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Research Paper#Communication Systems, AirComp, Digital Modulation🔬 ResearchAnalyzed: Jan 3, 2026 17:07

Digital AirComp with Complement Coding

Published:Dec 31, 2025 11:16
1 min read
ArXiv

Analysis

This paper addresses limitations of analog signals in over-the-air computation (AirComp) by proposing a digital approach using two's complement coding. The key innovation lies in encoding quantized values into binary sequences for transmission over subcarriers, enabling error-free computation with minimal codeword length. The paper also introduces techniques to mitigate channel fading and optimize performance through power allocation and detection strategies. The focus on low SNR regimes suggests a practical application focus.
Reference

“The paper theoretically ensures asymptotic error free computation with the minimal codeword length.”

PermalinkArXiv
Research Paper#Wireless Communication, Reinforcement Learning, UAV, RIS🔬 ResearchAnalyzed: Jan 3, 2026 08:42

Throughput Optimization in UAV-Mounted RIS using DRL

Published:Dec 31, 2025 10:36
1 min read
ArXiv

Analysis

This paper addresses a practical problem in wireless communication: optimizing throughput in a UAV-mounted Reconfigurable Intelligent Surface (RIS) system, considering real-world impairments like UAV jitter and imperfect channel state information (CSI). The use of Deep Reinforcement Learning (DRL) is a key innovation, offering a model-free approach to solve a complex, stochastic, and non-convex optimization problem. The paper's significance lies in its potential to improve the performance of UAV-RIS systems in challenging environments, while also demonstrating the efficiency of DRL-based solutions compared to traditional optimization methods.
Reference

“The proposed DRL controllers achieve online inference times of 0.6 ms per decision versus roughly 370-550 ms for AO-WMMSE solvers.”

PermalinkArXiv
Research Paper#Vision-Language Models, Agentic Reasoning, Reinforcement Learning🔬 ResearchAnalyzed: Jan 3, 2026 15:38

SenseNova-MARS: Agentic Reasoning with Tools via RL

Published:Dec 30, 2025 16:31
1 min read
ArXiv

Analysis

This paper introduces SenseNova-MARS, a novel framework that enhances Vision-Language Models (VLMs) with agentic reasoning and tool use capabilities, specifically focusing on integrating search and image manipulation tools. The use of reinforcement learning (RL) and the introduction of the HR-MMSearch benchmark are key contributions. The paper claims state-of-the-art performance, surpassing even proprietary models on certain benchmarks, which is significant. The release of code, models, and datasets further promotes reproducibility and research in this area.
Reference

“SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5.”

PermalinkArXiv
Research#Signal Processing🔬 ResearchAnalyzed: Jan 10, 2026 10:36

Novel Approach to Signal Processing with Low-Rank MMSE Filters

Published:Dec 16, 2025 21:54
1 min read
ArXiv

Analysis

This ArXiv article likely presents a novel approach to signal processing, potentially improving the performance and efficiency of Minimum Mean Square Error (MMSE) filtering. The use of low-rank representations and regularization suggests an effort to address computational complexity and overfitting concerns.
Reference

“The article's topic is related to Low-rank MMSE filters, Kronecker-product representation, and regularization.”

PermalinkArXiv