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Babak Hossein Khalaj

Publications and source records attributed to Babak Hossein Khalaj.

At least 19 recordsLinked to original sources

When Linear RUL Labels Disagree with Vibration Degradation: A Stage-Aware Target and Dual-Scale Predictor Evaluated on XJTU-SY and IMS

Remaining useful life (RUL) studies commonly treat the label as fixed, although clock-linear labels may decline while measured vibration remains nearly stable and then changes rapidly near failure. We separate target design from prediction. A development-only pipeline constructs an oriented vibration health indicator, identifies chronological early, middle, and late stages, and fits a continuous linear-quadratic-exponential degradation-state target. A compact CNN-LSTM and Transformer learn the target from causal feature sequences, and validation-fitted Ordered Weighted Averaging combines their outputs. In a bearing-wise XJTU-SY hold-out, all bearings ending in 5 are excluded from fitted preprocessing, training, early stopping, and fusion. The fused predictor obtains an RMSE of 0.0608, an MAE of 0.0392, and an R-squared value of 0.9617, with the Transformer providing most of the accuracy. Target shape is assessed independently on three documented IMS failed-bearing trajectories. Against the best anchored linear fit to the same vibration-derived reference, the stage-aware curve reduces RMSE by 3.6-18.2% and MAE by 3.1-31.1%; the mean reductions are 10.2% and 15.0%, respectively. Conservative BIC differences of 128.8-368.1 favor the stage-aware representation, whereas moving-block bootstrap intervals cross zero. Thus, stage-dependent targets better describe the evaluated vibration-derived degradation states, but the evidence remains descriptive because only three official IMS runs are available. The study establishes a measurement-oriented target-validity framework, not a universal nonlinear law for physical time-to-failure or robust cross-domain prediction.

eess.SP↗

Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware

In-cabin driver monitoring systems (DMS) must recognize distraction- and drowsiness-related behaviors with low latency under strict constraints on compute, power, and cost. We present a single-camera in-cabin driver behavior recognition system designed for deployment on two low-cost edge platforms: Raspberry Pi 5 (CPU-only) and the Google Coral development board with an Edge Tensor Processing Unit (Edge TPU) accelerator. The proposed pipeline combines (i) a compact per-frame vision model, (ii) a confounder-aware label taxonomy to reduce confusions among visually similar behaviors, and (iii) a temporal decision head that triggers alerts only when predictions are both confident and sustained. The system supports 17 behavior classes. Training and evaluation use licensed datasets plus in-house collection (over 800,000 labeled frames) with driver-disjoint splits, and we further validate the deployed system in live in-vehicle tests. End-to-end performance reaches approximately 16 FPS on Raspberry Pi 5 using 8-bit integer (INT8) inference (per-frame latency <60 ms) and approximately 25 FPS on Coral Edge TPU (end-to-end latency ~40 ms), enabling real-time monitoring and stable alert generation on embedded hardware. Finally, we discuss how reliable in-cabin perception can serve as an upstream signal for human-centered vehicle intelligence, including emerging agentic vehicle concepts.

cs.CV↗

5G Network Automation Using Local Large Language Models and Retrieval-Augmented Generation

This demonstration showcases the integration of a lightweight, locally deployed Large Language Model (LLaMA-3 8b Q-4b) empowered by retrieval augmented generation (RAG) to automate 5G network management, with a strong emphasis on privacy. By running the LLM on local or edge devices ,we eliminate the need for external APIs, ensuring that sensitive data remains secure and is not transmitted over the internet. Although lightweight models may not match the performance of more complex models like GPT-4, we enhance their efficiency and accuracy through RAG. RAG retrieves relevant information from a comprehensive database, enabling the LLM to generate more precise and effective network configurations based on natural language user input. This approach not only improves the accuracy of the generated configurations but also simplifies the process of creating and configuring private networks, making it accessible to users without extensive networking or programming experience. The objective of this demonstration is to highlight the potential of combining local LLMs and RAG to deliver secure, efficient, and adaptable 5G network solutions, paving the way for a future where 5G networks are both privacy-conscious and versatile across diverse user profiles.

cs.NI↗

Automated Fault Detection in 5G Core Networks Using Large Language Models

With the rapid growth of data volume in modern telecommunication networks and the continuous expansion of their scale, maintaining high reliability has become a critical requirement. These networks support a wide range of applications and services, including highly sensitive and mission-critical ones, which demand rapid and accurate detection and resolution of network errors. Traditional fault-diagnosis methods are no longer efficient for such complex environments.\cite{b1} In this study, we leverage Large Language Models (LLMs) to automate network fault detection and classification. Various types of network errors were intentionally injected into a Kubernetes-based test network, and data were collected under both healthy and faulty conditions. The dataset includes logs from different network components (pods), along with complementary data such as system descriptions, events, Round Trip Time (RTT) tests, and pod status information. The dataset covers common fault types such as pod failure, pod kill, network delay, network loss, and disk I/O failures. We fine-tuned the GPT-4.1 nano model via its API on this dataset, resulting in a significant improvement in fault-detection accuracy compared to the base model. These findings highlight the potential of LLM-based approaches for achieving closed-loop, and operator-free fault management, which can enhance network reliability and reduce downtime-related operational costs for service providers.

cs.NI↗

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.

cs.IT↗

Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals

Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and management of cardiovascular diseases. However, deploying robust AF detection models across different datasets with significant domain variations remains a challenge. In this paper, we use test-time data augmentation (TTA) to address the cross-domain problem and enhance AF detection performance. We use a publicly available dataset for training - Physionet Computing in Cardiology Challenge 2017 -, while collecting a distinct test set, creating a cross-domain scenario. We employ a neural network architecture that integrates transformer-based encoding of ECG signals and convolutional layers for spectrogram feature extraction. The model combines the latent representations obtained from both encoders to classify the input signals. By incorporating TTA during inference, we enhance the model's performance, achieving an F1 score of 76.6\% on our test set. Furthermore, our experiments demonstrate that the model becomes more resilient to perturbations in the input signal, enhancing its robustness. We show that TTA can be effective in addressing the cross-domain problem, where training and test data originate from disparate sources. This work contributes to advancing the field of AF detection in real-world scenarios.

eess.SP↗

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%.

cs.IT↗

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

cs.CR↗

Multi-Transmitter Coded Caching with Secure Delivery over Linear Networks -- Extended Version

In this paper, we consider multiple cache-enabled end-users connected to multiple transmitters through a linear network. We also prevent a totally passive eavesdropper, who sniffs the packets in the delivery phase, from obtaining any information about the original files in cache-aided networks. Three different secure centralized multi-transmitter coded caching scenarios namely, secure multi-transmitter coded caching, secure multi-transmitter coded caching with reduced subpacketization, and secure multi-transmitter coded caching with reduced feedback, are considered and closed-form coding delay and secret shared key storage expressions are provided. As our security guarantee, we show that the delivery phase does not reveal any information to the eavesdropper using the mutual information metric. Moreover, we investigate the secure decentralized multi-transmitter coded caching scenario, in which there is no cooperation between the clients and transmitters during the cache content placement phase and study its performance compared to the centralized scheme. We analyze the system's performance in terms of Coding Delay and guarantee the security of our presented schemes using the Mutual Information metric. Numerical evaluations verify that security incurs a negligible cost in terms of memory usage when the number of files and users are scaled up, in both centralized and decentralized scenarios. Also, we numerically show that by increasing the number of files and users, the secure coding delay of centralized and decentralized schemes became asymptotically equal.

cs.IT↗

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.

cs.IT↗

Energy Efficiency Through Joint Routing and Function Placement in Different Modes of SDN/NFV Networks

Network function virtualization (NFV) and software defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV networks in order to serve these services is a time consuming process and costly for mobile operators. This paper focuses on energy efficiency during the transition of mobile core networks (MCN) to full SDN/NFV networks, and explores how energy efficiency can be addressed during such migration. We propose a general system model containing a combination of legacy nodes and links, in addition to newly introduced NFV and SDN nodes. We refer to this system model as partial SDN and hybrid NFV MCN which can cover different modes of SDN and NFV implementations. Based on this framework, we formulate energy efficiency by considering joint routing and function placement in the network. Since this problem belongs to the class of non-linear integer programming problems, to solve it efficiently, we present a modified Viterbi algorithm (MVA) based on multi-stage graph modeling and a modified Dijkstra's algorithm. We simulate this algorithm for a number of network scenarios with different fractions of NFV and SDN nodes, and evaluate how much energy can be saved through such transition. Simulation results confirm the expected performance of the algorithm which saves up to 70% energy compared to network where all nodes are always on. Interestingly, the amount of energy saved by the proposed algorithm in the case of hybrid NFV and partial SDN networks can reach up to 60-90% of the saved energy in full NFV/SDN networks.

cs.NI↗

DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond

In this paper, we employ deep reinforcement learning to develop a novel radio resource allocation and packet scheduling scheme for different Quality of Service (QoS) requirements applicable to LTEadvanced and 5G networks. In addition, regarding the scarcity of spectrum in below 6GHz bands, the proposed algorithm dynamically allocates the resource blocks (RBs) to licensed users in a way to mostly preserve the continuity of unallocated RBs. This would improve the efficiency of communication among the unlicensed entities by increasing the chance of uninterrupted communication and reducing the load of coordination overheads. The optimization problem is formulated as a Markov Decision Process (MDP), observing the entire queue of the demands, where failing to meet QoS constraints penalizes the goal with a multiplicative factor. Furthermore, a notion of continuity for unallocated resources is taken into account as an additive term in the objective function. Considering the variations in both channel coefficients and users requests, we utilize a deep reinforcement learning algorithm as an online and numerically efficient approach to solve the MDP. Numerical results show that the proposed method achieves higher average spectral efficiency, while considering delay budget and packet loss ratio, compared to the conventional greedy min-delay and max-throughput schemes, in which a fixed part of the spectrum is forced to be vacant for unlicensed entities.

eess.SP↗

Cloud-Aided Interference Management with Cache-Enabled Edge Nodes and Users

This paper considers a cloud-RAN architecture with cache-enabled multi-antenna Edge Nodes (ENs) that deliver content to cache-enabled end-users. The ENs are connected to a central server via limited-capacity fronthaul links, and, based on the information received from the central server and the cached contents, they transmit on the shared wireless medium to satisfy users' requests. By leveraging cooperative transmission as enabled by ENs' caches and fronthaul links, as well as multicasting opportunities provided by users' caches, a close-to-optimal caching and delivery scheme is proposed. As a result, the minimum Normalized Delivery Time (NDT), a high-SNR measure of delivery latency, is characterized to within a multiplicative constant gap of $3/2$ under the assumption of uncoded caching and fronthaul transmission, and of one-shot linear precoding. This result demonstrates the interplay among fronthaul links capacity, ENs' caches, and end-users' caches in minimizing the content delivery time.

cs.IT↗

Optimum Transmission Delay for Function Computation in NFV-based Networks: the role of Network Coding and Redundant Computing

In this paper, we study the problem of delay minimization in NFV-based networks. In such systems, the ultimate goal of any request is to compute a sequence of functions in the network, where each function can be computed at only a specific subset of network nodes. In conventional approaches, for each function, we choose one node from the corresponding subset of the nodes to compute that function. In contrast, in this work, we allow each function to be computed in more than one node, redundantly in parallel, to respond to a given request. We argue that such redundancy in computation not only improves the reliability of the network, but would also, perhaps surprisingly, reduce the overall transmission delay. In particular, we establish that by judiciously choosing the subset of nodes which compute each function, in conjunction with a linear network coding scheme to deliver the result of each computation, we can characterize and achieve the optimal end-to-end transmission delay. In addition, we show that using such technique, we can significantly reduce the transmission delay as compared to the conventional approach. In some scenarios, such reduction can even scale with the size of the network. More precisely, by increasing the number of nodes that can compute the given function in parallel by a multiplicative factor, the end-to-end delay will also decrease by the same factor. Moreover, we show that while finding the subset of nodes for each computation, in general, is a complex integer program, approximation algorithms can be proposed to reduce the computational complexity. In fact, for the case where the number of computing nodes for a given function is upper-bounded by a constant, a dynamic programming scheme can be proposed to find the optimum subsets in polynomial times. Our numerical simulations confirm the achieved gain in performance in comparison with conventional approaches.

cs.IT↗

$K$--User Interference Channel with Backhaul Cooperation: DoF vs. Backhaul Load Trade--Off

In this paper, we consider multiple-antenna $K$-user interference channels with backhaul collaboration in one side (among the transmitters or among the receivers) and investigate the trade-off between the rate in the channel versus the communication load in the backhaul. In this investigation, we focus on a first order approximation result, where the rate of the wireless channel is measured by the degrees of freedom (DoF) per user, and the load of the backhaul is measured by the entropy of backhaul messages per user normalized by $\log$ of transmit power, at high power regimes. This trade-off is fully characterized for the case of even values of $K$, and approximately characterized for the case of odd values of $K$, with vanishing approximation gap as $K$ grows. For full DoF, this result establishes the optimality (approximately) of the most straightforward scheme, called Centralized Scheme, in which the messages are collected at one of the nodes, centrally processed, and forwarded back to each node. In addition, this result shows that the gain of the schemes, relying on distributed processing, through pairwise communication among the nodes (e.g., cooperative alignment) does not scale with the size of the network. For the converse, we develop a new outer-bound on the trade-off based on splitting the set of collaborative nodes (transmitters or receivers) into two subsets, and assuming full cooperation within each group. In continue, we further investigate the trade-off for the cases, where the backhaul or the wireless links (interference channel) are not fully connected.

cs.IT↗

Multi-agent Learning for Cooperative Large-scale Caching Networks

Caching networks are designed to reduce traffic load at backhaul links, by serving demands from edge-nodes. In the past decades, many studies have been done to address the caching problem. However, in practice, finding an optimal caching policy is still challenging due to dynamicity of traffic and scalability caused by complex impact of caching strategy chosen by each individual cache on other parts of network. In this paper, we focus on cache placement to optimize the performance metrics such as hit ratio in cooperative large-scale caching networks. Our proposed solution, cooperative multi-agent based cache placement (CoM-Cache) is based on multi-agent reinforcement learning framework and can seamlessly track the content popularity dynamics in an on-line fashion. CoM-Cache is enable to solve the problems over a spectrum from isolated to interconnected caches and is designed flexibly to fit any caching networks. To deal with dimensionality issue, CoM-Cache exploits the property of locality of interactions among caches. The experimental results report CoM-Cache outperforms base-line schemes, however at the expense of reasonable additional complexity.

cs.NI↗

On Multi-Server Coded Caching in the Low Memory Regime

In this paper we determine the delivery time for a multi-server coded caching problem when the cache size of each user is small. We propose an achievable scheme based on coded cache content placement, and employ zero-forcing techniques at the content delivery phase. Surprisingly, in contrast to previous multi-server results which were proved to be order-optimal within a multiplicative factor of 2, for the low memory regime we prove that our achievable scheme is optimal. Moreover, we compare the performance of our scheme with the uncoded solution, and show our proposal improvement over the uncoded scheme. Our results also apply to Degrees-of-Freedom (DoF) analysis of Multiple-Input Single-Output Broadcast Channels (MISO-BC) with cache-enabled users, where the multiple-antenna transmitter replaces the role of multiple servers. This shows that interference management in the low memory regime needs different caching techniques compared with medium-high memory regimes discussed in previous works.

cs.IT↗

Physical-Layer Schemes for Wireless Coded Caching

We investigate the potentials of applying the coded caching paradigm in wireless networks. In order to do this, we investigate physical layer schemes for downlink transmission from a multiantenna transmitter to several cache-enabled users. As the baseline scheme we consider employing coded caching on top of max-min fair multicasting, which is shown to be far from optimal at high SNR values. Our first proposed scheme, which is near-optimal in terms of DoF, is the natural extension of multiserver coded caching to Gaussian channels. As we demonstrate, its finite SNR performance is not satisfactory, and thus we propose a new scheme in which the linear combination of messages is implemented in the finite field domain, and the one-shot precoding for the MISO downlink is implemented in the complex field. While this modification results in the same near-optimal DoF performance, we show that this leads to significant performance improvement at finite SNR. Finally, we extend our scheme to the previously considered cache-enabled interference channels, and moreover, we provide an Ergodic rate analysis of our scheme. Our results convey the important message that although directly translating schemes from the network coding ideas to wireless networks may work well at high SNR values, careful modifications need to be considered for acceptable finite SNR performance.

cs.IT↗