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5G promises to usher in the industrial 4.0 era. In that era, intricately managed autonomous industrial sites with for example remotely controller equipment and autonomous units promise previously unseen levels of efficiency. Although such scenarios are elusive, they come with strict long-since established safety requirements. To uphold such requirements, intelligent industrial 5G networks, that actively take into account prevailing conditions and dynamics of the workers on the site, the equipment, and the network, are needed. Little is known about the dynamics of actual industrial 5G networks and the interplay between network performance and QoE. In this paper, as a step towards intelligent industrial 5G networks, we measure network performance for an industrial 5G network, and conduct QoE experiments with remote controlled industrial equipment on an operational site. The results revealed unexpected relationships between QoE and network performance that shows how important domain-specific knowledge is when researching intelligent industrial 5G networks.

This paper presents a novel user plane framework, tailored for different 5G services with diverse and conflicting key performance indicators. Initially, this paper identifies the major challenges in the legacy user-plane approaches and highlights the up-to-date 5G standardization activities in this area. It further analyzes new functional requirements related to service-oriented design and the introduction of new mechanisms to address them. Subsequently, this paper discusses how various user plane design decisions related to the control/user plane split options, network slicing, and radio access network (RAN)-core network (CN) interfacing can potentially impact the overall 5G architecture. For the latter, some key RAN/CN interface considerations and the interactions with CN given different protocols and quality of service models are investigated.

5G is expected to operate in a wide frequency range to support new challenging use-cases. Multi- RATs (Radio Access Technologies): NR (New Radio) and evolved LTE (Long Term Evolution) will together constitute 5G. Utilizing NR at high frequencies will have a significant impact on radio propagation conditions with e.g. unfavorable higher path loss and increased outdoor-to-indoor penetration losses. In order to provide a reliable communication from the outset of 5G deployment and to minimize the standardization and implementation complexity, 5G UP (User Plane) instances of 5G AIs (Air Interface) related to evolved LTE and NR need to be aggregated on a certain layer of the protocol stack. This paper sheds light on how to integrate 5G AIs into a single 5G AI framework and explores which protocol stack layer could be used as aggregation layer. Inter-RAT hard handover is the state of the art technique to integrate multiple RATs in order to support mobility and reliability across different RATs. However, the hard handover incurs a transmission interruption which stands as an obstacle along the way of accomplishing 5G design. According to simulation results, a common PDCP (Packet Data Convergence Protocol) layer improves the hard handover functionality and stands out as a basis for tight interworking between evolved LTE and NR. By means of simulation, it is shown that the multi-RAT UP aggregation can achieve three times higher user throughput, when NR is using 28 GHz and LTE 2 GHz, compared to stand-alone NR.

The standardization of the next generation 5G radio access technology has just started in 3GPP with the ambition of making it commercially available by 2020. There are a number of features that are unique for 5G radio access compared to the previous generations such as a wide range of carrier frequencies and deployment options, diverse use cases with very different user requirements, small-size base stations, self-backhaul, massive MIMO, and large channel bandwidths. In this article, we propose a flexible physical layer for the NR to meet the 5G requirements. A symmetric physical layer design with OFDM is proposed for all link types, including uplink, downlink, device-to-device, and backhaul. A scalable OFDM waveform is proposed to handle the wide range of carrier frequencies and deployments.

This paper proposes a unified way of describing 5G air interface (AI) design proposals using a 5G service/frequency map based on work carried out as part of 5G-PPP/H2020 project “METIS-II”. It then crucially proposes a design framework and suitability assessment process for 5G AI candidates. The proposed assessment methodology focuses on “harmonization Key Performance Indicators, or KPIs” and how to measure them (qualitatively / quantitatively). The paper proposes that evaluation of 5G AI candidates should, in addition to performance, include the “extent of harmonization”, which is defined in this paper. The case is argued that these harmonization KPIs are essential when assessing new 5G AI technologies. Additionally, an initial overview of different User Plane aggregation approaches is provided. We then discuss the types of Application Program Interfaces (APIs) which may need to be offered to higher layers, as well as a broad set of 5G Control Plane features and how AI considerations could take these into account.

The standardization of 5G, referred to as new radio (NR), has started. The 5G air interface (AI) is envisioned to comprise a number of AIs including the evolution of Long Term Evolution (LTE) and new NR AIs operating at frequencies from below 1GHz and up to 100GHz. One of the key challenges is the design of an overall 5G AI that efficiently combines these AIs into one 5G AI framework. This paper examines how the AIs can be harmonized into a single 5G AI framework, while minimizing the complexity of standardization and implementation. The harmonization among the 5G AIs could take place at any layer: at the physical layer (PHY), at the Medium Access Control layer (MAC) or at the Packet Data Convergence Protocol (PDCP) layer. Among various detailed harmonization options that are being investigated in this context, for the integration of the new AIs at the physical layer, cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) waveform with various numerologies is considered as a basis and the MAC layer harmonization can be built on top of the PHY layer, where the CP-OFDM-related AIs of different numerologies are catered for different future services, bands, and cell types. Also, harmonization at the PDCP layer appears to be the most viable harmonization option between the evolved LTE and the new AIs. The results of the first conducted 5G AI analysis will constitute discussion topics for other 5G-PPP projects as well as 3GPP.

The paradigm shift where realtime communication will be handled on the web is soon becoming a reality. With only a few lines of Javascript code developers can easily turn a web browser into a voice or video terminal using the WebRTC API. While the IETF is actively working on standardizing the RTCWeb protocol to enable realtime communication across browsers and to avoid failures in session setups and enhance the session’s Quality of Experience (QoE), there is still a lack of a standardized congestion control mechanisms in the area. This paper proposes and evaluates a congestion avoidance mechanism and a rate adaptation model for WebRTC interactive video sessions in LTE networks where the model is based on realtime available bandwidth estimation and measurements. Our simulation results show that the proposed model provides a fair bandwidth allocation between TCP traffic flows and realtime media traffic flows. The results also show that excessive video frame delays and packet losses can be prevented with the proposed congestion avoidance mechanism. Consequently the proposed model improves performance and perceived QoE of WebRTC. Validerad; 2014; 20140129 (karand) NIMO - Nordic Interaction and Mobility Research Platform