Improving QoS for streaming data transmission over 6G networks using reconfigurable intelligent surfaces (RIS)

This paper investigates a RIS‑assisted 6G framework operating at 300 GHz (THz sub‑band) to enhance end‑to‑end Quality of Service (QoS) for streaming data under ultra-reliable low-latency communication (URLLC) constraints, focusing on reliability, latency, and throughput across line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. By programmatically shaping the propagation environment, RIS elevates NLoS Signal-to-Noise Ratio (SNR) from approximately − 20 dB to above 45 dB, reduces Bit Error Rate (BER) from error‑prone levels to below 5.5 × 10⁻¹⁰, and shortens packet delay from roughly 16.5ms to near 2ms, while increasing throughput from ~ 1 Gbps to ~ 20 Gbps under matched assumptions, thereby meeting stringent URLLC targets for reliability and latency. The modeling consolidates noise power, path loss, RIS array gain, capacity, BER, and an SNR‑dependent latency relation with clearly stated assumptions and citations and introduces coding‑aware BER and an improved 300 GHz channel model including atmospheric absorption for realism. Robustness is demonstrated via multi‑run statistics on SNR, BER, delay, and throughput, and a benchmarking subsection contrasts RIS (this work) with CF‑mMIMO and relay baselines under matched or normalized scenarios to support comparative verification claims.