1 Introduction

The growth of telecommunication sector and the increasing number of smart phones have forced cellular network providers to serve higher data rates than previously served data rates. That’s why the Mobile Network Operators need a techno-economic approach to deal with the demand for increasing data rates. Therefore, an algorithm is necessary to be developed to cope up with the delay, path loss and received signal strength. [1] The mobile radio channel places fundamental limits on the efficiency of wireless communication systems.The major distinctive characteristic in which 5G wireless differs from the traditional wireless systems is that 5G allocates high bandwidth in unit area, possibly by exploiting Cognitive Radio, for enabling large number of connected devices for longer duration. [35] The radio path between generator and analyser can severely obstructed by vertical buildings, foothills,vehicles and vegetation expect direct path. Modeling the path loss (PL) has one of the most problematic phase as compared to the wired one, which is normally design on observed measurements.

Figure 1 demonstrates the 2D view of two ray ground reflection model for the concerned scenario. The ray indicated in color blue collides with the ground bends by the angle 𝜃 before reaching to mobile station (MS). Same is the case for far-wall and random ray. The large-scale PL typically studied in 3GPP release 17 for outdoor and indoor environment. After the free space propagation model, a number of well-known outdoor propagation models have been used to predict PL over irregular terrain like Longley-Rice, Durkin’s, Okumura-Hata and the extension of hata COST-231. For In-building, the Log-distance and Ericsson Breakpoint models are common with attenuation factor and signal penetration loss. All aforementioned models have been used to predict the signal strength over measurements and transmitter–receiver (Tx-Rx) separation in the specific scenario [2] - [3]. Well, each model is resulting with different characteristics, in [2], the measurement were carry out 0.9 [GHz] and 1.8 [GHz] for Outdoor to Indoor (O2I) environment based on COST-231 model. However, Millimeter-wave (mmWave) identified as primary fuel for the fifth generation (5G) mobile communication system. The band range about 30 [GHz] to 300 [GHz] have number of unregistered radio channels and can deliver [GHz] level bands of contiguous ranges, and therefore becoming a significant entrant in provision the high-throughput wireless connection for future mobile standard. As compared the high operating frequencies with traditional ranges under 6 [GHz], the attenuation factor experienced high on each metre due to the small-wavelengths and as well atmosphere [4] - [5]. As a result of the above mentioned problem, 5G networks have to work on latest trends. On the other hand due to the cognition handling in the mmWave networks, this is becoming very complicated and complex. [18] In-building, 60 [GHz] spectrum has been proposed to offer determined throughput for direct and indirect path. So far, a number of studies have been directed to measure the signal strength and attenuation of materials at higher frequency band for cognitive radio networks. Following the both Line-of-Sight (LoS) and None-Line-of-Sight(NLoS) environments, the intensive measurements campaigns for the perception of PL of several obstacles at 28 [GHz], 38 [GHz], 60 [GHZ] and 73 [GHz] In-building workplace environment were studied in [6] - [10].

Fig. 1
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Mobile Radio Path Distribution

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As above mentioned studies, there is still a large slot to inquire the dimensions of mmWave to achieve the ultimate objective as follows. The most recent works are emphasized on 28 [GHz] and the 38 [GHz] frequency bands considering as primary vehicle, however there are still other possible bands up to 300 [GHz] that can be further discovered. (See Table 1)

Table 1 Recent Studies Related to Propagation
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For purpose of demonstration, the method of images (ray-tracing) have been designed in Fig 1. The geometry presented in Fig. 1 illustrate the path difference between direct ray, ground ray, random ray and far wall ray effectively; although, for establishing the applied models in radio propagation based on ray-tracing, several years have gone into the study and exploration to make estimations that can give back the specific environment [1]. The method of ray-tracing for radio channel was derived from image-theory. Geometric optics and uniform theory of refraction were used to analyse that all transmitted rays reached by the receiver. Basically, the ray-tracing methods are used to model electromagnetic environment by projecting the propagating paths between source and destination. In the method of images, the shooting and bouncing ray approach is the key driver for the paths estimation [11] - [12]. To make computational algorithm less complex and to decrease the amout of memory used, walls of the building are illustrated as smooth surfaces and flat slabs. This simplification forces the reflected and diffracted rays to be tackled using “image-RT approach” and to the Uniform Theory of Diffraction (UTD) [20] (Table 2).

