Browsing by Subject "Physical layer security"

Now showing 1 - 14 of 14

Open Access
Artificial-noise-aided secure transmission over finite-ınput ıntersymbol ınterference channels
(IEEE, 2018-06) Hanoğlu, Serdar; Aghdam, Sina Rezaei; Duman, Tolga M.
We propose an artificial noise (AN) injection strategy for securing communication over finite-input intersymbol interference (lSI) channels. The technique relies on injection of colored noise whose power spectral density has the least match with the spectrum of the main channel in a certain sense. By evaluation of an achievable secrecy rate, we demonstrate that the proposed AN injection based solution results in a considerable improvement over the existing approaches, especially when the eavesdropper works at high signal-to-noise ratios (SNRs).
Open Access
Deep neural network based precoding for wiretap channels with finite alphabet inputs
(IEEE, 2021-04-28) Gümüş, Mücahit; Duman, Tolga M.
We consider secure transmission over multi-input multi-output multi-antenna eavesdropper (MIMOME) wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits the function approximation abilities of deep neural networks (DNNs) for solving the required power allocation problem. It is demonstrated that the DNN learns the optimal power allocation without any performance degradation compared to the conventional technique with a significant reduction in complexity.
Open Access
Deep-learning for communication systems: new channel estimation, equalization, and secure transmission solutions
(2023-08) Gümüş, Mücahit
Traditional communication system design takes a model-based approach that aims to optimize relevant performance metrics using somewhat simple and tractable channel and signal models. For instance, channel codes are designed for simple additive white Gaussian or fading channel models, channel equalization algorithms are based on mathematical models for inter-symbol interference (ISI), and channel estimation techniques are developed with the underlying channel statistics and characterizations in mind. Through utilizing superior mathematical models and expert knowledge in signal processing and information theory, the model-based approach has been highly successful and has enabled development of many communication systems until now. On the other hand, beyond 5G wireless communication systems will further exploit the massive number of antennas, higher bandwidths, and more advanced multiple access technologies. As communication systems become more and more complicated, it is becoming increasingly important to go beyond the limits of the model-based approach. Noting that there have been tremendous advancements in learning from data over the past decades, a major research question is whether machine learning based approaches can be used to develop new communication technologies. With the above motivation, this thesis deals with the development of deep neural network (DNN) solutions to address various challenges in wireless communications. We ﬁrst consider orthogonal frequency division multiplexing (OFDM) over rapidly time-varying multipath channels, for which the performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier in-terference (ICI). We focus on improving the overall system performance by designing DNN architectures for both channel estimation and data demodulation. In addition, we study OFDM over frequency-selective channels without cyclic preﬁx insertion in an eﬀort to improve the overall throughputs. Speciﬁcally, we design a recurrent neu-ral network to mitigate the eﬀects of ISI and ICI for improved symbol detection. Furthermore, we explore secure transmission over multi-input multi-output multi-antenna eavesdropper wiretap channels with ﬁnite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which beneﬁts from the generalized singular value decomposition to obtain independent streams and exploits function approximation abilities of DNNs for solving the required power allocation problem. We also propose a DNN technique to jointly optimize the data precoder and the power allocation for artiﬁcial noise. We use extensive numerical examples and computational complexity analyses to demonstrate the eﬀectiveness of the proposed solutions.
Open Access
Joint precoder and artificial noise design for MIMO wiretap channels with finite-alphabet inputs based on the cut-off rate
(Institute of Electrical and Electronics Engineers Inc., 2017) Aghdam, S. R.; Duman, T. M.
We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the precoder matrix and an exhaustive search over the power levels allocated to the AN. We also propose algorithms to reduce the complexities of direct ergodic secrecy rate maximization by: 1) maximizing a cut-off rate-based approximation for the ergodic secrecy rate, simplifying the mutual information expression, which lacks a closed-form and 2) diagonalizing the channels toward the legitimate receiver and the eavesdropper, which allows for employing a per-group precoding-based technique. Our numerical results reveal that jointly optimizing the precoder and the AN outperforms the existing solutions in the literature, which rely on the precoder optimization only. We also demonstrate that the proposed low complexity alternatives result in a small loss in performance while offering a significant reduction in computational complexity.
Open Access
Low complexity precoding for MIMOME wiretap channels based on cut-off rate
(IEEE, 2016) Aghdam, Sina Rezaei; Duman, Tolga M.
