The journey towards realizing the full potential of 5G technology has begun, with the rollout of 5G networks already underway. However, despite this progress, the future of 5G remains uncertain due to the lackluster response to these initial rollouts. Users are experiencing slower speeds, inaccuracies, and difficulties in adopting 5G-specific technologies and resources. So, now let us see how Edge AI Is Gearing Up and Driving a Better 5G Shift along with Reliable Mobile Network Monitoring Tools, Mobile Network Drive Test Tools, Mobile Network Testing Tools and Reliable LTE RF drive test tools in telecom & Cellular RF drive test equipment in detail.
One solution to these challenges lies in embracing software-defined and virtualized models, coupled with artificial intelligence (AI) and machine learning (ML). This approach has the potential to significantly reduce hardware costs and address latency issues. According to an IDC analyst report, organizations can reduce capital expenditure (CAPEX) while improving performance and agility by centralizing and virtualizing their networking infrastructure. Network Edge, leveraging software-defined networking (SDN) and network functions virtualization (NFV) platforms, plays a crucial role in enhancing edge networking.
Edge computing enables real-time interactions by empowering edge devices to process and compute data locally. Edge AI unlocks new possibilities for realizing the full potential of 5G. According to IDC, the edge AI software market is projected to grow from $355 million in 2018 to an astounding $1.12 trillion by 2023. Similarly, the edge computing market is expected to grow from $3.6 billion in 2020 to $15.7 billion by 2025, according to Markets and Markets data.
The integration of edge AI is critical for the success of 5G for several reasons:
Deployment of 5G technology without intelligence and edge computing models may not fully deliver on the promise of high-speed and low-latency networks.
As 5G increases network complexities with numerous endpoints, AI becomes essential for improving operational efficiency.
Edge computing significantly reduces latency and enhances security in 5G implementations, thereby improving network efficiencies and user experiences.
Now, let’s delve into the role of edge and AI in the evolving network model and the contributions of various stakeholders:
Device as an edge: End devices equipped with embedded AI capabilities are expected to become increasingly prevalent. Strategy Analytics predicts that within five years, nearly every smartphone chip will incorporate AI capabilities beyond current standards.
Enterprise premise as an edge: Enterprises can leverage edge computing capabilities within their facilities, machinery, vehicles, and other systems to enhance processing and decision-making.
Operator network as an edge: Multi-Access Edge Computing (MEC), located within operators’ networks, including data centers, base stations, edge routers, gateways, and central offices, offers additional edge computing capabilities.
This shift in the network model towards the edge, coupled with AI capabilities, has the potential to drive cost efficiencies, improve performance, and deliver next-generation services in the 5G ecosystem.
While edge AI is revolutionizing the 5G landscape, cloud-based AI remains a formidable player. Cloud-based AI enables on-device processing, further accelerating digital transformation—a trend that gained momentum during the COVID-19 pandemic.
Calsoft’s technology-driven approach and dedicated engineers are poised to facilitate faster connectivity through edge computing and AI/ML, bringing us closer to realizing the full potential of 5G. Contact our experts today for a free consultation and embark on the journey towards unlocking the possibilities of 5G together.
