Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Mulugeta Kassaw Tefera
 -- 4714 2022-05-19 11:08:09 |
2 format correct
Vivi Li
 -15 word(s) 4699 2022-05-20 04:23:39 | |
3 format correct
Vivi Li
 -6 word(s) 4693 2022-05-23 08:06:01 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Tefera, M.K.; , .; Jin, Z.; Zhang, S. AI Enabling Technologies in Physical Layer Security. Encyclopedia. Available online: https://encyclopedia.pub/entry/23116 (accessed on 16 May 2024).
Tefera MK,  , Jin Z, Zhang S. AI Enabling Technologies in Physical Layer Security. Encyclopedia. Available at: https://encyclopedia.pub/entry/23116. Accessed May 16, 2024.
Tefera, Mulugeta Kassaw, , Zengwang Jin, Shengbing Zhang. "AI Enabling Technologies in Physical Layer Security" Encyclopedia, https://encyclopedia.pub/entry/23116 (accessed May 16, 2024).
Tefera, M.K., , ., Jin, Z., & Zhang, S. (2022, May 19). AI Enabling Technologies in Physical Layer Security. In Encyclopedia. https://encyclopedia.pub/entry/23116
Tefera, Mulugeta Kassaw, et al. "AI Enabling Technologies in Physical Layer Security." Encyclopedia. Web. 19 May, 2022.
AI Enabling Technologies in Physical Layer Security
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s22093589

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods.

physical layer security optimization approaches information theory signal processing techniques resource allocation AI techniques

1. Introduction

Currently, 5G wireless networks are fully commercialized worldwide and B5G networks are in the process of development and are supposed to be deployed within the next few years. With the fast adoption of 5G technology, the number of users who utilize wireless mobile networks has increased exponentially over the last few years. It is expected that this rapid growth will continue to increase enormously due to the deployment of more smart applications and new enabling technologies within the upcoming B5G networks. Future wireless networks will not only provide much higher spectral efficiency/data rates and lower latency but will also provide new services and technologies that can be applied in various vertical industries [1]. The presence of ubiquitous connections and the growing number of devices that are connected to the internet are expected to cause a challenge for efficient and reliable resource management systems [2][3][4]. Moreover, the rapid growth of the massive number of new devices that are connected to the internet and the Internet of Things (IoT) is expected to cause a serious risk to network security if not manipulated properly [5]. Therefore, when considering all of these capabilities, it can be seen that there is a need for robust security mechanisms across all segments of 5G and B5G networks.
Conventionally, high-layer cryptography-based techniques have been widely adopted to deal with any discrepancies that are associated with information confidentiality, which include data authentication, secret key establishment and secret dissemination [6]. However, with the advancement in the computing capacities of eavesdropping devices, the above-mentioned techniques may not be sufficient or may even become unsuitable when an extra secure channel is required for secret key generation [7]. Recently, physical layer security (PLS) has emerged as a promising means of addressing the eavesdropping computational capability of secure transmission problems [8][9][10][11][12]. Compared to complex cryptography techniques, PLS does not depend on the computational capacity of the eavesdropping devices and, therefore, it has the benefit of reducing computational costs and resource consumption. From the perspective of information-theoretic fundamentals, it has been found that PLS can achieve secure and reliable communication even when network intruders have very strong computing capabilities [13]. This approach to information security is especially effective since it does not rely on underlying computational capabilities, but rather on the characteristics of the transmission media, such as noise, fading and interference, and it provides security guarantees that are independent from the computing power of the eavesdropper. In general, the PLS approach presents distinct advantages and is well suited for distributed processing systems and dynamic network configurations. Therefore, the PLS approach can be used as an alternative supplement for computationally demanding high-layer technologies to further ensure data security.
Although the PLS can be precisely evaluated using popular performance metrics, such as secrecy capacity, secrecy rate, secrecy throughput, etc., which are discussed in detail in the literature, security performance is quantified by maximizing the performance difference between legitimate channels and wiretap channels [14][15][16][17]. This is intuitive since PLS aims to enhance the received signal quality at the intended receiver or reduce the performance of the wiretap channel relative to the legitimate channel. In this circumstance, there is a need to allocate transmission power based on the states of the legitimate and eavesdropper channels in order to improve the PLS, as transmission power affects the signal quality at the intended receiver and eavesdropper. However, the allocation of power in PLS is a challenging task. It relies heavily on the prior information that the transmitter has on the channel state information (CSI) of the intended receiver and the eavesdropper. Most of the optimization functions in PLS are non-convex because of the characteristics of the logarithmic subtraction in security performance metrics. For instance, when the transmission power increases, the capacity and reliability of the main channel improve [13]. On the other hand, the capacity of the eavesdropper channel may also improve and the probability of eavesdropping increases. Therefore, there is no universal approach to achieving a global optimization for non-convex power allocation. Several research works have been conducted to formulate and solve these optimization problems in order to obtain stronger security [18]. In [19], instead of maximizing the secrecy capacity of the main channel, suboptimal power allocation was presented to minimize the SINR at an unintended receiver. However, the minimization of the SINR at an unintended receiver is not assessed by direct performance metrics and the security measure cannot ensure a non-negative rate of transmission. Moreover, a joint subcarrier and power allocation mechanism were proposed in [20] for maximizing the secrecy capacity of OFDMA-based wireless networks. Nevertheless, the performance of secrecy gain can be enhanced by limited power allocation. Consequently, it is hard to achieve global quality of service (QoS) constraints for secure transmission systems.
The mainstream studies on PLS as a method for characterizing an achievable security performance against eavesdropping have been extensively investigated from the fundamental viewpoints of information theory for different communication scenarios and channel types and under different assumptions on the knowledge of CSI. These studies have inspired the development of many signal processing design techniques [21][22][23][24]. In this context, a large number of research works have been conducted, which have contributed insightful thoughts and opportunities to the understanding of practical security designs, optimization techniques, technology status, etc. For example, in [25][26][27], key technologies, technical challenges, and countermeasures were reviewed from the fundamental viewpoints of design strategies that involve physical-layer authentication, secret key generation, wiretap coding, and multi-antenna techniques, and relay cooperation. Moreover, the authors in [28][29] presented an extensive investigation of multi-antenna techniques in multi-user wireless networks using different assumptions on the availability of CSI. Providing security for multi-antenna techniques is an effective and powerful approach in PLS that can offer higher spatial degrees of freedom. The survey paper in [30] also provided a comprehensive overview of secure transmission designs from the viewpoints of information theory and optimization problems using security performance metrics. Furthermore, a comprehensive overview of fundamental classification and applications of existing PLS techniques was presented by [31]. On the other hand, the challenges that face PLS were reviewed in [32]. It can be seen that the hurdles facing PLS are issued from different assumptions regarding the characteristics of wireless channels and eavesdropper models.
Naturally, the concept of optimization techniques in PLS plays a pivotal role in practical security design and thus, has received considerable attention from the research community. In this entry, due to the importance of secure transmission design in most practical scenarios, researchers were motivated to conduct a systematic overview of this research direction. It has to be noted that these studies have been extensively investigated and have been published in many PLS research works. Nevertheless, researchers outline a summary of some of the interesting studies in Table 1. In contrast to the aforementioned works, researchers provides a brief overview of recent results and technical challenges for the system design and optimization techniques for 5G wireless networks. The main focus of this entry is the existing techniques and design strategies for PLS optimization, optimization problems, and the solutions that are related to wireless PLS. Moreover, it inclusively discusses the applications of several enabling and computing technologies that could improve the corresponding research challenges. In order to address the limitations of existing optimization challenges, ML and AI technologies need to be efficiently integrated into 5G networks in order to produce better security and resource management. The use of ML and AI technologies within the field of mobile communication infrastructure has made significant progress in ensuring security, reliability, and resource allocations in a dynamic, robust and trustworthy way [33][34][35][36].
Table 1. Summary of related studies.
Existing Papers Research Issues Important Content
[12] Examine the security threats and corresponding defense methods in PHY security. Summary of the security requirements and threats in wireless networks considering the network protocols at various levels of data layers. Additionally, a comprehensive review of state-of-the-art PHY security and existing security protocols for 13 various wireless networks.
[25] A comprehensive survey on the basic theories and key technologies of PHY security. Discussion of the key technologies, limitations, and solutions of PHY security from the perspective of security coding, physical-layer authentication, secret key generation, and multi-antenna techniques.
[26] Security threats and the corresponding countermeasure techniques. Technologies, security attacks, and defense mechanisms in PHY security using game-theoretic approaches.
[27] Overview of the key technologies of PHY security. Recent technologies, optimization techniques, and limitations of PHY security from the perspective of information-theoretic security and wiretap channels.
[28] A comprehensive investigation of multi-antenna techniques. Survey of multi-antenna techniques in multi-user networks for improving the security performance of PHY security, but not considering CSI accuracy.
[29] A brief survey on multi-antenna techniques. Investigation on multi-antenna techniques in PHY security for improving secrecy performance considering the accuracy of CSI.
[30] A comprehensive overview of the optimization and design strategies of PHY security. Survey on security designs and optimization techniques from the viewpoints of information theory and security metrics in wireless PHY security.
[31] Comprehensive overview of all existing PHY security techniques. Classification of the existing PHY security techniques and brief discussion of the big picture they can be easily understood and applied in different communication systems.
[32] Challenges of PHY security in real-world systems. Identification of the existing assumptions and opportunities for applying PHY security in practical applications.
[34] A comprehensive investigation of AI and edge
computing (EC) for PLS.		 Identification of the existing challenges in the design and optimization of PLS and design of an enhancement scheme for PLS application.

2. Fundamental Concepts

2.1. Generic System Model

The typical network is where problems with PLS arise, which consists of a transmitter, a legitimate (intended) receiver and a malicious eavesdropper, represented by Alice, Bob and Eve, respectively. Figure 1 shows the general Alice–Bob–Eve model of a PLS system, in which the transmitter and receiver communicate through the legitimate channel and the eavesdropper makes a limited set of observations through a wiretap channel [37]. In this setup, the eavesdropper aims to decode or obtain sensitive information from historical observations of received signals. In other words, the objective of Alice is to use a secure transmission strategy that can send a confidential message to Bob while making sure that the eavesdropper is kept ignorant and is not able to glean any useful information from the transmitted signals. In order to achieve security in this case, PLS techniques are appropriately designed by exploiting the channel characteristics, such as noise, dispersion, fading, interference, etc., along with efficient secure transmission strategies, such that the information being sent from source to receiver is kept confidential from both active and passive eavesdroppers.
As illustrated in Figure 1, the transmitted message signal M is encoded into X with a length n and transmitted via a wireless medium. The signals that are received by Eve and Bob are designated by RE and RB, respectively. The entropy function of the transmitting signal is denoted by H(M), while the residual uncertainty of Eve’s observations is given by H(M/RE). Taking the environment and scenarios into consideration, the availability of CSI at Alice, Bob and Eve varies from full or partial channel knowledge to zero information. The a priori knowledge that is available to Alice regarding the channel information of the legitimate and eavesdropper channels is crucial for the determination of the corresponding secrecy optimization scheme and PLS design. Nevertheless, in practical scenarios, it is logical to assume that the Alice is aware of the statistical information of the eavesdropper but not of its instantaneous realizations. Such information includes the type of channel distribution, the average gain of the received signal, the spatial direction and the line-of-sight (LOS) component.
Sensors 22 03589 g001 550
Figure 1. Generic system model of an Alice–Bob–Eve channel.

