Physical-Layer Security for 6G E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Meet the wireless security challenges of the future with this key volume
The 6th generation of wireless communication technology—known as 6G—promises to bring both revolutionary advances and unique challenges. Secure communications will be harder than ever to achieve under the new integrated ground, air, and space networking paradigm, with increased connectivity creating the potential for increased vulnerability. Physical-layer security, which draws upon the physical properties of the channel or network to secure information, has emerged as a promising solution to these challenges.
Physical-Layer Security for 6G provides a working introduction to these technologies and their burgeoning wireless applications. With particular attention to heterogeneous and distributed network scenarios, this book offers both the information-theory fundamentals and the most recent developments in physical-layer security. It constitutes an essential resource for meeting the unique security challenges of 6G.
Physical-Layer Security for 6G readers will also find:
- Analysis of physical-layer security in the quality of security framework (QoSec)
- Detailed discussion of physical-layer security applications in visible light communication (VLC), intelligence reflecting surface (IRS), and more
- Practical use cases and demonstrations
Physical-Layer Security for 6G is ideal for wireless research engineers as well as advanced graduate students in wireless technology.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Part I: Preliminaries
1 Foundations of Physical-Layer Security for 6G*
1.1 Coding Mechanisms
1.2 Coding for Physical-Layer Security
1.3 Engineering and Learning Channels
References
Note
2 Coding Theory Advances in Physical-Layer Secrecy
2.1 Introduction
2.2 Wiretap Coding Schemes Based on Coset Coding
2.3 Wiretap Coding Schemes Based on Invertible Extractors
2.4 Finite-Length Results
References
Notes
Part II: Physical-Layer Security in Emerging Scenarios
3 Beamforming Design for Secure IRS-Assisted Multiuser MISO Systems
3.1 Introduction
3.2 System Model
3.3 Resource Allocation Optimization Problem
3.4 Solution of the Optimization Problem
3.5 Experimental Results
3.6 Conclusion
3.7 Future Extension
3.A Appendix – Proof of Theorem 3.1
References
Notes
4 Physical-Layer Security for Optical Wireless Communications
4.1 Introduction
4.2 PLS for SISO VLC
4.3 PLS for MISO VLC
4.4 PLS for Multiuser VLC
4.5 PLS for VLC with Emerging Technologies
4.6 Open Challenges and Future Works
References
5 The Impact of Secrecy on Stable Throughput and Delay
5.1 Introduction
5.2 System Model
5.3 Stability Region for the General Case
5.4 Stability Region Analysis: Receivers with Different Decoding Abilities
5.5 Impact of Secrecy on Delay Performance
5.6 Results and Discussion
5.7 Conclusion
References
6 Physical-Layer Secrecy for Ultrareliable Low-Latency Communication
6.1 Introduction
6.2 Background
6.3 System Model
6.4 Impact of Secrecy on Stable Throughput
6.5 Impact of Secrecy on Latency
6.6 Results and Discussion
6.7 Conclusion
References
Part III: Integration of Physical-Layer Security with 6G Communication
7 Security Challenges and Solutions for Rate-Splitting Multiple Access
7.1 Introduction
7.2 Security Issues in RSMA
7.3 How Much of the Split Signal Should Be Revealed?
7.4 Secure Beamforming Design for RSMA Transmission
7.5 Conclusion
References
Note
8 End-to-End Autoencoder Communications with Optimized Interference Suppression*
8.1 Introduction
8.2 Related Work
8.3 System Model
8.4 Performance Evaluation of AEC Considering the Effects of Channel, Quantization, and Embedded Implementation
8.5 Data Augmentation to Train the AE Model Using GANs
8.6 Methods to Suppress the Effects of Interference
8.7 AE Communications with Interference Suppression for MIMO Systems
8.8 Conclusion
References
Note
9 AI/ML-Aided Processing for Physical-Layer Security
9.1 Introduction
9.2 Proposed Metrics for RF Fingerprinting and SKG
9.3 Power Domain Preprocessing
9.4 Conclusions
References
10 Joint Secure Communication and Sensing in 6G Networks
10.1 Introduction
10.2 Related Work and Motivation
10.3 System Model
10.4 Secret Key Generation Protocol
10.5 Measurement Setup
10.6 Results and Discussion
Acknowledgments
References
Part IV: Applications
11 Physical-Layer Authentication for 6G Systems
11.1 Authentication by Physical Parameters
11.2 Challenge-Response PLA for 6G
11.3 Intelligent PLA Based on Machine Learning
References
12 Securing the Future e-Health: Context-Aware Physical-Layer Security
12.1 Introduction
12.2 PHYSEC Key Generation
12.3 Key-less PHYSEC for Medical Image Transmission
12.4 Proof-of-Concept Study
12.5 Conclusions and Future Directions
References
Notes
13 The Role of Non-terrestrial Networks: Features and Physical-Layer Security Concerns
13.1 Non-terrestrial Networks for 6G
13.2 Physical-Layer Security in Non-terrestrial Networks
13.3 Conclusions
References
Notes
14 Quantum Hardware-Aware Security for 6G Networks
14.1 Introduction
14.2 Preliminaries
14.3 Secret Communication
14.4 Covert Communication
14.5 Conclusion
Acknowledgments
References
Notes
15 Leveraging the Physical Layer to Achieve Practically Feasible Confidentiality and Authentication
15.1 Introduction
15.2 System Model
15.3 Confidentiality at the Physical Layer in Practical Settings
15.4 Authentication at the Physical Layer in Practical Settings
15.5 Numerical Experiments
15.6 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 System parameters.
Chapter 8
Table 8.1 The architecture of the AE.
Table 8.2 Comparison of model size.
Table 8.3 Comparison of inference time.
Table 8.4 Comparison of BER for .
Table 8.5 Comparison of BER for .
Table 8.6 Comparison of BER for .
Table 8.7 The network sizes and the total number of parameters for the AEC’s...
Table 8.8 Architecture of the generator and discriminator networks of the co...
Table 8.9 Evaluation of BER as a function of .
Table 8.10 The size of the inputs and outputs for the encoder and decoder in...
Table 8.11 BER of a MIMO system taking interference into account.
Chapter 9
Table 9.1 The layer configuration and activation function for AE1.
Table 9.2 AE: key results for Quadriga.
Chapter 10
Table 10.1 SKG rates for different scenarios.
Chapter 15
Table 15.1 Communication parameters for
PLA
.
List of Illustrations
Chapter 1
Figure 1.1 Channel coding over a discrete memoryless channel.
Figure 1.2 Soft-covering over a discrete memoryless channel.
Figure 1.3 Source coding with side information for a discrete memoryless sou...
Figure 1.4 Privacy amplification from a discrete memoryless channel.
Figure 1.5 Secure communication over the wiretap channel.
Figure 1.6 Secret-key generation from source model.
Chapter 2
Figure 2.1 An illustration of the binning technique. Each message is mappe...
Figure 2.2 An example of factor graph for the binary linear code defined b...
Figure 2.3 The binary erasure wiretap channel .
Figure 2.4 Range of eavesdropper’s parameters such that the dual wiretap L...
