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.

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.

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.

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.

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.

Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping

Learning representation from unlabeled time series data is a challenging problem. Most existing self-supervised and unsupervised approaches in the time-series domain do not capture low and high-frequency features at the same time. Further, some of these methods employ large scale models like transformers or rely on computationally expensive techniques such as contrastive learning. To tackle these problems, we propose a non-contrastive self-supervised learning approach efficiently captures low and high-frequency time-varying features in a cost-effective manner. Our method takes raw time series data as input and creates two different augmented views for two branches of the model, by randomly sampling the augmentations from same family. Following the terminology of BYOL, the two branches are called online and target network which allows bootstrapping of the latent representation. In contrast to BYOL, where a backbone encoder is followed by multilayer perceptron (MLP) heads, the proposed model contains additional temporal convolutional network (TCN) heads. As the augmented views are passed through large kernel convolution blocks of the encoder, the subsequent combination of MLP and TCN enables an effective representation of low as well as high-frequency time-varying features due to the varying receptive fields. The two modules (MLP and TCN) act in a complementary manner. We train an online network where each module learns to predict the outcome of the respective module of target network branch. To demonstrate the robustness of our model we performed extensive experiments and ablation studies on five real-world time-series datasets. Our method achieved state-of-art performance on all five real-world datasets.

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.

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

Person re-identification (re-ID) aims 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 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 regression 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 Regression 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 and outperforms the state-of-the-art GAN based models for re-ID in 4 benchmark datasets. The proposed model is robust to occlusion, scale and illumination, thereby outperforms the state-of-the-art models in terms of improvement over baseline.

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.

Decoding Orders and Power Allocation for Untrusted NOMA: A Secrecy Perspective

The amalgamation of non-orthogonal multiple access (NOMA) and physical layer security is a significant research interest for providing spectrally-efficient secure fifth-generation networks. Observing the secrecy issue among multiplexed NOMA users, which is stemmed from successive interference cancellation based decoding at receivers, we focus on safeguarding untrusted NOMA. Considering the problem of each user's privacy from each other, the appropriate secure decoding order and power allocation (PA) for users are investigated. Specifically, a decoding order strategy is proposed which is efficient in providing positive secrecy at all NOMA users. An algorithm is also provided through which all the feasible secure decoding orders in accordance with the proposed decoding order strategy can be obtained. Further, in order to maximize the sum secrecy rate of the system, the joint solution of decoding order and PA is obtained numerically. Also, a sub-optimal decoding order solution is proposed. Lastly, numerical results present useful insights on the impact of key system parameters and demonstrate that average secrecy rate performance gain of about 27 dB is obtained by the jointly optimized solution over different relevant schemes.