arXiv subjects

Deepak Mishra

Publications and source records attributed to Deepak Mishra.

At least 55 records · Page 3Linked to original sources

arXiv2023

QoS Aware Transmit Beamforming for Secure Backscattering in Symbiotic Radio Systems

This paper focuses on secure backscatter transmission in the presence of a passive multi-antenna eavesdropper through a symbiotic radio (SR) network. Specifically, a single-antenna backscatter device (BD) aims to transmit confidential information to a primary receiver (PR) by using a multi-antenna primary transmitter's (PT) signal, where the received symbols are jointly decoded at the PR. Our objective is to achieve confidential communications for BD while ensuring that the primary system's quality of service (QoS) requirements are met. We propose an alternating optimisation algorithm that maximises the achievable secrecy rate of BD by jointly optimising primary transmit beamforming and power sharing between information and artificial noise (AN) signals. Numerical results verify our analytical claims on the optimality of the proposed solution and the proposed methodology's underlying low complexity. Additionally, our simulations provide nontrivial design insights into the critical system parameters and quantify the achievable gains over the relevant benchmark schemes.

cs.IT↗

arXiv2023

Joint Optimization of IRS Deployment and Passive Beamforming to Enhance the Received Power

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for beyond fifth-generation (B5G) and 6G networks conceived from metamaterials that smartly tunes the signal reflections via a large number of low-cost passive reflecting elements. However, the IRS-assisted communication model and the optimization of available resources needs to be improved further for more efficient communications. This paper investigates the enhancement of received power at the user end in an IRS assisted wireless communication by jointly optimizing the phase shifts at the IRS elements and its location. Employing the conventional Friss transmission model, the relationship between the transmitted power and reflected power is established. The expression of received power incorporates the free space loss, reflection loss factor, physical dimension of the IRS panel, and radiation pattern of the transmit signal. Also, the expression of reflection coefficient of IRS panel is obtained by exploiting the existing data of radar communications. Initially exploring a single IRS element within a two-ray reflection model, we extend it to a more complex multi-ray reflection model with multiple IRS elements in 3D Cartesian space. The received power expression is derived in a more tractable form, then, it is maximized by jointly optimizing the underlying underlying variables, the IRS location and the phase shifts. To realize the joint optimization of underlying variables, first, the phase shifts of the IRS elements are optimized to achieve constructive interference of received signal components at the user. Subsequently, the location of the IRS is optimized at the obtained optimal phase shifts. Numerical insights and performance comparison reveal that joint optimization leads to a substantial 37% enhancement in received power compared to the closest competitive benchmark scheme.

cs.IT↗

arXiv2023

Secure Transmission in NOMA-enabled Industrial IoT with Resource-Constrained Untrusted Devices

The security of confidential information associated with devices in the industrial Internet of Things (IIoT) network is a serious concern. This article focuses on achieving a nonorthogonal multiple access (NOMA)-enabled secure IIoT network in the presence of untrusted devices by jointly optimizing the resources, such as decoding order and power allocated to devices. Assuming that the devices are resource-constrained for performing perfect successive interference cancellation (SIC), we characterize the residual interference at receivers with the linear model. Firstly, considering all possible decoding orders in an untrusted scenario, we obtain secure decoding orders that are feasible to obtain a positive secrecy rate for each device. Then, under the secrecy fairness criterion, we formulate a joint optimization problem of maximizing the minimum secrecy rate among devices. Since the formulated problem is non-convex and combinatorial, we first obtain the optimal secure decoding order and then solve it for power allocation by analyzing Karush-Kuhn-Tucker points. Thus, we provide the closed-form global-optimal solution of the formulated optimization problem. Numerical results validate the analytical claims and demonstrate an interesting observation that the conventional decoding order and assigning more power allocation to the weak device, as presumed in many works on NOMA, is not an optimal strategy from the secrecy fairness viewpoint. Also, the average percentage gain of about 22.75%, 50.58%, 94.59%, and 98.16%, respectively, is achieved by jointly optimized solution over benchmarks ODEP (optimal decoding order, equal power allocation), ODFP (optimal decoding order, fixed power allocation), FDEP (fixed decoding order, equal power allocation), and FDFP (fixed decoding order, fixed power allocation).

cs.IT↗

arXiv2023

Secrecy Outage Probability Analysis for Downlink Untrusted NOMA Under Practical SIC Error

