Mobile Network Optimization: A Guide For 2G And 3G Network Optim !FULL!
Open radio access networks offer the option of placing network functions in different places along the signal path. That option, called a functional split, lets network engineers optimize performance and make tradeoffs.
Mobile Network Optimization: A Guide For 2G And 3G Network Optim
When the RAN is opened up horizontally, it could bring in a new range of low-cost radio players, and it gives mobile operators a choice to optimize deployment options for specific performance requirements at much better cost.
RAN Functional Splits3GPP considered the split concept (DU and CU) for 5G from the beginning of writing its specifications. The DU is responsible for real time layer 1 (L1, physical layer) and lower layer 2 (L2) which contains the data link layer and scheduling functions. The CU is responsible for non-real time, higher L2 and L3 (network layer) functions.
With the increase in deployment footprint, fiber and availability of required front hauls can be challenging. By distributing protocol stacks between different components (different splits), network engineers and providers can focus on addressing the tight requirements for a near-perfect FH between RU, DU and CU.
Which split?The choice of how to split NR functions in the architecture depends on some factors related to radio network deployment scenarios, constraints and intended supported use cases. Three key ones are:
Mobile operators need the flexibility to pick and choose different splits based on the same COTS-based hardware and network components. The flexibility comes from using different software implementations. Protocol layers can reside in different components based on fronthaul availability and deployment scenarios. This approach will reduce the cost of operations and total cost for mobile operators.
To take full advantage of split architecture that can deliver interoperability, ability to select best-of-breed components, and scalability, any network needs support for2G, 3G, 4G, and 5G baseband functions (Figure 6). For the best latency support requirement, baseband functions DU and CU software are decoupled from hardware and are deployed on NFVI or as containers. An MNO can use any VM requirements and/or any hypervisor or orchestration provider to enable these functional splits.Different RAN functional splits work for different use cases.
One split might not fit all. A solution that can support many technologies including not just 4G and 5G, but 2G and 3G, is the most attractive to mobile network operators (MNOs) as it will simplify network management and reduce cost. Split 7.2 is the best for 4G and 5G; split 8 will be the best option for 2G and 3G. Both options can run over 7.2 Open RAN radios.
RAN Functional Split 6: Small cell splitThe Small Cell nFAPI (network FAPI) interface in RAN functional split 6 of Figure 7 is enabling the Open RAN ecosystem by allowing any small cell CU/DU to connect to any small cell radio unit (S-RU). 5G FAPI encourages competition and innovation among suppliers of small cell platform hardware, platform software, and application software by providing a common API. These interfaces will help network architects by letting them mix distributed and central units from different vendors.
The RAN DU real-time L2 functions and baseband processing. In the O-RAN Alliance working group, the DU is proposed to support multiple RUs. To properly handle the digital signal processing and accelerate network traffic, an FPGA can be used. Hardware acceleration is considered a requirement for 5G but less so for 2G, 3G, and even 4G.
Ericsson and Nokia are looking at GPU-based acceleration for some virtual RAN (vRAN) workloads, especially for 5G M-MIMO and for AI. Reducing overall cost will be a priority, and a solution around GP processor architectures to deliver the most efficient and cost-effective compute, storage and network elements will drive the innovation.
In 2G and 3G, the mobile architectures had controllers that were responsible for RAN orchestration and management. With 4G, overall network architecture became flatter and the expectation was that, to enable optimal subscriber experience, base stations would use the X2 interface to communicate with each other to handle resource allocation. This created the proverbial vendor lock-in as different RAN vendors had their own flavor of X2, and it became difficult for an MNO to have more than one RAN vendor in a particular location. The O-RAN Alliance went back to the controller concept to enable best-of-breed Open RAN.
As many 5G experiences require low latency, 5G specifications like Control and User Plane Separation (CUPS), functional RAN splits, and network slicing require advanced RAN virtualization combined with SDN. This combination of virtualization (NFV and containers) and SDN is necessary to enable configuration, optimization and control of the RAN infrastructure at the edge before any aggregation points. This is how the RAN Intelligent Controller (RIC) for Open RAN was born - to enable eNB/gNB functionalities as X-Apps on northbound interfaces. Applications like mobility management, admission control, and interference management are available as apps on the controller, which enforces network policies via a southbound interface toward the radios. RIC provides advanced control functionality, which delivers increased efficiency and better radio resource management. These control functionalities leverage analytics and data-driven approaches, including advanced ML/AI tools to improve resource management capabilities.
The separation of functionalities on southbound and northbound interfaces enables more efficient and cost-effective radio resource management for real-time and non-real-time functionalities, as the RIC customizes network optimization for each network environment and use case.
Virtualization (NFV or containers) creates software app infrastructure and a cloud-native environment for RIC, and SDN enables those apps to orchestrate and manage networks to deliver network automation for ease of deployment.
O-RAN defined overall RIC architecture consists of four functional software elements: DU software function, multi-RAT CU protocol stack, the near-real time RIC itself, and the orchestration/NMS layer with Non-Real Time RIC. They all are deployed as VNFs or containers to distribute capacity across multiple network elements with security isolation and scalable resource allocation. They interact with RU hardware to make it run more efficiently and to be optimized real-time as a part of the RAN cluster to deliver a better network experience to end users.
Non-Real-Time RAN Intelligent Controller (Non-RT RIC) functionality includes configuration management, device management, fault management, performance management, and lifecycle management for all network elements in the network. It is similar to Element Management (EMS) and Analytics and Reporting functionalities in legacy networks. All new radio units are self-configured by the Non-RT RIC, reducing the need for manual intervention, which will be key for 5G deployments of Massive MIMO and small cells for densification. By providing timely insights into network operations, MNOs use Non-RT RIC to better understand and, as a result, better optimize the network by applying pre-determined service and policy parameters. Its functionality is internal to the SMO in the O-RAN architecture that provides the A1 interface to the Near-Real Time RIC. The primary goal of Non-RT RIC is to support intelligent RAN optimization by providing policy-based guidance, model management and enrichment information to the near-RT RIC function so that the RAN can be optimized. Non-RT RIC can use data analytics and AI/ML training/inference to determine the RAN optimization actions for which it can leverage SMO services such as data collection and provisioning services of the O-RAN nodes.
Trained models and real-time control functions produced in the Non-RT RIC are distributed to the Near-RT RIC for runtime execution. Network slicing, security and role-based Access Control and RAN sharing are key aspects that are enabled by the combined controller functions, real-time and non-real-time, across the network.
The Near-RT RIC leverages embedded intelligence and is responsible for per-UE controlled load-balancing, RB management, interference detection and mitigation. This provides QoS management, connectivity management and seamless handover control. Deployed as a VNF, a set of VMs, or CNF, it becomes a scalable platform to on-board third-party control applications. It leverages a Radio-Network Information Base (R-NIB) database which captures the near real-time state of the underlying network and feeds RAN data to train the AI/ML models, which are then fed to the Near-RT RIC to facilitate radio resource management for subscribers. Near-RT RIC interacts with Non-RT RIC via the A1 interface to receive the trained models and execute them to improve the network conditions.
PageSpeed Insights analyzes a page to see if it follows our recommendations for making a page render in under a second on a mobile network. Research has shown that any delay longer than a second will cause the user to interrupt their flow of thought, creating a poor experience. Our goal is to keep the user engaged with the page and deliver the optimal experience, regardless of device or type of network.
Meeting the one second ATF criteria on mobile devices poses unique challenges which are not present on other networks. Users may be accessing your site through a variety of different 2G, 3G, and 4G networks. Network latencies are significantly higher than a wired connection, and consume a significant portion of our 1000 ms budget to render the ATF content.4G is the dominant network type around the world, you should expect that the majority of users will be accessing your page on a 4G network. For this reason, we have to assume that each network request will take, on average, 100 milliseconds.