arXiv subjects

Seyit Karatepe

Publications and source records attributed to Seyit Karatepe.

2 recordsLinked to original sources

arXiv2026

SINR-Aware Adaptive Offloading and Resource Allocation in Shared-Spectrum Integrated Terrestrial and Non-Terrestrial Networks

Sharing the sub-6~GHz spectrum between terrestrial networks and low Earth orbit satellites introduces cross-tier interference in both directions, as terrestrial transmissions can degrade satellite-link reception, while satellite transmissions can impair the performance of terrestrial links. Conventional reference signal received power (RSRP)-based offloading, designed for separate frequency bands, does not account for this cross-tier interference. We study offloading and resource allocation in an integrated terrestrial and non-terrestrial network through a two-timescale framework in which signal-to-interference-plus-noise ratio (SINR)-aware adaptive traffic offloading (SAATO) updates user associations from filtered SINR and load-aware expected throughput, switching a user only across a hysteresis band, while joint cross-tier fractional programming (Joint-FP) optimizes resource allocation for the resulting associations. Under an equal-share resource allocation model, the resulting decision metric approximates the change in proportional-fair utility with an error that decays with load. We prove that joint allocation under the sum-rate utility is nondeterministic polynomial-time (NP)-hard and that the utility sequence accepted by Joint-FP is non-decreasing and convergent. At full spectral overlap, RSRP-based offloading leaves 44\% of user samples below 0.5~Mbps (outage), whereas SAATO with Joint-FP, given instantaneous channel knowledge, leaves none and increases geometric-mean throughput by 8\% and 2\% under half and full spectral overlap, respectively, relative to power control without offloading. It also limits tier switching to at most 0.05 switches per user per minute.

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arXiv2026

A Multi-Objective Learning Approach for Adaptive Waveform Selection in Integrated Sensing and Communications Systems

Integrated Sensing and Communications (ISAC) has emerged as a key enabler for sixth generation (6G) wireless systems by jointly supporting data transmission and environmental awareness within a unified framework. However, communication and sensing functionalities impose inherently conflicting performance requirements, particularly in heterogeneous networks where users may demand sensing only, communication only, or joint services. Selecting a waveform that satisfies diverse service demands therefore becomes a challenging multi objective decision problem. In this paper, a multi objective learning approach for adaptive waveform selection in ISAC systems is proposed. A simulation driven evaluation framework is developed to assess multiple waveform candidates across communication, sensing, and joint performance metrics. Instead of enforcing scalar utility aggregation, waveform performance is represented in a multi dimensional objective space where Pareto optimal candidates are identified for each scenario. A dataset is generated by varying user demand distributions and channel conditions, and multi-label targets are constructed based on Pareto dominance. Machine learning models are trained to learn the mapping between network conditions and Pareto optimal waveform sets, enabling fast waveform selection under dynamic network states. Simulation results demonstrate that the proposed framework effectively adapts waveform selection to heterogeneous service requirements while preserving sensing communication trade offs, providing a forward-looking perspective for 6G and beyond ISAC deployments.

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