Radio Propagation Modelling for Smart Environments in 5G/6G Networks

Jiliang Zhang, Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, UK.
Jie Zhang, Department of Electronic and Electrical Engineering, the University of Sheffield, Sheffield, UK, and Ranplan Wireless Network Design Ltd., Cambridge, UK.

Abstract
Over 80% of mobile traffic takes place in buildings. Building layouts and materials have a significant impact on radio propagation in indoor environments. While wireless link capacity is approaching the Shannon limit, optimizing building layouts and modifying material electromagnetic properties become a promising way to enhance in-building wireless communication performance. We foresee that in 6G, smart environments (SEs) enabled by wireless friendly building structures and materials will either interact with wireless networks or be part of them. With SEs, radio channels become much more dynamic, which gives rise to challenges in radio propagation modelling. In this article, we first present the latest results on channel models for SEs with integrated network devices. Then, we examine dynamic radio propagation modelling for SEs with metamaterials (MMs) and intelligent reflecting surfaces (IRSs). Next, we present results on outdoor-indoor radio propagation in neighborhood small cell networks. After that, we shed lights on wireless performance modelling for buildings. Finally, we summarize our key findings and highlight a few research directions on radio propagation modelling for SEs in 6G.

1. Introduction
Over 80% of mobile traffic takes place in buildings [1]. The wireless communication industry has used various network deployment strategies to provide indoor wireless coverage and capacity cost-effectively, for example, relying on outdoor radio base stations (BSs) to provide indoor coverage in the 2G/3G eras, and moving towards, deploying indoor networks to meet both coverage and capacity demands in the 4G era, and densifying the indoor BSs with large-scale antenna arrays (LSAA) to address the increasing traffic demands in the 5G era [2].

Radio propagation model is fundamental for wireless system design, network performance evaluation, wireless protocol comparison, radio network planning and continuous network optimization. Indoor radio propagation models have been investigated in the last few decades. Detailed classifications of state-of-the-art radio propagation models can be found in [3].

Locating most BSs indoors brings new challenges to indoor radio propagation modeling due to the natural complexity of the indoor environment. Current radio propagation models need to be refined as the densification of indoor BSs with LSAAs generates new communication scenarios that have not been well considered in existing channel models.

Moreover, we believe that wireless efficiency is an intrinsic property of a building environment [4-6]. In the future, wireless communications, like the safety, insulation, and daylighting performance, will become a utility for buildings. While designing buildings, architects must consider wireless efficiency of building layouts and building materials carefully.
Therefore, we identify the following new communication scenarios, which are emerging but have not been well investigated in existing channel models.

2. Smart environments with integrated network devices
Indoor BSs equipped with LSAAs are a promising technology to address the capacity crunch problem in-building. However, in order to guarantee a low spatial correlation, the space intervals among antenna elements of the array have to be larger than half wavelength. LSAAs will increase the physical dimension of the BS, generating negative consequences on weight and visual impact. The strict space constraints on antenna housing will force 5G/B5G LSAA design to exploit more advanced technologies to support a higher level of visually obscured integration in the natural surroundings of the users.

Figure 1. Building material embedded LSAA BS in a typical indoor environment.

Therefore, venue owners/users may prefer to deploy the BSs in positions that will not lead to inconveniencing usages of a room. One possible solution is to deploy BS antennas close to an interior wall of the building. Another potential solution is to embed antennas into building structures, e.g., concrete walls and glasses, as shown in Fig. 1. When deploying the BS antennas close to the wall, the loss caused by the conduction of the material can be avoided in comparison with integrating them into the building structures. In contrast, while integrating the BS antennas into the building material, more antenna elements can be employed in a limited space to facilitate LSAA deployment because the electrical length of the antenna is reduced. Especially in the industrial environment, deploying BSs in the workspace may increase the risk of accidents. Under this situation, the BS antennas must be embedded in building materials.

Therefore, the interactions between the indoor radio propagation and the wall/building material should not be neglected in indoor radio propagation modeling. To investigate the impact of wall on a BS close to it, we have developed a statistic model by integrating the wall reflection multi-path component into the independent and identically distributed Rician channel model [7,8]. Therein, the wall is modeled as a single-layer dielectric. Moreover, we have designed a building material embedded antenna [9] and proposed an empirical model to predict the impact of a single-layer building material on the performance of antenna embedded in it [10].

