arXiv subjects

Rahul Jashvantbhai Pandya

Publications and source records attributed to Rahul Jashvantbhai Pandya.

12 recordsLinked to original sources

arXiv2026

Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence

Sixth-generation (6G) wireless networks are envisioned as AI-native systems in which semantic communication - transmitting task-relevant meaning rather than raw bits - moves beyond Shannon's classical bit-pipe model. Large language models (LLMs) dominate semantic encoding but are unsuitable for 6G user equipment and IoT devices, given prohibitive memory, energy, and latency costs. Tiny language models (TinyLMs) - compressed via TinyML techniques into kilobyte-to-megabyte memory and milliwatt power budgets - are the missing bridge between LLM-level semantic encoding and 6G edge hardware, yet no prior work systematically maps TinyML techniques onto semantic communication architectures for this purpose. This survey closes that gap through a two-axis taxonomy connecting six compression families (quantization, pruning, knowledge distillation, low-rank adaptation, neural architecture search, hybrid pipelines) to five semantic communication architectures (end-to-end joint source-channel coding, split learning, federated learning, knowledge-graph-assisted, and multi-task/cross-modal communication), synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier. Representative results include a CNN-Transformer encoder achieving 22 dB PSNR at 33.33% semantic-representation size reduction; a symbolic protocol machine reducing a neural MAC protocol from 4.55 MB to 1 KB (99.98% smaller) with zero performance loss; federated bidirectional knowledge distillation converging under joint model-and-data heterogeneity where FedAvg-style averaging underperforms; and knowledge-graph-assisted probability graphs cutting transmission energy by 65%. The survey identifies nine open research challenges for TinyLM-enabled 6G semantic communication, including two not previously articulated in the literature.

eess.SP↗

arXiv2026

Bridging the Semantic Gap in 6G: Tiny Language Models Under the Latency-Accuracy-Size Trilemma

Sixth-generation (6G) wireless networks are expected to serve as AI-native infrastructure, transmitting meaning rather than mere bits -- a shift that makes semantic communication the central paradigm for next-generation connectivity. Deep learning-based semantic encoders show compelling gains in bandwidth efficiency; however, their dependence on large transformer models with hundreds of millions of parameters is at odds with the sub-millisecond latency, microjoule energy budgets, and kilobyte memory footprints of the constrained IoT and edge devices that will dominate 6G endpoints. Tiny language models (t-LMs) -- compact, quantised, task-specialised models deployable on microcontrollers, mobile system-on-chips, and edge accelerators -- are the enabling technology for closing this gap. This review provides a unified treatment of (i) the theoretical foundations of semantic information, covering semantic entropy, channel capacity, and rate-distortion theory; (ii) a two-axis taxonomy of t-LM-based semantic communication systems across five architecture classes and six compression paradigms; (iii) a survey of model compression techniques -- quantisation, pruning, knowledge distillation, low-rank adaptation, split computing, and neural architecture search -- through the lens of semantic quality preservation; and (iv) semantic-aware resource allocation frameworks for 6G multi-user networks. Evidence across the surveyed literature shows that compression can reduce semantic encoder size by up to 99.98% while preserving task accuracy, that split computing achieves device-side encoders with as few as 640 parameters, and that knowledge graph integration cuts transmission energy by 65%. Seven open challenges are identified, spanning theoretical gaps, system design, knowledge-base management, post-quantum security, and hardware co-design, with a 3GPP standardisation roadmap toward IMT-2030.

eess.SP↗

arXiv2026

Semantic Communication for 6G Networks: A Trade-off between Distortion Criticality and Information Representability

In this work, a self-attention based conditional generative adversarial network (SA-cGAN) framework for the sixth generation (6G) semantic communication system is proposed, explicitly designed to balance the trade-off between distortion criticality and information representability under varying channel conditions. The proposed SA-cGAN model continuously learns compact semantic representations by jointly considering semantic importance, reconstruction distortion, and channel quality, enabling adaptive selection of semantic tokens for transmission. A knowledge graph is integrated to preserve contextual relationships and enhance semantic robustness, particularly in low signal-to-noise ratio (SNR) regimes. The resulting optimization framework incorporates continuous relaxation, submodular semantic selection, and principled constraint handling, allowing efficient semantic resource allocation under bandwidth and multi-constraint conditions. Simulation results show that, although SA-cGAN achieves modest syntactic bilingual evaluation understudy scores at low SNR to approximately 0.72 at 20 dB, it significantly outperforms conventional and JSCC-based schemes in semantic metrics, with semantic similarity, semantic accuracy, and semantic completeness consistently improving above 0.90 with SNR. Additionally, the model exhibits adaptive compression behavior, aggressively reducing redundant content while preserving critical semantic information to maintain fidelity. The convergence of training loss further validates stable and efficient learning of semantic representations. Overall, the results confirm that the proposed SA-cGAN model effectively captures distortion-invariant semantic representations and dynamically adapts transmitted content based on distortion criticality and information representability for meaning-centric communication in future 6G networks.

eess.SP↗

arXiv2025

Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer

Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) architecture, the ``Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios, with its efficacy showcased using the SIPaKMeD dataset. The performance of this DL model was observed to be similar to the state-of-the-art models, with accuracy variations of a mere 2\% to 3\%, achieved using just 1.7 million learnable parameters, which is approximately 85 times less than the VGG-19 model. Furthermore, we introduced a multi-task learning technique that simultaneously performs segmentation and classification tasks and begets an Intersection over Union score of 0.83 and a classification accuracy of 90\%. The final stage of the workflow employs a probabilistic approach for risk assessment, extracting feature vectors to predict the likelihood of normal cells progressing to malignant states, which can be utilized for the prognosis of cervical cancer.

eess.IV↗

arXiv2024

AI-Based Copyright Detection Of An Image In a Video Using Degree Of Similarity And Image Hashing

The expanse of information available over the internet makes it difficult to identify whether a specific work is a replica or a duplication of a protected work, especially if we talk about visual representations. Strategies are planned to identify the utilization of the copyrighted image in a report. Still, we want to resolve the issue of involving a copyrighted image in a video and a calculation that could recognize the degree of similarity of the copyrighted picture utilized in the video, even for the pieces of the video that are not featured a lot and in the end perform characterization errands on those edges. Machine learning (ML) and artificial intelligence (AI) are vital to address this problem. Numerous associations have been creating different calculations to screen the identification of copyrighted work. This work means concentrating on those calculations, recognizing designs inside the information, and fabricating a more reasonable model for copyrighted image classification and detection. We have used different algorithms like- Image Processing, Convolutional Neural Networks (CNN), Image hashing, etc. Keywords- Copyright, Artificial Intelligence(AI), Copyrighted Image, Convolutional Neural Network(CNN), Image processing, Degree of similarity, Image Hashing.

cs.CV↗

arXiv2022

A Survey on Semantic Communications for Intelligent Wireless Networks

With deployment of 6G technology, it is envisioned that competitive edge of wireless networks will be sustained and next decade's communication requirements will be stratified. Also 6G will aim to aid development of a human society which is ubiquitous and mobile, simultaneously providing solutions to key challenges such as, coverage, capacity, etc. In addition, 6G will focus on providing intelligent use-cases and applications using higher data-rates over mill-meter waves and Tera-Hertz frequency. However, at higher frequencies multiple non-desired phenomena such as atmospheric absorption, blocking, etc., occur which create a bottleneck owing to resource (spectrum and energy) scarcity. Hence, following same trend of making efforts towards reproducing at receiver, exact information which was sent by transmitter, will result in a never ending need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (context) of received data as opposed to only reproducing correct transmitted data. This in turn will require less bandwidth, and will reduce bottleneck due to various undesired phenomenon. In this respect, current article presents a detailed survey on recent technological trends in regard to semantic communications for intelligent wireless networks. We focus on semantic communications architecture including model, and source and channel coding. Next, we detail cross-layer interaction, and various goal-oriented communication applications. We also present overall semantic communications trends in detail, and identify challenges which need timely solutions before practical implementation of semantic communications within 6G wireless technology. Our survey article is an attempt to significantly contribute towards initiating future research directions in area of semantic communications for intelligent 6G wireless networks.

cs.NI↗

arXiv2022

Survey on Wireless Information Energy Transfer (WIET) and Related Applications in 6G Internet of NanoThings (IoNT)

This article contains an overview of WIET and the related applications in 6G IoNT. Specifically, to explore the following, we: (i) introduce the 6G network along with the implementation challenges, possible techniques, THz communication and related research challenges, (ii) focus on the WIET architecture, and different energy carrying code words for efficient charging through WIET, (iii) discuss IoNT with techniques proposed for communication of nano-devices, and (iv) conduct a detailed literature review to explore the implicational aspects of the WIET in the 6G nano-network. In addition, we also investigate the expected applications of WIET in the 6G IoNT based devices and discuss the WIET implementation challenges in 6G IoNT for the optimal use of the technology. Lastly, we overview the expected design challenges which may occur during the implementation process, and identify the key research challenges which require timely solutions and which are significant to spur further research in this challenging area. Overall, through this survey, we discuss the possibility to maximize the applications of WIET in 6G IoNT.

cs.NI↗

arXiv2022

Survey on Internet of Things enabled by 6G Wireless Networks

The 6G wireless technology is visualized to revolutionize multiple customer services with the Internet of Things (IoT), thereby contributing to a ubiquitous intelligent society comprising autonomous systems. In this chapter, we conduct a detailed survey on the IoT networks with 6G wireless networks and investigate the trending possibilities provided by the 6G technology within the IoT networks and the related utilization; Firstly, we detail the breakthrough IoT technologies and the technological drivers which are anticipated to strengthen IoT networks in future. Next, we present the relevant use cases detailing the discussion on the role of the 6G technology within a broad spectrum of IoT potential applications. Lastly, we highlight the several research scope and challenges and list the potential research needs and encourage further research within the thrust area of IoT enabled by 6G networks.

cs.NI↗

arXiv2022

A Survey of Machine Learning Algorithms for 6G Wireless Networks

The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional algorithms with deep learning AI techniques have dramatically reduced the power consumption and improved the system performance. Further, implementation of ML algorithms also enables the wireless network service providers to (i) offer high automation levels from distributed AI/ML architectures applicable at the network edge, (ii) implement application-based traffic steering across the access networks, (iii) enable dynamic network slicing for addressing different scenarios with varying quality of service requirements, and (iv) enable ubiquitous connectivity across the various 6G communication platforms. In this chapter, we review/survey the ML techniques which are applicable to the 6G wireless networks. and also list the open problems of research which require timely solutions.

cs.NI↗

arXiv2022

A Survey on Technological Trends to Enhance Spectrum Efficiency in 6G Communications

The research community has already identified that, by 2030, 5G networks will reach the capacity limits, and hence, will be inadequate to support next generation bandwidth-hungry, ubiquitous, intelligent services, and applications. Therefore, in view of sustaining the competitive edge of wireless technology and stratifying the next decade's communication requirements both, industry and research community have already begun conceptualizing the 6G technology. This article presents a detailed survey on the recent technological trends which address the capacity issues and enhance the spectrum-efficiency in 6G Communications. We present these trends in detail and then identify the challenges that need solutions before the practical deployment to realize 6G communications. Our survey article attempts to significantly contribute to initiating future research directions in the area of spectrum-efficiency in 6G communications.

cs.NI↗

arXiv2020

Deep Learning Based Load Balancing for improved QoS towards 6G

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future 6G intelligent wireless networks. Load balancing presents an efficient, cost-effective method to improve the data process capability, throughput, and expand the bandwidth, thus enhancing the adaptability and availability of networks. Hence a load balancing algorithm based on Long Short Term Memory(LSTM) deep neural network is proposed through which the coverage area of base station changes according to geographic traffic distribution, catering the requirement for future generation 6G heterogeneous network. The LSTM model performance is evaluated by considering three different scenarios, and the results were presented. Load variance coefficient(LVC) and load factor(LF) are introduced and validated over two wireless network layouts(WNL) to study the Quality of Service(QoS) and load distribution. The proposed method shows a decrease of LVC by 98.311% and 99.21% for WNL1, WNL2 respectively.

eess.SP↗

arXiv2020

AEVB-Comm: An Intelligent CommunicationSystem based on AEVBs

In recent years, applying Deep Learning (DL) techniques emerged as a common practice in the communication system, demonstrating promising results. The present paper proposes a new Convolutional Neural Network (CNN) based Variational Autoencoder (VAE) communication system. The VAE (continuous latent space) based communication systems confer unprecedented improvement in the system performance compared to AE (distributed latent space) and other traditional methods. We have introduced an adjustable hyperparameter beta in the proposed VAE, which is also known as beta-VAE, resulting in extremely disentangled latent space representation. Furthermore, a higher-dimensional representation of latent space is employed, such as 4n dimension instead of 2n, reducing the Block Error Rate (BLER). The proposed system can operate under Additive Wide Gaussian Noise (AWGN) and Rayleigh fading channels. The CNN based VAE architecture performs the encoding and modulation at the transmitter, whereas decoding and demodulation at the receiver. Finally, to prove that a continuous latent space-based system designated VAE performs better than the other, various simulation results supporting the same has been conferred under normal and noisy conditions.

eess.SP↗