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Transmitter (a) and (c) and receiver (b) and (d) configurations in the frequency bands 0.8 to 66.5 GHz.
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The International Telecommunications Union Radiocommunication Sector (ITU‐R) Study Group 3 identified the need for a number of radio channel models in anticipation of the World Radiocommunications Conference in 2019 when the frequency allocation for 5G will be discussed. In response to the call for propagation path loss models, members of the study...
Citations
... A custom-designed Durham University's channel sounder [27,28] was used to conduct SISO 3D indoor directional channel measurements in factory and office environments shown in Figure 1a, with the corresponding Tx and Rx locations in Figure 1b. At a repetition frequency of 1.22 kHz, the measurements were carried out with 4.5 and 6 GHz bandwidths centred at 38.31 and 70.28 GHz, respectively. ...
Single‐input single‐output (SISO) three‐dimensional (3D) wideband indoor directional measurements collected in a factory environment and an office environment at 38 and 70 GHz are presented. 3D single‐input multiple‐output (SIMO) dual polarised measurements with 1 × 2 antenna configurations were also carried out in a meeting room, a conference room, and an office room at the 60 GHz band. The measurements cover both azimuth and elevation by rotating the directional antenna (RDA) at the receiver side. Different statistical channel parameters such as power delay profile, power angle profile, root‐mean‐square delay spread, angular spread, and path loss were estimated for different possible antenna orientations between the transmitter and the receiver, which include line‐of‐sight, obstructed line‐of‐sight, and non‐line‐of‐sight. The polarisation effects on path loss models and the delay and angular spread models based on the surface area of the environment are studied. The results will be valuable for the design of indoor millimetre wave cellular networks.
... The state of the art, Durham University's channel sounder, upgraded to cover the frequency range (100-300 GHz) was used to conduct multi-frequency wideband indoor measurements at the science site campus of Durham University in industrial environment, as shown in Fig. 1(a), with the corresponding measurements layout as shown in Fig. 1(b) [1][2]. The measurements were conducted at a centre frequency of 145 GHz and 294 GHz with 18 GHz and 36 GHz bandwidth, respectively with a waveform repetition frequency of 1.22 kHz. ...
... Thus, measurements in the mm-wave band have been reported with the goal of providing data and models to characterise the temporal, spatial, and frequency-dependence behaviour of the radio propagation channel e.g. [1][2][3]. Obtaining detailed propagation channel characteristics, such as power delay profile PDP, RMS DS, AS, and PL in the mm-wave bands in different environments is essential for the design of 5G networks [4][5]. In this paper, wideband outdoor channel measurements in two of the frequency bands (39 GHz and 70 GHz) identified by the World Radiocommunications Conference in 2019 (WRC-19) for 5G wireless communication systems are presented by employing the rotated directional antenna-based (RDA) method. ...
... The PDPs were then used to estimate the RMS delay spread and angular spread using equations 1 and 2 [7][8] respectively and to estimate the received power and the path loss. (1) where N is the total number of delay bins in each PDP, and are the delay and the power of the path. ...
... F OR a number of years, the U.S. Federal Communications Commission (FCC) has been investigating opportunities for mid-band spectrum (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) GHz) that would accommodate new broadband technologies [1]. As far back as 2003, the FCC sought stakeholders' comments about a new interference metric to expand unlicensed use in the 6525-6700 MHz band and portions of the 12.75-13.25 ...
... Several studies have investigated similar frequency bands in an effort to improve propagation model fidelity. In [10], the authors report path loss measurements at 6 and 10 GHz in urban environments for distances of less than 1.6 km. Similarly, [11] derives measurements-based models at 2 and 18 GHz for urban environments and at distances of less than 1.5 km. ...
... In addition to the FI, CI and FI with reconstructed data models, the data was compared with two empirical models used in the cellular industry: WINNER II and 3GPP. The accuracy of each model is evaluated using the mean error ( ) 10 (ℎ − 1) − 14 10 (ℎ − 1) + 6 10 ( /5) < <5km NLOS (44.9 − 6.55 10 (ℎ )) 10 ( ) + 34.46 + 5.83 10 (ℎ ) + 23 10 ( /5) 50m< <5km 3GPP LOS 28 + 22 10 ( ) + 20 10 ( ) 10m< 2 < 28 + 40 10 ( ) + 20 10 ( ) − 9 10 ( ( Table III. The mean error is defined by ...