However, there are still challenges remaining in channel modeling considering the interaction between building materials and BSs with LSAAs. The major challenge is that building materials are not ideal smooth single-layer materials in general. On one hand, the analytical mapping between the MIMO channel matrix and the properties of multi-layer materials is difficult to derive because reflection characteristic is jointly influenced by the incident angle of EM waves as well as each individual material layer’s permittivity and thickness [11]. On the other hand, the surface of the building materials may not be smooth enough, and thus, the wave is diffused [12]. How to model the radio propagation considering the interaction between the BS and the rough building material is still unknown. Moreover, the properties of the building material may be inhomogeneous. When modelling the indoor radio propagation, we cannot know the exact EM properties of the building material in advance, and therefore, the size of antenna element and antenna array may not match the building material well. When modelling indoor radio propagation, we have to investigate its robustness on the inhomogeneity of EM properties.

3. Metamaterial-aided communications
With the emergence of smart built environments, MMs are considered as a key technology in 6G mobile communications. MMs enable innovative ways for communications such as switching walls between unidirectional and bidirectional modes [13], using toric/parabolic surfaces that homogenize the strength of the indoor signal [14,15], and using intelligent reflecting surfaces (IRSs) to control multipath components [16,17] and improve the degree of freedom (DoF) of MIMO channel by integrating multiple scattering elements [18]. In the indoor environment, it is natural to assume that all MMs are mounted on building structures. The application scenarios of MMs under indoor environments can be summarized as in Fig. 2.

Figure 2. Typical application scenarios of MMs in indoor environments.

Most of the state-of-the-art works on radio propagation modelling with MMs consider the MM as a device. In recent works, pathloss models of EM wave reflected by MMs have been measured and modelled [19,20]. The pathloss models reveal that how MMs generate controllable multipath components. A future research direction is to model radio propagates in an MM-integrated indoor environment considering the controllable multipath components generated by the MM.

4. Pure neighborhood small cell
While deploying BSs in-building, the leakage power from buildings could be made use of by an outdoor user that cannot be satisfied by outdoor BSs. From this perspective, the novel neighborhood small cell (NSC) network system has been proposed [21,22]. Conventionally, outdoor users are either jointly served by NSC BSs and macro-cell BSs or switch between NSC networks and macro-cell networks.
Due to urbanization, indoor small-cell (SC) BSs are densified naturally to address the increasing traffic demands. Considering that: (1) most users are located indoors, and (2) macro-cell BSs cost high deployment operation and significant energy consumption [23], we foresee that the hybrid macro-cell/NSC network will be replaced by a pure NSC network in the dense urban area [24]. In the pure NSC network, indoor SC BSs need to serve outdoor users via indoor-to-outdoor (I2O) signal transmission. To offset the penetration loss of the building envelope, CoMP techniques [25] will be applied.

Outdoor-to-indoor (O2I) radio propagation has been well investigated considering the conventional coverage of indoor users by macro-cell BSs [26]. However, published works on the I2O radio propagation modelling are relatively scarce. O2I radio propagation model cannot be directly applied in the I2O scenario using reciprocity. In the O2I scenario, the outdoor macro-cell BS is mounted above rooftop levels of surrounding buildings. Whereas in the I2O scenario, the outdoor user is located on the ground, so that the radio propagation characteristics are different from the O2I scenario.

Therefore, I2O radio propagation has to be further investigated for the NSC network design, evaluation, deployment and optimization. Future research directions for I2O radio propagation modelling are outlined as follows. Firstly, the impact of building material-BS interaction on the I2O radio propagation needs to be investigated, as in 3.1. Secondly, the correlation between links from different indoor SC BSs has to be taken into account because it significantly impacts the performance of CoMP technologies [27,28]. Thirdly, the penetration loss of building structures for various building layout needs to be empirically modelled to provide tractable but sufficiently accurate tools for NSC evaluation.

5. Building wireless performance modelling
As the link level throughput is approaching the Shannon capacity limit nowadays, improving wireless communication systems alone is no longer feasible nor cost effective to address the exploding indoor data traffic. Hence, it is now urgent to investigate how to design and modify indoor environment to enhance the building wireless efficiency (BWE).

Figure 3. Impact of building designs on indoor wireless coverage, predicted by the intelligent ray-launching algorithms in Ranplan Professional® [39].

BWE has never been used in the building design process. As a result, the desirable BWE may be unachievable in a built environment. For the first time, we have identified the idea that every building has an intrinsic wireless performance which is independent of how densely small cells are deployed. In this Section, we review our ground-breaking work that bridges building design and wireless network deployment.