We propose a low complexity transmit signal design scheme for achieving information-theoretic secrecy over a MIMO wiretap channel driven by finite-alphabet inputs. We assume that the transmitter has perfect channel state information (CSI) of the main channel and also knows the statistics of the eavesdropper's channel. The proposed transmission scheme relies on jointly optimizing the precoder matrix and the artificial noise so as to maximize the achievable secrecy rates. In order to lower the computational complexity associated with the transmit signal design, we employ a design metric using the cut-off rate instead of the mutual information. We formulate a gradient-descent based optimization algorithm and demonstrate via extensive numerical examples that the proposed signal design scheme can yield an enhanced secrecy performance compared with the existing solutions in spite of its relatively lower computational complexity. The impacts of the modulation order as well as the number of antennas at the transmitter and receiver ends on the achievable secrecy rates are also investigated.
Open Access
On secrecy rate analysis of spatial modulation and space shift keying
(IEEE, 2015) Aghdam, Sina Rezaei; Duman, Tolga M.; Di Renzo, M.
Spatial modulation (SM) and space shift keying (SSK) represent transmission methods for low-complexity implementation of multiple-input multiple-output (MIMO) wireless systems in which antenna indices are employed for data transmission. In this paper, we focus our attention on the secrecy behavior of SSK and SM. Using an information-theoretic framework, we derive expressions for the mutual information and consequently compute achievable secrecy rates for SSK and SM via numerical evaluations. We also characterize the secrecy behavior of SSK by showing the effects of increasing the number of antennas at the transmitter as well as the number of antennas at the legitimate receiver and the eavesdropper. We further evaluate the secrecy rates achieved by SM with different sizes of the underlying signal constellation and compare the secrecy performance of this scheme with those of general MIMO and SIMO systems. The proposed framework unveils that SM is capable of achieving higher secrecy rates than the conventional single-antenna transmission schemes. However, it underperfoms compared to a general MIMO system in terms of the achievable secrecy rates.
Open Access
Optimal parameter encoding strategies for estimation theoretic secure communications
(2019-12) Göken, Çağrı
Physical layer security has gained a renewed interest with the advances in modern wireless communication technologies. In estimation theoretic security, secrecy levels are measured via estimation theoretic tools and metrics, such as mean-squared error (MSE), where the objective is to perform accurate estimation of the parameter at the intended receiver while keeping the estimation error at the eavesdropper above a certain level. This framework proves useful both for analyzing the achievable performance under security constraints in parameter estimation problems, and for designing low-complexity, practical methods to enhance security in communication systems. In this dissertation, we investigate optimal deterministic encoding of random scalar and vector parameters in the presence of an eavesdropper, who is unaware of the encoding operation. We also analyze optimal stochastic encoding of a random parameter under secrecy constraints in a Gaussian wiretap channel model, where the eavesdropper is aware of the encoding strategy at the transmitter. In addition, we perform optimal parameter design for secure broadcast of a parameter to multiple receivers with fixed estimators. First, optimal deterministic encoding of a scalar parameter is investigated in the presence of an eavesdropper. The aim is to minimize the expectation of the conditional Cram´er-Rao bound (ECRB) at the intended receiver while keeping the MSE at the eavesdropper above a certain threshold. The eavesdropper is modeled to employ the linear minimum mean-squared error (LMMSE) estimator based on the encoded version of the parameter. First, the optimal encoding function is derived in the absence of secrecy constraints for any given prior distribution on the parameter. Next, an optimization problem is formulated under a secrecy constraint and various solution approaches are proposed. Also, theoretical results on the form of the optimal encoding function are provided. Furthermore, a robust parameter encoding approach is developed. In this case, the objective is to maximize the worst-case Fisher information of the parameter at the intended receiver while keeping the MSE at the eavesdropper above a certain level. The optimal encoding function is derived when there exist no secrecy constraints. Next, to obtain the solution of the problem in the presence of the secrecy constraint, the form of the encoding function that maximizes the MSE at the eavesdropper is explicitly derived for any given level of worst-case Fisher information. Then, based on this result, a low-complexity algorithm is provided to calculate the optimal encoding function for the given secrecy constraint. Numerical examples are presented to illustrate the theoretical results for both the ECRB and worst-case Fisher information based designs. Second, optimal deterministic encoding of a vector parameter is investigated in the presence of an eavesdropper. The objective is to minimize the ECRB at the intended