Non-orthogonal multiple access (NOMA) serves multiple users simultaneously via the same resource block by exploiting superposition coding at the transmitter and successive interference cancellation (SIC) at the receivers. Under practical considerations, perfect SIC may not be achieved. Thus, residual interference (RI) occurs inevitably due to imperfect SIC. In this work, we first propose a novel model for characterizing RI to provide a more realistic secrecy performance analysis of a downlink NOMA system under imperfect SIC at receivers. In the presence of untrusted users, NOMA has an inherent security flaw. Therefore, for this untrusted users' scenario, we derive new analytical expressions of secrecy outage probability (SOP) for each user in a two-user untrusted NOMA system by using the proposed RI model. To further shed light on the obtained results and obtain a deeper understanding, a high signal-to-noise ratio approximation of the SOPs is also obtained. Lastly, numerical investigations are provided to validate the accuracy of the desired analytical results and present valuable insights into the impact of various system parameters on the secrecy rate performance of the secure NOMA communication system.

cs.IT↗

arXiv2023

Secrecy Outage Probability Analysis for Downlink NOMA with Imperfect SIC at Untrusted Users

Non-orthogonal multiple access (NOMA) has come to the fore as a spectrally efficient technique for fifth-generation networks and beyond. At the same time, NOMA faces severe security issues in the presence of untrusted users due to successive interference cancellation (SIC)-based decoding at receivers. In this paper, to make the system model more realistic, we consider the impact of imperfect SIC during the decoding process. Assuming the downlink mode, we focus on designing a secure NOMA communication protocol for the considered system model with two untrusted users. In this regard, we obtain the power allocation bounds to achieve a positive secrecy rate for both near and far users. Analytical expressions of secrecy outage probability (SOP) for both users are derived to analyze secrecy performance. Closed-form approximations of SOPs are also provided to gain analytical insights. Lastly, numerical results have been presented, which validate the exactness of the analysis and reveal the effect of various key parameters on achieved secrecy performance.

cs.IT↗

arXiv2023

Untrusted NOMA with Imperfect SIC: Outage Performance Analysis and Optimization

Non-orthogonal multiple access (NOMA) has come to the fore as a spectral-efficient technique for fifth-generation and beyond communication networks. We consider the downlink of a NOMA system with untrusted users. In order to consider a more realistic scenario, imperfect successive interference cancellation is assumed at the receivers during the decoding process. Since pair outage probability (POP) ensures a minimum rate guarantee to each user, it behaves as a measure of the quality of service for the pair of users. With the objective of designing a reliable communication protocol, we derive the closed-form expression of POP. Further, we find the optimal power allocation that minimizes the POP. Lastly, numerical results have been presented which validate the exactness of the analysis, and reveal the effect of various key parameters on achieved pair outage performance. In addition, we benchmark optimal power allocation against equal and fixed power allocations with respect to POP. The results indicate that optimal power allocation results in improved communication reliability.

cs.IT↗

arXiv2023

User-Pair Selection for QoS-Aware Secrecy Rate Maximization in Untrusted NOMA

Non-orthogonal multiple access (NOMA) has been recognized as one of the key enabling technologies for future generation wireless networks. Sharing the same time-frequency resource among users imposes secrecy challenges in NOMA in the presence of untrusted users. This paper characterizes the impact of user-pair selection on the secrecy performance of an untrusted NOMA system. In this regard, an optimization problem is formulated to maximize the secrecy rate of the strong user while satisfying the quality of service (QoS) demands of the user with poorer channel conditions. To solve this problem, we first obtain optimal power allocation in a two-user NOMA system, and then investigate the user-pair selection problem in a more generalized four user NOMA system. Extensive performance evaluations are conducted to validate the accuracy of the proposed results and present valuable insights on the impact of various system parameters on the secrecy performance of the NOMA communication system.

cs.IT↗

arXiv2023

On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.

cs.AI↗

arXiv2023

Distilling Calibrated Student from an Uncalibrated Teacher

Knowledge distillation is a common technique for improving the performance of a shallow student network by transferring information from a teacher network, which in general, is comparatively large and deep. These teacher networks are pre-trained and often uncalibrated, as no calibration technique is applied to the teacher model while training. Calibration of a network measures the probability of correctness for any of its predictions, which is critical in high-risk domains. In this paper, we study how to obtain a calibrated student from an uncalibrated teacher. Our approach relies on the fusion of the data-augmentation techniques, including but not limited to cutout, mixup, and CutMix, with knowledge distillation. We extend our approach beyond traditional knowledge distillation and find it suitable for Relational Knowledge Distillation and Contrastive Representation Distillation as well. The novelty of the work is that it provides a framework to distill a calibrated student from an uncalibrated teacher model without compromising the accuracy of the distilled student. We perform extensive experiments to validate our approach on various datasets, including CIFAR-10, CIFAR-100, CINIC-10 and TinyImageNet, and obtained calibrated student models. We also observe robust performance of our approach while evaluating it on corrupted CIFAR-100C data.

cs.CV↗

arXiv2023

Automated classification of Chandra X-ray point sources using machine learning methods

A large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning methods. The aim of this paper is to find a suitable automated classifier to identify the point X-ray sources in the Chandra Source Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs), low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs), cataclysmic variables (CVs), and pulsars. The catalogue consists of approx 3,17,000 sources, out of which we select 2,77,069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use multi-wavelength features, such as magnitudes in optical/UV bands from Gaia-EDR3, SDSS and GALEX, and magnitudes in IR bands from 2MASS, WISE and MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC 2.0. We find the Light Gradient Boosted Machine, an advanced decision tree-based machine learning classification algorithm, suitable for our purpose and achieve $93\%$ precision, $93\%$ recall score and 0.91 Mathew's Correlation coefficient score. With the trained classifier, we identified 54,770 (14,066) sources with more than $3σ$ (4$σ$) confidence, out of which there are 32,600 (8,574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46) HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method can also be useful for identifying sources of other catalogues reliably.

astro-ph.HE↗

arXiv2022

Workload Similarity Analysis using Machine Learning Techniques

Finding the similarity between two workload behaviors is helpful in 1. creating proxy workloads 2. characterizing an unknown workload's behavior by matching its behavior against known workloads. In this article, we propose a method to measure the similarity between two workloads using machine learning-based analysis of the performance telemetry data collected for the execution runs of the two workloads. We also demonstrate the accuracy of the technique by measuring the similarity between a variety of know benchmark workloads.

cs.PF↗

arXiv2022

Privacy Preserving Release of Mobile Sensor Data

Sensors embedded in mobile smart devices can monitor users' activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the risk of accessing and potentially disclosing sensitive information of individuals to the apps that may lead to privacy breaches. In this paper, we aim to minimize privacy leakages that may lead to user identification on mobile devices through user tracking and distinguishability while preserving the functionality of apps and services. We propose a privacy-preserving mechanism that effectively handles the sensor data fluctuations (e.g., inconsistent sensor readings while walking, sitting, and running at different times) by formulating the data as time-series modeling and forecasting. The proposed mechanism also uses the notion of correlated noise-series against noise filtering attacks from an adversary, which aims to filter out the noise from the perturbed data to re-identify the original data. Unlike existing solutions, our mechanism keeps running in isolation without the interaction of a user or a service provider. We perform rigorous experiments on benchmark datasets and show that our proposed mechanism limits user tracking and distinguishability threats to a significant extent compared to the original data while maintaining a reasonable level of utility of functionalities. In general, we show that our obfuscation mechanism reduces the user trackability threat by 60\% across all the datasets while maintaining the utility loss below 0.5 Mean Absolute Error (MAE). We also observe that our mechanism is more effective in large datasets. For example, with the Swipes dataset, the distinguishability risk is reduced by 60\% on average while the utility loss is below 0.5 MAE.

cs.CR↗

arXiv2022

GITz: Graphene-assisted IRS Design for THz Communication

Graphene-based intelligent reflecting surface (GIRS) has been proved to provide a promising propagation environment to enhance the quality of high frequency terahertz (THz) wireless communication. In this paper, we characterize GIRS for THz communication (GITz) using material specific parameters of graphene to tune the reflection of the incident wave at IRS. In particular, we propose a GITz design model considering the incident signal frequency material level parameters like conductivity, Fermi-level, patch width to control the reflection amplitude (RA) at the communication receiver. We have obtained the closed-form expression of RA for an accurate design and characterization of GIRS, which is incomplete in the existing research due to the inclusion of only phase-shift. The numerical simulation results demonstrate the effectiveness of the proposed characterization by providing key insights.

eess.SP↗

arXiv2021

Circuit Characterization of IRS to Control Beamforming Design for Efficient Wireless Communication

Intelligent reflecting surface (IRS) has emerged as a transforming solution to enrich wireless communications by efficiently reconfiguring the propagation environment. In this paper, a novel IRS circuit characterization model is proposed for practical beamforming design incorporating various electrical parameters of the meta-surface unit cell. Specifically, we have modelled the IRS control parameters, phase shift (PS) and reflection amplitude (RA) at the communication receiver, in addition to the circuit level parameter, variable effective capacitance $C$ of IRS unit cell. We have obtained closed-form expressions of PS, RA and $C$ in terms of transmission frequency of signal incident to IRS and various electrical parameters of IRS circuit, with a novel touch towards an accurate analytical model for a better beamforming design perspective. Numerical results demonstrate the efficacy of the proposed characterization thereby providing key design insights.