Figure 2.5 The basic polarization technique for .
Figure 2.6 The channel polarization construction for .
Figure 2.7 Modular wiretap codes based on invertible extractors.
Figure 2.8 Comparison of the lower bound on secrecy rates of Reed–Muller and...
Chapter 3
Figure 3.1 Illustration of the considered IRS-enhanced wireless networks inv...
Figure 3.2 Illustration of the tangent space for a point at a unit circle ...
Figure 3.3 Illustration of the flow and the key optimization steps of the ov...
Figure 3.4 Illustration of the adopted simulation setup.
Figure 3.5 System SSR (bits/s/Hz) versus the BS maximum transmit power (dBm)...
Figure 3.6 SSR (bits/s/Hz) versus the number of users averaged over differen...
Chapter 4
Figure 4.1 The indoor SISO VLC wiretap system.
Figure 4.2 Limited dynamic range of a typical LED.
Figure 4.3 Secrecy capacity and different achievable secrecy rates.
Figure 4.4 The indoor MISO VLC wiretap system.
Figure 4.5 The SOP versus the number of active EDs for various ZF beamformin...
Figure 4.6 The average secrecy capacity and achievable secrecy rates versus ...
Figure 4.7 The indoor multiuser VLC wiretap system.
Figure 4.8 Secrecy rate of the MISO VLC beamforming system.
Figure 4.9 Configuration of the IRS: each reflecting element can direct the ...
Figure 4.10 The relationship between the secrecy rate and the quantity of IR...
Chapter 5
Figure 5.1 -User BC with secrecy constraint with having jamming ability....
Figure 5.2 The stability region for 2-user BC.
Figure 5.3 Impact of full-duplex ability of receiver on the stability regi...
Figure 5.4 The stability region for : , , , , , , , , , , and [...
Figure 5.5 Impact of degree of self-interference cancelation on the stabilit...
Figure 5.6 Impact of jamming power on average packet delay at the legitimate...
Figure 5.7 Impact of power of the interfering signal on average packet delay...
Chapter 6
Figure 6.1 Interplay between reliability, secrecy and latency.
Figure 6.2 Rayleigh fading wiretap channel with a trusted jammer and queue a...
Figure 6.3 Impact of power at the transmitter and jammer on the average serv...
Figure 6.4 Impact of arrival rate on latency (average delay and average AoI)...
Figure 6.5 Impact of power budget of jammer on latency (average delay and av...
Chapter 7
Figure 7.1 Overall architecture of RSMA scheme.
Figure 7.2 Internal eavesdropping attack in RSMA systems.
Figure 7.3 The proposed power-allocation scheme. (a) Secrecy rate versus a...
Figure 7.4 Ergodic secrecy rate and sum rate versus . (a) Ergodic secrecy r...
Figure 7.5 Ergodic sum rate versus ergodic secrecy rate.
Figure 7.6 WSR versus desired secrecy rate, , with , , and SNR = 20 dB.
Figure 7.7 Power allocation versus desired secrecy rate, , with , , and S...
Chapter 8
Figure 8.1 Interfaces of different system model components for the proposed ...
Figure 8.2 System model.
Figure 8.3 BER in an AWGN channel.
Figure 8.4 BER in an AWGN channel when considering channel impairments where...
Figure 8.5 BER of different number of bits for channel use of 1.
Figure 8.6 BER of different number of bits for channel use of 4.
Figure 8.7 Signal constellation comparison at dB SNR.
Figure 8.8 Signal constellation comparison at 30 dB SNR.
Figure 8.9 EVM results for different channel uses keeping the bits per symbo...
Figure 8.10 Comparison of the BER performance for channels, between FPM an...
Figure 8.11 System model of the conditional WGAN-GP.
Figure 8.12 BER with 100 real samples.
Figure 8.13 BER with 1000 real samples.
Figure 8.14 Error performance evaluation for channel use of 1.
Figure 8.15 Error performance evaluation for channel use of 2.
Figure 8.16 Error performance evaluation for channel use of 4.
Figure 8.17 Assessing the EVM versus BER for AEC without interference suppre...
Figure 8.18 Assessing the EVM versus BER for AEC that is trained with RS to ...
Figure 8.19 Visualization of jamming signal interfering with only one symbol...
Figure 8.20 Assessing the AE models trained on single-symbol interference fo...
Figure 8.21 The BER performance comparison between the AEC and conventional ...
Chapter 9
Figure 9.1 Positions of nodes in the synthetic Quadriga dataset.
Figure 9.2 TVD versus .
Figure 9.3 Separation of neighbors for the original signal and the princ...
Figure 9.4 Trade-off between CC and MP for SNR for the Quadriga dataset. D...
Figure 9.5 Trade-off between CC and MP for for the Quadriga dataset. (a) A...
Figure 9.6 Evolution of with for for the Quadriga dataset.
Chapter 10
Figure 10.1 Physical-layer-based SKG between Alice and Bob.
Figure 10.2 A filterbank comprising five filters. The transmit signal has a ...
Figure 10.3 Diagram depicting the measurement setup (made using floorplaner....
Figure 10.4 LoS measurement setup.
Figure 10.5 NLoS measurement setup.
Figure 10.6 Histograms of power measurements captured across various environ...
Figure 10.7 Bit mismatch probability at Bob and Eve after quantization. Diff...
Figure 10.8 Rate of unsuccessful reconciliation at Alice and Bob.
Figure 10.9 Rate of unsuccessful reconciliation at Eve.
Figure 10.10 Evaluation of the conditional min-entropy for different numbers...
Figure 10.11 Average SKG rate for different numbers of quantization regions,...
Chapter 11
Figure 11.1 Model of the RIS-assisted MIMO communication system.
Figure 11.2 Challenges of traditional authentication methods.
Figure 11.3 Intelligent PLA based on machine-learning algorithms.
Figure 11.4 Training accuracy of intelligent PLA based on the BPNN algorithm...
Figure 11.5 Authentication accuracy of intelligent PLA based on different ma...
Chapter 12
Figure 12.1 Learning-assisted PHYSEC key generation for wireless-powered coo...
Figure 12.2 Echo state network neural architecture.
Figure 12.3 Observation mismatches for learning-aided versus conventional PH...
Figure 12.4 Inference time for ESN versus fully-connected network.
Figure 12.5 Secret key rate comparison for different number of UTs.
Figure 12.6 Secret key rate comparison of the direct and TB-MALA schemes
Figure 12.7 Secure medical image transmission.
Figure 12.8 Required time for to complete recovering the entire file versu...
Figure 12.9 Recovered medical image at versus the recovered one at Eves....
Figure 12.10 Implemented testbed for group SKG.
Figure 12.11 Raw RSSI values obtained from the testbed.
Figure 12.12 Secrecy performance in terms of mutual information metric.
Figure 12.13 Achieved secret key rates for 50 runs of group-based PHY SKG al...
Chapter 13
Figure 13.1 The NTN scenario in which UAVs, HAPs, and satellites are deploye...
Figure 13.2 General model for a PLA scheme. Alice and Eve generate signals w...