2.2. Wireless Adversary Models

Due to the inherent characteristics of wireless communication, such as broadcast nature, openness and the superposition of the transmission medium, wireless networks are extremely vulnerable to security attacks. Within the field of security, an adversary refers to a wireless attacker who aims to disrupt or prevent transmitted signals from reaching the intended receiver. Therefore, it is important to consider adversaries when designing a secure strategy in order to measure the effectiveness of the proposed security system. Security attacks on wireless networks can be mainly classified into two types: active attacks and passive attacks [38]. The concept of passive or active attacks is typically to do with whether the adversary is actively injecting into the communication system or is merely listening to the ongoing transmissions [39]. Due to the various types of adversaries and the vast nature of attacks, there is a need to identify the assumptions, goals and capabilities on which these different types of PLS are designed and the potential challenges that could prevent PLS from succeeding.
A relevant set of goals is important for the modeling of a rigorous and strong adversary model. Naturally, a stronger adversary, i.e., with prior knowledge or more resources, could be able to attack the wireless network more successfully. With the adversary model, the assumptions of the adversary include its environment and resources, such as competency, knowledge, equipment, devices, etc. The adversary aims to compromise and obtain secret data content from within the communication system. These sets of data can be the legitimate transmission data or the property of the authorized user, e.g., energy consumption [40][41]. Due to their exposure to various types of attacks, communication channels are required to have determined capacities that enable them to resist and alleviate wireless attacks. These capabilities provide the adversary with reliable interactions that are based on the context of secure transmission systems. In fact, many of the PLS approaches assume that Alice has no knowledge of the eavesdropper’s CSI as the adversary is passive (i.e., not actively modifying the data, just silently reading and observing the communication system). On the other hand, some studies have found that Alice is sometimes assumed to know about the CSI of the eavesdropper [42][43][44]. Furthermore, active attacks have sometimes been observed, such as the denial of service, replay and node malfunction attacks that are employed against PHY security approaches. As a security community, researchers need to adopt a strong adversary model in which the adversary is cleverer and more active. Under the implications of an adversary being an active attack, intruders use more intrusive and aggressive methods that aim to deteriorate the received signal quality for the intended user.
The essential characteristics of a secure transmission system include authentication, availability, integrity, access control, and secret dissemination [38]. These can be established through appropriately designed signal processing strategies and channel coding techniques. The adversary model that is used in most PLS approaches is different from that used by the traditional cryptography and security community. Therefore, for the purpose of addressing the challenges that face PLS, it is important to bridge the gaps between the various adversary models that are used by the different communities. In the current entry, the PLS mainly focuses on the premise that the eavesdropper is passive, i.e., it does not communicate with the other nodes in order to conceal its presence.

3. Research Directions for System Designs and Optimization Concepts

Mainstream studies on PLS system design can be generally summarized by two main approaches. The first is related to secrecy features, which particularly focuses on the characterization of security capacity and eavesdropping or the more general trade-off between attainable secrecy capacity and confidentiality equivocation based on the concepts of information-theoretic fundamentals. The second approach is related to secure system design, which mainly focuses on the design and optimization of secure transmission strategies through the use of signal processing techniques [37][45][46][47].
Many conventional technologies in physical layer security, without the consideration of secrecy communication, can be reconstructed for secure data transmission under the fundamental framework of PLS. To realize the basic optimization problems and performance metrics of PLS, the main issues and research directions are expected to include three candidates for secure design strategies, as illustrated in Figure 2. These research topics are signal processing techniques, secure resource allocation and secure antenna selection/cooperative nodes. Improvements in secret communication within PLS can be supported by these research topics [30]. The signal processing techniques utilize secure precoding and beamforming to achieve the design of efficient secure communication strategies. The secure precoding and beamforming designs fully utilize the characteristics of multi-node and multi-antenna scenarios, which may form virtual or massive MIMO networks. Resource allocation, which is usually adopted within conventional communication systems, includes the allocation of power and subcarriers. It mainly focuses on resource management systems that utilize multi-faceted wireless resources, including power, time slots and frequency. Cooperative nodes or secure antenna selection, such as jammer selection, relay node selection and user selection, which are widely used in multiple node scenarios, have been fully explored as promising methods to improve the design of PLS. Such techniques attempt to select appropriate cooperative nodes or antennae from a candidate set to enhance the efficiency of secure design strategies.
Figure 2. Secure transmission strategies to improve the security and robustness of physical layer designs.
Despite the unparalleled advantages of the research approaches that are mentioned above, it is worth noting that some drawbacks do exist. To achieve a fine-grained security performance, only using a single research approach may be difficult or even insufficient for future wireless systems. Therefore, the joint use of some of the above techniques that is based on several enabling technologies would be more efficient for ensuring the security of the whole transmission system [34]. Typical examples include joint user scheduling and resource allocation and the trade-off between reliability, security performance, latency, energy consumption, etc. 

