Demo Data from VLDB 2026 / reviewers in the wild / expert
Tauseef Ahmad Siddiqui
dblp:391/7162
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0001-6584-2805ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Vehicular, aerial and satellite networks · 44% Physical-layer communications · 44% Wireless networking · 6% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Vehicular, aerial and satellite networks › satellite communication
LEO satellite communication |
0.9 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Physical-layer communications
multiple access |
0.9 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Physical-layer communications › multiple access › non-orthogonal multiple access
rate-splitting multiple access |
0.9 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Vehicular, aerial and satellite networks
satellite networks |
0.9 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Wireless networking › cognitive radio
spectrum sharing |
0.3 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Cellular and mobile networks › device-to-device communication
underlay communication |
0.3 | 1 | 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary Network · IEEE Trans. Commun. 2025 |
Methods — techniques the papers use, named apart from their topics
power allocation optimization · 0.9dueling double deep q-learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Enhanced Learning-Based Hybrid Optimization Framework for RSMA-Aided Underlay LEO Communication With Non-Collaborative Terrestrial Primary NetworkabstractLow Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network. Zain Ali 0001, Wali Ullah Khan, Muhammad Asif 0005, Asim Ihsan, Abdelrahman Elfikky, Khaled M. Rabie, Tauseef Ahmad Siddiqui, Symeon Chatzinotas, Octavia A. Dobre |
IEEE Trans. Commun. | 7 |