Table 2 Ray Tracing Techniques Comparison
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Figure 2 indicates the recent related work. The author [1] predicted path loss as well as received signal strength in outdoor and indoor environment respectively. Following the above image theory, the Ray Tracing (RT) based algorithm was designed by Ikegami et al [13]. The earlier well-known RT methods, brute force RT [14] and image theory [15] faced some limitations. The brute force check all possible paths using regular angular sampling method, however due to the increasing angular separation and dropping angles could result in the loss of some paths. On the other side, the image theory technique is not fit for complex environments, since its point to point ray targeting and unreliability of radiated rays. The proposed method can calculate the losses in densely populated area for both outdoor and indoor environment.

Fig. 2
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Recent related work

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The rest of this article is structured as follows. The In-building offices environment (in which measurement set-up is installed) is discussed in Section 2. The Path Loss (PL) mathematical modeling is inquired in Section 3. The smart 3D ray tracing algorithm for the under consideration scenario is explain in Section 5. The analysis of obtained results is discussed in Section 6. Finally, we draw a conclusion and future work in Section 7.

2 Measurement environment

The measurement set-up comprising on urban macro cell (UMa) mounted with small base stations has been considered. Both devices are assumed to be working on same radio frequencies (60 [GHz]) under subscriber group (CSG). Considering the capacity coverage, throughput and traffic offload, we deployed the small base station MetroLinq (ML). In our scenario, all Tx were on fixed location and users (Rx) were moving in indoor. The propagation measurements at 60 [GHz] were inquired in summer of 2020 at outdoor and indoor environment located at the State Life Building of Lyallpur, Pakistan.

The satellite view of the concerned scenario for urban area is shown in Fig. 3. The four high-rise buildings B1, B2, B3 & B4 were investigated in proposed scenario for pathloss and angular power, where elevation of main experimental building B2 is 50.4 [m]. B2 comprises of 13 office floors. The parking area and basement are not included during measurement campaign. There are two random reflector cars (C1 and C2) in main street in red and white color, also marked in the photograph in Fig. 3. The transmitter tinted with blue ring is set at a height of 25 [m] with a 110 [m] Tx-Rx maximum separation. The distance from UMa to User Interface (UIs) fluctuate as the user change their location from O2I or indoor to indoor (I2I). The material used in building also varies as the outer walls of B1 and B3 are fabricated with infrared coated glass and B4 outer-inner mostly made-up with cemented and plywood structure.

Fig. 3
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Measurement Environment

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For I2I scenario, Fig. 4a shows the locations of Tx mounted on the walls of the corridor. The height of the Tx as indicated in the fig is 2.7 [m]. Figure 4b indicates the set-up and receiving equipment. The users on the floor are distributed in such a fashion that there are 8 to 16 mobile users on each floor all the time. At each position, the Tx is piloted to the direction of the office while the Rx was set aside in the horizontal height and moved in azimuth-domain from 0∘ to 360∘ with continual increments. To considering the measurement, the Tx-Rx maximum altitude are aligned 2500-5040 cm respectively. The rest of parameters and measurements are in Table 3.

Fig. 4
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Experimental Arrangements

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Table 3 Simulation Setup
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Figure 5 is the complete floor plan of the under consideration building. There are three Acess Points (APs) i.e. Tx1, Tx2 and Tx3 marked with green circles, mounted on the wall and the receiver illustrated with red circle is inside the concerned room. Considering the above measurement setup, our research gear is to compute the PL on 60 [GHz] mmWave using cognitive 3D RT algorithm, which will examine the performance of UMa and ML in outdoor and indoor environment respectively. The measurement objective is to check the behaviour of algorithm over losses due to in-between obstacles and also to compute the received signal strength of remote users within the building.

Fig. 5
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Tx and Rx indoor locations

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3 Path loss modelling

The PL is the inverse of the instantaneous local channel gain [16]. Electromagnetic waves are well known for the attributes like reflection, diffraction and scattering. The loss estimation comprises on building structure as well as its electromagnetic wave interaction with different objects. The substances includes the reflection constants, permittivity, conductivity, roughness and polarization information.

