Towards the Flexible and Efficient Implementation of the 5G-NR RAN Physical Layer

Coutinho, Fabio D. L.; Domingues, Jose D.; Marques, Paulo; Pereira, Samuel Santos; Silva, Hugerles S.; Oliveira, Arnaldo S. R.

doi:10.1109/rws50353.2021.9360353

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…The research results on the implementation of 5G-NR DU and RU on FPGA are outlined in an article by [11]. The 5G-NR DU receiver, combined with the RU receiver, was implemented in the ZCU102 evaluation kit with the integrated AD9371 transceiver.…”

Section: Statement Of the Problemmentioning

confidence: 99%

Implementation of Functional Block Radio Unit Based on System-on-Chip

Ibraimov

2023

Eurasian phys. tech. j.

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This article discusses the implementation of the Radio Unit functional block based on the System-on-Chip. The primary focus was on integrating Radio Unit blocks such as modulation and Fast Fourier Transform on Field-Programmable Gate Array. Technical aspects of design, module testing, and Radio Unit block performance optimization are thoroughly examined. The results demonstrate that when separating the functionality of the 7.3 technology Fifth Generation (5G) radio block, the modulation module uses the minimum Field-Programmable Gate Array resources compared to other blocks. The Fast Fourier Transform block can meet delay requirements at the maximum Field-Programmable Gate Array size and clock frequency of 250 MHz. This article serves as a resource for engineers and researchers interested in optimizing the development and integration process of high-performance functional blocks in modern radio systems.

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Section: Statement Of the Problemmentioning

confidence: 99%

Implementation of Functional Block Radio Unit Based on System-on-Chip

Ibraimov

2023

Eurasian phys. tech. j.

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“…The software solution to implement stack cutting option defined by 3GPP at split option 7, between RU and DU, [54] gNB (ISW) Survey papers, theoretical & mathematical concepts [76], [86], [87], [88], [89], [90], [91], [92], [57], [93], [94] Mathematical & theoretical analysis…”

Section: Fpgamentioning

confidence: 99%

Survey on 5G Second Phase RAN Architectures and Functional splits

Khan¹,

Nidhi²,

OdetAlla³

et al. 2022

Preprint

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<p>The Radio Access Network (RAN) architecture evolves with different generations of mobile communication technologies and forms an indispensable component of the mobile network architecture. The main component of the RAN infrastructure is the base station, which includes a Radio Frequency unit and a baseband unit. The RAN is a collection of base stations connected to the core network to provide coverage through one or more radio access technologies. The advancement towards cloudnative networks has led to centralizing the baseband processing of radio signals. There is a trade-off between the advantages of RAN centralization (energy efficiency, power cost reduction, and the cost of the fronthaul) and the complexity of carrying traffic between the data processing unit and distributed antennas. 5G networks hold high potential for adopting the centralized architecture to reduce maintenance costs while reducing deployment costs and improving resilience, reliability, and coordination. Incorporating the concept of virtualization and centralized RAN architecture enables to meet the overall requirements for both the customer and Mobile Network Operator. Functional splitting is one of the key enablers for 5G networks. It supports Centralized RAN, virtualized Radio Access Network, and the recent Open Radio Access Networks. This survey provides a comprehensive tutorial on the paradigms of the RAN architecture evolution, its key features, and implementation challenges. It provides a thorough review of the 3rd Generation Partnership Project functional splitting complemented by associated challenges and potential solutions. The survey also presents an overview of the fronthaul and its requirements and possible solutions for implementation, algorithms, and required tools whilst providinga vision of the evaluation beyond 5G second phase.</p>

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“…Also, they need prior knowledge of spectrum sparsity which may not be available in a dynamic environment. From an architecture perspective, in 5G and next-generation wireless networks, distributed base station approach where radio unit (RU), distributed unit (DU), and central unit (CU) may not be co-located is being explored [21], [22]. Such deployment demands the mapping of SNS algorithms on hardware platforms such as system on chip (SoC) under the limited computing resource constraints due to remote locations of the RU and DU.…”

Section: Introductionmentioning

confidence: 99%

Hardware–Software Co-Design of Statistical and Deep-Learning Frameworks for Wideband Sensing on Zynq System on Chip

Rajesh

Darak

Jain

et al. 2023

IEEE Trans. VLSI Syst.

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With the introduction of spectrum sharing and heterogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters. However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA. Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning-based architectures and efficiently map them on the ZSoC platform as second contribution. Via hardware-software co-design, different versions of the proposed architecture obtained by partitioning between software (ARM processor) and hardware (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths are presented for these architectures.

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Towards the Flexible and Efficient Implementation of the 5G-NR RAN Physical Layer

Cited by 10 publications

References 4 publications

Implementation of Functional Block Radio Unit Based on System-on-Chip

Implementation of Functional Block Radio Unit Based on System-on-Chip

Survey on 5G Second Phase RAN Architectures and Functional splits

Hardware–Software Co-Design of Statistical and Deep-Learning Frameworks for Wideband Sensing on Zynq System on Chip

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