arXiv subjects

Vipindev Adat Vasudevan

Publications and source records attributed to Vipindev Adat Vasudevan.

14 recordsLinked to original sources

arXiv2026

Optimum Peer-Turbo: A Scalable and Efficient Solution for P2P Broadcasting

Blockchain systems such as Solana or Monad employ tree- or star-shaped broadcast topologies in which a single source node disseminates message shards to a set of target peers within a strictly bounded time window. In these architectures, shard propagation must complete before the next consensus step, making timely delivery to a large fraction of the validator set essential. A fundamental limitation of such designs is that the outbound bandwidth of the source node constitutes the primary system bottleneck. In this paper, we introduce peer Turbo, a technique that allows target nodes to exchange shards using Random Linear Network Coding (RLNC), thereby assisting each other in completing decoding without requiring explicit shard state coordination. We use a tractable fluid approximation of the degree of freedom distribution of peer-Turbo-enabled systems show that this approach reduces source bandwidth required for a set service quality by up to one order of magnitude, or equivalently reduces propagation latency by one order of magnitude under fixed bandwidth constraints.

cs.IT↗

arXiv2026

Sub-Band Full Duplex Resource Allocation: A Predictive Deep Reinforcement Learning Approach

This paper presents a predictive deep learning framework for dynamic sub-band allocation in Sub-Band Full Duplex (SBFD) systems, addressing the challenge of balancing uplink (UL) and downlink (DL) performance under highly dynamic traffic conditions. The key contribution lies in integrating a hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) model for traffic forecasting with a Double Deep Q-Network (DDQN) for real-time resource allocation. Using both predicted traffic and current queue states, the proposed system enables proactive scheduling based on traffic demand. Evaluation results show that the prediction model achieves high accuracy in capturing bursty traffic patterns, while the DDQN agent effectively adapts UL/DL split ratios according to traffic variations. The framework improves spectrum utilization, reduces queue buildup, and avoids inefficient static configurations. The proposed approach demonstrates that combining predictive intelligence with reinforcement learning significantly enhances the efficiency and adaptability of SBFD systems, making it a strong candidate for autonomous resource management in future 6G networks.

cs.NI↗

arXiv2026

From Indexing to Coding: A New Paradigm for Data Availability Sampling

The data availability problem is a central challenge in blockchain systems and lies at the core of the accessibility and scalability issues faced by platforms such as Ethereum. Modern solutions employ several approaches, with data availability sampling (DAS) being the most self-sufficient and minimalistic in its security assumptions. Existing DAS methods typically form cryptographic commitments on codewords of fixed-rate erasure codes, which restrict light nodes to sampling from a predetermined set of coded symbols. In this paper, we introduce a new approach to DAS that modularizes the coding and commitment process by committing to the uncoded data while performing sampling through on-the-fly coding. The resulting samples are significantly more expressive, enabling light nodes to obtain, in concrete implementations, up to multiple orders of magnitude stronger assurances of data availability than from sampling pre-committed symbols from a fixed-rate redundancy code as done in established DAS schemes using Reed Solomon or low density parity check codes. We present a concrete protocol that realizes this paradigm using random linear network coding (RLNC).

cs.CR↗

arXiv2026

Revisiting the Interface between Error and Erasure Correction in Wireless Standards

Modern 5G communication systems implement a combination of error correction and feedback-based erasure correction (HARQ/ARQ) as reliability mechanisms, which can introduce substantial delay and resource inefficiency. We propose forward erasure correction using network coding as a more delay-efficient alternative. We present a mathematical characterization of network delay for existing reliability mechanisms and network coding. Through simulations in a network slicing environment, we demonstrate that network coding not only improves the in-order delivery delay and goodput for the applications utilizing the slice, but also benefits other applications sharing the network by reducing resource utilization for the coded slice. Our analysis and characterization point towards ideas that require attention in the 6G standardization process. These findings highlight the need for greater modularity in protocol stack design that enables the integration of novel technologies in future wireless networks.

cs.NI↗

arXiv2025

Rethinking Reliability Using Network Coding: a Practical 5G Evaluation

This work presents the design and implementation of a real-time network coding system integrated into the IP layer of a 5G testbed, offering an alternative to conventional retransmission-based reliability mechanisms such as ARQ and HARQ. Using a netfilter-based packet interception framework, we inject forward erasure correction using Random Linear Network Coding (RLNC) into live traffic between a gNB and UE over a 3GPP RF link. We evaluate a block coding scheme, analyzing its impact on throughput, jitter, and resource usage. Results show that with appropriate code rate selection, RLNC can fully recover from packet losses using fewer transmissions than ARQ/HARQ and maintain a high throughput, particularly under moderate-to-high packet loss rates. These findings demonstrate that network coding can effectively replace retransmission-based reliability in future wireless systems, with the potential for more efficient resource utilization.

