TechLTEworld

5G & AI Native 6G PDSCH Chain and AIML in Phy Layer

The video walks through the transformation of the physical‑layer digital signal processing (DSP) chain across three generations of mobile radio—4G LTE, 5G NR, and the emerging 6G vision. It frames the discussion around the PDSCH pipeline and how each generation redefines the role of the DSP block. In 4G the DSP is a fixed‑point processor that executes static algorithms for channel estimation, OFDM demodulation, coding and MIMO processing. 5G upgrades this to an adaptive DSP that employs LMS, Kalman‑type filters, and early‑stage machine‑learning tricks to track fast‑varying channels, perform interference cancellation, and support massive MIMO beamforming. The speaker emphasizes that these adaptive techniques already blend rule‑based signal processing with data‑driven adjustments. The 6G outlook shifts to an AI‑native architecture where learned models are embedded inside the traditional DSP flow. Examples cited include auto‑encoders that redesign QAM constellations, neural receivers that replace fixed equalizers, and AI‑driven waveform selection among OFDM, OTFS, and GFDM. The presenter also explains why CNNs, RNNs or transformers are unsuitable for certain low‑latency tasks, highlighting model‑fit as a key design decision. If realized, AI‑enhanced DSP could continuously retrain on live traffic, delivering higher spectral efficiency, lower power consumption, and self‑healing capabilities. However, the transition raises challenges in data collection, real‑time training, and deployment on ASICs, FPGAs or GPUs. Operators and chip makers must therefore invest in end‑to‑end pipelines that marry telecom standards with scalable machine‑learning infrastructure.