5G and Beyond – Hierarchical and Modular Radio Resource Management
The evolution of mobile wireless systems into heterogeneous networks, along with the introduction of fifth-generation systems, significantly increased the complexity of radio resource management. Current mobile networks consist of a multitude of spectrum bands, use cases, system features, licensing schemes, radio technologies, and network layers. Fig. 1 below shows the evolution of LTE towards 5G along with key features to visualize the complexity increase.
Additionally, the traffic demand is uneven in terms of spatial and temporal domains, calling for a dynamic approach to radio resource allocation. To cope with these complexities, a generic and adaptive scheme is required for the efficient operation of heterogeneous networks. This article proposes using a hierarchical and modular framework as an approach to cover the mentioned challenges and to generalize this scheme to different network layers. The proposed management solution is based on three main components: specialized solutions for individual requirements, exposed to the coordination layer, through abstraction middleware. In this approach, new items can be added as “plugins.”
Hierarchical and modular Radio Resource Management architecture
The system complexity increased significantly with the introduction of new generations, new features and diverse services along with 5G as discussed within this post. We propose to handle the heterogeneity of radio access technologies (RATs), spectrum bands and types, devices, services, and features by a hybrid framework with three main components: a unified upper layer (handling the context-independent of the underlying technology), an abstraction middle layer (enabling an “easy” add-on of the techniques below and making the upper layer independent of the specifics of the specialized solutions), and a specialized lower layer (to best serve a particular purpose).
We propose to create the framework using the following actions: encapsulate (the solution), simplify (the solution), hide (the solution), expose (the simplified version of the solution), and use a coordination scheme. The discussed architecture is shown in Fig. 2 below.
The proposed approach can be applied in the following example use cases: unified medium access control (MAC) design, unified flow control, unified traffic steering (UTS), unified Radio Environment Maps (REM), unified Self-Organizing Networks (SON), the combination of those, as well as the network slicing framework (the details are elaborated in ).
Fig. 3 below shows the combination of unified MAC, UTS, and unified flow control. The selection of a particular RAT (e.g., LTE or NR) can be based on the common set of parameters. UTS can decide to use several nodes with different RATs (e.g., a master node, MN, with LTE and secondary node, SN, with NR) for one user and another configuration (e.g., master on NR and secondary on LTE) for another user. In this context, the unified scheduler needs to coordinate the resources for both users without looking into the specifics of each radio interface. Unified MAC is focused on a set of users (MAC layer provides access to multiple users in a single cell), while UTS is focused on a specific user (decides which users to assign to a particular cell). Unified MAC operates on various lower-layer PHY schemes, while UTS uses a common set of RRC procedures and operates on a set of RATs and features.
Management of network resources, within the complexity visible in today’s mobile networks and ongoing developments, should be addressed in a generic manner to be able to operate those networks efficiently. The development does not stop, while backward compatibility is one of the requirements in practically implemented systems. It is not possible to design a new system to just cope with the new requirements from scratch. This results in the need to
add new items to the existing system on different levels (e.g., new RAT, new function, new algorithm, new service), which requires adjusting the existing networks.
The adaptation of the existing system to the new requirements and functionalities is a complex task. We propose to solve it by a unified and hierarchical approach — where the specifics are separated from the system’s coordination and new items can be added as “plugins” to the architecture. We claim that it is possible to generalize the approach and provide a framework to cope with those challenges on various network layers. It is aimed at simplification of the introduction of new elements. We also claim that the unification, hybridization, and hierarchization can be the approach for future network management, including “RRM-low,” “RRM-high,” SON, and network orchestration.
Of course, there is a price to pay for this kind of approach – e.g. design of the abstraction layer — the more details related to the lower-layer mechanisms are used (allowing more flexibility for control), the more complicated the abstraction layer is. In contrast, the fewer details (simplifying the abstraction layer), the fewer the adjustment possibilities for the control of lower-layer mechanisms. You may find larger discussion on the cost and challenges of the proposed approach in the article .
