arXiv subjects

Hamid Behroozi

Publications and source records attributed to Hamid Behroozi.

At least 19 recordsLinked to original sources

arXiv2025

Enhancing Energy and Spectral Efficiency in IoT-Cellular Networks via Active SIM-Equipped LEO Satellites

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy-spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

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arXiv2025

Physical Layer Group Key Generation With the Aid of Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RIS) have the ability to alter the wireless environment by making changes in the impinging signal. While RIS has been extensively studied for enhancing wireless communications, its potential for facilitating group key generation (GKG) remains unexplored. In this study, we exploit the RIS to make the aggregate reflecting channels of different user terminals (UTs) as similar as possible to be able to extract common group secret keys from their channels. Specifically, the RIS will adjust its parameters to pave the way for GKG based on the physical channels of the UTs. Our method exploits the already gathered channel state information (CSI) in the RIS to beneficially design the phase shifts and does not impose additional probing burden on the network. We consider both passive RIS (PRIS) and active RIS (ARIS) to generate the group keys. The PRIS is widely adopted in physical layer key generation (PLKG) studies due to its use of passive elements, whereas the ARIS demonstrates superior capability in aligning the aggregate reflected channels among nodes in the GKG scenario, as demonstrated in this study. We will exploit various optimization methods like successive convex approximation (SCA) and semidefinite relaxation with Gaussian randomization (SDR-GR) to address the raised optimization problems. Unlike most of the studies in the literature, our scheme can achieve a high GKG rate in static environments as well. Finally, we will examine the performance of the proposed method by normalized mean squared error (NMSE), key error rate (KER), key generation rate (KGR) and key randomness metrics. Our numerical results verify that for the equal available power budget, the ARIS significantly outperforms PRIS in NMSE and KER, achieving more than four times higher KGR.

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arXiv2025

Energy Efficient RSMA-Based LEO Satellite Communications Assisted by UAV-Mounted BD-Active RIS: A DRL Approach

This paper proposes an advanced non-terrestrial communication architecture that integrates Rate-Splitting Multiple Access (RSMA) with a Beyond-Diagonal Active Reconfigurable Intelligent Surface (BD-ARIS) mounted on a UAV under the coverage of a Low Earth Orbit (LEO) satellite. The BD-ARIS adopts a group-connected structure to enhance signal amplification and adaptability, while RSMA enables efficient multi-user access by dividing messages into common and private components. The system jointly optimizes satellite beamforming, UAV positioning, power allocation, and rate-splitting ratios to maximize the overall energy efficiency (EE). To solve the resulting non-convex and high-dimensional problem, we employ three state-of-the-art deep reinforcement learning (DRL) algorithms: Trust Region Policy Optimization (TRPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Asynchronous Advantage Actor-Critic (A3C). Moreover, realistic models for the power consumption of both the UAV and the BD-ARIS are considered. Simulation results reveal that TRPO consistently achieves the best performance in terms of EE and sum rate, especially under high transmit powers and challenging deployment scenarios. TD3 converges faster and performs competitively in moderate settings, while A3C suffers from instability due to its high variance. Additionally, the robustness of each algorithm under channel state information (CSI) uncertainty is evaluated, confirming TRPO resilience to imperfect observations. Overall, the proposed RSMA-BD-ARIS framework significantly outperforms conventional RIS-assisted designs and provides a scalable, energy-efficient solution for 6G and massive IoT applications in non-terrestrial networks.

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arXiv2024

Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the tradeoff between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided.

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arXiv2024

Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description

Existing 3D facial emotion modeling have been constrained by limited emotion classes and insufficient datasets. This paper introduces "Emo3D", an extensive "Text-Image-Expression dataset" spanning a wide spectrum of human emotions, each paired with images and 3D blendshapes. Leveraging Large Language Models (LLMs), we generate a diverse array of textual descriptions, facilitating the capture of a broad spectrum of emotional expressions. Using this unique dataset, we conduct a comprehensive evaluation of language-based models' fine-tuning and vision-language models like Contranstive Language Image Pretraining (CLIP) for 3D facial expression synthesis. We also introduce a new evaluation metric for this task to more directly measure the conveyed emotion. Our new evaluation metric, Emo3D, demonstrates its superiority over Mean Squared Error (MSE) metrics in assessing visual-text alignment and semantic richness in 3D facial expressions associated with human emotions. "Emo3D" has great applications in animation design, virtual reality, and emotional human-computer interaction.

cs.CV↗

arXiv2023

Secure Deep-JSCC Against Multiple Eavesdroppers

In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.

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arXiv2022

Intelligent Reflecting Surface Assisted Secret Key Generation Under Spatially Correlated Channels in Quasi-Static Environments

Physical layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by enabling them to change their secret keys significantly faster and more efficient. However, due to the reliance of PLKG techniques on channel medium, reaching a high secret key rate is challenging in static environments. Recently, exploiting intelligent reflecting surface (IRS) as a means to induce randomness in static wireless channels has received significant research interest. However, the impact of spatial correlation between the IRS elements is rarely studied. To be specific, for the first time, in this contribution, we take into account a spatially correlated IRS which intends to enhance the secret key generation (SKG) rate in a static medium. Closed form analytical expressions for SKG rate are derived for the two cases of random phase shift and equal random phase shift for all the IRS elements. We also analyze the temporal correlation between the channel samples to ensure the randomness of the generated secret key sequence. We further formulate an optimization problem in which we determine the optimal portion of time within a coherence interval dedicated for the direct and indirect channel estimation. We show the accuracy and the fast convergence of our proposed sequential convex programming (SCP) based algorithm and discuss the various parameters affecting spatially correlated IRS assisted PLKG.

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arXiv2022

Secure SWIPT in the Multiuser STAR-RIS Aided MISO Rate Splitting Downlink

Recently, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a novel technology that provides 360 coverage and new degrees-of-freedom (DoFs). They are also capable of manipulating signal propagation and simultaneous wireless information and power transfer (SWIPT). This paper introduces a novel STAR-RIS-aided secure SWIPT system for downlink multiple input single output rate-splitting multiple access (RSMA) networks. The transmitter concurrently communicates with the information receivers (IRs) and sends energy to untrusted energy receivers (UERs). The UERs are also capable of wiretapping the IR streams. We assume that the channel state information (CSI) of the IRs is known at the information transmitter, but only imperfect CSI for the UERs is available at the energy transmitter. By exploiting RSMA, the base station splits the messages of the IRs into common and private parts. The former is encoded into a common stream that can be decoded by all IRs, while the private messages are individually decoded by their respective IRs. We find the precoders and STAR-RIS configuration that maximizes the achievable worst-case sum secrecy rate of the IRs under a total transmit power constraint, a sum energy constraint for the UERs, and subject to constraints on the transmission and reflection coefficients. The formulated problem is non-convex and has intricately coupled variables. To tackle this challenge, a suboptimal two-step iterative algorithm based on the sequential parametric convex approximation method is proposed. Simulations demonstrate that the RSMA-based algorithm implemented with a STAR-RIS enhances both the rate of confidential information transmission and the total spectral efficiency. Furthermore, our method surpasses the performance of both orthogonal multiple access (OMA) and non-OMA (NOMA).

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arXiv2022

Secrecy Rate of the Cooperative RSMA-Aided UAV Downlink Relying on Optimal Relay Selection

The Cooperative Rate-Splitting (CRS) scheme, proposed evolves from conventional Rate Splitting (RS) and relies on forwarding a portion of the RS message by the relaying users. In terms of secrecy enhancement, it has been shown that CRS outperforms its non-cooperative counterpart for a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC). Given the massive connectivity requirement of 6G, we have generalized the existing secure two-user CRS framework to the multi-user framework, where the highest-security users must be selected as the relay nodes. This paper addresses the problem of maximizing the Worst-Case Secrecy Rate (WCSR) in a UAV-aided downlink network where a multi-antenna UAV Base-Station (UAV-BS) serves a group of users in the presence of an external eavesdropper (Eve). We consider a practical scenario in which only imperfect channel state information of Eve is available at the UAV-BS. Accordingly, we conceive a robust and secure resource allocation algorithm, which maximizes the WCSR by jointly optimizing both the Secure Relaying User Selection (SRUS) and the network parameter allocation problem, including the RS transmit precoders, message splitting variables, time slot sharing and power allocation. To circumvent the resultant non-convexity owing to the discrete variables imposed by SRUS, we propose a two-stage algorithm where the SRUS and network parameter allocation are accomplished in two consecutive stages. With regard to the SRUS, we study both centralized and distributed protocols. On the other hand, for jointly optimizing the network parameter allocation we resort to the Sequential Parametric Convex Approximation (SPCA) algorithm. Our numerical results show that the proposed solution significantly outperforms the existing benchmarks for a wide range of network loads in terms of the WCSR.

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arXiv2022

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

UAVs are capable of improving the performance of next generation wireless systems. Specifically, UAVs can be exploited as aerial base-stations (UAV-BS) for supporting legitimate ground users in remote uncovered areas or in environments temporarily requiring high capacity. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective secrecy throughput of the UAV-aided uplink, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the effective network secrecy throughput in the uplink, the transmission rate vs. power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect CSI of both the legitimate users and of the potential eavesdroppers (Eves). We then propose a novel transformation of the associated probabilistic constraints for decoupling the variables, so that our design problem can be solved by alternatively activating the related block coordinate decent programming. In the model considered, each user transmits a superposition of two messages to a UAV-BS, each having different transmit power and the UAV-BS uses a SIC technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the \epsilon-constrainted transmit power of each user is calculated by harnessing SPCA programming.

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arXiv2021

Temporal Action Localization Using Gated Recurrent Units

Temporal Action Localization (TAL) task which is to predict the start and end of each action in a video along with the class label of the action has numerous applications in the real world. But due to the complexity of this task, acceptable accuracy rates have not been achieved yet, whereas this is not the case regarding the action recognition task. In this paper, we propose a new network based on Gated Recurrent Unit (GRU) and two novel post-processing methods for TAL task. Specifically, we propose a new design for the output layer of the conventionally GRU resulting in the so-called GRU-Split network. Moreover, linear interpolation is used to generate the action proposals with precise start and end times. Finally, to rank the generated proposals appropriately, we use a Learn to Rank (LTR) approach. We evaluated the performance of the proposed method on Thumos14 and ActivityNet-1.3 datasets. Results show the superiority of the performance of the proposed method compared to state-of-the-art. Specifically in the mean Average Precision (mAP) metric at Intersection over Union (IoU) of 0.7 on Thumos14, we get 27.52% accuracy which is 5.12% better than that of state-of-the-art methods.

cs.CV↗

arXiv2021

On Learning-Assisted Content-Based Secure Image Transmission for Delay-Aware Systems with Randomly-Distributed Eavesdroppers -- Extended Version

In this paper, a learning-aided content-based wireless image transmission scheme is proposed, where a multi-antenna-aided source wishes to securely deliver an image to a legitimate destination in the presence of randomly distributed eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we propose a transmission scheme, where the source employs a hybrid method to realize both the error-free data delivery of public regions containing less-important pixels; and an artificial noise (AN)-aided transmission scheme to provide security for the regions containing large amount of information. Moreover, in order to reinforce system's security, fountain-based packet delivery is adopted: First, the source node encodes image packets into fountain-like packets prior to sending them over the air. The secrecy of our proposed scheme will be achieved if the legitimate destination correctly receives the entire image source packets, while conforming to the latency limits of the system, before Eves can obtain the important regions. Accordingly, the secrecy performance of our scheme is characterized by deriving the closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed wireless image delivery scheme leverages the deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided with some useful engineering insights which illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and the delay requirement.

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arXiv2021

DTW-Merge: A Novel Data Augmentation Technique for Time Series Classification

In recent years, neural networks achieved much success in various applications. The main challenge in training deep neural networks is the lack of sufficient data to improve the model's generalization and avoid overfitting. One of the solutions is to generate new training samples. This paper proposes a novel data augmentation method for time series based on Dynamic Time Warping. This method is inspired by the concept that warped parts of two time series have similar temporal properties and therefore, exchanging them between the two series generates a new training sample. The proposed method selects an element of the optimal warping path randomly and then exchanges the segments that are aligned together. Exploiting the proposed approach with recently introduced ResNet reveals improved results on the 2018 UCR Time Series Classification Archive. By employing Gradient-weighted Class Activation Mapping (Grad-CAM) and Multidimensional Scaling (MDS), we manifest that our method extract more discriminant features out of time series.

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arXiv2021

Secrecy Rate Maximization for Hardware Impaired Untrusted Relaying Network with Deep Learning

This paper investigates the physical layer security design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Taking into account the hardware impairments, and power budget constraints, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aimed at maximizing average secrecy rate (ASR). The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to improve the ASR while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.

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arXiv2020

Fairness-Oriented User Association in HetNets Using Bargaining Game Theory

In this paper, the user association and resource allocation problem is investigated for a two-tier HetNet consisting of one macro Base Station (BS) and a number of pico BSs. The effectiveness of user association to BSs is evaluated in terms of fairness and load distribution. In particular, the problem of determining a fair user association is formulated as a bargaining game so that for the Nash Bargaining Solution (NBS) abiding the fairness axioms provides an optimal and fair user association. The NBS also yields in a Pareto optimal solution and leads to a proportional fair solution in the proposed HetNet model. Additionally, we introduce a novel algorithmic solution in which a new Coalition Generation Algorithm (CGA), called SINR-based CGA, is considered in order to simplify the coalition generation phase. Our simulation results show the efficiency of the proposed user association scheme in terms of fairness and load distribution among BSs and users. In particular, we compare the performance of the proposed solution with that of the throughput-oriented scheme in terms of the max-sum-rate scheme and show that the proposed solution yields comparable average data rates and overall sum rate.

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arXiv2020

Can a Multi-Hop Link Relying on Untrusted Amplify-and-Forward Relays Render Security?

Cooperative relaying is utilized as an efficient method for data communication in wireless sensor networks and the Internet of Things (IoT). However, sometimes due to the necessity of multi-hop relaying in such communication networks, it is challenging to guarantee the secrecy of cooperative transmissions when the relays may themselves be eavesdroppers, i.e., we may face with the untrusted relaying scenario where the relays are both necessary helpers and potential adversary. To obviate this issue, a new cooperative jamming scheme is proposed in this paper, in which the data can be confidentially communicated from the source to the destination through multiple untrusted relays. In our proposed secure transmission scheme, all the legitimate nodes contribute to providing secure communication by intelligently injecting artificial noises to the network in different communication phases. For the sake of analysis, we consider a multi-hop untrusted relaying network with two successive intermediate nodes, i.e, a three-hop communications network. Given this system model, a new closed-form expression is presented in the high signal-to-noise ratio (SNR) region for the Ergodic secrecy rate (ESR). Furthermore, we evaluate the high SNR slope and power offset of the ESR to gain an insightful comparison of the proposed secure transmission scheme and the state-of-arts. Our numerical results highlight that the proposed secure transmission scheme provides better secrecy rate performance compared with the two-hop untrusted relaying as well as the direct transmission schemes.

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arXiv2020

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

This paper proposes compressed domain signal processing (CSP) multiple input multiple output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an 8-fold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar thus enabling faster target detection. Receiver operating characteristic (ROC) curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based compressed sensing algorithms.

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arXiv2018

Sparse Antenna and Pulse Placement for Colocated MIMO Radar

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this work, the Cram'er-Rao lower bound (CRLB) for the angle and velocity estimator is employed as a performance metric to design the antenna and pulse placement. It is shown that the CRLB derived for two targets is a more appropriate criterion in comparison with the single-target CRLB since the two-target CRLB takes into account both the mainlobe width and sidelobe level of the ambiguity function. In this paper, several algorithms for antenna and pulse selection based on convex and submodular optimization are proposed. Numerical experiments are provided to illustrate the developed theory.

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