Secure Symbol-Level Precoding in MU-MISO Wiretap Systems

Liu, Rang; Li, Ming; Liu, Qian; Swindlehurst, A. Lee

doi:10.1109/tifs.2020.2988127

Cited by 19 publications

(10 citation statements)

References 44 publications

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“…Since the Lagrangian dual function (32) has very simple constraints, it can be efficiently solved using standard iterative search algorithms. Given the high complexity required to calculate the first and second order partial derivatives of (32a), we adopt the derivative-free Hooke-Jeeves Pattern Search algorithm [38], which is a popular local search algorithm whose convergence has been proven in [39]. The details are omitted due to space limitations.…”

Section: B Bcd Algorithmmentioning

confidence: 99%

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Liu,

Li,

Liu

et al. 2021

Preprint

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Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multioutput (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multiuser communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbollevel precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.

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Section: B Bcd Algorithmmentioning

confidence: 99%

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Liu,

Li,

Liu

et al. 2021

Preprint

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“…Fig. 2 shows the TTP performance versus the SEP requirement ε for (N, K) = (32,30) and for various QAM constellation sizes. It is seen that the SLP schemes, both SLP and Semi-ZF SLP, outperform OLB and ZF.…”

Section: Slp For Ttp Minimizationmentioning

confidence: 99%

“…Later, Alodeh et al extended this interference manipulating concept to quadrature amplitude modulation (QAM) constellations [24] and many subsequent works followed this adaptation [20,[25][26][27]. Lately, SLP has been applied to a number of scenarios, such as MIMO orthogonal frequency division multiplexing [28,29], physical-layer security [30,31] and intelligent reflecting surface [32].…”

Section: Introductionmentioning

confidence: 99%

Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

Liu,

Shao,

et al. 2021

Preprint

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Symbol-level precoding (SLP) has recently emerged as a new paradigm for physical-layer transmit precoding in multiuser multi-input-multi-output (MIMO) channels. It exploits the underlying symbol constellation structure, which the conventional paradigm of linear precoding does not, to enhance symbol-level performance such as symbol error probability (SEP). It allows the precoder to take a more general form than linear precoding. This paper aims to better understand the relationships between SLP and linear precoding, subsequent design implications, and further connections beyond the existing SLP scope. Our study is built on a basic signal observation, namely, that SLP can be equivalently represented by a zero-forcing (ZF) linear precoding scheme augmented with some appropriately chosen symbol-dependent perturbation terms, and that some extended form of SLP is equivalent to a vector perturbation (VP) nonlinear precoding scheme augmented with the above-noted perturbation terms. We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees and under quadrature amplitude modulation (QAM) constellations; and ii) derive heuristic but computationally cheaper SLP designs. We also touch on the analysis side by showing that, under the total power minimization criterion, the basic ZF scheme is a near-optimal SLP scheme when the QAM order is very high-which gives a vital implication that SLP is more useful for lower-order QAM cases. Numerical results further indicate the merits and limitations of the different SLP designs derived from the perturbed ZF and VP interpretations.

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“…Although not originally conceived as a PLS method, SLP is more secure than block-level precoding, like zero-forcing (ZF) [22], as the precoder is redesigned for each symbol period (SP). In [23], [24], secure SLP precoding schemes were proposed in the context of a multiple-input single-output (MISO) wiretap channel while considering only a single-antenna Eve. In [25], the authors proposed to exploit the statistical characteristics of the received signal at Eve in order to improve its detection performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures

Mayouche

Martins

Tsinos

et al. 2021

IEEE Open J. Veh. Technol.

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In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve), who is a legit user trying to eavesdrop other users. In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. The proposed ML frameworks allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding runtime, security, and power consumption. Simulation results validate both the MLbased eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users.

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Secure Symbol-Level Precoding in MU-MISO Wiretap Systems

Cited by 19 publications

References 44 publications

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures

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