STAR-RIS-Assisted 6G Service Multiplexing
IEEE Transactions on Network Science and Engineering, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/tnse.2026.3734043
- Dergi Adı: IEEE Transactions on Network Science and Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Enhanced mobile broadband (eMBB), massive machine type communication (mMTC), simultaneous transmit ting and reflecting reconfigurable intelligent surface (STAR-RIS), ultra-reliable and low-latency communication (uRLLC)
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
Modern wireless networks are rapidly evolving to accommodate a diverse range of service requirements, broadly categorized into ultra-reliable and low-latency communications (uRLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). Meeting this service heterogeneity demands an integration of cutting-edge communication technologies. In this work, we examine a heterogeneous wideband wireless network scenario, in which a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) significantly boosts the network coverage of a base station (BS) to concurrently support uRLLC, eMBB, and mMTC services. The practical impacts of hardware impairment, beam squint and imperfection of channel state information are taken into account. Due to the large number of mMTC users, the data rate constraint of eMBB users, as well as the stringent delay/reliability requirements of uRLLC users, we formulate a resource allocation problem that maximizes the number of admitted mMTC users while satisfying the requirements of eMBB and uRLLCusers. This is done by simultaneously adjusting three key decision variables: the BS beamforming vector, the STAR RIS coefficient matrix, and the time-user scheduling vector. This optimization is tailored to strict constraints, including the BS's transmit power budget and the specific quality-of-service requirements of each service type. To address this intricate problem, we devise an iterative sub-optimal solution strategy by integrating tools from generalized Benders decomposition, block coordinate descent, semi-definite programming, successive convex approximation and augmented Lagrangian method. Simulation results analyze the performance of the studied system under user mobility assumption through Gauss-Markov model. It is revealed that the proposed resource allocation strategy considerably en hances the number of admitted mMTC users and effectively meets the heterogeneous requirements of eMBB and uRLLC users.