This letter investigates propagation at 7 and 13 GHz
in urban locations motivated by coexistence studies in the recently
opened 6 GHz band (5925-7125 MHz) and in the similar 13
GHz band (12700-13250 MHz). Path loss measurements were
collected in four urban areas in both line-of-sight (LOS) and
non-LOS (NLOS) conditions. We derive floating-intercept (FI)
and close-in free space reference distance (CI) path loss models
and achieve RMS errors between 5.6 and 9.0 dB. We investigate
the applicability of WINNER II and 3GPP models at these
frequencies and find that the LOS case provides a conservative
estimate of propagation loss.
... In [6], the authors discussed RMS delay spread and path loss, showing that RMS delay spread increases with path loss. In [45], the authors presented large-scale radio channel measurements, performed in urban and suburban environments of different countries, in a frequency range from 0.8 GHz up to 73 GHz. This work presents an overview of the path loss model adopted in the recommendation recommendation ITU-R P.1411-10 (2019-08) and gives the data analysis with the approach to derive this adopted path loss model. ...
The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.
... According to the coverage of each site, the number of sites is required. The equations in this section can be used to determine how much coverage each location has and how many 5G NSA sites are needed by using the equation in (4) [15]. ...
... Most of the time operator demanding signal in shadow fading area is also accounted in this Eq. 1 to observe real signal prediction. Reference distance is another factor to suggest real path loss of signal near the tower for installation engineer [7,8]. Standard deviation is calculated based on received signal strength recorded for different environments using statistical analysis at Mat Lab version 14.6. ...
... Others parameter which directly affect the propagation in NLOS communication is considered in outdoor path loss model like: LEE & COST -231 Models. These terms are included in publication [6][7][8][9]. ...
... Referring to Fig. 4 in comparative analysis, the response of a standard model used in publication like log distance model for indoor communication is also almost flat. References [5][6][7][8][9] reveals that slight variation in signal is only possible if the range from Base station to Mobile station is of short distance and LOS. Therefore the analysis of path loss is feasible to almost 80 to 90% in synchronization with standard path loss log distance model. ...
The effective performance of LTE network is analyzed using signal strength measurements in fading environments. It is one of the primary experimental methodologies for planning and designing a cellular network for given coverage area. In this paper, we present the divergence of path loss between two environments i.e. indoor and outdoor and its effect on coverage and capacity very effectively. The received signal strength and data transfer rate is analyzed at different locations for user’s satisfaction. Standard deviation of signal with path loss exponents are calculated based on received signal strength in the operating network areas. It is in the range of 4 to 5.3 in the vicinity of entire urban areas. Path loss using indoors and outdoors model were analyzed effectively and presented. Based on the received signal strength a new path loss model is proposed to meet the requirements of current environment for tuning the path loss model. It is also suggested that, the removal of permanent object within the vicinity of 200 m in front of antenna is considerably a better solution of propagation of signal. The improvement of 27 dBm signal strength around 14 km area is presented with well-installed network if antenna is free from clutter zone. The analyzed parameter may be very effective for 5G communication considering a future network of this era.
... According to the coverage of each site, the number of sites is required. The equations in this section can be used to determine how much coverage each location has and how many 5G NSA sites are needed by using the equation in (4) [15]. ...
... In any case, when different channel sounders are deployed by different organizations to record measurements [14], a meaningful comparison across bands is difficult to obtain since specific calibration procedures will vary from organization to organization. This is true even when different channel sounders are used by the same organization [4]- [8], [15] since calibration is never perfect. ...
Millimeter-wave (mmWave) communications promise Gigabit/s data rates thanks to the availability of large swaths of bandwidth between 10–100 GHz. Although cellular operators prefer the lower portions of the spectrum due to popular belief that propagation there is more favorable, the measurement campaigns to confirm this – conducted by ten organizations thus far – report conflicting results. Yet it is not clear whether the conflict can be attributed to the channel itself – measured in different environments and at different center frequencies – or to the differences in the organizations’ channel sounders and sounding techniques. In this paper, we propose a methodology to measure mmWave frequency dependence, using the 26.5–40 GHz band as an example. The methodology emphasizes calibration of the equipment so that the measurement results represent the channel alone (and not the channel coupled with the channel sounder). Our results confirm that free-space propagation is indeed frequency invariant – a well understood phenomena but to our knowledge reported nowhere else at mmWave to date. More interestingly, we found that specular paths – the strongest after the line-of-sight path and so pivotal to maintaining connectivity during blockage – are the least invariant compared to weaker diffracted and diffuse paths.
... The dataset is considered as presented in [36]. It has been measured in three countries (UK, South Korea, and Japan) from 0.8 GHz to 73 GHz in different environments including urban-low rise, urban high-rise, and suburban environments. ...
Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.
