60 research outputs found
Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives
© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements
A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead
Physical layer security which safeguards data confidentiality based on the
information-theoretic approaches has received significant research interest
recently. The key idea behind physical layer security is to utilize the
intrinsic randomness of the transmission channel to guarantee the security in
physical layer. The evolution towards 5G wireless communications poses new
challenges for physical layer security research. This paper provides a latest
survey of the physical layer security research on various promising 5G
technologies, including physical layer security coding, massive multiple-input
multiple-output, millimeter wave communications, heterogeneous networks,
non-orthogonal multiple access, full duplex technology, etc. Technical
challenges which remain unresolved at the time of writing are summarized and
the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication
Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints
Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance
Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints
Recently, the combination of cognitive radio networks with the nonorthogonal multiple
access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also
supporting large numbers of wireless communication connections. However, cognitive NOMA networks
are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome
this drawback, many techniques have been proposed, such as optimal power allocation and interference
cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able
to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by
using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power
allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security
constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station
and the leakage probability for the eavesdropper are obtained with imperfect channel state information.
Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance.
Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN)
and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput
of the secondary network. These optimization algorithms guarantee not only the performance of the primary
users but also the security constraints of the secondary users. Finally, simulations are presented to validate
our research results and provide insights into how various factors affect system performance
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
Hybrid satellite-terrestrial relay network: proposed model and application of power splitting multiple access
The development of hybrid satellite-terrestrial relay networks (HSTRNs) is one of the driving
forces for revolutionizing satellite communications in the modern era. Although there are many unique
features of conventional satellite networks, their evolution pace is much slower than the terrestrial
wireless networks. As a result, it is becoming more important to use HSTRNs for the seamless integration
of terrestrial cellular and satellite communications. With this intent, this paper provides a comprehensive
performance evaluation of HSTRNs employing non-orthogonal multiple access technique. The terrestrial
relay is considered to be wireless-powered and harvests energy from the radio signal of the satellite.
For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying
protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic
capacities are derived for each relaying protocol. Extensive simulations are performed to verify the
accuracy of the obtained closed-form expressions. The results provided in this work characterize the
outage and capacity performance of such a HSTRN.publishe
URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence
The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5{th} generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3{rd} generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR
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