3.1. Main Technical Challenges in System Design

Keyless-based PLS techniques are well established transmission strategies that can enhance the performance variation between the main transmission channel and wiretap channels. Unlike conventional transmission methods, the optimization objectives, the conditions of constrained optimization problems and the performance parameters that are associated with SINR-based security techniques are based on the characteristics of wireless channels and information theory secrecy metrics. For secure physical layer design and optimization schemes, the selection of suitable performance metrics is crucial. As illustrated in Figure 3, multi-dimensional security and resource management strategies typically contain secure resource allocation and signal processing methods for cooperative wireless networks. Resource allocation systems are employed by the network operator, which are shown in the top position; simultaneously, the signal processing schemes are operated by transmitters, which are positioned on the bottom. Multiple-antenna techniques have been widely used in wireless networks due to their spatial degrees of freedom, which are offered by multi-service networks. Although secure multi-antenna techniques are widely implemented in various design approaches, they can also be mathematically modeled as optimization problems to find the most favorable transmission solutions. This can be realized and optimized by using information-theoretic security metrics to design secure beamforming and precoding, appropriate antenna or relay node selection and resource allocation. Most of the problems in PLS are caused by non-convex optimization due to the characteristics of quadratic programming functions in the performance metrics. Many researchers have carried out extensive work on formulating and solving these optimization issues in order to obtain the maximum level of security [48]. By considering the complexity of these non-convex constraint functions, several signal processing techniques and optimization methods have been developed to solve the corresponding optimization problems.
Figure 3. An illustration of multi-dimensional security and resource management within a multi-service wireless network.
Although many optimization methods have been developed to cope with the non-convexity of the optimization problems in PLS schemes, there are still many challenges to solve within the existing schemes; specifically, the basic assumptions that are related to system design and channel coding models. Given the limited resources in wireless networks, such as energy and bandwidth, a common problem that is associated with resource management is the adequate exploitation of the resource constraints to attain the conditions of the information-theoretic security metrics. In a transmission system with limited resources, design and optimization schemes must be considered for the resource allocation to the various consumers who use the data over the network so as to achieve a good performance. Several existing works have discussed the fundamental resource management issues within multi-dimensional wireless networks, such as power allocation, subcarrier allocation and joint power and subcarrier allocation [49][50][51][52][53][54][55][56][57][58]. The existing secure resource allocation methods are generally based on communication nodes with three terminals (i.e., the Alice–Bob–Eve model) and lack sufficient investigation into multi-user scenarios and heterogeneous networks. On the other hand, the existing design and optimization of signal processing schemes are commonly based on the parallel fading channel and AWGN, with few investigations on massive MIMO and millimeter wave channels. Therefore, the PLS is still a great potential prospect for 5G wireless communication systems to mitigate all of these challenges. As discussed above, the complexity of networks and channels can affect the corresponding optimization objectives, which results in additional non-convex optimization problems that are hard to address. The major challenge in the existing signal processing methods is the computational cost of implementing optimal secure precoding and beamforming schemes in practice. 

3.2. Optimization in PHY Security: Current Status and Main Issues

The information-theoretic studies on the investigation and characterization of attainable rates of security performance for multi-dimensional secure wireless networks that are presented in [31][59][60][61] have inspired the optimization and system design of many signal processing techniques. From the fundamental viewpoints of optimization objectives, four representative techniques have been adopted in this area: (1) convex optimization; (2) secure beamforming; (3) artificial noise (AN); and (4) zero-forcing (ZF) precoding.

3.2.1. Convex Optimization Techniques

Convex optimization has been widely used in PLS because it can effectively evaluate information-theoretic security metrics to find the most favorable transmission solutions. In PLS, the optimization problem refers to the problem of determining the performance metrics, such as power consumption, secrecy outage probability, secrecy capacity or secrecy rate, etc. In the existing studies on PLS, there are some convex optimization techniques that are widely used to resolve optimization problems, such as in [62]. As mentioned earlier, due to the properties of the logarithmic subtraction functions in the performance metrics, most of the optimization issues in PLS are either non-convex or quadratic programming functions. These non-convex optimizations are adopted to solve problems that are related to non-linear constraints and objective functions. Therefore, non-convex optimization problems can be used in the design of complex global and optimal solutions. Some common examples of non-convex optimization problems include secure power allocation, power minimization and beamforming. In order to achieve optimal solutions, the original non-convex problems need to be converted into convex problems using other functions, such as SDP. Semi-definite programming (SDP) can be used to optimize a linear constraint function under non-negative and linear equality constraints. In general, in order to solve the problems of non-convexity optimization in PLS system design, many optimization methods have been discussed in the literature, such as semi-definite relaxation (SDR), alternating search, dual decomposition, etc. [37][49][63].

3.2.2. Secure Beamforming Techniques

The basic idea of beamforming is to transmit signals effectively in the direction of Bob and Eve, which aims to enhance the signal quality of the main transmission channel and degrade the quality of wiretap channels. Beamforming is a typical signal processing method that has been shown to be a promising means to achieve PLS [47][64][65][66]. The optimization concern of secure beamforming-based designs is to reduce the interference signal at the intended receiver so that the desired recipient can receive the desired QoS via the transmitted signal. For secure beamforming, the optimization problems are neither concave nor convex due to the characteristics of the logarithmic functions in the performance metrics. Due to this, optimization problems are solved using basic numerical calculations in many situations, for example, as in [67][68][69][70][71]. However, the complexity of the numerical methods is computationally prohibitive, which affects the design of secure beamforming within practical applications. In order to reduce the complexity, some low-computational algorithms have been explored in the literature, which facilitate practical application scenarios, such as in [72][73][74][75][76][77]. Therefore, due to their simple design schemes, the developed algorithms are attractive alternatives that attempt to maximize secrecy performance and, at the same time, minimize interference to the secure transmission.

