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Rahim Tafazolli

6 papers indexed

arxiveess.SP2026-07-14

LiTCom: A Lightweight Transmitter and Inference-Capable Receiver Framework for 6G Uplink

Chunmei Xu, Siqi Zhang, Zhi Ding, Yi Ma, Rahim Tafazolli

This paper introduces LiTCom, a lightweight transmitter and inference-capable receiver framework, designed to enable robust 6G uplink communication under low signal-to-noise (SNR) conditions. It embraces the resource asymmetry between edge devices and the network infrastructure.…

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arxivcs.CRcs.AI2026-07-10

Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests

Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli

Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic tel…

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arxivcs.ITeess.SP2026-07-01

Fundamental Limits of Random Downlink Integrated Sensing and Communication over Rician Channels

Marziyeh Soltani, Mahtab Mirmohseni, Rahim Tafazolli, Mark F. Flanagan

This paper studies the stochastic performance of a downlink multiple-input multiple-output integrated sensing and communication (ISAC) system over Rician fading channels. Rician fading is important in line-of-sight (LoS)-dominated deployments, where a deterministic propagation co…

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arxivcs.NIcs.DC2026-06-29

SubEdge: A Subscriber-Centric Edge Computing Subsystem in 6G Networks for AI

Abdirazak Ali Asir Rage, Riccardo Pozza, Rahim Tafazolli

Beyond traditional connectivity, 6G is envisioned to transform mobile networks into a distributed fabric that provides native integrated communication, computing, and intelligence services. AI-native terminals (e.g., robots, autonomous vehicles, and smart glasses) require real-ti…

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arxiveess.SP2026-06-29

Effective Depth in Joint Source-Channel Coding: An Implicit Equilibrium Analysis

Kaiwen Yu, Gang Wu, Xiaodong Xu, Yi Ma, Rahim Tafazolli

A fundamental design question in deep joint source-channel coding (Deep JSCC) remains insufficiently explored: given a channel signal-to-noise ratio (SNR), what effective computation depth is required for semantic reconstruction? Existing Deep JSCC systems typically employ fixed-…

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crossrefSensors2023-10-28Cited by 30

Recent Advances in Machine Learning for Network Automation in the O-RAN

Mutasem Q. Hamdan, Haeyoung Lee, Dionysia Triantafyllopoulou, Rúben Borralho, Abdulkadir Kose, Esmaeil Amiri, et al.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to d…

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