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5G Radio Access Network Optimizations for Improved End-to-End Performance

Feature Topic


The 3GPP standardization of the first 5G New Radio (NR) is rapidly progressing, with the first release of non-stand-alone specifications planned to be ready in early 2018, while support for stand-alone operation comes half a year later. It has already been agreed that the 5G NR will include a new Quality-of-Service (QoS) architecture, enhanced protocol stack for the radio access network (RAN), and support for a variety of network implementations (distributed and centralized). All has been driven by the challenging targets to fulfill the requirements for enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). In order to support such a wide gamut of applications, 5G NR introduces a new and efficient FEC (Forward Error Correction) solution that is scalable and power efficient. The medium access control (MAC) and physical (PHY) layers introduce support for configurable PHY numerologies, new multipoint coherent and non-coherent transmission schemes, a new dynamic frame structure, and plurality of new scheduling options and formats, and massive MIMO, etc.

At the higher RAN layers, a new Access Stratum (AS) sub-layer with a Service Data Application Protocol is introduced that open new opportunities for enhanced quality-of-experience (QoE) management and more efficient interaction with the lower layer MAC scheduler. At the Packet Data Protocol Control (PDCP) layer, options for PDCP packet duplication are introduced for enhance reliability. On the control plane, a new radio resource control (RRC) state machinery is introduced to more efficiently leverage the tradeoffs between terminal power consumption, access latency, and control signaling overhead, etc. Hence, the 3GPP standard for the 5G NR will bring a large number of new options, including support for various distributed and centralized network infrastructure implementations. Especially, it is anticipated that there will be impact on the RAN architecture, as the NR architecture may not be flat and can allow building a multi-vendor infrastructure. Additionally, different possibilities in terms of spectrum bands and access would be available for the deployment: from low to high frequency bands, and for licensed or unlicensed access.  However, the specifications will not dictate how to best utilize all these new degrees of freedom, and hence it is an active field of research how to best take advantage of these new opportunities to achieve the desired E2E performance for the consumers.

For this Feature Topic (FT), we invite authors to submit their papers that offer recommendations and analysis that relate to 5G NR optimizations of the RAN part to achieve the best possible end-to-end (E2E) performance, including contributions on E2E performance definitions of particular relevance for 5G and their relationship to the NR RAN.

The Feature Topic scope includes, but is not limited to, the following topics of interest:

  • 5G NR QoS and end-to-end awareness.
  • 3GPP NR architecture options and E2E performance for different services.
  • Novel E2E-aware RRM algorithms for improved 5G performance.
  • Efficient multiplexing of highly diverse services with different QoS requirements.
  • 5G NR multi-node connectivity for superior E2E performance.
  • 5G NR non-stand-alone vs stand-alone operations.
  • Enhanced massive MIMO and improved E2E performance for different service types.
  • Multi-connectivity management for improving E2E performance and minimizing interference.
  • Novel self-optimizing schemes of the new RAN functionalities and parameters.
  • Coordination schemes, for operations below 6 GHz for boosting the performance.
  • Performance analysis and operational principles of the 5G NR RRC state machinery.
  • 5G NR RAN performance optimization and orchestration recommendations for new vertical use cases.
  • Models and relations between traditional radio KPIs and E2E performance.
  • Solutions related to wireless self-backhauling.


Panagiotis Demestichas
University of Piraeus, Greece

Klaus Pedersen
Nokia Bell Labs, Denmark

Raquel Barco
University of Malaga, Spain

Marie-Helene Hamon
Orange, France

Fu-Chun Zheng
Harbin Institute of Technology, China

Arunabha Ghosh
AT&T Labs, Austin, Texas, USA