This post describes the hierarchical and modular resource management architecture, which is based on my Ph.D. thesis and the article “A Hierarchical and Modular Radio Resource Management Architecture for 5G and Beyond“ published in IEEE Communications Magazine .
 M. Dryjanski and A. Kliks, „A Hierarchical and Modular Radio Resource Management Architecture for 5G and Beyond,” in IEEE Communications Magazine, vol. 58, no. 7, pp. 28-34, July 2020
 3GPP RP-151569, “Release 13 Analytical View Version,” Sept. 2015.
 M. Rahnema and M. Dryjanski, From LTE to LTE-Advanced Pro and 5G, Artech House, 2017.
 E. Calvanese Strinati et al., “6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication,” IEEE Vehic. Tech. Mag., vol. 14, no. 3, Sept. 2019.
 M. Szydeko and M. Dryjaski, “3GPP Spectrum Access Evolution Towards 5G,” EAI Endorsed Trans. Cognitive Commun., vol. 3, no. 10, Dec. 2016–Mar. 2017.
 M. Dryjaski and M. Szydeko, “A Unified Traffic Steering Framework for LTE Radio Access Network Coordination,” IEEE Commun. Mag., vol. 54, no. 7, July 2016.
 O. Bulakci et al., “Agile Resource Management for 5G: A METIS-II Perspective,” Proc. IEEE Conf. Standards for Commun. and Networking, 2015.
 T. Chen and N. Nikaein, “Towards Software Defined 5G Radio Access Networks,” IEEE Softwarization, Mar. 2016.
 M. Sooriyabandara et al., “Generic Interface Architecture Supporting Cognitive Resource Management in Future Wireless Networks,” IEEE Commun, Mag., vol. 49, no. 9, Sept. 2011.
 FP7 5GNOW, “Final MAC/Networking Concepts,” Deliverable 4.2, 03.2015.
 K. Pedersen et al., “Agile 5G Scheduler for Improved E2E Performance and Flexibility for Different Network Implementations,” IEEE Commun. Mag., vol. 56, no. 3, Mar. 2018.
 L. Wan et al., “4G/5G Spectrum Sharing: Efficient 5G Deployment to Serve Enhanced Mobile Broadband and Internet of Things Applications,” IEEE VTM, vol. 13, no. 4, Dec. 2018.
 S. Hmlinen, H. Sanneck, and C. Sartori, LTE Self-Organising Networks (SON): Network Management Automation for Operational Efficiency, Wiley, 2012.
 C. Chang and N. Nikaein, “RAN Runtime Slicing System for Flexible and Dynamic Service Execution Environment,” IEEE Access, vol. 6, June 2018.
 X. Foukas et al., “Network Slicing in 5G: Survey and Challenges,” IEEE Commun. Mag., vol. 55, no. 5, May 2017.
Note: ETSI is the copyright holder of LTE, LTE-Advanced, and LTE Advanced Pro and 3GPP 5G Logos. LTE is a trademark of ETSI. RIMEDO Labs is authorized to use the LTE, LTE-Advanced, LTE-Advanced Pro, and 3GPP 5G logos and the acronyms LTE and 3GPP.
Marcin Dryjanski received his Ph.D. (with distinction) from the Poznan University of Technology in September 2019. Over the past 12 years, Marcin served as an R&D engineer and consultant, technical trainer, technical leader, advisor and a board member. Marcin has been involved in 5G design since 2012, when he was a work-package leader in the FP7 5GNOW project. From 2018, he is a Senior IEEE Member. He is a co-author of many articles on 5G and LTE-Advanced Pro and a co-author of the book „From LTE to LTE-Advanced Pro and 5G” (M. Rahnema, M. Dryjanski, Artech House 2017). From October 2014 to October 2017, he was an external advisor at Huawei Technologies Sweden AB, working on algorithms and architecture of the RAN network for LTE-Advanced Pro and 5G systems. Marcin is co-founder of Grandmetric, where he served as a board member and wireless architect between 2015 and 2020. Currently, he serves as CEO and principal consultant at RIMEDO Labs.
You can reach Marcin at: email@example.com