5G: An overview of Channels characteristics and modelling techniques

Bhardwaj, Kapil; Singh, Anant; Sachan, Vibhav Kumar

doi:10.1109/pdgc.2018.8745875

Cited by 4 publications

(7 citation statements)

References 38 publications

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“…Decreasing the connection time T also decreases the time averaged gain, that is, G N,K m (T = 60 s) > G N,K m (T = 10 s) > G N,K m (T = 1 s) for any fixed m, N or K. This observation is explained by the fact that the more independent UE sets are served in the averaging time-span T avg (or, equivalently, the less the T value is), the closer G N,K m approaches the normalized average of instantaneous array patterns A(θ, ϕ, t) over the cell. Conversely, in the limit of a single UE served with T = T avg , as follows from ( 10), (11), (12), the time-averaged gain is the instantaneous BS pattern maximum. In this case, the CB beamforming realizes the maximum theoretical gain G max for the codebook directions coinciding with the maxima of the BS antenna element's individual pattern.…”

Section: Normalized Time-averaged Gainmentioning

confidence: 99%

“…It has been shown that the RT method reproduces key parameters of measured Massive MIMO channels [10], whereas the state-of-the-art statistical model (WINNER-II) tends to underestimate the amount of correlation in the channels of closely spaced UEs [11]. Various other approaches to 5G channel modeling have been proposed in the literature and we direct an interested reader to the recent overview articles in [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Ray-Tracing-Based Numerical Assessment of the Spatiotemporal Duty Cycle of 5G Massive MIMO in an Outdoor Urban Environment

et al. 2020

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In the near future, wireless coverage will be provided by the base stations equipped with dynamically-controlled massive phased antenna arrays that direct the transmission towards the user. This contribution describes a computational method to estimate realistic maximum power levels produced by such base stations, in terms of the time-averaged normalized antenna array gain. The Ray-Tracing method is used to simulate the electromagnetic field (EMF) propagation in an urban outdoor macro-cell environment model. The model geometry entities are generated stochastically, which allowed generalization of the results through statistical analysis. Multiple modes of the base station operation are compared: from LTE multi-user codebook beamforming to the more advanced Maximum Ratio and Zero-Forcing precoding schemes foreseen to be implemented in the massive Multiple-Input Multiple-Output (MIMO) communication protocols. The influence of the antenna array size, from 4 up to 100 elements, in a square planar arrangement is studied. For a 64-element array, the 95th percentile of the maximum time-averaged array gain amounts to around 20% of the theoretical maximum, using the Maximum Ratio precoding with 5 simultaneously connected users, assuming a 10 s connection duration per user. Connection between the average array gain and actual EMF levels in the environment is drawn and its implications on the human exposure in the next generation networks are discussed.

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Section: Normalized Time-averaged Gainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ray-Tracing-Based Numerical Assessment of the Spatiotemporal Duty Cycle of 5G Massive MIMO in an Outdoor Urban Environment

et al. 2020

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“…While 5G technology promises substantial advancements in mobile network performance within urban and suburban regions, realizing these benefits requires careful consideration of unique challenges such as building penetration and complex terrain [7]. Building Penetration, particularly at higher frequencies like mmWave, poses significant obstacles for 5G NR signals.…”

Section: Introductionmentioning

confidence: 99%

“…Building Penetration, particularly at higher frequencies like mmWave, poses significant obstacles for 5G NR signals. Similarly, complex terrain including uneven landscapes, hills, and geographical features can disrupt signal propagation, leading to coverage gaps and impacting overall network performance [7]. Addressing these challenges is essential for the successful deployment and optimization of 5G technology in urban and suburban environments.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks

Zreikat,

Mathew

2024

Computers

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5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hot spot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G.

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“…Accurate channel model is regarded as the foundation of future wireless network. Generally, deterministic and semi-deterministic modeling are the two mainstream methods of channel modeling [2]. However, this method has many limitations, including low computational efficiency, excessive computing power consumption, and it requires complicated simulation.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

Zhang¹,

He²

2023

Preprint

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As an emerging approach, deep learning plays an increasingly influential role in channel modeling. Traditional ray tracing (RT) methods of channel modeling tend to be inefficient and expensive. In this paper, we present a superresolution (SR) model for channel characteristics. Residual connection and attention mechanism are applied to this convolutional neural network (CNN) model. Experiments prove that the proposed model can reduce the noise interference generated in the SR process and solve the problem of low efficiency of RT. The mean absolute error of our channel SR model on the PL achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT took only 52% of the time in theory. Compared with vision transformer (ViT), the proposed model also demonstrates less running time and computing cost in SR of channel characteristics.

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5G: An overview of Channels characteristics and modelling techniques

Cited by 4 publications

References 38 publications

Ray-Tracing-Based Numerical Assessment of the Spatiotemporal Duty Cycle of 5G Massive MIMO in an Outdoor Urban Environment

Ray-Tracing-Based Numerical Assessment of the Spatiotemporal Duty Cycle of 5G Massive MIMO in an Outdoor Urban Environment

Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks

Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

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