129 research outputs found
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
An introduction of intelligent interconnectivity for people and things has
posed higher demands and more challenges for sixth-generation (6G) networks,
such as high spectral efficiency and energy efficiency, ultra-low latency, and
ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output
(mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent
reflecting surface (IRS), are two promising technologies for coping with these
unprecedented demands. Given their distinct capabilities, integrating the two
technologies to further enhance wireless network performances has received
great research and development attention. In this paper, we provide a
comprehensive survey of research on RIS-aided CF mMIMO wireless communication
systems. We first introduce system models focusing on system architecture and
application scenarios, channel models, and communication protocols.
Subsequently, we summarize the relevant studies on system operation and
resource allocation, providing in-depth analyses and discussions. Following
this, we present practical challenges faced by RIS-aided CF mMIMO systems,
particularly those introduced by RIS, such as hardware impairments and
electromagnetic interference. We summarize corresponding analyses and solutions
to further facilitate the implementation of RIS-aided CF mMIMO systems.
Furthermore, we explore an interplay between RIS-aided CF mMIMO and other
emerging 6G technologies, such as next-generation multiple-access (NGMA),
simultaneous wireless information and power transfer (SWIPT), and millimeter
wave (mmWave). Finally, we outline several research directions for future
RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure
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
IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities
Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces
(IRS) are gaining traction as transformative technologies for upcoming wireless
networks. The IRS-aided UAV communication, which introduces IRSs into UAV
communications, has emerged in an effort to improve the system performance
while also overcoming UAV communication constraints and issues. The purpose of
this paper is to provide a comprehensive overview of IRSassisted UAV
communications. First, we provide five examples of how IRSs and UAVs can be
combined to achieve unrivaled potential in difficult situations. The
technological features of the most recent relevant researches on IRS-aided UAV
communications from the perspective of the main performance criteria, i.e.,
energy efficiency, security, spectral efficiency, etc. Additionally, previous
research studies on technology adoption as machine learning algorithms. Lastly,
some promising research directions and open challenges for IRS-aided UAV
communication are presented
Reconfigurable Intelligent Surface for Physical Layer Security in 6G-IoT: Designs, Issues, and Advances
Sixth-generation (6G) networks pose substantial security risks because
confidential information is transmitted over wireless channels with a broadcast
nature, and various attack vectors emerge. Physical layer security (PLS)
exploits the dynamic characteristics of wireless environments to provide secure
communications, while reconfigurable intelligent surfaces (RISs) can facilitate
PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight
security solution can be designed for low-end Internet of Things (IoT) devices,
depending on the design scenario and communication objective. This article
discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and
jamming attacks. The theoretical background and literature review of RIS-aided
PLS are discussed, and design solutions related to resource allocation,
beamforming, artificial noise, and cooperative communication are presented. We
provide simulation results to show the effectiveness of RIS in terms of PLS. In
addition, we examine the research issues and possible solutions for RIS
modeling, channel modeling and estimation, optimization, and machine learning.
Finally, we discuss recent advances, including STAR-RIS and malicious RIS.Comment: Accepted for IEEE Internet of Things Journa
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G
Due to the explosive growth in the number of wireless devices and diverse
wireless services, such as virtual/augmented reality and
Internet-of-Everything, next generation wireless networks face unprecedented
challenges caused by heterogeneous data traffic, massive connectivity, and
ultra-high bandwidth efficiency and ultra-low latency requirements. To address
these challenges, advanced multiple access schemes are expected to be
developed, namely next generation multiple access (NGMA), which are capable of
supporting massive numbers of users in a more resource- and
complexity-efficient manner than existing multiple access schemes. As the
research on NGMA is in a very early stage, in this paper, we explore the
evolution of NGMA with a particular focus on non-orthogonal multiple access
(NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review
the fundamental capacity limits of NOMA, elaborate on the new requirements for
NGMA, and discuss several possible candidate techniques. Moreover, given the
high compatibility and flexibility of NOMA, we provide an overview of current
research efforts on multi-antenna techniques for NOMA, promising future
application scenarios of NOMA, and the interplay between NOMA and other
emerging physical layer techniques. Furthermore, we discuss advanced
mathematical tools for facilitating the design of NOMA communication systems,
including conventional optimization approaches and new machine learning
techniques. Next, we propose a unified framework for NGMA based on multiple
antennas and NOMA, where both downlink and uplink transmissions are considered,
thus setting the foundation for this emerging research area. Finally, several
practical implementation challenges for NGMA are highlighted as motivation for
future work.Comment: 34 pages, 10 figures, a survey paper accepted by the IEEE JSAC
special issue on Next Generation Multiple Acces
Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks
Abstract Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum-rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations on the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information
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