3.2.3. Artificial Noise (AN) Techniques

AN is an effective technique for multi-antenna systems and has been extensively considered within PLS in order to enhance communication security even further. The essence of the AN technique is to generate noise in the null space of the main transmission channel; simultaneously, it is also designed within the range of the wiretap channel [78]. The basic idea is to generate an AN signal that is based on the assumptions of the eavesdropper so that the intended receiver is not affected. This technique aims to degrade the quality of the unintended receiver’s channel by adding a noisy signal, which is used to interrupt their attacking abilities [79][80][81]. On the other hand, it is designed to avoid interference leakage for the intended user. In multi-antenna scenarios, it is possible to adjust the directions of the transmit and AN signal jointly through spatial beamforming by exploiting the degrees of freedom to optimize the performance metrics. In practice, performance of the AN technique depends on the knowledge of the CSI of the eavesdropper at the transmitter. Naturally, knowledge of the full CSI of both the wiretap and legitimate channels is important for practical security design and optimization schemes to attain the optimal performance of secure transmission. When the CSI of the Eve channel is known at the source Alice, the maximum spatial degrees of freedom are applicable, i.e., the beamformer is perfectly designed. However, according to practical scenarios, the CSI of the eavesdropper is usually either imperfect or unknown. Several methods have been proposed in the literature to deal with these assumptions, such as those that are presented in [82][83]. They provide the optimal power allocation and secure beamforming scheme between AN and transmit signals in order to maximize the achievable secrecy performance. The major strength of the AN design is that the offered secrecy performance matches the SNR well because when the SNR increases at the eavesdropper, the power of the noise signal increases, along with the transmitting power.

3.2.4. Zero-Forcing (ZF) Precoding Techniques

Precoding is another signal processing method that is used to deliver signals to the null space of wiretap channels. In ZF precoding schemes, the source Alice that uses multiple transmit antennae is based on the cancelation of unwanted signals at the destination node in multi-user or multi-stream data transmission [84]. Some authors provided an iterative algorithm to control the interference signals that are leaked to unauthorized users. The design of secure precoding that is based on ZF beamforming was discussed in [85] and requires both the complete CSI of the main transmission and wiretap channels at the transmitter and that the number of intended user antennae is greater than that of the eavesdropper. To be more specific, the security performance of ZF precoding becomes very poor when knowledge of the CSI of the eavesdropper at the transmitter is limited, which makes it less applicable.
In summary, the concepts of optimization objectives, constrained conditions and corresponding performance parameters were discussed based on four basic signal processing techniques in order to easily understand design and optimization problems. Although optimization problems that use convex optimization achieve better secrecy performance than the conventional secure beamforming and zero-forcing precoding techniques, convex optimization is more complex and expensive to apply to different services within practical scenarios. To tackle these network complexity and computational costs, service providers should develop solutions to ensure reliability and provide secure resource management to their customers in a robust, intelligent and reliable way. In the next section, researchers discuss the use of AI and ML technologies as key enablers for achieving the above-mentioned system performance metrics.