3.1 Path loss modeling for urban-macro O2I

The total path loss for Line-of-Sight (LoS) and None-Line-of-Sight (NLoS) is modeled here as [2]:

$$ L_{P_{TOT}}=L_{P_{TYP}}+L_{P_{TB}}+L_{P_{IB}}+Z(0,{\sigma_{P}^{2}})+ Z(0,\sigma_{SF}^{2})+\mu $$
(1)

where \(L_{P_{TYP}}\) is the typical path loss, \(L_{P_{TB}}\) is the through building dispersion losses from concrete outer-wall and \(L_{P_{IB}}\) is the in-Building loss where radio signal penetrate in different materials. The standard deviation for the dispersion losses is derived as \({\sigma _{P}^{2}}\) and \(\sigma _{SF}^{2}\) is the average deviation for the shadow fading. μ is used for vehicle penetration loss and O2I vehicle penetration loss models are correct for upto 0.6-60 [GHz].

$$ L_{P_{TYP}}=L_{P_{UMa}}(S_{3D_{out}}+S_{3D_{in}}) $$
(2)

where \(L_{P_{UMa}}\) is the urban macro path loss, \(S_{3D_{out}}\) is the three dimensional outdoor distance from the tip of Tx to the outer wall of the building and \(S_{3D_{in}}\) is the inside three dimensional distance. In Fig. 6 it can be seen that 3D distance can be computed by adding both indoor and outdoor distances as well as the height of the HUI. Figure 6 describe the floor-wise descriptions of parameters, either it is LoS or NLoS and also guide about path as received through ground or through wall.

Fig. 6
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Floor-wise descriptions of parameters

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Figure 7a indicates the empirical CDF which suddenly irrupts at about 80 [m] which is clearly a sign of concern for modelling the algorithm. Figure 7b shows the outage probability of BS for different frequencies.

$$ L_{P_{UMa-LoS}}=\left\{\begin{array}{ll} L_{P_{1}} & 10[m]\leqslant S_{2D} \leqslant S_{BP}^{\prime}\\ L_{P_{2}} & S_{BP}^{\prime}\leqslant S_{2D} \leqslant 5[m] \end{array}\right. $$
(3)

where \(L_{P_{UMa-LoS}}\) indicates the function used for the propagation loss in urban-macro scenario, in LoS case if the Tx − Rx distance ≤ breakpoint and which is given as,

$$ S_{BP^{\prime}}=13.33h^{\prime}_{BS}h^{\prime}_{UI}f_{c} $$
(4)

where fc is the high operating frequency in [Hz], and \(h^{\prime }_{BS}\) and \(h^{\prime }_{UI}\) are the effective antenna altitudes of base station and user interface. To compute \(h^{\prime }_{BS}\) and \(h^{\prime }_{UI}\) we need to subtract the environment influence hE = 1 from respective antenna heights. So far according to base station and user interface brake-point distance, we follow the \(L_{P_{2}}\) in our measurement from given equations:

$$ L_{P_{1}}=32.2+40*log_{10}(S_{3D})+20*log_{10}(f_{c}) $$
(5)
$$ \begin{array}{@{}rcl@{}} L_{P_{2}}&=&32.2+40*log_{10}(S_{3D})+20*log_{10}(f_{c})\\ &-&10*log_{10}((S_{BP^{\prime}})^{2}+(h_{BS}-h_{UI})^{2}) \end{array} $$
(6)

To follow the urban-macro, the shadow fading factor for LoS is 4 [dB] and for NLoS is 6 [dB] when associated with a height 25 [m] or above. However in case of NLoS, we follow the max condition (b) for 10 [m] \(\leq S_{BP^{\prime }} \leq 5 [Km]\) which apply as:

$$ \begin{array}{@{}rcl@{}} &&L_{P^{\prime}_{UMa-NLoS}}=13.54 +39.08 *log_{10}({S_{3D}})\\ &&+20*log_{10}(f_{c})-0.6*(h_{UI}-1.5) \end{array} $$
(7)

In (3), (5), (6) and (7) S3D is the total of outdoor and indoor distance or \(S_{3D_{out}}+S_{3D_{in}}\), which is defined as:

$$ S_{3D_{out}}+S_{3D_{in}} = S_{3D} = \sqrt{(S_{2D_{out}} + S_{2D_{in}})^{2} + (h_{BS}-h_{UI})^{2}} $$
(8)

here, S2D is two dimensional distance in-between base station and user interface of the building. It can also be denoted as \(S_{2D_{out}}+S_{2D_{in}}\). Furthermore, the material permeation loss over frequencies demonstrated with the help of Table 2 are modelled here as [8]

$$ L_{P_{TW}}=L_{P_{ad}}-10log_{10}\sum\limits_{i=1}^{N}F_{i}10^{\tfrac{L_{substance_{-i}}}{-10}} $$
(9)

\(L_{P_{TW}}\) is an additional propagation loss which is to be studied through the outer-wall-loss over frequencies, \(L_{substance_{-i}}\) is refer as the permeation loss, Fi is fraction of the ith substances and N is the number of substances involved in wall penetration loss. The concerned building is constructed with multiple substances. Our simulator is not only modelled for traditional low-loss concrete structure but also for the high loss metal coated-glass for B1 and B2 individually shown in Table 2.

Fig. 7
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CDF and Probability

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In Table 2, LSG is the low path loss through standard glass, LCon is the loss through concrete and LMCG is the high loss through metal coated glass. Moreover, the use of such high loss glass at this time seems to be more predominant in organizational towers than in home construction in some countries of the world. The indoor loss of the building is given by [2]

$$ L_{P_{IB}}=\sum\limits_{i=1}^{M}\gamma S_{2D_{IN}} $$
(10)

In (10), γ is the material dependent constant as used inside the building. The value of γ vary for low, moderate and high penetration loss due to material M.

3.2 Path loss modelling I2I

The base stations operating on higher frequencies are much smaller in size and have greater throughput. The 60 [GHz] based small base station (SBS) therefore will not overcrowd poles, rooftops and corridors. Furthermore, a huge amount of noise-free-spectrum (6 – 12 [GHz]) makes the 60 [GHz] band perfect for high gain point-to-point and point to multipoint uses. The 60 [GHz] mmWave is categorized as very sensitive to oxygen-absorption which means signals spread from a 60 [GHz] mmWave will not travel as far as lower frequencies. Frequency reuse factor makes it perfect not only for countryside, but also for densely populated deployments [7]. Moreover, co-location of several components on a single tower is possible due to narrow-beam widths which are attained from both high-gain directional antennas as well as dedicated beamforming method. These benefits illustrate that 60 [GHz] avoid the cell-interference problems usually contained by other frequencies [7]. Measurements were performed after office hours for smooth readings. There are rooms and workshops, the doors were shut throughout the measurement along both side of its central corridor. A wall mounted Tx “ML” considered here in office building corridor operating at 60 [GHz] frequency and shown in Fig. 4a. The B1 first floor (open office) in the mentioned area is composed of concrete, plywood, coated glass and other materials. The thickness of the concrete walls that separate the main office from corridor on both sides is within the range of 24-65 [cm]. The indoor corridor Tx height is 2.7 [m] and is separated with Rx distance 6 [m] inside the office. For indoor path loss, we selected the 3GPP loss models with supportive range (6 [GHz] to 100 [GHz]) for LoS and NLoS to get the accurate results [9]. With the increasing width of the corridor and increasing the frequency, COST-231 model provides simulation results which are not considerable when compared to measurements. In [19], the author addressed the same issue. Considering the above factors, we have chosen 5G-3GPP model for our work. Figure 8 represent that, for inside first floor we need to mount three Tx in corridor to cover the traffic. In 3GPP open office scenario states that: [2]

$$ \begin{array}{ll} L_{P_{ofc-LoS}}=32.4 + 17.3log_{10}(S_{3D})+20log_{10}(f_{c}) & 1[m]\leq S_{3D}\leq 100[m] \end{array} $$
(11)
$$ L_{P_{ofc-NLoS}}=max(L_{P_{ofc-LoS}},L_{P_{ofc-NLoS}}) $$
(12)
$$ \begin{array}{ll} L_{P_{ofc-NLoS}}=38.3log_{10}(S_{3D})+17.20 + 24.9log_{10}(f_{c}) & 1[m]\leq S_{3D}\leq 86[m] \end{array} $$
(13)
Fig. 8
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High, Moderate and Low Level path loss through building external wall

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In (11) and (13), open office path loss LoS and NLoS is the sum of free space PL and path loss exponent (PLE), where denotes the referred frequency of base station. S3D is the three dimension distance between user interface and base station. Beside the open office scenario, 3GPP path loss models can be inquired in variety of places including shopping malls, street canyon, urban and rural areas. The omnidirectional PL models frequency supportive is in-between 0.5 [GHz] to 100 [GHz]. It is challenging to find the accurate received power level during complex indoor office scenario on higher operating frequencies. The Friis’s equation is the simplest technique in radio propagation to compute the received power level in free space as used below

$$ \frac{P_{t}}{P_{r}}=G_{t}.G_{r}(\frac{\lambda }{4\pi r})^{2} $$
(14)

where Pr is the received power sensitivity, Pt is the transmitted power, and are the antenna gains of transmitter and receiver respectively. Where and denotes the power level and Tx-Rx separation distance accordingly. In Fig. 4a two nearest wall mounted Tx used in corridor to map the power level over random Rx positions. On the other hand, the signal penetration loss inside locations has more influence due to fluctuation of frequencies. It can be seen in Figs. 9a and 9b, which the office propagation loss is directly proportional at higher frequencies and that creates the RSS level relatively low at higher frequencies in indoor scenario.

Fig. 9
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Received angular power at 28 [GHz] and 60 [GHz]

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Figures 9a and 9b illustrates that the received angular power is greater for LoS region and the received angular power for Tx simulation consequences provided in this article are in a close connection with the measurement outcomes described at reference [17]. It is experienced from the results that a sufficient indoor service can be provisioned at 28 [GHz] and 60 [GHz]. However, it is hard to provide the service to indoor mobile user at 60 [GHz] using an out-of-doors Tx even in such a small-cell environment.

4 Ray tracing concept

The idea of rays is pretty familiar to all over the experience of sun light. Rays are supposed to transmit beside a straight-line. The plane of incident is defined as the plane containing the incident, reflected and transmitted rays. In our observed case it might be a moving object (car) or static (building). As E-field is ⊥ to B-field, E-field is polarized vertically and magnetic field is polarized along horizontally. Figure 10 tells us about the free space permittivity. The dielectric constant depends upon the material on which the ray strikes. And is the standard deviation of electric field. Consider a specific ray from Omni transmitter which collide with obstacle the EM-field exerts force along horizontal i.e X-axis. The RT concept is used in mobile propagation projection more sensibly using Maxwell’s and Snell’s law. The electric field estimation can be expressed by

$$ E_{t}=E_{i}+E_{r} $$
(15)

where Et express the transmitted field, Ei incident angle and Er material permittivity. Considering the polar Cartesian coordinate of the system, we have:

$$ E_{i}\cos \phi_{i}-E_{r}\cos \phi_{r}=E_{t}\cos \phi_{t} $$
(16)

According to the superposition principal, the Rx detect only those rays that will have positive value of reflection coefficients. Taking into account the movement of EM-waves perpendicular as well as the parallel directions with respect to field, the coefficient of reflection become where signifies angles between the “incident rays” and the “reflected planes” and is the material permittivity of the reflected plane.

$$ {\Gamma}_{\parallel}=\frac{E_{r}}{E_{i}}=\frac{E_{i}\cos \phi-\sqrt{\eta^{2}-\sin^{2}\phi}}{E_{i}\cos {\varPhi} +\sqrt{\eta^{2}-\sin^{2}\phi}} $$
(17)
$$ {\Gamma}_{\perp}=\frac{E_{r}}{E_{i}}=\frac{E_{i}\cos \phi-\sqrt{\eta^{2}+\sin^{2}\phi}}{E_{i}\cos {\varPhi} +\sqrt{\eta^{2}+\sin^{2}\phi}} $$
(18)

In (17) and (18), ϕ is the penetrated phase angle for reflected rays and Φ is the angle for incident rays.

Fig. 10
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Ray concept for plane and normal to plane of incidence

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5 Proposed cognitive 3D RT method for UMa and indoor SBS

Cognitive Smart 3D RT is a method to find the path of each ray accurately, by recognizing and regenerating the usable paths considering the sun light theory but in the inverse order, from the receiver back to its transmitted point. Each generated ray contains some straight-lines, which are the cause of reflection, deflection and transmitted paths etc. For the reason of sorting the Tx direction of a throwing ray, the angle on the parallel surface, intended from a reference path, is called the parallel angle. Likewise, the azimuths angle on a perpendicular surface, intended from a reference path, is called the perpendicular angle. The 3D geometrical drawing of the building and floor plan is all thorough considered using MATLAB Simulink. Which consists on the structural information including concrete block walls, windows, glass type, ceilings, vehicles, tops, grid and floors. To compute and identification the path of rays, electromagnetic high-low effects on walls and on other materials are also inquired in this model as shown in Table 4. The database contain the information of concerned buildings location and distance between Tx and Rx classified in Table 4. Furthermore, based on the measurement campaign we have designed an exhaustive 3D RT propagation algorithm on 60 [GHz] mm-Wave, which uses computer-generated reality based rays and reality based simulations. It can receive the maximum transmitted propagation paths including reflected and diffracted rays regardless of any particular geometry. This smart algorithm is also autonomous in sense of treating the edges equally, either ground reflected or random diffracted. Figure 10 explains the concept of rays originating from a source and dispersing in different directions.

Table 4 High operating frequencies properties over different construction materials
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The behavior of algorithm comprises on two major phases Firstly find all possible sequences paths of outdoor and indoor, threw in various directions (horizontally or vertically) and that may produce the losses. Secondly compute the reflected and diffracted edges point for RT and finally sum-up all the path losses, i.e., loss through walls, coated glass, standard glass, wood and all metals used in scenario.

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6 Results validation and discussion

The results validation and discussions are provided here in the form of path loss over distance, received signal power level and outcomes from proposed smart 3D RT algorithm in this simulation. The simulations were done using dimension layouts of the building which were designed in the MATLAB Simulink.

6.1 Cognitive ray tracing O2I environment

Figures 14a and 14b show 2D and 3D respectively is the representation of rays transmitted from the tip of Tx on 25 [m] height and penetrated in third floor of building through number of obstacles. A smart cognitive RT algorithm is executed in MATLAB. Figure 14a illustrates the location of Rx which is inside the B2 as marked in green color. In Fig. 14b three dimensional illustration of the proposed algorithm is presented with its building set-up, where Tx is erected outside and simulated rays in red and green color compared on multiple receiving locations. It can be seen that the incident rays sometime reached via direct path and sometime finite number of reflection, transmission and diffraction occurred due to ground and building edges. Fig. 15a shows the path loss trend when Tx and Rx are parallel to each other in term of their height, i.e., 25 [m] and their separation distance 50 [m]. We have seen that on direct path, the loss is comparatively low around 147 [dB] and presented in red color as there are less obstacles. On the other hand, Fig. 15b is the representation of high path loss 170 [dB] for 110 [m] separation distance. In 3D path loss graph, the dimmest blue square clearly indicates that the rays are received through indirect path as shown in Fig. 15. The reason behind is that the B4 is raised in the front of concerned B2 with 30 [m] height and there is no direct path on receiving end. So it can be seen the path loss is radically increased from 4th floor to ground floor as rays received through scatters. Moreover, when concerned frequency compared with 28 [GHz] operating frequency, it has been noticed that the Path Loss Exponent (PLE) also increased due to the small wave length and variation in between Tx and Rx distance. Figures 14 and 15 also shows the impact of signal strength over distance, where on the right side of graph the scale presented the path loss in dB which is directly propositional to the distance as well as received power. For simulating the rays in smart algorithm, the B1 and B3 edges are work as perfect mirror, in which the angle of incidence is parallel to the angle of reflection and transmitted rays are discarded during computation. The receivers are pointing on multiple floors in B2, so in this case the incidence ray first penetrate into ceilings and then finally reach to receiver. To compute the losses of plywood, ceilings, standard glass, metal coated glass and concrete, we have added the penetration models for all related hindrances in this work. Normally, the ceilings are made with concrete, so in this work the concrete penetration parameters are similar for ceilings loss.

6.2 Smart ray tracing for indoor corridor and open office

Figure 8 shows through wall penetration loss for a range of frequencies. The variation in loss models is due to the material used in buildings and small wave length of higher radio channels. Figure 11 shows the indoor 3D architecture (floor wise) with corridor length and open office partitions on different receiving points, which is planned in MATLAB. The reflectivity of the partitions and the edges of the partitions can be found out by detecting the path of rays. Figure 12 shows the antenna pattern for Omni-Directional, its beam-width (degree), height [m] and radio frequency (GHz) values are given in Table 3 parameter set-up.

Fig. 11
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Indoor 3D architecture in MATLAB

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Fig. 12
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Antenna Pattern for Omni-Directional at 60 [GHz]

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Figure 13 explains the open office and corridor received power (Tx-Rx) simulation and significance provided in this article have close connection with the measurement outcomes described at reference [17]. It is experienced from the results that a sufficient indoor service can be provisioned at 28 [GHz] and 60 [GHz]. However, it is bit hard to provide the services to indoor users at 60 [GHz] using an out-of-doors Tx, even in a small cell environment. Figure 13 is the graphical representation of path loss from corridor to open office. The heat-maps show power radiated by transmitter at different locations. The yellow color in the graph shows the reception sensitivity which becomes darker when receiver gets far away from the radiated point, where the highest signal throughput is -120 dBm approx in (Fig. 13b) Tx2 location. The weak signal strength is noticeable from the walls in 1st reflection which is affected by reflection and diffraction. Furthermore, Fig. 13 shows the received power -113 dBm for LoS at perpendicular angle (azimuth plane). Altogether, we improved ray pointing technique to make the proposed 3D ray tracing algorithm more efficient, suitable and smart. From the results and considerations, it is clear that the suggested ray tracing method has significant impact on mobile radio propagation in term of large scale path loss, received angular power and number of received rays. The results also explained here that (Figs. 14 and 15)

  • For LoS on 60 [GHz] the RSS is -40 dBm on 90∘ angle and compare to 28 [GHz] the RSS is -75 dBm. Secondly, for NLoS on same angle and frequencies the RSS value is -95 dBm and -80 dBm respectively. So above mentioned ideals values also shows great agreement with literature [5], where it can be seen clearly that received angular power is improved individually -35 dBm and -15 dBm on high operating frequency.

  • Distance is directly proportional to pathloss. The heapmap tells us that the increasing the distances and number of hurdles could result in greater pathloss.

Fig. 13
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Radiated power for different locations of Tx

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Fig. 14
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2D and 3D representation of transmitted rays

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Fig. 15
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Heapmaps for different Tx-Rx separations

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7 Conclusions

In this article, we presented a smart 3D ray tracing algorithm to examine outdoor and indoor radio propagation at 60 [GHz] and results also compared with our previous work. The algorithm pointing more rays only in the predefined region, which improve the computational efficiency in densely populated area. With the help of real time capabilities, geospatial awarnesses, the path loss, signal strength and propagation delays can be determined in cognitive radio networks. [19].

The path loss and reception sensitivity were validated by the use of the recent 3GPP mobile propagation model. The article findings suggest that the millimeter wave’s propagation heavily attenuates with the increasing separation between the transmitter and the receiver. Besides this, the obstacles and hurdles in the path of waves can cause reflection and diffractions which can result in large path loss and low received signal strength. The Simulator used in this article is designed in MATLAB. This can be used for any site regardless of the geometry of the buildings and environment chosen. By changing the antenna parameters, physical layout of the building, material used in building (glass, concrete), operating frequency), location of Tx-Rx and separation between Tx-Rx, this simulator can be used for any environment. The results gap show that the proposed ray tracing is more perfect with respect to RSS and path loss. The contributions of this study is expected to guide further research on 60 [GHz] and 73 [GHz] mmWave particularly in-building environment.