Figure 13.3 General system model for the position integrity problem.
Figure 13.4 Missed detection probability versus the SNR on the legitimate ...
Figure 13.5 Performance of the optimal attack strategy, with LRT and GLRT de...
Chapter 14
Figure 14.1 Quantum wiretap channel models. (a)
classical/classical-quantum
Figure 14.2 The basic idea of covert communication is to prevent the attacke...
Chapter 15
Figure 15.1 General system model for confidentiality and authentication at t...
Figure 15.2 Number of transmission slots required to achieve bits of
PLS
t...
Figure 15.3 Average MD probability with different numbers of subcarriers , ...
Figure 15.4 Measure of authentication performance with different training se...
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright
About the Editors
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
Physical-Layer Security for 6G
Edited by
Parthajit Mohapatra
Department of Electrical Engineering, Indian Institute of Technology Tirupati, India
Nikolaos Pappas
Department of Computer and Information Science, Linköping University, Campus Valla, Sweden
Arsenia Chorti
Information, Communications and Imaging (ICI) Group of the ETIS Lab UMR8051, CY Cergy Paris Universite, Cergy, France
Stefano Tomasin
Department of Information Engineering, University of Padova, Padova, Italy
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Library of Congress Cataloging-in-Publication Data
Names: Mohapatra, Parthajit, editor. | Pappas, Nikolaos, 1982- editor. | Chorti, Arsenia, editor. | Tomasin, Stefano, editor.
Title: Physical-layer security for 6G / edited by Parthajit Mohapatra, Nikolaos Pappas, Arsenia Chorti, Stefano Tomasin.
Description: Hoboken, New Jersey : Wiley, [2024] | Includes index.
Identifiers: LCCN 2024034415 (print) | LCCN 2024034416 (ebook) | ISBN 9781394170913 (hardback) | ISBN 9781394170920 (adobe pdf) | ISBN 9781394170937 (epub)
Subjects: LCSH: 6G mobile communication systems--Security measures.
Classification: LCC TK5103.252 .P48 2024 (print) | LCC TK5103.252 (ebook) | DDC 621.3845/6--dc23/eng/20240819
LC record available at https://lccn.loc.gov/2024034415
LC ebook record available at https://lccn.loc.gov/2024034416
Cover Design: Wiley
Cover Image: © koiguo/Getty Images
About the Editors
Parthajit Mohapatra is an associate professor in the Department of Electrical Engineering, Indian Institute of Technology Tirupati, India. His primary research interests include wireless communication and current research focuses on physical-layer secrecy, short packet communication, and union of physical and network layers. Parthajit received the PhD degree from the Indian Institute of Science, Bangalore, in 2015.
Nikolaos Pappas is an associate professor and a docent in the Department of Computer and Information Science, Linköping University, Linköping, Sweden. His main research interests include the field of wireless communication networks with an emphasis on semantics-aware goal-oriented communications, age of information, energy harvesting networks, and network-level cooperation. Nikolaos received the PhD degree in computer science from the University of Crete, Greece, in 2012.
Arsenia Chorti is a professor (first class) at Ecole Nationale Supérieure de l’Electronique et de ses Applications, Cergy, Ile de France. Dr. Chorti received the Habilitation pour Diriger des Recherches in 2020.
Stefano Tomasin is a full professor in the Department of Information Engineering, University of Padova, Italy. Stefano received the PhD degree in telecommunications engineering from the University of Padova, Italy, in 2003.
List of Contributors
Francesco Ardizzon
Department of Information Engineering
University of Padova
Padova
Italy
Marco Baldi
Department of Information Engineering
Università Politecnica delle Marche
Ancona
Italy
Igor Bjelaković
Technische Universität Berlin
Berlin
Germany
and
Fraunhofer Heinrich-Hertz-Institut
Berlin
Germany
Matthieu Bloch
School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA
USA
Arsenia Chorti
Wireless Connectivity Group
Barkhausen Institut
Dresden
Germany
and
ETIS UMR8051, CY University
ENSEA, CNRS
Cergy
France
Laura Crosara
Department of Information Engineering
University of Padova
Padova
Italy
Kemal Davaslioglu
Nexcepta
Gaithersburg, MD
USA
Tugba Erpek
Nexcepta
Gaithersburg, MD
USA
He Fang
School of Electronic and Information Engineering
Soochow University
Sochow
China
Matthias Frey
Department of Electrical and Electronic Engineering
The University of Melbourne
Parkville, Victoria
Australia
Marco Giordani
Department of Information Engineering
University of Padova
Padova
Italy
Shenjie Huang
School of Engineering
The University of Edinburgh
Edinburgh
UK
Eduard Jorswieck
Institute for Communications Technology
TU Braunschweig
Brunswick
Germany
Babak Khalaj
EE Department
Sharif University of Technology
Tehran
Iran
Juliane Krämer
Universität Regensburg
Regensburg, Bayern
Germany
Nicola Laurenti
Department of Information Engineering
University of Padova
Padova
Italy
Mehdi Letafati
EE Department
Sharif University of Technology
Tehran
Iran
Laura Luzzi
ETIS, UMR 8051, CY Cergy Paris Université
ENSEA, CNRS
Cergy-Pontoise
France
Christos Masouros
Department of Electronic and Electrical Engineering
University College London
London
UK
Amitha Mayya
Wireless Connectivity Group
Barkhausen Institut
Dresden
Germany
Miroslav Mitev
Wireless Connectivity Group
Barkhausen Institut
Dresden
Germany
Parthajit Mohapatra
Department of Electrical Engineering
Indian Institute of Technology Tirupati
Tirupati
India
Derrick Wing Kwan Ng
School of Electrical Engineering and Telecommunications
University of New South Wales
Sydney, New South Wales
Australia
Janis Nötzel
Emmy-Noether Group Theoretical Quantum Systems Design
Technische Universität München
München, Bayern
Germany
Nikolaos Pappas
Department of Computer and Information Science
Linköping University
Linköping
Sweden
Majid Safari
School of Engineering
The University of Edinburgh
Edinburgh
UK
Yalin Sagduyu
Nexcepta
Gaithersburg, MD
USA
Abdelhamid Salem
Department of Electronic and Electrical Engineering
University College London
London
UK
and
Department of Electronic and Electrical Engineering
University of Benghazi
Benghazi
Libya
Robert Schober
Institute for Digital Communications
Friedrich-Alexander-University Erlangen Nürnberg
Erlangen
Germany
Linda Senigagliesi
Department of Information Engineering
Università Politecnica delle Marche
Ancona
Italy
Mahdi Shakiba Herfeh
ETIS UMR8051, CY University
ENSEA, CNRS
Cergy
France
Sotiris Skaperas
ETIS UMR8051, CY University
ENSEA, CNRS
Cergy
France
Mohammad Dehghani Soltani
School of Engineering
The University of Edinburgh
Edinburgh
UK
Muralikrishnan Srinivasan
Department of Electronics Engineering
Indian Institute of Technology (BHU)
Varanasi
India
Sławomir Stańczak
Technische Universität Berlin
Berlin
Germany
and
Fraunhofer Heinrich-Hertz-Institut
Berlin
Germany
Stefano Tomasin
Department of Information Engineering
University of Padova
Padova
Italy
Xianbin Wang
Department of Electrical and Computer Engineering
Western University
London, Ontario
Canada
Dongfang Xu
Academy of Interdisciplinary Studies
The Hong Kong University of Science and Technology
Kowloon, Hong Kong
China
Michele Zorzi
Department of Information Engineering
University of Padova
Padova
Italy
Preface
6G is envisioned to provide hyperconnectivity between users and objects, with interconnected machines being the dominant users. The widespread adoption of wireless devices presents unique challenges for ensuring secure communications. To meet the requirements of various services such as Ultra-Reliable Low Latency Communication (URLLC) and massive Machine Type Communication (mMTC), it is required to redesign existing physical-layer-based techniques to take into account the quality of service requirements such as reliability, latency, and energy consumption. The book aims to provide a comprehensive background on the physical-layer security and advances in the context of 6G networks.
