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Bilkent University

Academic institutioneurope · tr
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Research library176linked papers
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Selected work

Representative Papers

Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC

Jan 18, 2024arXiv.org

This work addresses energy-efficient integrated sensing and communication (ISAC) in cell-free massive MIMO downlink systems under joint ultra-reliable low-latency communication (URLLC) and multistatic sensing constraints. Method: It proposes the first end-to-end energy-saving optimization framework jointly modeling both sensing processing energy consumption and communication transmission energy consumption. To tackle the non-convex joint optimization of transmit power and transmission blocklength, two efficient algorithms are developed—based on feasible point pursuit-successive convex approximation (FPP-SCA) and concave–convex procedure (CCP)—with fractional programming incorporated to handle the energy-efficiency ratio objective. Contribution/Results: The proposed joint design significantly reduces total energy consumption compared to conventional decoupled approaches. Numerical results show that increasing the number of access points raises sensing energy consumption; raising the sensing SINR threshold enlarges total energy consumption while narrowing the performance gap between the two algorithms.

10 citations1 influentialRead paper

Test smells in LLM-Generated Unit Tests

Oct 14, 2024arXiv.org

This study addresses the understudied issue of test smells in large language model (LLM)-generated unit tests. Method: We conduct the first large-scale empirical analysis of test smells across 20,505 Java class-level test suites drawn from five sources: human-written tests, EvoSuite-generated tests, and LLM-generated tests from GPT-3.5, GPT-4, Mistral, and Mixtral. Our multi-benchmark, cross-model analysis framework encompasses over 770,000 test cases, leveraging dual smell-detection tools—TsDetect and JNose—across 34,635 open-source projects and the TestBench benchmark. Contribution/Results: We identify prevalent smells—including Assertion Roulette and Magic Number Test—in LLM-generated tests; their occurrence patterns are significantly influenced by prompting strategies, context length, and model scale. Notably, LLM-generated tests exhibit smell profiles closer to human-written tests than to search-based software testing (SBST) outputs, suggesting potential training data contamination. These findings provide critical empirical grounding for developing smell-aware test generation frameworks.

4 citationsRead paper

Ordered Probabilistic Choice

Apr 01, 2025

This paper addresses the challenge of identifying heterogeneous individual-level choice behaviors from macro-level aggregate selection data. To this end, it establishes, for the first time, a systematic theoretical linkage between ordered probit choice models and copula theory, mapping individual heterogeneity onto the structural form of copula functions. The authors propose an analytically tractable representation based on extreme-value theory, enabling unique and unbiased identification of both heterogeneity types and their mixing weights. Methodologically, the approach integrates copula modeling, extreme-value function analysis, and structural identification theory to derive a general closed-form extreme-value representation. This framework overcomes key limitations of conventional aggregate modeling—such as loss of behavioral granularity and identifiability constraints—thereby substantially improving the accuracy, interpretability, and structural fidelity of micro-behavioral inference. It introduces a novel paradigm for discrete choice analysis, behavioral econometrics, and multivariate dependence modeling.

1 citationsRead paper

Markov Insertion/Deletion Channels: Information Stability and Capacity Bounds

Jan 29, 2024

This work investigates the information stability and existence of Shannon capacity for Markovian add-delete channels subject to synchronization errors (insertions/deletions) with memory. Specifically, it considers channels where errors are modeled by a stationary ergodic finite-state Markov chain. The paper extends information stability theory—previously established only for memoryless synchronous channels—to this class of Markovian insertion/deletion channels, constituting the first such generalization. By integrating ergodic theory, the asymptotic equipartition property (AEP), and stationarity of Markov chains, the authors construct a generalized typical set framework and develop joint typicality analysis to rigorously establish information stability. Consequently, the Shannon capacity is proven to exist for these channels. This result provides a foundational capacity-theoretic basis for practical memory-inclusive communication systems—such as DNA-based data storage—and furnishes rigorous theoretical guarantees for the design of capacity-approaching coding schemes.

1 citationsRead paper
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