eess.SP↗

arXiv2021

Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

The objective of person re-identification (re-ID) is to retrieve a person's images from an image gallery, given a single instance of the person of interest. Despite several advancements, learning discriminative identity-sensitive and viewpoint invariant features for robust Person Re-identification is a major challenge owing to the large pose variation of humans. This paper proposes a re-ID pipeline that utilizes the image generation capability of Generative Adversarial Networks combined with pose clustering and feature fusion to achieve pose invariant feature learning. The objective is to model a given person under different viewpoints and large pose changes and extract the most discriminative features from all the appearances. The pose transformational GAN (pt-GAN) module is trained to generate a person's image in any given pose. In order to identify the most significant poses for discriminative feature extraction, a Pose Clustering module is proposed. The given instance of the person is modelled in varying poses and these features are effectively combined through the Feature Fusion Network. The final re-ID model consisting of these 3 sub-blocks, alleviates the pose dependence in person re-ID. Also, The proposed model is robust to occlusion, scale, rotation and illumination, providing a framework for viewpoint invariant feature learning. The proposed method outperforms the state-of-the-art GAN based models in 4 benchmark datasets. It also surpasses the state-of-the-art models that report higher re-ID accuracy in terms of improvement over baseline.

cs.CV↗

arXiv2021

Novel Outage-Aware NOMA Protocol for Secrecy Fairness Maximization Among Untrusted Users

Observing the significance of spectrally-efficient secure non-orthogonal multiple access (NOMA), this paper proposes a novel quality of service (QoS) aware secure NOMA protocol that maximizes secrecy fairness among untrusted users. Considering a base station (BS) and two users, a novel decoding order is designed that provides security to both users. With the objective of ensuring secrecy fairness between users, while satisfying their QoS requirements under BS transmit power budget constraint, we explore the problem of minimizing the maximum secrecy outage probability (SOP). Closed-form expression of pair outage probability (POP) and optimal power allocation (PA) minimizing POP are obtained. To analyze secrecy performance, analytical expressions of SOP for both users are derived, and individual SOP minimization problems are solved using concept of generalized-convexity. High signal-to-noise ratio approximation of SOP and asymptotically optimized solution minimizing this approximation is also found. Furthermore, a global-optimal solution from secrecy fairness standpoint is obtained at low computational complexity, and tight approximation is derived to get analytical insights. Numerical results present useful insights on globally optimized PA which ensure secrecy fairness and provide performance gain of about 55.12%, 69.30%, and 19.11%, respectively, compared to fixed PA and individual users' optimal PAs. Finally, a tradeoff between secrecy fairness performance and QoS demands is presented.

cs.IT↗

arXiv2021

Secrecy Fairness Aware NOMA for Untrusted Users

Spectrally-efficient secure non-orthogonal multiple access (NOMA) has recently attained a substantial research interest for fifth generation development. This work explores crucial security issue in NOMA which is stemmed from utilizing the decoding concept of successive interference cancellation. Considering untrusted users, we design a novel secure NOMA transmission protocol to maximize secrecy fairness among users. A new decoding order for two users' NOMA is proposed that provides positive secrecy rate to both users. Observing the objective of maximizing secrecy fairness between users under given power budget constraint, the problem is formulated as minimizing the maximum secrecy outage probability (SOP) between users. In particular, closed-form expressions of SOP for both users are derived to analyze secrecy performance. SOP minimization problems are solved using pseudoconvexity concept, and optimized power allocation (PA) for each user is obtained. Asymptotic expressions of SOPs, and optimal PAs minimizing these approximations are obtained to get deeper insights. Further, globally-optimized power control solution from secrecy fairness perspective is obtained at a low computational complexity and, asymptotic approximation is obtained to gain analytical insights. Numerical results validate the correctness of analysis, and present insights on optimal solutions. Finally, we present insights on global-optimal PA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%, respectively are achieved, compared to fixed PA and individual users' optimal PAs.

cs.IT↗

arXiv2021

Decoding Orders for Securing Untrusted NOMA

This letter focuses on exploring a new decoding order to resolve the secrecy issue among untrusted nonorthogonal multiple access users. In this context, firstly, we identify the total number of possible decoding orders analytically. Then, we propose a decoding order strategy ensuring positive secrecy rate for all users, and obtain the number of feasible secure decoding orders based on this proposed strategy numerically. Thereafter, we present a suboptimal policy to avoid the computational complexity involved in finding the best secure decoding order. Numerical results illustrate that the suboptimal solution provides a performance gain of about 137% over therelevant benchmark.

cs.IT↗