Statistical analysis

The boxplots displayed in Figure 3-8 show DTLZ1 (In Figure 3-8a, C1-3D, and C1-5D correspond to C1-DTLZ1 problem for three and five dimensions), DTLZ2 (In Figure 3-8b, C2-3D, and C2-5D correspond to C2-DTLZ2 problem for three and five dimensions), and DTLZ3 (In Figure 3-8c, C1-3D, and C1-5D correspond to C1-DTLZ3 problem for three and five dimensions) problems have a better statistical performance followed by DTLZ5 and DTLZ7 problems with low dispersion of data around the median. Different from the DTLZ4 problem that faces severe convergence issues, especially for three-objective and five-objective

3.4 Performance results 37

20 0 40 60 100 80 120 140

3D 5D 8D 10D C1-3DC1-5D I-3D I-5D 2000

(a) DTLZ1 test problem.

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(b) DTLZ2 test problem.

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(c) DTLZ3 test problem.

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(d) DTLZ4 test problem.

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(e) DTLZ5 test problem.

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(f) DTLZ7 test problem.

Figure 3-8: IGD boxplots for normalized DTLZ1-4, DTLZ5, and DTLZ7 test problems.

Twenty realizations.

cases with a high distribution of data and the median located near the outliers. Finally, the IDTLZ1 (In Figure 3-8a, I-3D, and I-5D correspond to the IDTLZ1 problem for three and five dimensions) problem has high IGD values, but with good statistical behavior in term of low data dispersion.

4 Real-Time Emulation Methodologies for Centralized Radio Access Networks

In this chapter, we propose a framework for prototyping real-time C-RANs in a scalable software-only environment, built on top of the OpenAirInterface platform, and develop at EURECOM as part of my international internship during 2017 [51]. It takes advantage of the IF4P5 interface that exchanges in-phase and quadrature signals in the frequency domain.

Our approach valid for the fourth-generation and fifth generation of cellular systems allows system-level validations employing synthetic networks composed of RRUs and UEs, instead of physical radio units. It speeds up emulations using general-purpose processors and fast Ethernet transport ports. Also, it generates traffic stimulus to upper layers of the protocol stack, avoids uncertainties of software-defined radio front-ends, and enables the synthetic network scalability. Previous achievements are possible by executing optimized instructions available in modern multicore hardware and eliminating some blocks of the orthogonal fre-quency division multiplexing chain. The new frefre-quency-domain framework accelerates emu-lations ten-fold compared to the time domain. It supports generic x86 computers up to 3 Remote Radio Units and 3 User Equipment and allows the seamless switch from real-time IP-based emulations to real-world testbeds. The number of nodes can be increased using High-Performance Computing (HPC) that delivers much higher efficiency. Developers will find this framework interesting to prototype a specific technique applied to the C-RAN be-fore launching to the market, as we show with the coordinated scheduling proof-of-concept in chapter 5.

4.1. Introduction

Complex, dense, and scalable C-RANs scenarios will deploy the base-band processing in generic data centers and built on virtualized network components, general-purpose hardware, open-source software, and standardized interfaces. They will use macrocells to accomplish ubiquitous coverage and small cells to increase the capacity per area. Also, they will employ functional splits to support a myriad of applications with diverse bandwidth requirements and to build virtual end-to-end networks. Previous challenges will encourage mobile network operators to deploy flexible, load-aware, autonomous, versatile, and efficient ecosystems to provide ultra-reliable and mission-critical network services.

Traditionally, we use real testbeds, network simulations, and network emulations for prototy-ping C-RANs. Each of them has different trade-offs among reproducibility, scalability, and applicability [126], as explained in chapter 2. First, real testbeds conceive real-time world systems that are too expensive, applicable, but not scalable. They challenge the reprodu-cibility because of the large number of parameters imposed by operators in a multi-vendor deployment. Second, network simulations are reproducible and scalable. They have applicabi-lity concerns because models might hide important tasks that reduce the realistic validation certainty. Finally, network emulations are replicable, applicable, and less scalable compared with network simulations. They are either affordable or too expensive depending on user cases, the purpose of the experiment, and the platform employed.

From the above prototyping standpoints, we selected the OAI network emulation platform because it allows a system-level analysis of scalable, real-time, software-only, and 3rd Ge-neration Partnership Project (3GPP) standard-compliant scenarios. However, the primary concern is their scalability. We improve it by optimizing software functions of the multipath channel taking advantage of the Intel architecture with SIMD instructions, and neglecting some blocks of the Orthogonal Frequency Division Multiplexing (OFDM) chain such as IFFT, FFT, and Cyclic Prefix functions. Previous techniques increase the computation time efficiency of OFDM chain functions using frequency-domain methodologies compared to the time domain and support both downlink and uplink transmissions. The OAI system-level network emulation platform [10] it is a convenient and flexible platform that harmonizes commoditization of 3GPP Radio systems and open-source [122]. Previously, abstract met-hodologies of the physical layer valid only for downlink transmissions that achieve 100-fold faster emulations are implemented to improve the scalability of the RAN, but with the absence of the propagation environment and coding/decoding functions [13][81].

We contribute to the prototyping area of C-RANs with a real-time, scalable, system-level, and frequency-domain emulation framework. It serves as a stepping stone for future C-RANs deployed in data-centers. This framework helps to face some challenges mobile operators, policy-makers, and regulatory authorities will study to have a better end-user experience.

It captures complexities associated with real-world scenarios, achieving reproducible and scalable results for all layers of the protocol stack. Finally, it is essential to mention that we might create a hybrid emulation framework mixing radio frequency hardware and synthetic networks. The former will be used for application testability and the latter for network scalability.