receiver while satisfying an individual secrecy constraint on the MSE of estimating each parameter at the eavesdropper. The eavesdropper is modeled to employ the LMMSE estimator based on the noisy observation of the encoded parameter without being aware of encoding. First, the problem is formulated as a constrained optimization problem in the space of vector-valued functions. Then, two practical solution strategies are developed based on nonlinear individual encoding and affine joint encoding of parameters. Theoretical results on the solutions of the proposed strategies are provided for various scenarios on channel conditions and parameter distributions. Finally, numerical examples are presented to illustrate the performance of the proposed solution approaches. Third, estimation theoretic secure transmission of a scalar random parameter is investigated in the presence of an eavesdropper. The aim is to minimize the estimation error at the receiver under a secrecy constraint at the eavesdropper; or, alternatively, to maximize the estimation error at the eavesdropper for a given estimation accuracy limit at the receiver. In the considered setting, the encoder at the transmitter is allowed to use a randomized mapping between two one-to-one and continuous functions and the eavesdropper is fully aware of the encoding strategy at the transmitter. For small numbers of observations, both the eavesdropper and the receiver are modeled to employ LMMSE estimators, and for large numbers of observations, the ECRB metric is employed for both the receiver and the eavesdropper. Optimization problems are formulated and various theoretical results are provided in order to obtain the optimal solutions and to analyze the effects of encoder randomization. In addition, numerical examples are presented to corroborate the theoretical results. It is observed that stochastic encoding can bring significant performance gains for estimation theoretic secrecy problems. Finally, estimation theoretic secure broadcast of a random parameter is investigated. In the considered setting, each receiver device employs a fixed estimator and carries a certain security risk such that its decision can be available to a malicious third party with a certain probability. The encoder at the transmitter is allowed to use a random mapping to minimize the weighted sum of the conditional Bayes risks of the estimators under secrecy and average power constraints. After formulating the optimal parameter design problem, it is shown that the optimization problem can be solved individually for each parameter value and the optimal mapping at the transmitter involves a randomization among at most three different signal levels. Sufficient conditions for improvability and nonimprovability of the deterministic design via stochastic encoding are obtained. Numerical examples are provided to corroborate the theoretical results.
Open Access
An overview of physical layer security with finite-alphabet signaling
(Institute of Electrical and Electronics Engineers Inc., 2019) Aghdam, Sina Rezaei; Nooraiepour, A.; Duman, Tolga M.
Providing secure communications over the physical layer with the objective of achieving secrecy without requiring a secret key has been receiving growing attention within the past decade. The vast majority of the existing studies in the area of physical layer security focus exclusively on the scenarios where the channel inputs are Gaussian distributed. However, in practice, the signals employed for transmission are drawn from discrete signal constellations such as phase shift keying and quadrature amplitude modulation. Hence, understanding the impact of the finite-alphabet input constraints and designing secure transmission schemes under this assumption is a mandatory step toward a practical implementation of physical layer security. With this motivation, this paper reviews recent developments on physical layer security with finite-alphabet inputs. We explore transmit signal design algorithms for single-antenna as well as multi-antenna wiretap channels under different assumptions on the channel state information at the transmitter. Moreover, we present a review of the recent results on secure transmission with discrete signaling for various scenarios including multi-carrier transmission systems, broadcast channels with confidential messages, cognitive multiple access and relay networks. Throughout the article, we stress the important behavioral differences of discrete versus Gaussian inputs in the context of the physical layer security. We also present an overview of practical code construction over Gaussian and fading wiretap channels, and discuss some open problems and directions for future research.
Open Access
Physical layer security for space shift keying transmission with precoding
(Institute of Electrical and Electronics Engineers Inc., 2016) Aghdam, S. R.; Duman, T. M.
We investigate the effect of transmitter side channel state information on the achievable secrecy rates of space shift keying. Through derivation of the gradient of the secrecy rate, we formulate an iterative algorithm to maximize the achievable secrecy rates. We also introduce two lower complexity signal design algorithms for different scenarios based on the number of antennas at the eavesdropper. Our results illustrate the effectiveness of the proposed precoding techniques in attaining positive secrecy rates over a wide range of signal to noise ratios. © 2016 IEEE.
Open Access
Physical layer security over frequency selective fading channels
(2016-01) Ayhan, Kadir
The inherent open nature of the transmission medium makes security a challenging issue in wireless networks. Physical layer security, which is an alternative or a complement to the cryptographic approaches, exploits the differences between the physical properties of different channels in order to provide secrecy. The idea is to ensure that the received signal at an eavesdropper is degraded compared to that of the legitimate receiver in some sense which guarantees that the confidential messages cannot be recovered by an unintended receiver. Over the last decade, various researchers have studied fundamental limits of physical layer security under different wiretap channel models, including Gaussian and fading channels, and with different assumptions on the transmitter’s knowledge on the channel state information. In this thesis, we study physical layer security over frequency selective fading channels modelling certain wireless links. Specifically, we investigate optimal and suboptimal power allocation schemes across frequencies with perfect and partial channel state information at the transmitter with the objective of providing secrecy. We demonstrate that frequency selectivity allows for positive secrecy rates even though the eavesdropper’s channel is not a degraded version of the desired user’s channel. We also analyse the impact of user mobility and the resulting time variations in the wireless medium on the achievable secrecy rates. Furthermore, we consider quantized channel state information at the transmitter and evaluate the secrecy rate loss due to limited feedback from the legitimate receiver to the transmitter. Our results reveal that the partial channel state information at the transmitter can still be helpful in providing positive secrecy rates.
Open Access
Queue management for two-user cognitive radio with delay-constrained primary user
(Elsevier, 2018) Mehr, K. A.; Niya, J. M.; Akar, Nail
In this paper, two novel Queue Management Policies (QMP) are proposed for Quality of Service (QoS) enhancement of a two-user Cognitive Radio Network (CRN) comprising a Primary User (PU) and Secondary User (SU), the latter having non-causal information on PU's messages (or packets). Specifically, we aim to maximize the throughput of the SU while satisfying the delay criterion of the Primary User (PU). The first proposed QMP is a hybrid interweave/overlay scheme where all the SU's resources are devoted to the transmission of PU's packets. The second proposed QMP adaptively uses all or some of the SU's resources towards the transmission of the PU's packet, this decision being based on the packet's delay experienced in the PU queue. For this adaptive QMP, a novel multi-regime Markov fluid queue model is proposed via which closed-form expressions are derived and validated for the exact delay distribution for Poisson PU traffic and exponentially distributed packet lengths. Using this analytical tool, we optimally tune the parameters of the adaptive QMP and we show through numerical examples that it consistently outperforms the hybrid interweave/overlay model as well as two other conventional schemes in terms of SU throughput. We also show that the performance improvement attainable by the proposed QMP depends on the intensity of PU traffic as well as the channel conditions. A heuristic suboptimal parameter tuning scheme is also proposed with lesser computational complexity.
Open Access
Randomized convolutional and concatenated codes for the wiretap channel
(2016-10) Nooraiepour, Alireza
Wireless networks are vulnerable to various kinds of attacks such as eavesdropping because of their open nature. As a result, security is one of the most important challenges that needs to be addressed for such networks. To address this issue, we utilize information theoretic secrecy approach and develop randomized channel coding techniques akin to the approach proposed by Wyner as a general method for confusing the eavesdropper while making sure that the legitimate receiver is able to recover the transmitted message. We first study the application of convolutional codes to the randomized encoding scheme. We argue how dual of a code plays a major role in this construction and obtain dual of a convolutional code in a systematic manner. We propose optimal and sub-optimal decoders for additive white Gaussian noise (AWGN) and binary symmetric channels and obtain bounds on the decoder performance extending the existing lower and upper bounds on the error rates of coded systems with maximum likelihood (ML) decoding. Furthermore, we apply list decoding to improve the performance of the sub-optimal decoders. We demonstrate via several examples that security gaps achieved by the randomized convolutional codes compete favorably with some of the existing coding methods. In order to improve the security gap hence the system performance further, we develop concatenated coding approaches applied to the randomized encoding scheme as well. These include serial and parallel concatenated convolutional codes and serial concatenation of a low density generator matrix code with a convolutional code. For all of these solutions low-complexity iterative decoders are proposed and their performance in the wiretap channel is evaluated in terms of the security gap. Numerical examples show that for certain levels of confusion at the eavesdropper, randomized serially concatenated convolutional codes oer the best performance.
Open Access
Secrecy rates of finite-input intersymbol interference channels
(2016-10) Hanoğlu, Serdar
Due to the broadcast nature of the communication medium, security is a critical problem in wireless networks. Securing the transmission at the physical layer is a promising alternative or complement to the conventional higher level techniques such as encryption. During the past decade, various studies have been carried out which investigate such possibilities in providing secrecy for different scenarios. On the other hand, secrecy over intersymbol interference (ISI) channels has not received significant attention, and much work remains to be done. With this motivation, we focus on secrecy rates of finite-input ISI channels for both fixed and fading channel coefficients. We argue that the secrecy rates of ISI channels can be computed by the forward recursion of the BCJR algorithm. Moreover, by utilizing Markov input distributions for transmission over the ISI channels, achievable secrecy rates can be increased. However, the existing iterative method in the literature to obtain the optimal Markov input distribution is computationally complex as many BCJR recursions are needed. Thus, we propose an alternative solution by introducing a codebook based approach. Particularly, among the existing Markov input distributions in the codebook, we propose to select the one which spectrally matches the main channel. Our numerical results reveal that the proposed low complexity approach undergoes a minimal loss with respect to the existing iterative algorithm while offering a considerably reduced complexity. We also propose injection of artificial noise (AN) to increase the secrecy rates, and show that this is especially useful for moderate and high signal to noise ratio (SNR) values where the use of Markov input distributions is not beneficial. We inject AN to frequencies where the eavesdropper's channel is better than the main channel. We show that this approach significantly increases the secrecy rates compared to the existing methods. Furthermore, we consider the effect of channel state information (CSI) on the secrecy rates, and demonstrate that availability of eavesdropper's CSI at the transmitter is highly beneficial in terms of the achievable secrecy rates.
Open Access
Secure multi-antenna transmission with finite-alphabet signaling
(2017-12) Aghdam, Sina Rezaei
With the ever-growing demand for services that rely on transmission over wireless networks, a challenging issue is the security of the transmitted information. Due to its open nature, wireless communications is prone to eavesdropping attacks. Typically, secrecy of the transmitted information is ensured with the aid of cryptographic techniques, which are deployed on upper layers of the network protocol stack. However, due to the need for key distribution and management, cryptographic solutions are difficult to implement in decentralized networks. Moreover, the security provided by key based solutions is not provable from a mathematical point of view. Physical layer security is an alternative or complement to the cryptographic techniques, which can resolve the complexities associated with key distribution and management. The basic principle of physical layer security is to exploit the randomness of the communication channels to allow a transmitter deliver its message to an intended receiver reliably while guaranteeing that a third party cannot infer any information about it. Much of the existing research in physical layer security focuses on investigating the information theoretic limits of secure communications. Among different techniques proposed, multiple-antenna based solutions have been shown to exhibit a high potential for enhancing security. Furthermore, Gaussian inputs are proved to be the optimal input distributions in a variety of scenarios. However, due to the high detection complexity, Gaussian signaling is not used in practice, and the transmission is carried out with the aid of symbols drawn from standard signal constellations. In this thesis, we develop several secure multi-antenna transmission techniques under the practical finite-alphabet input assumption. We first consider multipleinput multiple-output (MIMO) wiretap channels under finite-alphabet input constraints. We assume that the statistical channel state information (CSI) of the eavesdropper is available at the transmitter, and study two different scenarios regarding the transmitter's knowledge on the main channel CSI (MCSI) including availability of perfect and statistical MCSI at the transmitter. In each scenario, we introduce iterative algorithms for joint optimization of data precoder and arti ficial noise. We also propose different strategies to reduce the computational complexity associated with the transmit signal design. Moreover, we consider the setups with simultaneous wireless information and power transfer (SWIPT), and propose transmission schemes for achieving the trade-off between the secrecy rate and the harvested power. We demonstrate the efficacy of the proposed transmit signal design algorithms via extensive numerical examples. We also introduce several secure transmission schemes with spatial modulation and space shift keying (SSK). We derive an expression for the achievable secrecy rate, and develop precoder optimization algorithms for its maximization using transmitter side CSI. Furthermore, we introduce a group of secure SSK transmission schemes, which rely on dynamic antenna index assignment over reciprocal channels. Our results reveal that the fundamentally different working principle of SSK opens up new avenues for secure multi-antenna transmission.