5.1 Wireless-efficient building layouts
Intuitive examples of good or bad building layout in terms of BWE are illustrated in Fig. 3. Four transmit single-antenna elements with the same transmit power are evenly distributed to provide a benchmark. Coverage rate is computed assuming a 5 dB SINR threshold. It is predictable that for building layouts shown in Fig. 3(a, c), it is difficult to obtain a straightforward wireless deployment solution to achieve a coverage rate of 96%, which can be easily achieved in the building plan in Fig. 3(b). These examples demonstrate the importance of building design on in-building wireless performance.

The first step towards the building layout evaluation is to investigate the line-of-sight (LOS) probability while the BS and the user is randomly located in the building. In 3GPP channel model, the empirical LOS model is provided without consideration of building layout [26]. In [29], we, for the first time, derived an analytical LOS probability model for specific building layouts. In [30], the proposed model was extended to 3-D scenarios. In [31], a machine learning-based empirical LOS probability model is proposed with inputs of statistical building layout features.

Funded by a Eurostars project Build-Wise, which was ranked the 16th in 331 applications [32] in 2016 September competition, we have presented ground-breaking works that systematically evaluate building layouts in terms of wireless efficiency. (1) The interference gain (IG) and power gain (PG) were defined as two figures of merit (FoM) to evaluate the wireless efficiency of a building layout [33]. (2) To quickly and accurately compute the FoMs, an approach to derive exact closed-form expressions for these FoMs was proposed in [5]. (3) Based on the IG, we developed a novel approach to compute the optimum transmitting power to achieve the maximum IG of a building layout [6]. The proposed approaches and numerical result shed light to architects on evaluation and design of building layouts with desirable wireless efficiency, and to radio engineers on how to approach the intrinsic wireless performance of a building.

In the scope of wireless-efficient building layouts, we identify the following research directions. Firstly, impact of building layouts on other metrics such as area spectral efficiency should be applied to evaluate BWE. Secondly, the trade-off between the maximal tolerable density of transmit elements and the maximal achievable BWE needs to be comprehensively investigated. Thirdly, a building’s wireless performance considering spatially correlated shadowing should be considered. Moreover, future analysis will include a comprehensive building wireless performance evaluation considering diverse types of buildings such as office, shopping mall, airport, stadium and train station.

5.2 Wireless-efficient building materials
Building structures and materials significantly affect wireless communications in-building. New building materials and structures that achieve better BWE while keeping good physical properties needs to be well designed. However, how to design building materials to achieve adequate BWE has never been systematically researched. Identifying the wireless efficiency of building materials, we have defined two H2020 MSCA-Individual Fellowships, AceLSAA [34] and GATE [35]. The GATE project achieved 98.8% and was ranked 6th among over 900 applications in the ENG panel.

To lay a foundation for investigating the wireless efficiency of building materials, we have measured the EM properties of building materials. In [36], complex permittivity of four typical building materials, i.e., ceramic, granite, PVC, and wood, was extracted from practical measurement based on a vector network analyzer. In [12] and [37], we presented a measurement campaign of diffuse scattering pattern from six typical building materials, i.e., granite, marble, plasterboard, wooden board, rough stone and ceramic tile, over a frequency range from 40 GHz to 50 GHz. In [40], we presented a three-step minimum least square-based algorithm to accurately resolve two closely adjacent rays reflected from the front and the back surfaces of a board-shaped material while measuring the EM properties of building materials.

We have designed metrics that capture the wireless efficiency of building materials for simple single-layer building materials while putting the BS close to a wall. In [7], we defined the ergodic capacity in a room covered by the BS as a metric to evaluate the wireless efficiency of the wall. In [8], we defined the per-antenna power distribution as a metric to evaluate the impact of wall on the efficiency of the power amplifier in a BS equipped with LSAA.

In the scope of wireless-efficient building materials, we identify the following research directions. Firstly, more application scenarios, as listed in Fig. 2, should be considered while evaluating wireless efficiency of building materials. Secondly, we will evaluate the wireless efficiency of building materials while embedding BSs with LSAA into them. Thirdly, we will optimize composite building materials and MM-based building materials for better BWE. Fourthly, building materials will be designed and optimized targeting good BWE while keeping acceptable physical properties such as anisotropic strength, stiffness, and optical transparency. Finally, advanced building material manufacturing technologies, such as 3D concrete printing, will be applied to prototype wireless efficient building materials. Moreover, the building layout and building materials need to be jointly evaluated and optimized in terms of wireless efficiency.

In summary, our key findings on radio propagation modelling for SEs in 6G is listed in TABLE I.

Table 1. list of our contributions on radio propagation modelling for smart environments.

Publications	Research direction
[7-10]	Smart environments with integrated network devices
[24,38]	Pure neighborhood SC
[6,29-31,39]	Wireless efficient building layouts
[12,36,37,40]	Wireless efficient building materials

 

6. Conclusions
In the future, indoor radio propagation environment must be well designed to meet the requirement of future wireless traffic demand. In this review paper, we summarized studies of indoor radio propagation models and identified future research directions for intelligent indoor radio propagation environment. Beyond indoor radio propagation, the indoor environment itself should be carefully evaluated, modelled and optimized at the building design stage. Our current and future outcomes in this research direction will shed light to architects and civil engineers on design, evaluation and optimization of wireless-efficient building layouts and materials.

References

1. Cisco, “5G-thriving indoors, Cisco White Paper,” https://www.cisco.com/c/dam/en/us/solutions/collateral/serviceprovider/ultra-services-platform/5g-ran-indoor.pdf. Accessed: 20/04/2021.
2. J. G. Andrews, et al., “What will 5G be?” IEEE J. Sel. Area Commun., vol. 32, pp. 1065-1082, 2014.
3. C. X. Wang, et al., “A survey of 5G channel measurements and models,” IEEE Commun. Survey & Tut., vol. 20, no. 4, 2018.
4. EUROSTARS Project 11088 BuildWise (The development of a software tool to design buildings with tailored wireless performance), https://www.eurostars-eureka.eu/project/id/11088. Accessed: 20/04/2021.
5. J. Zhang, et al., “Wireless performance evaluation of building layouts: Closed-form computation of figures of merit,” IEEE Trans. Commun., early access.
6. J. Zhang, et al., “Wireless energy efficiency evaluation for buildings under design based on analysis of interference gain,” IEEE Trans. Veh. Tech., vol. 69, no. 6, pp. 6310-6324, 2020.
7. Y. Zhang, et al., “How friendly are building materials as reflectors to indoor LOS MIMO communications?,” IEEE Internet Things J., vol. 7, no. 9, pp. 9116-9127, Sept. 2020.
8. Y. Zhang, et al., “Effects of wall reflection on the per-antenna power distribution of ZF-precoded ULA for indoor mmWave MU-MIMO transmissions,” IEEE Commun. Lett., vol. 25, no. 1, pp. 13-17, Jan. 2021.
9. Y. Yao, et al., “Design of glass-integrated grid antenna using CMA for multiband indoor network,” ISAP, 2020.
10. J. Tan, et al., “Empirical formulas for performance prediction of concrete embedded antenna,” ISAP, 2020.
11. ITU-R, “Effects of building materials and structures on radiowave propagation above about 100 MHz P series radiowave propagation,” Recomm. ITU-R P.2040-1, Jul. 2015.
12. X. Liao, et al., “Experimental study of diffuse scattering from typical construction materials over 40-50GHz,” IEEE APCAP, 2018, pp. 216-218.
13. L. Subrt, et al., “Advanced modelling of intelligent walls for indoor smart environments,” EuCAP, 2014.
14. R. Kitta, et al., “Research of the indoor reflection wave control with phase control wall,” ISAP, 2012.
15. H. D. Hristov, et al., “Indoor signal focusing by means of Fresnel zone plate lens attached to building wall,” IEEE Trans. Ant. Prop., 2004.
16. M. D. Renzo, et al., “Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come,” EURASIP J. Wireless Commun. Netw., vol. 129, no. 1, May 2019, Art. no. 129.
17. E. Basar, et al., “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753-116773, Aug. 2019.
18. Ö. Özdogan, et al., “Using intelligent reflecting surfaces for rank improvement in MIMO communications,” IEEE ICASSP, 2020
19. W. Khawaja, et al., “Coverage enhancement for NLOS mmwave links using passive reflectors,” IEEE Open J. Commun. Soc., vol. 1, pp. 263-281, Jan. 2020.
20. Ö. Özdogan, et al., “Intelligent reflecting surfaces: Physics, propagation, and pathloss modeling,” IEEE Wireless Commun. Lett., vol. 9, no. 5, pp. 581-585, May 2020.
21. Qualcomm Inc., “Neighborhood small cells for hyper dense deployments: Taking HetNets to the next level,” White Paper, Feb. 2013.
22. N. Bhushan, et al., “Network densification: The dominant theme for wireless evolution into 5G,” IEEE Commun. Mag., vol. 52, no. 2, pp. 82-89, Feb. 2014.
23. W. Guo and T. O’Farrell, “Capacity-energy-cost tradeoff in small cell networks,” IEEE VTC, 2012, pp. 1-5.
24. W. Yang, et al., “On evaluation of indoor to outdoor communications using neighbourhood small cells,” IEEE Trans. Veh. Technol., vol. 69, no. 7, pp. 8045-8050, July 2020.
25. D. Gesbert, et al., “Multi-cell MIMO cooperative networks: A new look at interference,” IEEE J. Sel. Areas Commun., vol. 28, no. 9, pp. 1380-1408, Dec. 2010
26. 3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz (3GPP TR 38.901 version 14.3.0 Release 14),” 2018.
27. J. Zhang, et al., “Indoor multiple-user MIMO channel measurement and characterization,” IEEE CAMAD, 2018, pp. 1-5
28. L. Ding, et al., “Investigation of spatial correlation for two-user cooperative communication in indoor office environment,” IEEE ICCT, 2010, pp. 420-423
29. H. Zheng, et al., “Exact line-of-sight probability for channel modeling in typical indoor environments,” IEEE Ant. Wireless Prop. Lett., vol. 17, no. 7, pp. 1359-1362, July 2018
30. W. Yang, et al., “Line-of-sight probability for channel modeling in 3-D indoor environments,” IEEE Ant. Wireless Prop. Lett., vol. 19, no. 7, pp. 1182-1186, July 2020
31. W. Yang, et al., “Machine learning based indoor line-of-sight probability prediction,” ISAP, 2019, pp. 1-3.
32. https://www.eurostars-eureka.eu/project/id/11088. Accessed: 20/04/2021.
33. Ranplan Wireless Network Design, “Method for evaluating the building wireless performance,” Patent, 2020.
34. https://cordis.europa.eu/project/rcn/212652/factsheet/en. Accessed: 20/04/2021.
35. https://cordis.europa.eu/project/id/843133. Accessed: 20/04/2021.
36. J. Luo, et al., “Complex permittivity estimation for cloths based on QPSO method over (40 to 50) GHz,” IEEE Trans. Ant. Prop., vol. 69, no. 1, pp. 600-605, Jan. 2021
37. H. Tian, et al., “Effect level based parameterization method for diffuse scattering models at millimeter-wave frequencies,” IEEE Access, vol. 7, pp. 93286-93293, 2019.
38. W. Yang, et al., “On performance of ultra-dense neighborhood small cell networks in urban scenarios,” IEEE Commun. Lett., vol. 25, no. 4, pp. 1378-1382, April 2021.
39. www.ranplanwireless.com. Accessed: 20/04/2021.
40. J. Zhang, et al., “Two-ray reflection resolution algorithm for planar material electromagnetic property measurement at the millimeter-wave bands,” Radio Science, vol. 55, no. 3, pp. 1-14, March 2020.

