Enhanced Remote Areas Communications: The Missing Scenario for 5G and Beyond 5G Networks

Mendes, Luciano Leonel; Moreno, Carlos Salle; Marquezini, M. V.; Cavalcante, André; Neuhaus, Peter; Seki, Jorge; Aniceto, Nathália F. T.; Karvonen, Heikki; Vidal, Iván; Valera, Francisco; Barreto, Priscila América Solís Mendez; Caetano, Marcos F.; Dias, Wheberth; Fettweis, Gerhard

doi:10.1109/access.2020.3042437

Cited by 18 publications

(11 citation statements)

References 55 publications

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“…The Software-defined radio (SDR) approach is used to implement all the physical layer (PHY) and medium access control (MAC) functions of the base stations, which run at the CO server. It will be assumed that BSs will be used to provide connectivity to remote areas using eRAC, which means that low out-of-band emission is necessary [7]. The signal from BS is linearized by the proposed ML-based DPD approach and then applied to MZM.…”

Section: Radio-over-fiber Modelmentioning

confidence: 99%

“…One scenario that has been attracting attention, especially in continental-sized countries, is known as enhanced remote area communications (eRAC) [7]. In this scenario, new business models and cost-effective solutions are required to offer connectivity for subscribers in remote and rural areas [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, this network operating model is usually economically unattractive, since there are a few potential subscribers at these locations. Moreover, the capital expenditures (CAPEX) and frequency licenses have hindered the remote network deployment [7]. These restrictions must be overcome to finally offer connectivity in remote areas.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

et al. 2022

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This manuscript proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVM RMS ), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns.

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Section: Radio-over-fiber Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

et al. 2022

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“…Micro-operators are more likely to succeed in this, since they can easily engage with the community while meeting local needs, while also leveraging their infrastructure to reduce capital and operation costs to become profitable [2]. The micro-operator concept has been illustrated by its deployment in Peru to improve rural connectivity [30]. Micro-operators need to be able to dynamically adjust bandwidth to guarantee the functionality of the most important applications for locals (e.g., eHealth and e-learning) to ensure, for example, the number of visitors will not completely block the system.…”

Section: Reducing Rural-urban Digital Divide With 6g Telecommunication Networkmentioning

confidence: 99%

5G and 6G Broadband Cellular Network Technologies as Enablers of New Avenues for Behavioral Influence with Examples from Reduced Rural-Urban Digital Divide

2021

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Two of the key information technologies with very high expectations to change our contemporary society are the fifth generation broadband cellular network technologies, which are already emerging to markets, and the foreseen sixth generation broadband cellular network technologies currently under research and development. The core promise of these next generation technologies lies especially in lower latency for providing users feedback on their behavior; thus, growing opportunities for influencing users in their everyday contexts. This viewpoint article seeks to discuss how these opportunities may impact future information technology in general and especially persuasive design and research before 2030. In addition, we will address challenges regarding the promise of 5G and 6G technologies. Information and communication technology can support individuals’ behavioral change only if they can access the technology. In this article, we will exemplify this by presenting possible ways to minimize the digital divide between rural and urban areas, wherein lies a general danger that the divide would increase further.

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“…The Ultra Reliable Low Latency Communications (URLLC) [2] supports robust low latency connectivity with a target Round Trip Time (RTT) of 1 ms. The enhanced Remote Area Communications (eRAC) [3] provides connectivity in long-range and remote areas, achieving at least 100 Mbps at 50 km from the Base Station (BS) while exploiting the TV White Space (TVWS). Finally, the massive Machine Type Communications (mMTC) [4] promises connectivity to a plethora of powerlimited Internet of Things (IoT) devices.…”

Section: Introductionmentioning

confidence: 99%

MUSA Grant-Free Access Framework and Blind Detection Receiver

Aquino¹,

Barbosa²,

Chafii³

et al. 2021

JCIS

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Recently, a non-orthogonal multiple access scheme called multi-user shared access (MUSA) was proposed to provide massive connection capability of low-complexity devices in the 5G networks. MUSA achieves higher spectral efficiency allowing independent devices to transmit data on the same physical layer time-frequency resources. Furthermore, MUSA introduces a grant-free transmission and a blind multi-user detection at the receiver, reducing the complexity on the transmit side. This approach is interesting for Internet of Things applications over mobile communication networks, where the devices have limited power and processing capacity. The references available in the literature about this multiple access scheme do not bring sufficient details about the MUSA multi-user detector. This limitation makes it difficult to evaluate the MUSA performance and to propose improvements for this new technique. The main goal of this paper is to provide a framework describing the entire communication chain using MUSA as multiple access. This paper also brings a proposal for a blind multi-user detection, where the information about the MUSA parameters and the channel state information are unknown at the receiver side. The performance of the MUSA multi-user detector is improved by a deep learning based processing that enhances the quality of the channel estimation provided by a initial minimum mean square error estimator. The proposed deep neural network architecture employed to improve the channel estimation allows more users to share the same time-frequency resources for a given target block error rate, increasing the overall spectrum efficiency of the system.

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Enhanced Remote Areas Communications: The Missing Scenario for 5G and Beyond 5G Networks

Cited by 18 publications

References 55 publications

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

5G and 6G Broadband Cellular Network Technologies as Enablers of New Avenues for Behavioral Influence with Examples from Reduced Rural-Urban Digital Divide

MUSA Grant-Free Access Framework and Blind Detection Receiver

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