On the need of computing in future communication networks

Fitzek, Frank H. P.; Seeling, Patrick; Höschele, Thomas; Jacobfeuerborn, Bruno

doi:10.1016/b978-0-12-820488-7.00011-6

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…In this context, we consider network resources at the edge, including radio units (RUs), wireless spectrum, and a transport network that connects RUs to the edge cloud at the proximity of wireless infrastructure to provide low-latency computational service for different wireless use-cases. Although end-to-end network slicing architecture encompasses slicing core cloud and data network resources, this paper discusses the resource allocation problem at the edge for surveillance and Industry 4.0 use-cases (Garcia-Morales et al, 2019;Fitzek et al, 2020;Yuan and Muntean, 2020). In particular, we focus on managing computational resources at the edge, a.k.a multiaccess (mobile) edge computing (MEC), which manifests the computational infrastructure for wireless slices operation.…”

Section: System Design and Autoscaling Methodsmentioning

confidence: 99%

Auto-scaling edge cloud for network slicing

Mazied

Nikolopoulos

Hanafy

et al. 2023

Front. High Perform. Comput.

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This paper presents a study on resource control for autoscaling virtual radio access networks (RAN slices) in next-generation wireless networks. The dynamic instantiation and termination of on-demand RAN slices require efficient autoscaling of computational resources at the edge. Autoscaling involves vertical scaling (VS) and horizontal scaling (HS) to adapt resource allocation based on demand variations. However, the strict processing time requirements for RAN slices pose challenges when instantiating new containers. To address this issue, we propose removing resource limits from slice configuration and leveraging the decision-making capabilities of a centralized slicing controller. We introduce a resource control agent (RC) that determines resource limits as the number of computing resources packed into containers, aiming to minimize deployment costs while maintaining processing time below a threshold. The RAN slicing workload is modeled using the Low-Density Parity Check (LDPC) decoding algorithm, known for its stochastic demands. We formulate the problem as a variant of the stochastic bin packing problem (SBPP) to satisfy the random variations in radio workload. By employing chance-constrained programming, we approach the SBPP resource control (S-RC) problem. Our numerical evaluation demonstrates that S-RC maintains the processing time requirement with a higher probability compared to configuring RAN slices with predefined limits, although it introduces a 45% overall average cost overhead.

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Section: System Design and Autoscaling Methodsmentioning

confidence: 99%

Auto-scaling edge cloud for network slicing

Mazied

Nikolopoulos

Hanafy

et al. 2023

Front. High Perform. Comput.

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“…On the other hand, see that z [1] = f (m), this is, the function can be estimated from a single sample. This provides an even simpler demodulation scheme, although it requires high SNR.…”

Section: Function Estimatementioning

confidence: 99%

“…Future networks are expected to integrate computing capabilities and will consist of many devices capable of sensing the environment and taking decisions. In this vein, [1] highlights the relevance of computing in communication networks and motivates it with respect to a wide variety of use cases.…”

Section: Introductionmentioning

confidence: 99%

DCT-Based Air Interface Design for Function Computation

Gost

Pérez-Neira

Lagunas

2023

IEEE Open J. Signal Process.

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With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. Furthermore, the proposed modulation allows to recover both the measurement and the function in a single transmission. Our experiments show that this scheme outperforms the double side-band (DSB) modulation in terms of mean squared error (MSE). This can also be implemented with an agnostic receiver, in which the function is unkown to the receiver. Finally, the proposed modulation is compatible with some of the existing transmission technologies for sensor networks.

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“…In the context of 3G, when many frequency bands were allotted internationally, a serious demand for SDR emerged. SDR was utilised to reduce the number of possible baseband configurations [ 37 ].…”

Section: Software-defined Radiomentioning

confidence: 99%

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System

Saeed

Shah

Khan

et al. 2022

Sensors

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Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods is non-line-of-sight environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we suggest the intelligent reflecting surface (IRS) to assure high accuracy activity monitoring in complicated environments where traditional microwave sensing is ineffective. This work is based on reconfigurable IRS that can perform beam-forming/beam-steering and intelligent machine learning algorithms that can accurately recognise several human activities. For experimentation, the transmitter and receiver are positioned on two separate floors of a building. A complicated multi-floor scenario is created in order to test the IRS. Multiple activities such as sitting, standing, and walking are performed on the floor of the building by two individuals, a male and a female. It has been proven through an experiment that IRS technology increases detection accuracy by around 30% compared to conventional microwave sensing without IRS technology.

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On the need of computing in future communication networks

Cited by 14 publications

References 12 publications

Auto-scaling edge cloud for network slicing

Auto-scaling edge cloud for network slicing

DCT-Based Air Interface Design for Function Computation

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System

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