References

  1. Wang, C.X.; Haider, F.; Gao, X.; You, X.H.; Yang, Y.; Yuan, D.; Aggoune, H.M.; Haas, H.; Fletcher, S.; Hepsaydir, E. Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun. Mag. 2014, 52, 122–130.
  2. Rawat, S.; Chaturvedi, P. A QoS Aware RA Algorithm for OFDM Network. J. Eng. Educ. Transform. 2015, 2349–2473.
  3. Chou, S.F.; Pang, A.C.; Yu, Y.J. Energy-aware 3D unmanned aerial vehicle deployment for network throughput optimization. IEEE Trans. Wirel. Commun. 2019, 19, 563–578.
  4. Hindumathi, V.; Reddy, K.R.L. A Proficient Fair Resource Allocation in the Channel of Multiuser Orthogonal Frequency Division Multiplexing using a Novel Hybrid Bat-Krill Herd Optimization. Wirel. Pers. Commun. 2021, 120, 1449–1473.
  5. Zhou, L.; Wu, D.; Wei, X.; Dong, Z. Seeing Isn’t Believing: QoE Evaluation for Privacy-Aware Users. IEEE JSAC 2019, 37, 1656–1665.
  6. Zorgui, M. Wireless Physical Layer Security: On the Performance Limits of Secret-Key Agreement. Masters’s Thesis, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2015.
  7. Campagna, M.; Chen, L.; Dagdelen, O.; Ding, J.; Fernick, J.; Gisin, N.; Hayford, D.; Jennewein, T.; Lütkenhaus, N.; Mosca, M.; et al. Quantum Safe Cryptography and Security: An Introduction, Benefits, Enablers and Challenges; European Telecommunications Standards Institute: Sophia Antipolis, France, 2015; Volume 8, pp. 1–64.
  8. Shiu, Y.S.; Chang, S.Y.; Wu, H.C.; Huang, S.C.H.; Chen, H.H. Physical layer security in wireless networks: A tutorial. IEEE Wireless Commun. 2011, 18, 66–74.
  9. Mukherjee, A. Physical-layer security in the Internet of Things: Sensing and communication confidentiality under resource constraints. Proc. IEEE 2015, 103, 1747–1761.
  10. Liu, Y.; Chen, H.H.; Wang, L. Secrecy Capacity Analysis of Artificial Noisy MIMO Channels—An Approach based on Ordered Eigenvalues of Wishart Matrices. IEEE Trans. Inf. Forensics Secur. 2016, 99, 1.
  11. Yang, N.; Wang, L.; Geraci, G.; Elkashlan, M.; Yuan, J.; Di Renzo, M. Safeguarding 5G wireless communication networks using physical layer security. IEEE Commun. Mag. 2015, 53, 20–27.
  12. Wang, C.; Wang, H.M. Physical layer security in millimeter wave cellular networks. IEEE Trans. Wirel. Commun. 2016, 15, 5569–5585.
  13. Zou, Y.; Zhu, J.; Wang, X.; Hanzo, L. A survey on wireless security: Technical challenges, recent advances, and future trends. Proc. IEEE 2016, 104, 1727–1765.
  14. Barros, J.; Rodrigues, M.R. Secrecy capacity of wireless channels. In Proceedings of the 2006 IEEE International Symposium on Information Theory, Seattle, WA, USA, 9–16 July 2006; pp. 356–360.
  15. Jeon, H.; Kim, N.; Choi, J.; Lee, H.; Ha, J. Bounds on secrecy capacity over correlated ergodic fading channels at high SNR. IEEE Trans. Inf. Theory 2011, 57, 1975–1983.
  16. Saad, W.; Zhou, X.; Han, Z.; Poor, H.V. On the physical layer security of backscatter wireless systems. IEEE Trans. Wirel. Commun. 2014, 13, 3442–3451.
  17. Basciftci, Y.O.; Gungor, O.; Koksal, C.E.; Ozguner, F. On the secrecy capacity of block fading channels with a hybrid adversary. IEEE Trans. Inf. Theory 2015, 61, 1325–1343.
  18. Zhang, N.; Cheng, N.; Lu, N.; Zhang, X.; Mark, J.W.; Shen, X. Partner selection and incentive mechanism for physical layer security. IEEE Trans. Wirel. Commun. 2015, 14, 4265–4276.
  19. Bashar, S.; Ding, Z. Optimum power allocation against information leakage in wireless network. In Proceedings of the GLOBECOM 2009—2009 IEEE Global Telecommunications Conference, Honolulu, HI, USA, 30 November–4 December 2009; pp. 1–6.
  20. Wang, X.; Tao, M.; Mo, J.; Xu, Y. Power and subcarrier allocation for physical-layer security in OFDMA-based broadband wireless networks. IEEE Trans. Inf. Forensics Secur. 2011, 6, 693–702.
  21. Du, Y.N.; Han, S.; Xu, S.; Li, C. Improving secrecy under high correlation via discriminatory channel estimation. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–6.
  22. Hong, Y.W.P.; Lan, P.C.; Kuo, C.C.J. Signal Processing Approaches to Secure Physical Layer Communications in Multi-Antenna Wireless Systems; Springer: New York, NY, USA, 2014.
  23. Liu, R.; Trappe, W. Securing Wireless Communications at the Physical Layer; Springer: New York, NY, USA, 2010.
  24. Baldi, M.; Tomasin, S. (Eds.) Physical and Data-Link Security Techniques for Future Communication Systems; Springer International: Cham, Switzerland, 2016.
  25. Liu, Y.; Chen, H.H.; Wang, L. Physical layer security for next generation wireless networks: Theories, technologies, and challenges. IEEE Commun. Surv. Tutor. 2017, 19, 347–376.
  26. Sharma, R.K.; Rawat, D.B. Advances on security threats and countermeasures for cognitive radio networks: A survey. IEEE Commun. Surv. Tutor. 2014, 17, 1023–1043.
  27. Sanenga, A.; Mapunda, G.A.; Jacob, T.M.L.; Marata, L.; Basutli, B.; Chuma, J.M. An overview of key technologies in physical layer security. Entropy 2020, 22, 1261.
  28. Mukherjee, A.; Fakoorian, S.A.A.; Huang, J.; Swindlehurst, A.L. Principles of physical layer security in multiuser wireless networks: A survey. IEEE Commun. Surv. Tutor. 2014, 16, 1550–1573.
