arXiv subjects

Moritz Grundei

Publications and source records attributed to Moritz Grundei.

7 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

Pricing Innovation Under Latency Constraints: A Mean-Field Analysis of Coded Payload Delivery

We study pricing mechanisms for low-latency payload delivery in settings where participant rewards depend on the time required to reconstruct a payload. In such environments, the decoding time distribution determines deadline-meeting probabilities and therefore bounds a participant's willingness to pay for additional delivery rate. Using a mean-field formulation, we derive price-rate bounds from simple stochastic arrival models and instantiate them for (i) unsharded transmission and (ii) sharded delivery under three regimes: uncoded sharding, fixed-rate erasure coding, and rateless coding. These bounds yield a comparative characterization of how symbol usefulness translates into economic value under deadline-driven utilities. We further analyze a two-lane service consisting of a base lane and a Random Linear Network Coding (RLNC) fast lane. In this turbo decoding setting, a receiver combines shards arriving via both lanes to minimize time to decode. Under a fixed base-lane price-rate pair and an aggregate rate constraint, we derive a fast-lane pricing bound and show how even modest additional RLNC rate can generate measurable utility gains, depending on the base-lane propagation regime. The framework extends naturally to stepwise reward schedules with multiple deadlines, and we illustrate its applicability on representative scenarios motivated by blockchain message dissemination and latency-sensitive competition.

cs.IT↗

arXiv2026

OPTIMUM-DERAM: Highly Consistent, Scalable, and Secure Multi-Object Memory using RLNC

This paper introduces OPTIMUM-DERAM, a highly consistent, scalable, secure, and decentralized shared memory solution. Traditional distributed shared memory implementations offer multi-object support by multi-threading a single object memory instance over the same set of data hosts. While theoretically sound, the amount of resources required made such solutions prohibitively expensive in practical systems. OPTIMUM-DERAM proposes a decentralized, reconfigurable, atomic read/write shared memory (DeRAM) that: (i) achieves improved performance and storage scalability by leveraging Random Linear Network Codes (RLNC); (ii) scales in the number of supported atomic objects by introducing a new object placement and discovery approach based on a consistent hashing ring; (iii) scales in the number of participants by allowing dynamic joins and departures leveraging a blockchain oracle to serve as a registry service; and (iv) is secure against malicious behavior by tolerating Byzantine failures. Experimental results over a globally distributed set of nodes, help us realize the performance and scalability gains of OPTIMUM-DERAM over previous distributed shared memory solutions (i.e., the ABD algorithm [3])

cs.DC↗

arXiv2025

Decoding in the presence of ISI without interleaving -- ORBGRAND-AI

Inter symbol interference (ISI), which occurs in a wide variety of channels, is a result of time dispersion. It can be mitigated by equalization, which results in noise coloring. Inspired by the development of Approximate Independence in statistical physics, for such colored noise we propose a decoder called Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND-AI) that operates without the need for turbo equalization or interleaving. By foregoing interleaving, ORBGRAND-AI can deliver the same, or lower, block error rate (BLER) for the same amount of energy per information bit in an ISI channel as a state-of-the-art soft input decoder, such as Cyclic Redundancy Check Assisted-Successive Cancellation List (CA-SCL) decoding, with an interleaver. To assess the decoding performance of ORBGRAND-AI, we consider delay tap models and their associated colored noise. In particular, we examine a two-tap dicode ISI channel as well as an ISI channel derived from data from RFView, a physics-informed modeling and simulation tool. We investigate the dicode and RFView channel under a variety of imperfect channel state information assumptions and show that a second order autoregressive model adequately represents the RFView channel effect.

eess.SP↗

arXiv2025

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↗

arXiv2023

Using channel correlation to improve decoding -- ORBGRAND-AI

To meet the Ultra Reliable Low Latency Communication (URLLC) needs of modern applications, there have been significant advances in the development of short error correction codes and corresponding soft detection decoders. A substantial hindrance to delivering low-latency is, however, the reliance on interleaving to break up omnipresent channel correlations to ensure that decoder input matches decoder assumptions. Consequently, even when using short codes, the need to wait to interleave data at the sender and de-interleave at the receiver results in significant latency that acts contrary to the goals of URLLC. Moreover, interleaving reduces channel capacity, so that potential decoding performance is degraded. Here we introduce a variant of Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND), which we call ORBGRAND-Approximate Independence (ORBGRAND-AI), a soft-detection decoder that can decode any moderate redundancy code and overcomes the limitation of existing decoding paradigms by leveraging channel correlations and circumventing the need for interleaving. By leveraging correlation, not only is latency reduced, but error correction performance can be enhanced by multiple dB, while decoding complexity is also reduced, offering one potential solution for the provision of URLLC.

cs.IT↗

arXiv2022

Evaluation of a Gaussian Mixture Model-based Channel Estimator using Measurement Data

In this work, we use real-world data in order to evaluate and validate a machine learning (ML)-based algorithm for physical layer functionalities. Specifically, we apply a recently introduced Gaussian mixture model (GMM)-based algorithm in order to estimate uplink channels stemming from a measurement campaign. For this estimator, there is an initial (offline) training phase, where a GMM is fitted onto given channel (training) data. Thereafter, the fitted GMM is used for (online) channel estimation. Our experiments suggest that the GMM estimator learns the intrinsic characteristics of a given base station's whole radio propagation environment. Essentially, this ambient information is captured due to universal approximation properties of the initially fitted GMM. For a large enough number of GMM components, the GMM estimator was shown to approximate the (unknown) mean squared error (MSE)-optimal channel estimator arbitrarily well. In our experiments, the GMM estimator shows significant performance gains compared to approaches that are not able to capture the ambient information. To validate the claim that ambient information is learnt, we generate synthetic channel data using a state-of-the-art channel simulator and train the GMM estimator once on these and once on the real data, and we apply the estimator once to the synthetic and once to the real data. We then observe how providing suitable ambient information in the training phase beneficially impacts the later channel estimation performance.

cs.IT↗