cs.NI↗

arXiv2025

An Efficient Hybrid Key Exchange Mechanism

We present \textsc{CHOKE}, a novel code-based hybrid key-encapsulation mechanism (KEM) designed to securely and efficiently transmit multiple session keys simultaneously. By encoding $n$ independent session keys with an individually secure linear code and encapsulating each resulting coded symbol using a separate KEM, \textsc{CHOKE} achieves computational individual security -- each key remains secure as long as at least one underlying KEM remains unbroken. Compared to traditional serial or combiner-based hybrid schemes, \textsc{CHOKE} reduces computational and communication costs by an $n$-fold factor. Furthermore, we show that the communication cost of our construction is optimal under the requirement that each KEM must be used at least once.

cs.CR↗

arXiv2025

Cryptanalysis via Machine Learning Based Information Theoretic Metrics

The fields of machine learning (ML) and cryptanalysis share an interestingly common objective of creating a function, based on a given set of inputs and outputs. However, the approaches and methods in doing so vary vastly between the two fields. In this paper, we explore integrating the knowledge from the ML domain to provide empirical evaluations of cryptosystems. Particularly, we utilize information theoretic metrics to perform ML-based distribution estimation. We propose two novel applications of ML algorithms that can be applied in a known plaintext setting to perform cryptanalysis on any cryptosystem. We use mutual information neural estimation to calculate a cryptosystem's mutual information leakage, and a binary cross entropy classification to model an indistinguishability under chosen plaintext attack (CPA). These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem and the results can provide a useful empirical bound. We evaluate the efficacy of our methodologies by empirically analyzing several encryption schemes. Furthermore, we extend the analysis to novel network coding-based cryptosystems and provide other use cases for our algorithms. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy. It also identifies the faults in CPA-secure cryptosystems with faulty parameters, such a reduced counter version of AES-CTR. We also conclude that with our algorithms, in most cases a smaller-sized neural network using less computing power can identify vulnerabilities in cryptosystems, providing a quick check of the sanity of the cryptosystem and help to decide whether to spend more resources to deploy larger networks that are able to break the cryptosystem.

cs.CR↗

arXiv2024

Enhancing 5G Performance: Reducing Service Time and Research Directions for 6G Standards

This paper presents several methods for minimizing packet service time in networks using 5G and beyond. We propose leveraging network coding alongside Hybrid Automatic Repeat reQuest (HARQ) to reduce service time as well as optimizing Modulation and Coding Scheme (MCS) selection based on the service time. Our network coding approach includes a method to increase the number of packets in flight, adhering to the current standard of the 16 HARQ process limit, demonstrating that these strategies can enhance throughput and reduce latency. Experimental results show that network coding reduces service times by up to 7% in low SNR regimes, with greater reduction across all SNR as the number of packets in flight increases, suggesting that future 6G standards should consider increasing the number of HARQ processes for better performance.

cs.NI↗

arXiv2024

Leveraging AES Padding: dBs for Nothing and FEC for Free in IoT Systems

The Internet of Things (IoT) represents a significant advancement in digital technology, with its rapidly growing network of interconnected devices. This expansion, however, brings forth critical challenges in data security and reliability, especially under the threat of increasing cyber vulnerabilities. Addressing the security concerns, the Advanced Encryption Standard (AES) is commonly employed for secure encryption in IoT systems. Our study explores an innovative use of AES, by repurposing AES padding bits for error correction and thus introducing a dual-functional method that seamlessly integrates error-correcting capabilities into the standard encryption process. The integration of the state-of-the-art Guessing Random Additive Noise Decoder (GRAND) in the receiver's architecture facilitates the joint decoding and decryption process. This strategic approach not only preserves the existing structure of the transmitter but also significantly enhances communication reliability in noisy environments, achieving a notable over 3 dB gain in Block Error Rate (BLER). Remarkably, this enhanced performance comes with a minimal power overhead at the receiver - less than 15% compared to the traditional decryption-only process, underscoring the efficiency of our hardware design for IoT applications. This paper discusses a comprehensive analysis of our approach, particularly in energy efficiency and system performance, presenting a novel and practical solution for reliable IoT communications.

