Insights into Evolution of User-Centric Networks

The user-centric network has long been a research hotspot in mobile communication research, attracting substantial interest from both academia and industry. With the emergence of diverse new demands and the development of new technologies, user-centric access networks are becoming a key feature in future mobile communication systems.

ZTE identifies “user-centric networks” as one of the critical topics of 6G, and is gradually integrating research outcomes into 5G-A networks to fully leverage their role as a technological bridge, empowering a wide range of new scenarios and services.               

User-Centric Networks: The Core Path to a Stable User Experience

Traditional mobile networks deliver services in a "cell-centric" manner. This approach leads to an uneven user experience, with optimal performance at the cell's center, which deteriorates rapidly towards the edges and fluctuates significantly during user mobility. Such an approach is inadequate for meeting the demands of emerging 6G applications and scenarios, such as low-altitude internet of intelligence (IoI), vehicle-to-everything (V2X), and immersive communication services, all of which require a consistent and stable network experience.

User-centric networks represent a network architecture that departs from the traditional cell-centric approach. By dynamically adjusting network resources and coverage, they ensure that users consistently remain within an optimal service zone. Through intelligent collaboration among multiple transmit/receive points (TRPs), user-centric networks eliminate interference between TRPs, enhance spectrum utilization, and minimize the hardware challenges associated with increased RF and antenna requirements. This improves system scalability and reliability, ensuring stable and continuous network connections for users on the move (Fig. 1).                                                                          

After a comprehensive analysis, we believe the evolution of user-centric networks involves five key technology clusters: foundational multi-TRP collaboration techniques, distributed multi-TRP coordination, mobility enhancement technologies, intelligent collaborative networking, and collaborative architecture pooling.

Multi-TRP Collaboration: Laying the Foundation

Placing the user at the center of network services requires multiple TRPs to serve the user simultaneously. These TRPs must coordinate with efficiency comparable to centralized base stations, relying on two key foundational technologies:

• Improving air interface calibration and synchronization accuracy: Time synchronization is a fundamental requirement for 4G and 5G communication systems, with a basic synchronization accuracy requirement of ±1.5 µs. For multi-TRP collaboration, synchronization accuracy must be improved to the picosecond level to keep latency and phase differences between TRPs within acceptable limits. Failure to achieve this precision renders coherent merging and demodulation of signals unfeasible. Air interface calibration typically includes two methods: self-calibration of TRP antennas and terminal-assisted calibration.
• Intelligent management of TRP collaboration: TRP collaboration relies on obtaining essential prerequisite information, such as resource allocation and channel state information, from other TRPs. This requires dynamic management of collaboration relationships and links. In user-centric networks, the wireless conditions and service requirements of each user affect collaboration associations and links, which must be managed intelligently.

Deepening TRP Collaboration: Maximizing Performance

User-centric networks enhance the user experience through intelligent collaboration among multiple TRPs. The levels of collaboration between TRPs are categorized based on the type of data exchanged (Fig. 2):

• Level 1: Light interaction between TRPs to reduce interference.
• Level 2: Exchange of service data between TRPs through simultaneous multi-link transmission and reception improves the signal-to-noise ratio and enhances the demodulation capability of the base station.
• Level 3: In addition to service data exchange, air interface calibration and synchronization are performed between TRPs, enabling time and phase alignment during transmission and reception, thus achieving coherent power combining.
• Level 4: Beyond service data exchange and air interface calibration and synchronization, air interface channel information is also shared between TRPs. This enables joint beamforming and increases spatial dimensions, surpassing the shaping capabilities of individual RF units.

The more comprehensive the data exchanged between TRPs, the greater the performance enhancements, while the implementation complexity also increases exponentially. In 5G-A and 6G systems, Level 4 multi-TRP collaboration is targeted, where user-centric, extreme coordination among multiple TRPs delivers distributed Massive MIMO services. Distributed Massive MIMO dynamically adjusts TRPs based on user trajectories, offering more flexibility in handling interference and improving wireless performance. This approach is applicable to both high-capacity scenarios and low-latency, low-altitude coverage environments.

Mobility Enhancement Technologies: Reducing Handover Impact

The objective of user-centric networks is to offer consistent, efficient service regardless of user location, ensuring seamless handovers during mobility. Traditional inter-cell mobility management is based on L3 measurements and triggered by RRC signaling, requiring complete cell reconfiguration, which results in data transmission interruptions and significant signaling overhead. The performance impact from frequent handovers can be particularly severe.

To mitigate these effects, mobility enhancement technologies focus on two areas: reducing handover interruption latency and improving handover robustness.

• Reducing handover latency: In 5G-A, an LTM (L1/L2-triggered mobility mechanism) has been introduced to facilitate inter-cell mobility based on L1 measurements. Handover decisions are initiated by L1/L2 instructions, simplifying signaling configuration and reducing the negotiation process between base stations. This approach significantly reduces handover latency and minimizes handover failures.
• Improving handover robustness: The 5G standard introduces dual active protocol stack (DAPS) and conditional handover (CHO). DAPS allows the UE to maintain connections with both the source and target cells during handover until the target cell explicitly signals the release of the source cell connection. CHO enables the UE to select the target base station based on measurement results, reducing handover failures.

By leveraging intelligent or sensing-assisted communication, UE movement can be predicted, enabling more precise beam management. This approach optimizes beam selection and tracking, improving user experience.

Intelligent Collaborative Networking: Ensuring Real-Time Experience

In traditional service models, services are typically generic and standardized, which may fail to meet the UE-specific requirement. As a key feature of 5G-A/6G networks, user-centricity is characterized by flexible and reconfigurable intelligent networking capabilities. These capabilities adapt to evolving demands and environments, supporting emerging application scenarios and service models.

User-centric networks dynamically detect user behavior and service demands, adaptively constructing flexible cells. This network architecture includes the following key capabilities:

• Intelligent insight into scenarios and service needs: Network services are based on user location, real-time context, and data. By using intelligent algorithms, the network automatically adjusts service strategies to ensure users remain in the optimal service state.
• User-centric collaborative set generation: Based on actual user needs, the network dynamically adjusts service areas and constructs flexible cell collaboration sets, ensuring stable services for both users and applications.
• Continuous learning and iterative optimization: Utilizing machine learning and adaptive algorithms, the network continuously learns user behavior and needs, gradually optimizing service models and enhancing its understanding of user requirements to improve service quality.
• Deployment and management of diverse network architectures: Different user scenarios and applications demand varying network architectures. The network can flexibly organize and deploy resources, swiftly responding to new service demands. With efficient connection management and adaptive topology mechanisms, the network dynamically optimizes resource allocation based on user intent and interest sensing.

Future of Collaborative Architecture: Decoupling and Pooling

Network resources (e.g., bandwidth, storage, and computing power) can be virtualized and pooled for flexible allocation to different users and applications. The network is capable of monitoring and analyzing resource utilization in real-time, adjusting to user demands and network conditions, and automatically reconfiguring network nodes and resources to deliver optimal service. Collaborative architecture pooling includes baseband pooling and uplink/downlink decoupling.

The uplink/downlink decoupling architecture aggregates spatial resources from multiple TRPs, facilitating the adaptive selection of TRPs for uplink and downlink based on optimal principles. Meanwhile, multi-TRP pooling enables a TRP to support either uplink, downlink, or both, providing flexibility to adapt to different scenarios, network requirements, and traffic loads.

The evolution of user-centric networks will open a new chapter in mobile communications, creating a more seamless and smooth communication environment for users. It will meet the demands for stable network experiences in emerging applications and scenarios such as low-altitude Iol, V2X, and immersive services. This evolution will provide users with "full-bar" coverage everywhere, while laying a solid foundation for a smooth transition to 6G networks.