arXiv subjects

Tolga M Duman

Publications and source records attributed to Tolga M Duman.

5 recordsLinked to original sources

arXiv2026

Learning-Enhanced Composite DNA Data Storage Under Sampling Randomness and IDS Errors

DNA data storage offers a high-density, long-term alternative to conventional storage systems, addressing the exponential growth of digital data. Composite DNA extends this paradigm by leveraging mixtures of nucleotides to increase storage capacity beyond the four standard bases. In this work, composite DNA storage is modeled as a multinomial channel, and an analogy to digital modulation is established by representing composite letters on the three-dimensional probability simplex. To mitigate errors caused by sampling randomness, we derive transition probabilities for each constellation point which enables the computation of bit-wise log-likelihood ratios (LLRs) to employ practical channel codes for error correction. The framework is then extended to substitution and insertion-deletion-substitution (IDS) channels by proposing constellation update rules that account for these impairments. For the substitution channel, exact constellation updates enable exact LLR computation. In contrast, for the IDS channel, the large number of possible cases necessitates an approximate update rule, yielding approximate LLRs. To address this limitation, we further propose two learning-enhanced receiver architectures: (1) RhoNet, a learning-assisted model-based method that refines constellation points prior to analytical LLR computation, and (2) LLRNet, which directly estimates LLRs, representing a progressive transition from model-based analytical estimation to fully data-driven processing. Numerical results demonstrate reliable performance with existing low-density parity-check (LDPC) codes.

cs.IT↗

arXiv2025

Capacity Approximations for Insertion Channels with Small Insertion Probabilities

Channels with synchronization errors, exhibiting deletion and insertion errors, find practical applications in DNA storage, data reconstruction, and various other domains. Presence of insertions and deletions render the channel with memory, complicating capacity analysis. For instance, despite the formulation of an independent and identically distributed (i.i.d.) deletion channel more than fifty years ago, and proof that the channel is information stable, hence its Shannon capacity exists, calculation of the capacity remained elusive. However, a relatively recent result establishes the capacity of the deletion channel in the asymptotic regime of small deletion probabilities by computing the dominant terms of the capacity expansion. This paper extends that result to binary insertion channels, determining the dominant terms of the channel capacity for small insertion probabilities and establishing capacity in this asymptotic regime. Specifically, we consider two i.i.d. insertion channel models: insertion channel with possible random bit insertions after every transmitted bit and the Gallager insertion model, for which a bit is replaced by two random bits with a certain probability. To prove our results, we build on methods used for the deletion channel, employing Bernoulli(1/2) inputs for achievability and coupling this with a converse using stationary and ergodic processes as inputs, and show that the channel capacity differs only in the higher order terms from the achievable rates with i.i.d. inputs. The results, for instance, show that the capacity of the random insertion channel is higher than that of the Gallager insertion channel, and quantifies the difference in the asymptotic regime.

cs.IT↗

arXiv2025

On the Capacity of Insertion Channels for Small Insertion Probabilities

Channels with synchronization errors, such as deletion and insertion errors, are crucial in DNA storage, data reconstruction, and other applications. These errors introduce memory to the channel, complicating its capacity analysis. This paper analyzes binary insertion channels for small insertion probabilities, identifying dominant terms in the capacity expansion and establishing capacity in this regime. Using Bernoulli(1/2) inputs for achievability and a converse based on the use of stationary and ergodic processes, we demonstrate that capacity closely aligns with achievable rates using independent and identically distributed (i.i.d.) inputs, differing only in higher-order terms.

cs.IT↗

arXiv2025

Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing

Deep neural networks provide reliable solutions for many classification and regression tasks; however, their application in real-time wireless systems with simple sensor networks is limited due to high energy consumption and significant bandwidth needs. This study proposes a multi-sensor wireless inference system with memristor-based analog computing. Given the sensors' limited computational capabilities, the features from the network's front end are transmitted to a central device where an $L_p$-norm inspired approximation of the maximum operation is employed to achieve transformation-invariant features, enabling efficient over-the-air transmission. We also introduce a trainable over-the-air sensor fusion method based on $L_p$-norm inspired combining function that customizes sensor fusion to match the network and sensor distribution characteristics, enhancing adaptability. To address the energy constraints of sensors, we utilize memristors, known for their energy-efficient in-memory computing, enabling analog-domain computations that reduce energy use and computational overhead in edge computing. This dual approach of memristors and $L_p$-norm inspired sensor fusion fosters energy-efficient computational and transmission paradigms and serves as a practical energy-efficient solution with minimal performance loss.

cs.LG↗

arXiv2011

On the Capacity of Memoryless Channels with Synchronization Errors

Memoryless channels with synchronization errors as defined by a stochastic channel matrix allowing for symbol insertions and deletions in addition to random errors are considered. Such channels are information stable, hence their Shannon capacity exists. However, computation of the channel capacity is formidable, and only some upper and lower bounds on the capacity (for some special cases) exist. In this short paper, using a simple methodology, we prove that the channel capacity is a convex function of the stochastic channel matrix. Since the more widely studied model of an independent identically distributed (i.i.d.) deletion channel is a particular case, as an immediate corollary to this result we also argue that the i.i.d. deletion channel capacity is a convex function of the deletion probability. We further use this result to improve the existing capacity upper bounds on the deletion channel by a proper "convexification" argument. In particular, we prove that the capacity of the deletion channel, as the deletion probability d --> 1, is upper bounded by $0.4143(1-d)$ (which was also observed by a different (weaker) recent result).

cs.IT↗