cs.ET↗

arXiv2024

On the Benefits of Coding for Network Slicing

Network slicing has emerged as an integral concept in 5G, aiming to partition the physical network infrastructure into isolated slices, customized for specific applications. We theoretically formulate the key performance metrics of an application, in terms of goodput and delivery delay, at a cost of network resources in terms of bandwidth. We explore an un-coded communication protocol that uses feedback-based repetitions, and a coded protocol, implementing random linear network coding and using coding-aware acknowledgments. We find that coding reduces the resource demands of a slice to meet the requirements for an application, thereby serving more applications efficiently. Coded slices thus free up resources for other slices, be they coded or not. Based on these results, we propose a hybrid approach, wherein coding is introduced selectively in certain network slices. This approach not only facilitates a smoother transition from un-coded systems to coded systems but also reduces costs across all slices. Theoretical findings in this paper are validated and expanded upon through real-time simulations of the network.

cs.NI↗

arXiv2023

CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography. We propose applying this methodology directly to estimate the MI between plaintext and ciphertext in a chosen plaintext attack. The leaked information, if any, from the encryption could potentially be exploited by adversaries to compromise the computational security of the cryptosystem. We evaluate the efficiency of our approach by empirically analyzing multiple encryption schemes and baseline approaches. Furthermore, we extend the analysis to novel network coding-based cryptosystems that provide individual secrecy and study the relationship between information leakage and input distribution.

cs.CR↗

arXiv2023

CERMET: Coding for Energy Reduction with Multiple Encryption Techniques -- $It's\ easy\ being\ green$

This paper presents CERMET, an energy-efficient hardware architecture designed for hardware-constrained cryptosystems. CERMET employs a base cryptosystem in conjunction with network coding to provide both information-theoretic and computational security while reducing energy consumption per bit. This paper introduces the hardware architecture for the system and explores various optimizations to enhance its performance. The universality of the approach is demonstrated by designing the architecture to accommodate both asymmetric and symmetric cryptosystems. The analysis reveals that the benefits of this proposed approach are multifold, reducing energy per bit and area without compromising security or throughput. The optimized hardware architectures can achieve below 1 pJ/bit operations for AES-256. Furthermore, for a public key cryptosystem based on Elliptic Curve Cryptography (ECC), a remarkable 14.6X reduction in energy per bit and a 9.3X reduction in area are observed, bringing it to less than 1 nJ/bit.

cs.CR↗

arXiv2023

Practical Sliding Window Recoder: Design, Analysis, and Usecases

Network coding has been widely used as a technology to ensure efficient and reliable communication. The ability to recode packets at the intermediate nodes is a major benefit of network coding implementations. This allows the intermediate nodes to choose a different code rate and fine-tune the outgoing transmission to the channel conditions, decoupling the requirement for the source node to compensate for cumulative losses over a multi-hop network. Block network coding solutions already have practical recoders but an on-the-fly recoder for sliding window network coding has not been studied in detail. In this paper, we present the implementation details of a practical recoder for sliding window network coding for the first time along with a comprehensive performance analysis of a multi-hop network using the recoder. The sliding window recoder ensures that the network performs closest to its capacity and that each node can use its outgoing links efficiently.

cs.NI↗

arXiv2022

An Integrated Approach for Energy Efficient Handover and Key Distribution Protocol for Secure NC-enabled Small Cells

Future wireless networks must serve dense mobile networks with high data rates, keeping energy requirements to a possible minimum. The small cell-based network architecture and device-to-device (D2D) communication are already being considered part of 5G networks and beyond. In such environments, network coding (NC) can be employed to achieve both higher throughput and energy efficiency. However, NC-enabled systems need to address security challenges specific to NC, such as pollution attacks. All integrity schemes against pollution attacks generally require proper key distribution and management to ensure security in a mobile environment. Additionally, the mobility requirements in small cell environments are more challenging and demanding in terms of signaling overhead. This paper proposes a blockchain-assisted key distribution protocol tailored for MAC-based integrity schemes, which combined with an uplink reference signal (UL RS) handover mechanism, enables energy efficient secure NC. The performance analysis of the protocol during handover scenarios indicates its suitability for ensuring high level of security against pollution attacks in dense small cell environments with multiple adversaries being present. Furthermore, the proposed scheme achieves lower bandwidth and signaling overhead during handover compared to legacy schemes and the signaling cost reduces significantly as the communication progresses, thus enhancing the network's cumulative energy efficiency.

cs.NI↗