The initial part of the book offers an introduction to physical-layer secrecy and then discusses the coding theory advances in the context of 6G. It provides novel material on the role of physical-layer secrecy in emerging communication scenarios ranging from intelligent reflecting surfaces to non-terrestrial networks. Some of the key topics covered in the book are as follows:
Application of physical-layer security in emerging communication technologies such as visible light communication (VLC) and intelligent reflecting surfaces (IRS)
Impact of physical-layer secrecy on latency aspects of communication such as delay and age of information (AoI)
Physical-layer-based authentication
Joint secure communication and sensing
AI/ML-enabled physical-layer secrecy
Context-aware physical-layer secrecy
The book explores use cases and practical demonstrations to provide insights into how physical-layer security can address the unique challenges of 6G networks. The reader will obtain a view of physical-layer security as a technology ready to be considered for the upcoming 6th generation of cellular networks. The book will benefit a broad audience of engineers and scientists by helping them understand the functioning of a new type of security that will be included in future communication systems.
June 2024
Parthajit Mohapatra
Tirupati, India
Nikolaos Pappas
Linköping, Sweden
Arsenia Chorti
Cergy-Pontoise, France
Stefano Tomasin
Padova, Italy
Part IPreliminaries
1Foundations of Physical-Layer Security for 6G*
Matthieu Bloch
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Wireless connectivity has become a cornerstone of our modern societies, driving innovation and supporting an ever-growing range of services. With the increasingly sensitive nature of information transmitted over wireless networks, privacy and secrecy mechanisms have naturally become an integral part of new protocols and standards. While identified weaknesses of previous generation wireless protocols are typically addressed with the rollout of the next generation, challenges constantly emerge that must be proactively addressed. For instance, while 5G systems have addressed some of the security weaknesses identified in 4G systems, the attack surface of 5G networks has increased because of the heterogeneity of devices and the larger number of use cases [48], as exemplified by the growth of machine-to-machine communications [19]. Consequently, security has yet again already been identified as one of the main challenges that 6G networks must address [72].
Several security solutions have been considered to provide full-stack security, including lightweight cryptography for Internet of Things (IoT) devices [61], the use of post-quantum cryptography [3, 72], and physical-layer security (PLS) [11, 45], which has again re-emerged as a possible technology [32, 38, 49, 66, 74]. The key concept behind PLS is to exploit the random imperfections inherent to wireless channels and devices (noise, interference) to provide, e.g., secrecy or authentication, using physical-layer signal processing and coding algorithms [11, 56, 68]. While PLS may certainly not solve all 6G security challenges in isolation, its main benefits are (i) to provide a concrete framework in which security can be quantified, e.g., through the notion of secrecy capacity [68]; (ii) to treat security on par with other system-level metrics, such as power consumption, throughput, and latency, at the design stage; (iii) to reduce the attack surface at the physical layer by making eavesdropping extremely costly, if not ineffective; and (iv) to seamlessly integrate with security mechanisms in the upper layers of the protocol stack. In particular, ensuring confidentiality for ultralow-latency communications is a known challenge [1, 54] that PLS could help tackle [15, 58].
PLS was already discussed in the context of 5G networks [35, 66], and one should recognize that, with the exception of niche applications and use cases [73], PLS has not had much impact on deployed systems. This state of affairs can be attributed to a multitude of factors, both technological and conceptual, ranging from scientific challenges related to the foundation of PLS itself (e.g., how do we characterize and learn a passive eavesdropper’s channel?) to technological hurdles (e.g., how do we justify integrating new codes at the physical layer of a standard?). Nevertheless, 6G promises new unique features that may finally offer the opportunity to push PLS into widely deployed systems [16, 45]. In particular, the integration of sensing and communication, especially as it relates to enhancing the localization of devices, and the push toward higher frequencies in the mmWave region are offering new avenues to strengthen the case of PLS.
The objective of this chapter is twofold. First, we will review the seminal coding ideas behind PLS, which have been refined over the last two decades to provide a strong basis for discussing secrecy in a principled manner. Second, we will discuss how these principles may be used in the more specific context of 6G systems, with an eye toward engineering channels, developing dedicated hardware, and exploiting channel knowledge for security. Given the breadth of literature on the topic, this chapter does not do justice to many creative ideas, in particular those involving PLS in the context of networks of many devices for which the reader is referred to tutorial articles [46, 51, 66, 71]. The focus of the chapter is on point-to-point links, for they still capture the essence of the challenges that remain to address and the opportunities that have emerged and might represent the realistic use cases for which PLS could be deployed at scale.
1.1 Coding Mechanisms
The appeal of (PLS) can be largely attributed to the early work of Wyner [68], Csiszár and Körner [21], Ahlswede and Csiszár [2], and Maurer [43], that first established and analyzed the notion of secrecy capacity and secret-key capacity. We defer to Section 1.2 for exact definitions, suffice to say for now that these definitions are the counterparts of the traditional notion of channel capacity and that they quantify the maximum rate of information that can be transmitted or extracted reliably and confidentially over a channel that includes an eavesdropping adversary. While secrecy capacity and secret-key capacity therefore provide system-level metrics that can be optimized as a function of channel parameters to understand how much secrecy can be achieved in a network, the ability to operationalize them is fundamentally tied to the ability to design specific coding schemes to extract or encode information in signal. Said differently, in the same way that the notion of channel capacity is useful because good error-control codes exist, secrecy and secret-key capacity are useful because good secrecy codes exist. The objective of this section is to introduce four coding operations that shall enable PLS by providing operational meaning to what it means to enforce secrecy in Section 1.2.
1.1.1 Channel Coding
The problem of channel coding is illustrated in Figure 1.1. The objective consists in transmitting a uniformly distributed messages over uses of a discrete memoryless channel with known transition probability by encoding the message into a coded sequence . The set of coded sequences is called the codebook while is called the blocklength of the code. Upon receiving the corrupted signal , the receiver attempts construct a correct estimate of using its knowledge of the channel and the code. The performance of channel coding may be measured in terms of the rate of transmission and the probability of decoding error .
The seminal result established by Shannon [55] is that, asymptotically, reliable communication is possible as long as the rate does not exceed a channel-dependent quantity called the channel capacity. We state this result more formally as follows.
Theorem 1.1 Given a discrete memoryless channel with known transition probability , a distribution and any , there exists a blocklength and an encoder/decoder pair such that and where is the mutual information between the random variables and with joint distribution . The quantity is called the channel capacity since no higher such constant can be found.
Specific instances of such codes can be designed using low-density parity-check codes [24, 33, 34] or polar codes [4].
Figure 1.1 Channel coding over a discrete memoryless channel.
1.1.2 Soft Covering
The operation of channel coding can be interpreted as introducing structure in coded sequences that is resilient to the corruption of the noisy channel. A lesser known coding operation over channels consists in introducing structure in coded sequences that disappear when corrupted by noise. Formally, this coding operation called soft covering is illustrated in Figure 1.2. Consider a random variable with distribution transmitted over a discrete memoryless channel with known transition probability . The output of the channel is a new random variable with distribution obtained by taking the marginal of . Instead of transmitting the random variable , one can instead ask whether one can approximately simulate transmissions of the random variable using instead a uniformly distributed message encoded into sequences of length . The intuition is that -coded sequences might be sufficient to approximately cover all possible realizations of i.i.d. realizations of the random variable . The performance of soft covering may be measured in terms of the rate of transmission and the relative entropy , where is the distribution induced by the random choice of coded sequences while is the -fold product distribution of .
The fundamental result of soft covering, first identified by Wyner [67] but studied and refined later on by others [22, 26, 29, 30, 65], is that and are virtually indistinguishable as long as the rate does not fall below a quantity called the channel resolvability. We state this result more formally below.
Theorem 1.2 Given a discrete memoryless channel with known transition probability , a distribution , and any , there exists a blocklength and an encoder such that and , where is the mutual information between the random variables and with joint distribution . The quantity is called the channel resolvability since no lower such constant can be found.
The intuition behind Theorem 1.2 is that by exceeding the channel resolvability, the structure that exists within the coded sequences gets obfuscated by the channel noise and the distribution at the output of the channel resembles a product distribution. Note that Theorem 1.2 is not merely a consequence of Theorem 1.1 since Theorem 1.1 only ensures that reliable communication is impossible at rates above the channel capacity and some lingering structure could still be identified in the channel output. In contrast, Theorem 1.2 shows that some codes completely lose their structure at rates above the channel resolvability. The close resemblance in the expression of the channel resolvability and the channel capacity is slightly misleading and an artefact of the discrete memoryless nature of the channels considered here. In general, the channel resolvability exceeds the channel capacity for the same choice of input distribution [29].
Figure 1.2 Soft-covering over a discrete memoryless channel.
The usefulness of soft covering for security shall be developed in Section 1.2; for now, one can interpret soft covering as a coding mechanism that obfuscates the existence of coded information. Although the design of soft-covering codes has been much less investigated than channel codes, specific instances can be built using polar codes or a combination of channel codes and invertible extractors [12, 18, 31, 60].
1.1.3 Source Coding with Side Information
The problem of source coding with side information is illustrated in Figure 1.3. Consider a discrete memoryless source with two components and with known joint distribution . The objective is to encode realizations of the source into a message to decode a reconstruction using both and the associated realizations as side information. The performance of source coding with side information may be measured in terms of the compression rate and the probability of reconstruction error .
The fundamental result of source coding with side information, discovered by Slepian and Wolf [59], is the following.
Theorem 1.3 Given a discrete memoryless source with known joint distribution and any , there exists a blocklength and an encoder/decoder pair such that and , where is conditional entropy of given . In addition, is the lowest such constant that can be found.
Figure 1.3 Source coding with side information for a discrete memoryless source.
Figure 1.4 Privacy amplification from a discrete memoryless channel.
Again, specific instances of codes for source coding with side information can be constructed using low-density parity-check codes and polar codes [5, 37, 42].
1.1.4 Privacy Amplification
Privacy amplification plays a role dual to source coding with side information analogous to the role of soft covering with respect to channel coding. Instead of compressing a source while retaining enough dependence to enable reconstruction with side information, one can attempt to compress so much that all dependence against side information is lost. The operation of privacy amplification is illustrated in Figure 1.4. Consider a discrete memoryless source with two components and with known joint distribution . The objective is to encode realizations of the source into a message that is uniformly distributed and nearly independent of the associated sequence . The performance of privacy amplification can be measured in terms of the extraction rate and the relative entropy between the joint distribution induced by the code and the source, and the product distribution where is the uniform distribution on .
The fundamental results of privacy amplification, which can be found in several forms [2, 9] including the so-called left-over hash lemma, is as follows.
Theorem 1.4 Given a discrete memoryless source with known joint distribution and any , there exists a blocklength and an encoder such that and , where is conditional entropy of given . In addition, is the highest such constant that can be found.
Intuitively, Theorem 1.4 states that the maximum randomness that can be extracted from the output of a channel independently of the input is the entropy of the channel noise. The design of codes for privacy amplification is quite well understood, in particular is sufficed to use universal hash functions [14].
1.2 Coding for Physical-Layer Security
With the coding mechanisms of Section 1.1 at hand, designing codes for PLS follows almost naturally once the meaning of secrecy is given operational significance. We shall analyze the two canonical models of PLS: secure communication over the wiretap channel and secret-key generation from correlated observations of a source.
1.2.1 Secure Communication
The wiretap channel model is illustrated in Figure 1.5. The model captures a situation in which a transmitter attempts to reliably convey a message to a legitimate receiver over discrete memoryless channel with known transition probability , while avoiding leaking information to an eavesdropper who obtains its observations through another discrete memoryless channel with known transition probability . These distributions may be the marginals of a transition probability . The legitimate receiver attempts to construct a correct estimate of while the eavesdropper attempts to infer information about from its observations . The performance of the coding scheme is measured in terms of the secret communication rate , the probability of decoding error , and the secret information leakage . For reasons we detail next, it is convenient to introduce an auxiliary random message to randomize the encoding of the message , and to allow the encoder to be stochastic. These additional assumptions do not trivialize the problem since the randomness introduced is local to the encoder and not shared with any other party.
The secrecy metric is called semantic secrecy, for a small value ensures that the eavesdropper cannot infer any information about any function of the message [7]. Other metrics, such as the strong secrecy and the weak secrecy , have found their use in the literature [20, 44] but have gathered skepticism in the cryptographic community. Fortunately, most results derived under weaker secrecy metrics have been shown to hold for semantic secrecy, and the coding techniques discussed next provide tools to directly analyze semantic secrecy.
The crucial idea to understand how the design of codes for secure communication is the following observation [20, 65]. For any distribution , it holds that
One interpretation of the upper bound (1.1) is that a sufficient condition to ensure semantic secrecy is to ensure that all messages induce the same target distribution . Intuitively, from the perspective of the eavesdropper, identifying the message is nearly impossible because all messages induce the same output distribution. This deceptively simple observation allows one to then make a conceptual connection with the mechanisms of Section 1.1. Specifically, the ability to control the output distribution is precisely the role of soft covering. Hence, one can construct a code for the wiretap channel by (i) associating a soft-covering code of rate to every message , inducing the same target distribution at the output of the eavesdropper’s channel ; (ii) ensuring that the union of soft-covering codes forms a channel code of rate for the main channel . This construction mandates the use of the auxiliary message alluded to earlier to randomize the encoding of every soft-covering code; hence, the encoding of every secrecy message is stochastic, associating a randomly chosen codeword within a codebook to every message.
