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 -- 4714 2022-05-19 11:08:09 |
2 format correct -15 word(s) 4699 2022-05-20 04:23:39 | |
3 format correct -6 word(s) 4693 2022-05-23 08:06:01 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
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 18 June 2024).
Tefera MK,  , Jin Z, Zhang S. AI Enabling Technologies in Physical Layer Security. Encyclopedia. Available at: https://encyclopedia.pub/entry/23116. Accessed June 18, 2024.
Tefera, Mulugeta Kassaw, , Zengwang Jin, Shengbing Zhang. "AI Enabling Technologies in Physical Layer Security" Encyclopedia, https://encyclopedia.pub/entry/23116 (accessed June 18, 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

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].

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
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , ,
View Times: 603
Revisions: 3 times (View History)
Update Date: 23 May 2022
1000/1000
Video Production Service