  29. Chen, X.; Ng, D.W.; Gerstacker, W.H.; Chen, H.H. A Survey on Multiple-Antenna Techniques for Physical Layer Security. IEEE Commun. Surv. Tutor. 2017, 19, 1027–1053.
  30. Wang, D.; Bai, B.; Zhao, W.; Han, Z. A Survey of Optimization Approaches for Wireless Physical Layer Security. IEEE Commun. Surv. Tutor. 2019, 21, 1878–1911.
  31. Hamamreh, J.M.; Furqan, H.M.; Arslan, H. Classifications and applications of physical layer security techniques for confidentiality: A comprehensive survey. IEEE Commun. Surv. Tutor. 2018, 21, 1773–1828.
  32. Trappe, W. The challenges facing physical layer security. IEEE Commun. Mag. 2015, 53, 16–20.
  33. Mao, Q.; Hu, F.; Hao, Q. Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2018, 20, 2595–2621.
  34. Zhao, L.; Zhang, X.; Chen, J.; Zhou, L. Physical layer security in the age of artificial intelligence and edge computing. IEEE Wirel. Commun. 2020, 27, 174–180.
  35. Xiao, L.; Sheng, G.; Liu, S.; Dai, H.; Peng, M.; Song, J. Deep Reinforcement Learning-Enabled Secure Visible Light Communication against Eavesdropping. IEEE Trans. Commun. 2019, 67, 6994–7005.
  36. Gui, L.; He, B.; Zhou, X.; Yu, C.; Shu, F.; Li, J. Learning-Based Wireless Powered Secure Transmission. IEEE Wirel. Commun. Lett. 2019, 8, 600–603.
  37. Wyner, A.D. The wire-tap channel. Bell Syst. Tech. J. 1975, 54, 1355–1387.
  38. Li, H.; Wang, X. Physical-Layer Security Enhancement in Wireless Communication Systems. Master’s Thesis, the University of Western Ontario London, Ontario, Canada, 2013.
  39. Zheng, G.; Choo, L.C.; Wong, K.K. Optimal cooperative jamming to enhance physical layer security using relays. IEEE Trans. Signal Process. 2011, 59, 1317–1322.
  40. He, X.; Yener, A. On the Role of Feedback in Two-Way Secure Communication. In Proceedings of the 2008 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 26–29 October 2008; pp. 1093–1097.
  41. He, X.; Yener, A. Providing Secrecy When the Eavesdropper Channel is Arbitrarily Varying: A Case for Multiple Antennas. In Proceedings of the 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, 29 September–1 October 2010; pp. 1228–1235.
  42. Liao, W.C.; Chang, T.H.; Ma, W.K.; Chi, C.Y. QoS-based transmit beamforming in the presence of eavesdroppers: An optimized artificial-noise aided approach. IEEE Trans. Signal Process. 2011, 59, 1202–1216.
  43. Reboredo, H.; Xavier, J.; Rodrigues, M.R.D. Filter design with secrecy constraints: The MIMO Gaussian wiretap channel. IEEE Trans. Signal Process. 2013, 61, 3799–3814.
  44. Li, M.; Kundu, S.; Pados, D.A.; Batalama, S.N. Waveform design for secure SISO transmissions and multicasting. IEEE J. Sel. Areas Commun. 2013, 31, 1864–1874.
  45. Wang, H.M.; Xia, X.-G. Enhancing wireless secrecy via cooperation: Signal design and optimization. IEEE Commun. Mag. 2015, 53, 47–53.
  46. Hong, Y.W.P.; Lan, P.C.; Kuo, C.C.J. Enhancing physical-layer secrecy in multiantenna wireless systems: An overview of signal processing approaches. IEEE Signal Process. Mag. 2013, 30, 29–40.
  47. Yener, A.; Ulukus, S. Wireless physical-layer security: Lessons learned from information theory. Proc. IEEE 2015, 103, 1814–1825.
  48. Boyd, S.; Boyd, S.P.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK, 2004.
  49. Bai, B.; Chen, W.; Cao, Z. Outage optimal subcarrier allocation for downlink secure OFDMA systems. In Proceedings of the IEEE Global Communications Conference (GLOBECOM) Workshops, Austin, TX, USA, 8–12 December 2014; pp. 1320–1325.
  50. Jindal, A.; Bose, R. Resource allocation for secure multicarrier AF relay system under total power constraint. IEEE Commun. Lett. 2014, 19, 231–234.
  51. Chen, J.; Chen, X.; Gerstacker, W.H.; Ng, D.W.K. Resource allocation for a massive MIMO relay aided secure communication. IEEE Trans. Inf. Forensics Secur. 2016, 11, 1700–1711.
  52. Ng, D.W.K.; Lo, E.S.; Schober, R. Secure resource allocation and scheduling for OFDMA decode-and-forward relay networks. IEEE Trans. Wirel. Commun. 2011, 10, 3528–3540.
  53. Huang, J.; Swindlehurst, A.L. Robust secure transmission in MISO channels based on worst-case optimization. IEEE Trans. Signal Process. 2012, 60, 1696–1707.
  54. Jeong, C.; Kim, I.M. Optimal power allocation for secure multicarrier relay systems. IEEE Trans. Signal Process. 2011, 59, 5428–5442.
  55. Tsai, S.H.; Poor, H.V. Power allocation for artificial-noise secure MIMO precoding systems. IEEE Trans. Signal Process. 2014, 62, 3479–3493.
  56. Wang, L.; Elkashlan, M.; Huang, J.; Tran, N.H.; Duong, T.Q. Secure transmission with optimal power allocation in untrusted relay networks. IEEE Wireless Commun. Lett. 2014, 3, 289–292.
  57. Benfarah, A.; Tomasin, S.; Laurenti, N. Power allocation in multiuser parallel Gaussian broadcast channels with common and confidential messages. IEEE Trans. Commun. 2016, 64, 2326–2339.
  58. Zheng, T.X.; Wang, H.M. Optimal power allocation for artificial noise under imperfect CSI against spatially random eavesdroppers. IEEE Trans. Veh. Technol. 2016, 65, 8812–8817.
  59. Oggier, F.; Hassibi, B. The secrecy capacity of the MIMO wiretap channel. IEEE Trans. Inf. Theory 2011, 57, 4961–4972.
