arXiv subjects

Paeiz Azmi

Publications and source records attributed to Paeiz Azmi.

At least 19 recordsLinked to original sources

arXiv2026

Group Physical-Layer Authentication and Confidential Beamforming in Non-Terrestrial Networks

Coordinated high-altitude platform station (HAPS) networks need to authenticate group responses and protect private data under shared power constraints. This paper studies joint group physical-layer authentication (PLA) and confidential beamforming using a fresh secret tag superimposed on private data. For received attacks independent of the current tag, we derive exact finite-block expressions for false acceptance and legitimate rejection. These expressions translate authentication error targets into a required tag signal-to-interference-plus-noise ratio (SINR). We establish a nonzero asymptotic tag-energy floor and analyze residual frequency offset and peak-power restrictions. The legitimate receiver removes the tag by orthogonal projection, which discards one temporal dimension. For colluding eavesdroppers, we identify the spatial coverage condition under which artificial noise (AN) keeps their SINR bounded as data and AN powers increase together. We jointly optimize the private beams, tag beam, and AN covariance to minimize transmit power under authentication, secrecy, and per-HAPS power constraints. The method combines semidefinite relaxation (SDR), exact private-beam rank reduction, and feasible successive convex approximation. A corrected dual lower bound measures the remaining optimality gap. Numerical results show that increasing the tag length from 16 to 64 symbols reduces the required tag SINR by 7.16 dB. Joint beam design reduces mean transmit power by 27.4% relative to an optimized fixed-tag-direction baseline. The authentication guarantee requires fresh secret tags and excludes relays of the current response.

eess.SP↗

arXiv2026

Energy-Efficient Risk-Constrained Adaptive Semantic Communications Under Uncertain Adversarial Threats

Semantic communication systems based on deep joint source-channel coding can provide task-oriented wireless transmission, but adversarial robustness may require substantial radio and receiver-side defensive-computation energy. This paper develops an energy-efficient, risk-constrained semantic communication framework under source-domain, over-the-air (OTA), and potentially composite adversarial conditions. Long-term total energy, including communication and diffusion-purification energy, is minimized subject to average semantic-loss, trajectory-level tail-risk, and end-to-end latency constraints. A recurrent primal-dual proximal policy optimization controller jointly adapts transmit power, semantic/channel bandwidth ratio, and purification complexity using causal channel observations, semantic confidence, imperfect attack-belief estimates, and recurrent history. A conditional value-at-risk (CVaR) constraint explicitly limits severe semantic failures. On CIFAR-100, at the unseen source-domain level $ε=12/255$, the proposed controller achieves approximately $61\%$ robust Top-1 accuracy with $\mathrm{CVaR}_{0.9}\approx0.36$ while using about $9\%$ less total energy than diffusion-only defense. At the unseen OTA level $Γ_A=-5$ dB, it achieves approximately $52\%$ robust Top-1 accuracy, reduces semantic tail risk by $40\%$, and consumes about $8\%$ less total energy than diffusion-only defense. These results demonstrate energy-efficient semantic defense under partially observable adversarial conditions.

eess.SP↗

arXiv2026

Joint Deployment and Resource Management for Group-Tag-Based Physical-Layer Authentication and Collaborative Communication in NTN

Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) has primarily been studied from a security perspective, while its system-level integration with communication remains less explored. We propose the secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a system-level formulation for the joint management of collaborative group-tag and private-data delivery in NTN, where high-altitude platform station (HAPS) placement, user association, and transmission powers are jointly selected under service and secrecy constraints. Group-level authentication tags and user data are collaboratively transmitted by multiple HAPSs, with artificial noise (AN) mitigating information leakage. We jointly optimize HAPS placement, user association, and power allocation to balance deployment regularity, tag distribution, and user-rate fairness under secrecy, quality-of-service (QoS), power, and coverage constraints. The problem is addressed using a repair-based cross-entropy method (RCEM) and a graph-aware advantage actor-critic (A2C) algorithm (GA2C). Under colluding and non-colluding eavesdropper models, GA2C improves average secrecy spectral efficiency (SSE) by 135--139% over its single-connect baseline and 21.1--23.2% over its baseline without AN. Across ten independent random seeds, GA2C achieves approximately 23.4% and 17.9% higher mean per-user SSE under non-colluding and colluding eavesdropping, respectively, and a 16.2\% higher mean private-rate Jain's index than RCEM. These results quantify the gains of multi-node connectivity and AN within the proposed framework under the evaluated assumptions.

eess.SP↗

arXiv2026

TwinViT-DeepJSCC: Adversarially Robust Semantic Image Communication

Learning-based semantic communication is vulnerable to adversarial perturbations introduced before semantic encoding or over wireless channels. This paper proposes TwinViT-DeepJSCC, a preventive-corrective semantic image transceiver operating under a fixed channel-use budget. Two Vision Transformer (ViT)-based deep joint source-channel coding (DeepJSCC) branches learn complementary latent representations protected by sensitivity-aware masking. At the receiver, confidence-aware fusion, blind corruption-severity estimation, and signal-to-noise ratio (SNR)-severity-conditioned denoising diffusion implicit model (DDIM) purification mitigate residual corruption without requiring attack metadata. Experiments on the Canadian Institute for Advanced Research 100-class (CIFAR-100) dataset consider fast gradient sign method (FGSM), projected gradient descent (PGD), natural evolution strategies (NES), and Carlini-Wagner (CW) source-domain attacks, as well as random jamming and channel-aware adversarial waveforms over additive white Gaussian noise (AWGN) and block-flat Rayleigh fading. Under matched channel-use and attack budgets, TwinViT-DeepJSCC achieves maximum peak signal-to-noise ratio (PSNR) gains of approximately 9.5 dB under 20-step PGD and 10.8 dB under channel-aware waveform attacks over block-flat Rayleigh fading. Under PGD, it also improves Top-1 accuracy by up to approximately 38 percentage points over the undefended baseline and 13 percentage points over the strongest competing defense. Ablation results confirm the complementary contributions of the proposed transmitter- and receiver-side mechanisms.

cs.CR↗

arXiv2026

Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning

Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly to unseen environments. This paper proposes a secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a causal meta-learning framework for distributed PLA (DPLA) in NTNs. First, we design a multi-feature fingerprint that combines spatial, angular, combiner, subspace, and Doppler-delay features. The fingerprint is adaptive and distributed, as it fuses heterogeneous physical-layer features and measurements from multiple aerial nodes. Second, we formulate a structural causal model (SCM) to capture the relations among design choices, environmental factors, extracted features, and authentication outcomes. Third, we develop a model-agnostic meta-learning (MAML) strategy with invariant risk minimization (IRM) and causal consistency regularization for fast adaptation to unseen NTN environments with few labeled samples. Fourth, we propose a two-stage authentication scheme that performs local recognition and activates time-difference-of-arrival (TDOA) localization with a graph attention (GAT) network only when needed, which reduces backhaul overhead. Simulations show that SAFA-MZ achieves 92% accuracy and 96% AUC, outperforming centralized deep learning and single-feature baselines across diverse environments.

eess.SP↗

arXiv2024

Age of Information Minimization in UAV-Assisted Covert Communication: Trajectory and Beamforming Design

Unmanned aerial vehicles (UAVs) have the potential for time-sensitive applications. Due to wireless channel variation, received data may have an expiration time, particularly in critical situations such as rescue operations, natural disasters, or the military. Age of Information (AoI) is a metric that measures the freshness of received packets to specify the validity period of information. In addition, it is necessary to guarantee the privacy of confidential information transmission through air-to-ground links against eavesdroppers. This paper investigates UAV-assisted covert communication to minimize AoI in the presence of an aerial eavesdropper for the first time. However, to ensure the eavesdropper's error detection rate, UAV-enabled beamforming employs the power-domain non-orthogonal multiple access (PD-NOMA) technique to cover the covert user by a public user. PD-NOMA technique significantly improves the user's AoI, too. The joint optimization problem contains non-convex constraints and coupled optimization variables, including UAV trajectory, beamforming design, and the user's AoI which is challenging to derive a direct solution. We have developed an efficient alternating optimization technique to address the formulated optimization problem. Numerical results demonstrate the impact of the main parameters on the performance of the proposed communication system.

eess.SY↗

arXiv2022

Average Age of Information Minimization in Reliable Covert Communication on Time-Varying Channels

In this letter, we propose reliable covert communications with the aim of minimizing age of information (AoI) in the time-varying channels. We named the time duration that channel state information (CSI) is valid as a new metric, as age of channel variation (AoC). To find reliable covert communication in a fresh manner in dynamic environments, this work considers a new constraint that shows a relation between AoI and AoC. With the aid of the proposed constraint, this paper addresses two main challenges of reliable covert communication with the aim of minimizing AoI: 1) users packets with desirable size; 2) guaranteeing the negligible probability of Willies detection, in time-varying networks. In the simulation results, we compare the performance of the proposed constraint in reliable covert communication with the aim of minimizing AoI with conventional optimization of the requirement of information freshness in covert communications.

cs.IT↗

arXiv2022

AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties

Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. The proposed formulation is a nonconvex mixed-integer non-linear programming problem. To perform robust resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in deep reinforcement learning. Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. The simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%.

eess.SP↗

arXiv2022

A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power through frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. Results show improvement of success rate compared of some published method. Furthermore, the algorithm directly computes SINR after signal is detected by successive interference cancellation (SIC) and before any signal decoding. Moreover, because of the direct sense of physical channel, the presented algorithm can discount occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity through the eNode B connections. Simulation results in compare to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.

cs.NI↗

arXiv2021

AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks

Spectrum sharing is a method to solve the problem of frequency spectrum deficiency. This paper studies a novel AI based spectrum sharing and energy harvesting system in which the freshness of information (AoI) is guaranteed. The system includes a primary user with access rights to the spectrum and a secondary user. The secondary user is an energy harvesting sensor that intends to use the primary user spectrum opportunistically. The problem is formulated as partially observable Markov decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal policy. The purpose is to choose the best action adaptively in every time slot based on its situation in both overlay and underlay modes to minimize the average AoI of the secondary user. Finally, simulation experiments are performed to evaluate the effectiveness of the proposed scheme compared to the overlay mode. According to the results, the average AoI in the proposed system is less than that of the existing models, including only overlay mode. The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.

eess.SY↗

arXiv2021

Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction

In this study, an algorithm to blind and automatic modulation classification has been proposed. It well benefits combined machine leaning and signal feature extraction to recognize diverse range of modulation in low signal power to noise ratio (SNR). The presented algorithm contains four. First, it advantages spectrum analyzing to branching modulated signal based on regular and irregular spectrum character. Seconds, a nonlinear soft margin support vector (NS SVM) problem is applied to received signal, and its symbols are classified to correct and incorrect (support vectors) symbols. The NS SVM employment leads to discounting in physical layer noise effect on modulated signal. After that, a k-center clustering can find center of each class. finally, in correlation function estimation of scatter diagram is correlated with pre-saved ideal scatter diagram of modulations. The correlation outcome is classification result. For more evaluation, success rate, performance, and complexity in compare to many published methods are provided. The simulation prove that the proposed algorithm can classified the modulated signal in less SNR. For example, it can recognize 4-QAM in SNR=-4.2 dB, and 4-FSK in SNR=2.1 dB with %99 success rate. Moreover, due to using of kernel function in dual problem of NS SVM and feature base function, the proposed algorithm has low complexity and simple implementation in practical issues.

cs.LG↗

arXiv2020

Theoretical Accuracy Analysis of RSS-Based Range Estimation for Visible Light Communication

In this paper, an improved channel model of visible light communication (VLC) for ranging in presented. For indoor channel model of VLC, distance is estimated based on received signal strength. In this model, received shot noise as a distance-dependent parameter is considered in range estimation accuracy. Moreover, based on this model, the Cramer-Rao lower bound is computed as the theoretical limits on the performance and accuracy of any unbiased estimator. In this way, the effects of horizontal and vertical distances are investigated. In addition, the transmitted power effect on RSN and accordingly on CRLB is demonstrated.

eess.SP↗

arXiv2020

Cross Layer Resource Allocation in H-CRAN with Spectrum and Energy Cooperation

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both the spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in non-orthogonal multiple access (NOMA)-based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterwards, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for the all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in NOMA H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

eess.SP↗

arXiv2020

Joint Information Theoretic Secrecy and Covert Communication in the Presence of an Untrusted User and Warden

In this paper, we investigate joint information theoretic secrecy and covert communication in a single-input multi-output (SIMO) system where a transmitter (Alice) is communicating with two legitimate users (Bob and Carol). We consider that an untrusted user and a warden node are also present in the network attempting to attack the secure and covert communications to Bob and Carol, respectively. Specifically, Bob requires secure communications such that his messages from Alice are not decoded by the untrusted user, while Carol requires covert communications such that her messages from Alice are not detected by the warden. To do so, we consider that Alice transmits Carol's messages during selected time slots to hide them from the warden while also transmitting Bob's messages in each time slot contentiously. We formulate an optimization problem with the aim of maximizing the average rate subject to a covert communication requirement and a secure communications constraint. Since the proposed optimization problem is non-convex, we utilize successive convex approximation to obtain a tractable solution. We consider practical assumptions that Alice has imperfect knowledge of the warden's location and imperfect channel state information (CSI) of Bob and Carol. Our numerical examples highlight that the imperfect CSI at Carol has a more detrimental impact on the average rate compared to imperfect CSI at Bob.

eess.SP↗

arXiv2019

Robust Resource Allocation for PD-NOMA-Based MISO Heterogeneous Networks with CoMP Technology

In this paper, we consider a hybrid scheme of coordinated multi-point (CoMP) technology in MISO heterogeneous communication networks based on power domain non-orthogonal multiple access (PD-NOMA). We propose a novel method based on matching game with externalities to realize the hybrid scheme where the number of the cooperative nodes are variable. Moreover, we propose a new matching utility function to manage the interference caused by CoMP and NOMA techniques. We also devise robust beamforming to cope with the channel uncertainty. In this regard, we focus on both no CSI and partial CSI cases to increase the achievable data rate. We provide the complexity analysis of both schemes which shows that the complexity of the partial CSI approach is more than that of the no CSI method. Results evaluate the performance of proposed CoMP scheme and the sensibility of our methods, Index Terms, CoMP technology, hybrid scheme, matching game with externalities, PD-NOMA, robust beamforming, probabilistic constraint, no CSI, partial CSI.

cs.IT↗

arXiv2019

Information-Theoretic Security or Covert Communication

Information-theoretic secrecy, in particular the wiretap channel formulation, provides protection against interception of a message by adversary Eve and has been widely studied in the last two decades. In contrast, covert communications under an analogous formulation provides protection against even the detection of the presence of the message by an adversary, and it has drawn significant interest recently. These two security topics are generally applicable in different scenarios; however, here we explore what can be learned by studying them under a common framework. Under a similar but not identical mathematical formulation, we introduce power optimization problems for each of the secrecy and the covert communications scenario, and we exploit common aspects of the problems to employ similar tools in their respective optimizations. Moreover, due to the practical limitations, we assume only channel

cs.CR↗

arXiv2019

Covert Communication Using Null Space and 3D Beamforming

Covert communication is often limited in rate because it is difficult to hide the signal in the background noise. Recent work has shown that jamming can significantly improve the rate at which covert communications can be conducted; however, the rate could be improved further if the jamming incident on the intended receiver can be mitigated. Here, we consider a multiple-antenna jammer that employs beamforming to place the intended receiver in the null space of the jamming and a multi-antenna transmitter equipped with three-dimensional (3D) antennas that is able to beamform toward its intended recipient. To evaluate this design, we formulate an optimization problem and present an iterative algorithm to solve it. Numerical results consider both the rate of covert communications with the proposed architecture and the gap between the result from our optimization and that obtained from exhaustive search.

eess.SP↗

arXiv2019

Bit Error Probability Instead of Secrecy Rate Criterion to Enhance Performance for Secure Wireless Communication Systems

In this paper, we propose a new practical power allocation technique based on bit error probability (BEP) for physical layer security systems. It is shown that the secrecy rate that is the most commonly used in physical layer security systems, cannot be a suitable criterion lonely. Large positive values are suitable for the secrecy rate in physical layer security, but it does not consider the performance of the legitimate and adversary users. In this paper, we consider and analyze BEP for physical layer security systems because based on it, the performance of the legitimate and adversary users are guaranteed and it is needed to use lower power. BEP is calculated for the legitimate and adversary users and it is shown that BEP can be better criterion for performance evaluation of the physical layer security systems. Based on BEP, the optimum transmit power is obtained and a new definition for outage probability is proposed and obtained theoretically. Also, the proposed approach is applied for adversary users with unknown mode and the cooperative adversary users. Simulation results show that the proposed method needs more than 5dB lower power for different scenarios.

eess.SP↗