5G Artificial Intelligence: Qualcomm Presentation
Deliveringon the 5G vision
$13.2 trillion in goods & services by 2035
On-device AI processing augmented by edge cloudNew experiences
Privacy/security. New verticals. Immediacy. Private/public networks. Personalization. Customized/local value. Reliability
Process data at the source to scale AI and make sense of a digitized world
Cloud-centric AI .AI training and AI inference in the central cloud.Fully-distributed AI
With lifelong on-device learning.Partially-distributed AI.Power-efficient. on-device AI inference
Applying AI to overcome wireless challenges
Wireless challenges. Hard-to-model problems. Computational infeasibility of optimal solution. Efficient modem parameter optimization . Dealing with non-linearity. AI strengths. Determining appropriate representations for hard-to-model problems. Finding near-ideal and computationally realizable solutions. Modelling non-linear functions. Applying AI to solve difficult wireless challenges.
AI enables intelligent 5G network management. Enhanced service quality. Better mobility management, user localization, and user behavior and demand prediction. Higher network efficiency. More efficient scheduling, radio resource utilization, congestion control and routing. Simplified deployment. More capable Self Organizing Networks (SON) for e.g., mmWave network densification. Improved network security. More effective detection and defence against malicious attacks by analyzing a massive quantity of data.
On-device AI improves the 5G end-to-end system. Radio awareness. Environmental and contextual sensing that reduces access overhead and latency. On-deviceAI. Enhanced device experience. More intelligent beamforming & power management improve throughput, robustness, and battery life. Improved system performance. On-device inference reduces network data traffic for more efficient mobility and spectrum utilization. Better radio security. Detecting and defending against malicious base station spoofing and jamming with fingerprinting.
Radio awareness. Achieved by advanced on-device AI algorithms. Spectrum sensing and access. Predict activities of other devices for more efficient access and better scheduling to improve 5G system performance. Contextual awareness. Use device context (e.g., position, velocity, or in-car) derived from RF, sensors, traffic activities to improve device experience. Environment (RF) sensing. Detect gestures, movements, and objects by monitoring signal reflection patterns to enable new use cases.
5G Artificial Intelligence