Figure 1.5 Secure communication over the wiretap channel.
Assuming a given distribution , the rate must not exceed but can approach according to Theorem 1.1. Similarly, the rate much exceed but can approach according to Theorem 1.2. Consequently, the rate of secret messages must not exceed but can approach , which is essentially the secrecy capacity.
An additional subtlety is that the transmitter could artificially introduce noise before transmission, in the form of a discrete memoryless channel with transition probability so that the effective channel is the concatenation of and . This channel prefixing turns out to be required to achieve optimal secrecy rates [21, 63] and the secrecy capacity is given as follows.
Theorem 1.5 Given a discrete memoryless channel with known transition probability , a distribution , and any , there exists a blocklength and an encoder such that and , , where the random variables have joint distribution . The quantity is the largest such constant that can be found and is called the secrecy capacity.
This conceptual approach to constructing codes for secure communication over the wiretap channel leads to concrete code instance, see for instance [8, 12, 17, 40, 57, 62]. The constructions [6, 17, 57] particularly stand out for they suggest that the soft-covering codes can be created in a modular fashion by “hashing” a good channel code into subcodes and leveraging specific hash function that can be stochastically inverted.
One should note that the approach described above is not the approach that was historically followed to construct codes. Instead, earlier constructions used an astute observation by Wyner [68] to tie secrecy to reliability [12]. Despite many code constructions, e.g., [36, 50], this approach is fundamentally limited to weak secrecy [10]. Furthermore, the approach outlined here based on soft covering generalizes to adversarial channels [27, 47, 53] and allows one to ensure secrecy under much weaker assumptions regarding channel knowledge than our presentation suggests.
1.2.2 Secret-Key Generation
The source model for secret-key generation is illustrated in Figure 1.6. This model captures a scenario in which three parties observe the correlated signals , , , respectively, originating from a discrete memoryless source with distribution . The objective of the two parties observing and is to distill a secret key from their observations, treating as an eavesdropper. The distillation of the secret key is enabled by a public-authenticated channel of unlimited capacity over which the two parties can transmit messages collectively denoted . This assumption does not trivialize the problem because is known to the eavesdropper. In addition, the cost of authenticating a channel is negligible compared to the size of the distilled key [64]. The performance of the coding scheme is measured in terms of the secret key distillation rate , the probability of reconstruction error , and the secret information leakage . The encoding operations that define the messages can be generic, allowing for interactivity and randomization.
The design of secret-key generation codes follows an approach conceptually similar to that outlined in Section 1.2.1 for secure communication in an even more direct way. Specifically, secret keys may be distilled by exactly combining source coding with side information and privacy amplification so that (i) one associates a public index of rate to every sequence that enables the receiver observing to reconstruct ; (ii) one associates a secret key index of rate to every sequence that remains independent of the public index and independent of . The rate must exceed but can approach by Theorem 1.3. The sum rate must not exceed but can approach by Theorem 1.4. Consequently, the secret-key rate must not exceed but can approach , which is the essence of the secret-key capacity.
Figure 1.6 Secret-key generation from source model.
In general, the secret-key capacity is not known without placing additional restrictions on the source model. Nevertheless, upon remarking that the roles of and are interchangeable, the following result holds.
Theorem 1.6 Given a source model with joint distribution and any , there exists a secret-key generation code such that
and , . The secret key capacity is also known to be no greater .
This approach to generating secret keys has long been used in quantum key distribution [25] and is made much simpler than the construction of codes for the wiretap channel by the universal nature of privacy amplification [9] and the ability to ignore the exact structure of reconciliation [13] when applying privacy amplification.
Note that rewriting as suggests a connection between secure communication and secret-key generation. While this connection can be exploited [20, 52, 69], the instantiation of codes is more natural following the distinct coding mechanisms described earlier. In particular, the use of universal hash functions for privacy amplification offers some level of universality to the key generation process, such that minimal knowledge of the distribution is actually required to extract a key [9, 23].
1.3 Engineering and Learning Channels
The push toward the use of higher frequencies of the wireless spectrum is naturally offering opportunities for PLS and 6G. For instance, the need for narrow beams to overcome the path loss at mmWave is a natural way to make communications more directional and effectively create channels that are naturally “better” for an intended receiver than for an eavesdropper outside the beam [39, 70]. Perhaps more importantly, the push toward higher frequencies has sparkled more experimental interest for PLS, leading to the design of new RF-frontend circuits and measurements [28, 38, 41] that offer more concrete evidence of the potential and limits of PLS.
These exciting experimental works open up possibilities for PLS and can be broadly described as solutions to engineer channels that will enable PLS. Nevertheless, a complete integration with the coding mechanisms described in Sections 1.1 and 1.2 is still largely missing. More precisely, secrecy capacity and secret-key capacity are still often used to measure the “secrecy level” of a link but without deploying the coding schemes required to transform the metric into an actual operational guarantee for a transmission. The main impediment toward this integration is perhaps not the coding mechanisms themselves, as solutions exist as described earlier. Rather, the main hurdle is the need to learn the wireless environment as some degree of channel knowledge is required to properly tune the parameters of the coding scheme and provide security guarantees.
References
1
3GPP. Study on the security for 5G URLLC (Release 16). Technical Specification (TR) 33.825, 3rd Generation Partnership Project (3GPP), Mar 2019.
2
R. Ahlswede and I. Csiszár. Common randomness in information theory and cryptography. I. Secret sharing.
IEEE Transactions on Information Theory
, 39(4):1121–1132, Jul 1993. doi: 10.1109/18.243431.
3
G. Alagic et al. Status report on the second round of the NIST post-quantum cryptography standardization process. Technical Report NISTIR 8309,National Institute of Standards and Technology, Jul 2020.
4
E. Arikan. Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels.
IEEE Transactions on Information Theory
, 55(7):3051–3073, 2009. doi: 10.1109/TIT.2009.2021379.
5
E. Arikan. Source polarization. In
Proceedings of IEEE International Symposium on Information Theory
, pages 899–903, Austin, TX, Jun 2010. doi: 10.1109/ISIT.2010.5513567.
6
M. Bellare and S. Tessaro. Polynomial-time, semantic-secure encryption achieving the secrecy capacity, Jan 2012.
7
M. Bellare, S. Tessaro, and A. Vardy. Semantic security for the wiretap channel. In R. Safavi-Naini and R. Canetti, editors,
Advances in Cryptology & CRYPTO 2012
, volume 7417 of
Lecture Notes in Computer Science
, pages 294–311. Springer-Verlag, Berlin Heidelberg, 2012. ISBN 978-3-642-32008-8. doi: 10.1007/978-3-642-32009-58. hard-copy.
8
M. Bellare, S. Tessaro, and A. Vardy. A cryptographic treatment of the wiretap channel, Jan 2012.
9
C. H. Bennett, G. Brassard, C. Crépeau, and U. Maurer. Generalized privacy amplification.
IEEE Transactions on Information Theory
, 41(6):1915–1923, Nov 1995. doi: 10.1109/18.476316.
10
M. R. Bloch. Achieving secrecy: Capacity vs. resolvability. In
Proceedings of IEEE International Symposium on Information Theory
, pages 632–636, Saint Petersburg, Russia, Aug 2011. doi: 10.1109/ISIT.2011.6034207.
11
M. Bloch and J. Barros.
Physical-Layer Security: From Information Theory to Security Engineering
. Cambridge University Press, Oct 2011. doi: 10.1017/CBO9780511977985.
12
M. R. Bloch, M. Hayashi, and A. Thangaraj. Error-control coding for physical-layer secrecy.
Proceedings of IEEE
, 103(10):1725–1746, Oct 2015. ISSN 0018-9219. doi: 10.1109/JPROC.2015.2463678.
13
C. Cachin and U. M. Maurer. Linking information reconciliation and privacy amplification.
Journal of Cryptology
, 10(2):97–110, Mar 1997.
14
J. L. Carter and M. N. Wegman. Universal classes of hash functions.
Journal of Computer and System Sciences
, 18:143–154, 1979.
15
R. Chen, C. Li, S. Yan, R. Malaney, and J. Yuan. Physical layer security for ultra-reliable and low-latency communications.
IEEE Wireless Communications
, 26(5):6–11, Oct 2019. doi: 10.1109/mwc.001.1900051.
16
A. Chorti, A. N. Barreto, S. Kopsell, M. Zoli, M. Chafii, P. Sehier, G. Fettweis, and H. V. Poor. Context-aware security for 6G wireless: The role of physical layer security.
IEEE Communications Standards Magazine
, 6(1):102–108, Mar 2022. doi: 10.1109/mcomstd.0001.2000082.
17
R. A. Chou. Explicit codes for the wiretap channel with uncertainty on the eavesdropper’s channel. In
Proceedings of IEEE International Symposium on Information Theory
, pages 476–481, Vail, CO, Jun 2018. doi: 10.1109/ISIT.2018.8437777.
18
R. A. Chou, M. R. Bloch, and J. Kliewer. Low-complexity channel resolvability codes for the symmetric multiple-access channel. In
Proceedings of IEEE Information Theory Workshop
, pages 466–470, Hobart, Tasmania, Nov 2014. doi: 10.1109/ITW.2014.6970875.
19
Cisco. Cisco Annual Internet Report (2018–2023) White Paper, Mar 2020.
20
I. Csiszár. Almost independence and secrecy capacity.
Problems of Information Transmission
, 32(1):40–47, Jan-Mar 1996.
21
I. Csiszár and J. Körner. Broadcast channels with confidential messages.
IEEE Transactions on Information Theory
, 24(3):339–348, May 1978.
22
P. Cuff. Distributed channel synthesis.
IEEE Transactions on Information Theory
, 59(11):7071–7096, 2013. doi: 10.1109/TIT.2013.2279330.
23
Y. Dodis, R. Ostrovsky, L. Reyzin, and A. Smith. Fuzzy extractors: How to generate strong keys from biometrics and other noisy data.
SIAM Journal of Computing
, 38(1):97–139, Mar 2008. ISSN 0097-5397. doi: 10.1137/060651380.
24
R. G. Gallager.
Low Density Parity Check Codes
. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 1963.
25
N. Gisin, G. Ribordy, W. Tittel, and H. Zbinden. Quantum cryptography.
Reviews of Modern Physics
, 74:145–195, Mar 2002. doi: 10.1103/RevModPhys.74.145.
26
Z. Goldfeld, P. Cuff, and H. Permuter. Semantic-security capacity for wiretap channels of type II.
IEEE Transactions on Information Theory
, 62(7):3863–3879, 2016. ISSN 0018-9448. doi: 10.1109/TIT.2016.2565483.
27
Z. Goldfeld, P. Cuff, and H. H. Permuter. Arbitrarily varying wiretap channels with type constrained states.
IEEE Transactions on Information Theory
, 62(12):7216–7244, Dec 2016. doi: 10.1109/TIT.2016.2619701.
28
J. Guo, L. Poli, M. A. Hannan, P. Rocca, S. Yang, and A. Massa. Time-modulated arrays for physical layer secure communications: Optimization-based synthesis and experimental assessment.
IEEE Transactions on Antennas and Propagation
, 66(12):6939–6949, Dec 2018. doi: 10.1109/tap.2018.2870381.
29
T. S. Han and S. Verdú. Approximation theory of output statistics.
IEEE Transactions on Information Theory
, 39(3):752–772, May 1993. ISSN 0018-9448. doi: 10.1109/18.256486.
30
M. Hayashi. General nonasymptotic and asymptotic formulas in channel resolvability and identification capacity and their application to the wiretap channels.
IEEE Transactions on Information Theory
, 52(4):1562–1575, Apr 2006. doi: 10.1109/TIT.2006.871040.
31
M. Hayashi. Tight exponential analysis of universally composable privacy amplification and its applications.
IEEE Transactions on Information Theory
, 59(11):7728–7746, Nov 2013. ISSN 0018-9448. doi: 10.1109/TIT.2013.2278971.
32
L. Jiao, N. Wang, P. Wang, A. Alipour-Fanid, J. Tang, and K. Zeng. Physical layer key generation in 5G wireless networks.
IEEE Wireless Communications
, 26(5):48–54, Oct 2019. ISSN 1536-1284, 1558-0687. doi: 10.1109/MWC.001.1900061.
33
S. Kudekar, T. Richardson, and R. L. Urbanke. Spatially coupled ensembles universally achieve capacity under belief propagation.
IEEE Transactions on Information Theory
, 59(12):7761–7813, Dec 2013. doi: 10.1109/tit.2013.2280915.
34
M. Lentmaier, A. Sridharan, D. J. Costello, and K. Sh. Zigangirov. Iterative decoding threshold analysis for LDPC convolutional codes.
IEEE Transactions on Information Theory
, 56(10):5274–5289, Oct 2010. doi: 10.1109/TIT.2010.2059490.
35
G. Li, C. Sun, J. Zhang, E. Jorswieck, B. Xiao, and A. Hu. Physical layer key generation in 5G and beyond wireless communications: Challenges and opportunities.
Entropy
, 21(5):497, May 2019. ISSN 1099-4300. doi: 10.3390/e21050497.
36
R. Liu, Y. Liang, H. V. Poor, and P. Spasojević. Secure nested codes for type ii wiretap channels. In
Proceedings of IEEE Information Theory Workshop
, pages 337–342, Lake Tahoe, CA, Sept 2007. doi: 10.1109/ITW.2007.4313097.
37
A. D. Liveris, Z. Xiong, and C. N. Georghiades. Compression of binary sources with side information at the decoder using LDPC codes.
IEEE Communications Letters
, 6(10):440–442, Oct 2002.
38
X. Lu, S. Venkatesh, B. Tang, and K. Sengupta. Space-time modulated 71-to-76ghz mm-wave transmitter array for physically secure directional wireless links. In
Proceedings of IEEE International Solid- State Circuits Conference
, pages 86–88, Feb 2020. doi: 10.1109/ISSCC19947.2020.9062929.
39
J. Ma et al. Security and eavesdropping in terahertz wireless links.
Nature
, 563(7729):89–93, Nov 2018. ISSN 1476-4687. doi: 10.1038/s41586-018-0609-x. URL
https://www.nature.com/articles/s41586-018-0609-x
.
40
H. Mahdavifar and A. Vardy. Achieving the secrecy capacity of wiretap channels using polar codes.
IEEE Transactions on Information Theory
, 57(10):6428–6443, 2011. doi: 10.1109/TIT.2011.2162275.
41
N. S. Mannem, T.-Y. Huang, E. Erfani, S. Li, and H. Wang. A mm-wave transmitter MIMO with constellation decomposition array (CDA) for keyless physically secured high-throughput links. In
2021 IEEE Radio Frequency Integrated Circuits Symposium (RFIC)
. IEEE, Jun 2021. doi: 10.1109/rfic51843.2021.9490442.
42
J. Martinez-Mateo, D. Elkouss, and V. Martin. Key reconciliation for high performance quantum key distribution.
Scientific Reports
, 3:1576, Apr 2013. doi: 10.1038/srep01576.
43
U. M. Maurer. Secret key agreement by public discussion from common information.
IEEE Transactions on Information Theory
, 39(3):733–742, May 1993. doi: 10.1109/18.256484.
44
U. M. Maurer and S. Wolf. Information-theoretic key agreement: From weak to strong secrecy for free. In
Advances in Cryptology - Eurocrypt 2000
,
page 351
. Lecture Notes in Computer Science, B. Preneel, 2000.
45
M. Mitev, A. Chorti, H. V. Poor, and G. P. Fettweis. What physical layer security can do for 6G security.
IEEE Open Journal of Vehicular Technology
, 4:375–388, 2023. doi: 10.1109/ojvt.2023.3245071.
46
A. Mukherjee. Physical-layer security in the internet of things: Sensing and communication confidentiality under resource constraints.
Proceedings of the IEEE
, 103(10):1747–1761, Oct 2015. ISSN 1558-2256. doi: 10.1109/JPROC.2015.2466548.
47
M. Nafea and A. Yener. A new wiretap channel model and its strong secrecy capacity.
IEEE Transactions on Information Theory
, 64(3):2077–2092, Mar 2018. doi: 10.1109/TIT.2017.2786541.
48
NIS Cooperation Group. EU coordinated risk assessment of the cybersecurity of 5G networks. Technical report, European Commission, 2019.
49
P. Porambage, G. Gur, D. P. M. Osorio, M. Liyanage, A. Gurtov, and M. Ylianttila. The roadmap to 6G security and privacy.
IEEE Open Journal of the Communications Society
, 2:1094–1122, 2021. doi: 10.1109/ojcoms.2021.3078081.
50
V. Rathi, M. Andersson, R. Thobaben, J. Kliewer, and M. Skoglund. Performance analysis and design of two edge-type LDPC codes for the BEC wiretap channel.
IEEE Transactions on Information Theory
, 59(2):1048–1064, Feb 2013. ISSN 0018-9448. doi: 10.1109/TIT.2012.2219577.
51
P. A. Regalia, A. Khisti, Y. Liang, and S. Tomasin. Secure communications via physical-layer and information-theoretic techniques [scanning the issue].
Proceedings of the IEEE
, 103(10):1698–1701, Oct 2015. doi: 10.1109/jproc.2015.2473895
52
J. M. Renes and R. Renner. Noisy channel coding via privacy amplification and information reconciliation.
IEEE Transactions on Information Theory
, 57(11):7377–7385, 2011. doi: 10.1109/TIT.2011.2162226
53
R. F. Schaefer, H. Boche, and H. V. Poor. Secure communication under channel uncertainty and adversarial attacks.
Proceedings of the IEEE
, 103(10):1796–1813, Oct 2015. ISSN 0018-9219. doi: 10.1109/JPROC.2015.2459652
54
M. Shakiba-Herfeh, A. Chorti, and H. V. Poor. Physical layer security: Authentication, integrity, and confidentiality. In
Physical Layer Security
, pages 129–150. Springer International Publishing, 2021. doi: 10.1007/978-3-030-55366-1.
55
C. E. Shannon. A mathematical theory of communication.
The Bell System Technical Journal
, 27:379–423, 623–656, Jul, Oct 1948.
56
C. E. Shannon. Communication theory of secrecy systems.
The Bell System Technical Journal
, 28:656–715, 1948.
57
S. Sharifian, F. Lin, and R. Safavi-Naini. Hash-then-encode: A modular semantically secure wiretap code. In
Proceedings of the 2nd Workshop on Communication Security
, pages 49–63, Paris, France, Apr 2018. ISBN 978-3-319-59265-7
58
C. She, C. Sun, Z. Gu, Y. Li, C. Yang, H. V. Poor, and B. Vucetic. A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning.
Proceedings of the IEEE
, 109(3):204–246, Mar 2021. doi: 10.1109/jproc.2021.3053601
59
D. Slepian and J. K. Wolf. Noiseless coding of correlated information sources.
IEEE Transactions on Information Theory
, 19(4):471–480, Jul 1973.
60
R. Sultana and R. A. Chou. Explicit low-complexity codes for multiple access channel resolvability. In
Proceedings of 57th Annual Allerton Conference on Communication, Control, and Computing
, pages 116–123, Monticello, IL, Sept 2019.
61
M. S. Turan, K. McKay, Ç. Çalık, D. Chang, and L. Bassham. Status report on the first round of the NIST lightweight cryptography standardization process. Technical Report NISTIR 8268,National Institute of Standards and Technology, Oct 2019.
62
H. Tyagi and A. Vardy. Universal hashing for information-theoretic security.
Proceedings of the IEEE
, 103(10):1781–1795, Oct 2015. ISSN 0018-9219. doi: 10.1109/JPROC.2015.2462774
63
S. Watanabe and Y. Oohama. The optimal use of rate-limited randomness in broadcast channels with confidential messages.
IEEE Transactions on Information Theory
, 61(2):983–995, Feb 2015. ISSN 0018-9448. doi: 10.1109/TIT.2014.2382096
64
M. N. Wegman and J. L. Carter. New hash functions and their use in authentication and set equality.
Journal of Computer Sciences and Systems
, 22:265–279, 1981.
65
A. Winter, A. C. A. Nascimento, and H. Imai. Commitment capacity of discrete memoryless channels. In
Proceedings of 9th IMA International Conference
, pages 33–51, Cirencester, UK, 2003.
66
Y. Wu, A. Khisti, C. Xiao, G. Caire, K. Wong, and X. Gao. A survey of physical layer security techniques for 5G wireless networks and challenges ahead.
