The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology.INDEX TERMS 5G, new radio, cellular network dimensioning, network data, capacity model.
It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.
Analyzing and interpreting the exact behavior of new delay-based congestion control protocols with complex non-linear control loops is exceptionally difficult in highly variable networks such as cellular networks. This paper proposes a Model-Driven Interpretability (MDI) congestion control framework, which derives a model version of a delay-based protocol by simplifying a congestion control protocol's response into a guided random walk over a two-dimensional Markov model. We demonstrate the case for the MDI framework by using MDI to analyze and interpret the behavior of two delay-based protocols over cellular channels: Verus and Copa. Our results show a successful approximation of throughput and delay characteristics of the protocols' model versions across variable network conditions. The learned model of a protocol provides key insights into an algorithm's convergence properties.
This paper proposes an emergency scenario which is satellite assisted underwater radio communication network for emergency services. Identification of effective range of underwater radio communication with the integration of versatile NAV/COM devices is the main focus. Possible transmission paths have been discussed and effective numerical computations are carried out to determine the underwater communication range. Constraints like conductivity (σ), attenuation (α), refraction or interface loss and wavelength (λ) in water for underwater radio communication are considered.
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