  60. Khisti, A.; Wornell, G.W. Secure transmission with multiple antennas I: The MISOME wiretap channel. IEEE Trans. Inf. Theory 2010, 56, 3088–3104.
  61. Khisti, A.; Wornell, G.W. Secure transmission with multiple antennas—Part II the MIMOME wiretap channel. IEEE Trans. Inf. Theory 2010, 56, 5515–5532.
  62. Sheng, Z.; Tuan, H.D.; Sheng, Z.; Tuan, H.D.; Duong, T.Q.; Vincent Poor, H. Beamforming Optimization for Physical Layer Security in MISO Wireless Networks. IEEE Trans. Signal Process. 2018, 66, 3710–3723.
  63. Palomar, D.P.; Chiang, M. A tutorial on decomposition methods for network utility maximization. IEEE J. Sel. Areas Commun. 2006, 24, 1439–1451.
  64. Yaacoub, E.; Al-Husseini, M. Achieving physical layer security with massive MIMO beamforming. In Proceedings of the 2017 11th European Conference Antennas Propagation, EUCAP 2017, Paris, France, 19–24 March 2017; pp. 1753–1757.
  65. Shafiee, S.; Liu, N.; Ulukus, S. Towards the secrecy capacity of the Gaussian MIMO wire-tap channel: The 2-2-1 channel. IEEE Transactions on Information Theory 2009, 55, 4033–4039.
  66. Björnson, E.; Bengtsson, M.; Ottersten, B. Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure . IEEE Signal Process. Mag. 2014, 31, 142–148.
  67. Mo, J.; Tao, M.; Liu, Y.; Wang, R. Secure beamforming for MIMO two-way communications with an untrusted relay. IEEE Trans. Signal Process. 2014, 62, 2185–2199.
  68. Liu, Z.; Chen, C.; Bai, L.; Xiang, H.; Choi, J. Secure beamforming via amplify-and-forward relays in wireless networks with multiple eavesdroppers. In Proceedings of the IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, 10–14 June 2014; pp. 4698–4703.
  69. Jeong, C.; Kim, I.M.; Kim, D.I. Joint secure beamforming design at the source and the relay for an amplify-and-forward MIMO untrusted relay system. IEEE Trans. Signal Process. 2012, 60, 310–325.
  70. Zhao, P.; Zhang, M.; Yu, H.; Luo, H.; Chen, W. Robust beamforming design for sum secrecy rate optimization in MU-MISO networks. IEEE Trans. Inf. Forensics Secur. 2015, 10, 1812–1823.
  71. Shi, Q.; Xu, W.; Wu, J.; Song, E.; Wang, Y. Secure beamforming for MIMO broadcasting with wireless information and power transfer. IEEE Trans. Wirel. Commun. 2015, 14, 2841–2853.
  72. Wang, X.; Wang, K.; Zhang, X.D. Secure relay beamforming with imperfect channel side information. IEEE Trans. Veh. Technol. 2013, 62, 2140–2155.
  73. Zheng, G.; Arapoglou, P.D.; Ottersten, B. Physical layer security in multibeam satellite systems. IEEE Trans. Wirel. Commun. 2012, 11, 852–863.
  74. Nghia, N.T.; Tuan, H.D.; Duong, T.Q.; Poor, H.V. MIMO beamforming for secure and energy-efficient wireless communication. IEEE Signal Process. Lett. 2017, 24, 236–239.
  75. Nasir, A.A.; Tuan, H.D.; Duong, T.Q.; Poor, H.V. Secure and energy-efficient beamforming for simultaneous information and energy transfer. IEEE Trans. Wirel. Commun. 2017, 16, 7523–7537.
  76. Zhao, N.; Yu, F.R.; Sun, H. Adaptive energy-efficient power allocation in green interference-alignment-based wireless networks. IEEE Trans. Veh. Technol. 2014, 64, 4268–4281.
  77. Pu, W.; Xiao, J.; Zhang, T.; Luo, Z.Q. Overcoming dof limitation in robust beamforming: A penalized inequality-constrained approach. arXiv 2019, arXiv:1910.03365.
  78. Liu, S.; Hong, Y.; Viterbo, E. Practical secrecy using artificial noise. IEEE Commun. Lett. 2013, 17, 1483–1486.
  79. Li, Q.; Ma, W.K. Spatially selective artificial-noise aided transmit optimization for MISO multi-eves secrecy rate maximization. IEEE Trans. Signal Process. 2013, 61, 2704–2717.
  80. Lin, P.H.; Lai, S.H.; Lin, S.C.; Su, H.J. On secrecy rate of the generalized artificial-noise assisted secure beamforming for wiretap channels. IEEE J. Sel. Areas Commun. 2013, 31, 1728–1740.
  81. Yang, N.; Yan, S.; Yuan, J.; Malaney, R.; Subramanian, R.; Land, I. Artificial noise: Transmission optimization in multi-input single-output wiretap channels. IEEE Trans. Commun. 2015, 63, 1771–1783.
  82. Wang, B.; Mu, P.; Li, Z. Secrecy rate maximization with artificial noise-aided beamforming for MISO wiretap channels under secrecy outage constraint. IEEE Commun. Lett. 2015, 19, 18–21.
  83. Tang, Y.; Xiong, J.; Ma, D.; Zhang, X. Robust artificial noise aided transmit design for MISO wiretap channels with channel uncertainty. IEEE Commun. Lett. 2013, 17, 2096–2099.
  84. Reboredo, H.; Prabhu, V.; Rodrigues, M.R.; Xavier, J. Filter design with secrecy constraints: The multiple-input multiple-output Gaussian wiretap channel with zero forcing receive filters. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22–27 May 2011; pp. 3440–3443.
  85. Rezki, Z.; Alouini, M.S. On the finite-SNR diversity-multiplexing tradeoff of zero-forcing transmit scheme under secrecy constraint. In Proceedings of the IEEE International Conference Communication Workshops (ICC Workshops), Kyoto, Japan, 5–9 June 2011; pp. 1–5.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Artificial Intelligence
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mulugeta Kassaw Tefera
,
,
Zengwang Jin
,
Shengbing Zhang
View Times: 583
Revisions: 3 times (View History)
Update Date: 23 May 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback