This paper proposes a novel context-aware userdriven strategy to efficiently exploit all available bands and licensing regimes in ultra-dense deployments without prior knowledge about each combination. It relies first on fuzzy logic to estimate the suitability of each radio access technology (RAT) to support the requirements of various applications. Then, a fuzzy multiple attribute decision making (MADM) approach is developed to combine these estimates with the heterogeneous context components to assess the in-context suitability. Based on this metric, a spectrum management strategy is proposed to support interactive video sessions for a set of Bronze and Gold subscriptions. The results reveal that the proposed approach always assigns Gold users to the well-regulated licensed band, while switches Bronze users between licensed and unlicensed bands depending on the operating conditions. This results in a significant improvement of the quality-of-experience (QoE) compared to a baseline that exploits only licensed bands. Then, a comparative study is conducted between the available options to exploit unlicensed bands, namely Offloading and Sharing. The results show that the best option strongly depends on the existing load on WLAN. Therefore, a combined approach is proposed to efficiently switch between both options, which achieves the best QoE for all considered loads.
This paper considers an optimization problem that maximizes an aggregate utility, formulated as the weighted geometric mean of the "in-context" suitability of a set of radio access technologies (RATs) to support adaptive video streaming, subject to the existence of legacy data transfers. Motivated by the unfeasibility of solving the formulated problem centrally when the various RATs are loosely integrated (i.e., at core network (CN) level), a hybrid (i.e., network-assisted user-driven) strategy is devised to approximate its optimum solution. Unlike previous hybrid approaches, the proposed methodology exploits network assistance to ensure a friendly co-existence between adaptive video streaming clients and legacy users. It operates on different timescales, where the fastest timescale operation is performed on the video clients according to a policy that is tuned by the network on slower timescales. A user tuning on the fastest timescale (i.e., tens of ms) enables to adapt video streaming depending on the perceived quality-of-experience (QoE) and local components of the context (e.g., remaining credit and battery level). A small-cell tuning on a slower timescale (i.e., hundreds of ms) enables to preempt the resources used by legacy users based on the operating conditions (e.g., load and type of scheduler). Finally, a tuning performed by the network on the slowest timescale (i.e., few seconds) offloads legacy data transfers to unlicensed bands whenever the amount of interference on licensed bands reaches critical levels, which helps to sustain good QoE for all video clients. A cost-benefit analysis reveals that the proposed methodology performs closely to its centralized counterpart with much less control overhead on the radio interface.
5G systems are expected to advance on a number of aspects compared with current systems, for example, providing a 1000 times higher capacity, a much lower latency and improved quality of user experience. This paper presents the vision and approach followed by the H2020 project SPEED-5G. The approach is based on densification of small cells, exploitation of Multi-RAT, development of new resource management techniques and a more efficient use of spectrum. A novel 5G system architecture is proposed based on the Network Slicing paradigm, which enables a highly flexible, scalable and backwards compatible architecture. A core aspect is the definition of a new MAC layer that facilitates Multi-RAT access and allows prioritising and allocating traffic across heterogeneous access technologies.
This chapter presents initial results available from the European Commission H2020 5G PPP Phase 2 project SaT5G (Satellite and Terrestrial Network for 5G) . It specifically elaborates on the selected use cases and scenarios for satellite communications (SatCom) positioning in the 5G usage scenario of eMBB (enhanced mobile broadband), which appears the most commercially attractive for SatCom. After a short introduction to the satellite role in the 5G ecosystem and the SaT5G project, the chapter addresses the selected satellite use cases for eMBB by presenting their relevance to the key research pillars (RPs), their relevance to key 5G PPP key performance indicators (KPIs), their relevance to the 3rd Generation Partnership Project (3GPP) SA1 New Services and Markets Technology Enablers (SMARTER) use case families, their relevance to key 5G market verticals, and their market size assessment. The chapter then continues by providing a qualitative high-level description of multiple scenarios associated to each of the four selected satellite use cases for eMBB. Useful conclusions are drawn at the end of the chapter.
This paper presents initial results available from the European Commission Horizon 2020 5G Public Private Partnership Phase 2 project “SaT5G” (Satellite and Terrestrial Network for 5G).1 After describing the concept, objectives, challenges, and research pillars addressed by the SaT5G project, this paper elaborates on the selected use cases and scenarios for satellite communications positioning in the 5G usage scenario of enhanced mobile broadband.
This paper formulates an optimization problem thatmaximizes an aggregate utility that captures the “in-context” suit-ability of available radio access technologies (RATs) to supportadaptive video streaming subject to a single-homing constraint.To efficiently solve the considered problem, a novel network-assisted quality-of-experience (QoE)-driven methodology is de-vised, and its impact on the end-user devices is evaluated.The proposed approach is evaluated and benchmarked againstits distributed and centralized counterparts from a cost-benefitperspective. The results reveal that the proposed strategy sig-nificantly outperforms its distributed counterpart, and performsdifferently with respect to its centralized counterpart dependingon the number of video clients. At low loads, it performs similarlywith much less control overhead. At high loads, the proposedstrategy scales up well, while the centralized approach getsoverwhelmed by an increasing uplink signaling. A practicalityanalysis of the proposed strategy for battery-powered devicesreveals that its gain in terms of uplink signaling outweighs its costin terms of processing load, which results in a drastic reduction ofthe consumed energy. Therefore, the proposed solution providesa win-win situation, where the video clients can sustain goodQoE levels at reduced energy consumption, while the networkcan accommodate more users with existing capacity.
This paper proposes a novel spectrum utility (SU) metric that assesses the efficiency of spectrum usage by a set of heterogeneous applications. Unlike the traditional spectrum efficiency (SE), the proposed metric does not blindly consider the achievable bit-rate, but captures the most relevant performance metrics for each of the considered applications. Specifically, it is formulated as an aggregated utility that combines the satisfaction level with respect to the various requirements with an innovative pricing model based on it to derive the total revenue generated for the spectrum owner. To get insight into the usefulness of the proposed metric, the proposed methodology is instantiated for an illustrative use case, where a mixture of delay-sensitive (i.e., interactive video) and -tolerant (i.e., file transfer) applications are established in dense indoor deployments. The obtained results reveal that the proposed SU significantly outperforms the legacy SE in assessing how efficiently a limited frequency spectrum is utilised from the perspective of the total revenue, particularly when the quality-of-experience (QoE) perceived during video sessions is degraded. This calls for a novel SU-aware ecosystem, where the spectrum sharing models, billing policies and resource allocation mechanisms (e.g., medium access control (MAC) and radio resource management (RRM)) are jointly revisited to maximise the overall SU.
This paper extends traditional dynamic adaptive streaming over HTTP (DASH) to efficiently exploit all available bands and licensing regimes in a given context. A novel objective quality-of-experience (QoE) metric is proposed to capture the most relevant factors that impact user perception during streaming sessions. Based on it, a QoE-driven adaptation strategy is devised to jointly select the best radio access technology (RAT) and quality for each video segment depending on the various components of the context. It relies first on fuzzy logic to estimate the QoE provided by each available RAT subject to the uncertainty level associated with DASH clients. Then, a fuzzy multiple attribute decision making (MADM) methodology is developed to combine the QoE estimates with the heterogeneous components of the context to assess the in-context suitability levels. The proposed approach is applied to adapt video streaming across available RATs in dense deployments for a set of Bronze and Gold subscriptions. The results reveal that the proposed strategy always assigns Gold clients to the wellregulated licensed band, while switches Bronze clients between licensed and unlicensed bands depending on the operating conditions. It strikes a balance between maximising video quality and reducing playback stalling, which significantly improves the perceived QoE compared to the traditional DASH approach.