms of the eMBB spectral efficiency, URLLC reliability and low complexity. We show analytically and through simulations the efficacy of the proposed schemes over their existing counterparts
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