Embracing Innovation: Operator Strategies for Efficiency, Monetization, and Automation in 2024

As the telecommunications industry marches forward with 5G, operators find themselves standing at the edge of transformative change in 2024. This pivotal year will witness a strategic shift in how operators manage their networks, optimize their resources, and capitalize on emerging technologies. Telecom operators are poised to do more with less by embracing cloud-native automation and AI enabled networks. Simultaneously, with the growing concern for sustainability and energy costs reduction, energy efficiency will be a key focus area for operators, aiming to drive down their total cost of ownership (TCO). In this article, Mavenir’s Bejoy Pankajakshan, EVP, Chief Technology and Strategy Officer , delves into the predictions for 2024, offering insights into how operators are set to thrive in this dynamic landscape of automation, energy efficiency and monetization. #1: Operators will look to do more with less by embracing cloud-native automation in their networks Telecom operators are increasingly turning to cloud-native automation to modernize their networks and keep pace with the demands of 5G and hyperconnectivity. Cloud-native automation, which leverages containerized infrastructure, microservices architecture, and open-source tools is seen as a key enabler for network modernization and monetization. By adopting cloud-native automation, operators can achieve a higher level of automation in their networks, reduce operational complexity, and realize substantial cost savings over legacy approaches. This approach also allows for the efficient deployment, management, monitoring, and optimization of multi-cloud 5G networks, ultimately enabling the rapid innovation of new services. The primary focus of cloud-native Automation is to implement a common multi-cluster Kubernetes based automated deployments, leveraging GitOps approach. The Cloud-native Automation framework must have the ability to support following requirements: Planning & Design, which takes input from operators and automates the generation of network configuration files. Server Hardware configuration, which involves TOR Switch, BIOS, Firmware setup and Server OS installation. Multi-Cluster Kubernetes, which involves automation of K8s cluster Life Cycle Management. Application Life Cycle Management, which involves automation of Day 0, Day 1 & Day 2 processes of Application Life Cycle. Test Automation, which automates repetitive tests and analyze test results to improve software quality. While the transition to cloud-native automation presents challenges, such as migration of legacy workloads to cloud-native workloads and the management of complex Kubernetes clusters, its benefits in terms of network modernization and the creation of new revenue streams are proving to be compelling drivers for telecom operators. #2: Energy Efficiency will become a key Operator focus to drive down TCO Operators will continue to focus on achieving network sustainability targets and reducing network OPEX, by exploring various ways to improve energy efficiency. Energy Savings features will be introduced in all parts of the RAN ecosystem as the RAN is a greater contributor to total energy consumption in operator networks. Leveraging hardware (PA, chipsets), software, AI enhancements will improve Energy Efficiency. Power Amplifiers will move from Silicon based PAs to GaN PAs. Vendor capabilities such as PA shutdown, Micro DTX, MIMO/ mMIMO sleep modes will be increasingly adopted that results in lower energy consumption. Technology advancements such as Integrated HW accelerator in CPU, dynamic cell carrier shutdown, load balancing traffic from lightly loaded cells, optimized DU/CU sleep modes will be used to realize lower energy consumption. AI based solutions, hosted on the RAN Intelligent Controller (RIC) platform, will identify opportunities to turn off radios and bring them back on when traffic rises. This would greatly reduce the RAN power consumption, since most of the time, there is light load on the network. Improvements in O-cloud energy efficiency such as, adaptive shutdown of hardware, scaling down network functions, will support energy usage optimization across all network functions including the Core & RAN. In the packet core, various solutions such as NIC offload, offload to a white box switch router will help reduce the UPF energy consumption in response to dynamic traffic patterns. #3: AI/ML solutions will mature in the RAN and in the Packet Core The benefits of Open-RAN with open interfaces and access to the RAN data to support non-real time control and near-real-time control will draw clear contrast to traditional RAN. RAN Intelligent Controller solutions for energy savings and network slicing will move towards broader network deployments. The ability of AI software running on the near-RT RIC to support traffic steering of individual users to meet user SLA guarantees will bear evidence in trials. RIC platforms with multivendor apps will become more widely available. Native AI solutions in the RAN such as estimation of cell spectral efficiency to identify areas where additional carriers are needed, and prediction of short-term spectral efficiency to tune scheduler parameters, and trigger load balancing and handovers will gain maturity. Operators will bring in GenAI solutions for network fault diagnostics using LLMs customized with network data to troubleshoot network issues, such as performance degradation and misconfiguration issues. These will help Operators save significant time and effort. Telcos will embrace AI chatbots to proactively address customer issues that will lead to faster problem resolution, reduced customer churn, and increased customer satisfaction. In the packet core, network analytics supported by AI/ML algorithms will be more broadly deployed to predict UPF traffic load, user mobility patterns, network congestion. These will help reduce TCO and optimize user QoE. Methodologies to collect the necessary training data needed to train the various ML models will be discussed in various forums towards identifying a practical, viable solution to enable AI solutions in telecom networks. #4: Operators will increasingly be able to monetize their 5G network Telecom operators are exploring various strategies to monetize their 5G investments, with the migration to Standalone (SA) networks likely to gather pace in 2024. The time is ripe to reevaluate some of the monetization strategies and use cases. The strategies to monetize include the following: Migrating to 5G SA networks offers benefits such as network slicing capabilities, ultra-low latency communication, improved reliability, and simplified operations. Adoption of Cloud-native Principles allows for network disaggregation, flexible placement of workloads, open API operation model, reduced operational complexity with Cloud-native automation. Upgrading Billing & Charging systems should be Cloud-native by design and support flexible charging models with agility. Open Platforms Fosters developer communities with accessible APIs and tools to accelerate innovation. The key use case focus areas are: Mobile Edge Computing: This includes low-latency applications, industrial automation, enhanced entertainment, healthcare transformation. Network slicing: This includes high throughput video for video production, telehealth, ultra-low latency applications and high availability and security for critical services. Data monetization: Leverages the vast amounts of data generated by 5G networks to create new revenue streams through targeted advertising, and personalized services. Network exposure using open APIs: Allows third-party developers to access and utilize 5G network capabilities, fostering innovation and collaboration. 5G new calling for consumers and enterprises: Offers advanced communication and interactive services, by bringing in web applications to the native voice/video client with Mini-Apps which need not be downloaded to the client. While the transition to 5G requires high capital expenditures and the need for greater collaboration among carriers and brands, adoption of these strategies and implementing the above use cases would improve the return on operator investments by enhancing user experience and driving economic growth.