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Low-density parity-check (LDPC) codes form part of the IRIG-106 standard and have been successfully deployed for the Telemetry Group version of shaped-offset quadrature phase shift keying (SOQPSK-TG) modulation. Recently, LDPC code solutions have been proposed and optimized for continuous phase modulations (CPMs), including pulse code modulation/frequency modulation (PCM/FM) and the multi-h CPM developed by the Advanced-Range TeleMetry program (ARTM CPM), the latter of which was shown to perform around one dB from channel capacity. In this paper, we consider the effect of the random puncturing and shortening of these LDPC codes to further improve spectrum efficiency. We perform asymptotic analyses of the ARTM0 code ensembles and present numerical simulation results that affirm the robust decoding performance promised by LDPC codes designed for ARTM CPM. 

With the rapid growth of Internet of Vehicles (IoV) applications and the advancement of edge computing, resource-limited vehicles (and other IoT devices) increasingly rely on external servers to handle diverse and complex computational tasks. However, this dependence on external servers, which may be malicious or compromised, introduces significant security risks. Replication-based verifiable computing has been proposed as a solution to verify the accuracy of task results, but these approaches are vulnerable to collusion, where compromised servers return identical incorrect results to mislead the vehicle. Existing defenses against collusion either cannot ensure complete protection or become ineffective as the number of colluding servers rises. In this paper, we introduce CoVFeFE, a collusion-resilient verification framework designed to detect and mitigate collusion, even when the majority of servers are compromised. Our framework integrates a rapid detection mechanism that monitors computational conflicts, alongside a heuristic mitigation strategy that identifies and neutralizes colluding servers. Simulation results demonstrate that CoVFeFE outperforms existing solutions by successfully identifying all colluding servers, even when they constitute a majority > 50%) of the network. 

Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peerto- peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacypreserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE’s performance using Lightning Network data and prove its security using the Universal Composability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies.