Abhiram R. Gorle | Searcharxiv

arXiv2026

Cooperative Dynamic Spectrum Access for Cell-Free MISO Networks Using Spectrum Consumption Models

This paper incorporates uniform planar arrays into a cooperative dynamic spectrum access (DSA) mechanism based on the IEEE 1900.5.2 spectrum consumption model (SCM). Unlike prior work on SCM-based DSA that assume a fixed radiation pattern, the proposed DSA mechanism treats the radiation pattern as a design variable and jointly designs the precoder and spectrum allocation. The system model is a cell-free downlink, multiuser, multiple-input single-output network, an architecture that suits the dense indoor deployments of large-scale networks such as online retailers' fulfillment centers. The main objective is to reduce spectrum use while meeting a target signal-to-interference-plus-noise ratio at every receiver. To this end, this paper proposes a leakage-aware iterative water-filling precoder using uplink-downlink reciprocity, a power-control method that further encourages spectrum sharing, and a sequential admission algorithm that allocates spectrum to base stations based on the SCM compatibility test. Simulations in an indoor warehouse setting show that the proposed DSA mechanism better utilizes the spatial dimension for spectrum sharing.

eess.SP↗

arXiv2026

On the Fundamental Limits of LLMs at Scale

Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) reasoning degradation, (4) retrieval fragility, and (5) multimodal misalignment. While existing surveys describe these phenomena empirically, they lack a rigorous theoretical synthesis connecting them to the foundational limits of computation, information, and learning. This work closes that gap by presenting a unified, proof-informed framework that formalizes the innate theoretical ceilings of LLM scaling. First, computability and uncomputability imply an irreducible residue of error: for any computably enumerable model family, diagonalization guarantees inputs on which some model must fail, and undecidable queries (e.g., halting-style tasks) induce infinite failure sets for all computable predictors. Second, information-theoretic and statistical constraints bound attainable accuracy even on decidable tasks, finite description length enforces compression error, and long-tail factual knowledge requires prohibitive sample complexity. Third, geometric and computational effects compress long contexts far below their nominal size due to positional under-training, encoding attenuation, and softmax crowding. We further show how likelihood-based training favors pattern completion over inference, how retrieval under token limits suffers from semantic drift and coupling noise, and how multimodal scaling inherits shallow cross-modal alignment. Across sections, we pair theorems and empirical evidence to outline where scaling helps, where it saturates, and where it cannot progress, providing both theoretical foundations and practical mitigation paths like bounded-oracle retrieval, positional curricula, and sparse or hierarchical attention.

cs.LG↗