Jiliang Zhang (M'15, SM’19) received the B.E., M.E., and Ph.D. degrees from the Harbin Institute of Technology, Harbin, China, in 2007, 2009, and 2014, respectively. He was a Postdoctoral Fellow with Shenzhen Graduate School, Harbin Institute of Technology from 2014 to 2016, an Associate Professor with the School of Information Science and Engineering, Lanzhou University from 2017 to 2019, and a researcher at the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden from 2017 to 2018. He is now a Marie Curie Research Fellow at the Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, UK.
He serves as an Academic Editor for the Wireless Communications and Mobile Computing and a Topic Editor for the Electronics. His current research interests include, but are not limited to wireless channel modelling, modulation system, relay system, vehicular communications, ultra-dense small cell networks, and smart environment modelling.

Jie Zhang has held the Chair in Wireless Systems at the Department of Electronic and Electrical Engineering, University of Sheffield (www.sheffield.ac.uk) since Jan. 2011.
He is also Founder, Board Director and Chief Scientific Officer (CSO) of Ranplan Wireless (www.ranplanwireless.com), a public company listed on Nasdaq OMX. Ranplan Wireless produces a suite of world leading indoor and the only joint indoor-outdoor 5G/4G/WiFi network planning and optimization tools suites including Ranplan Professional and Collaboration-Hub, which are being used by the world’s largest mobile operators and network vendors across the globe.
Along with his students and colleagues, he has pioneered research in small cell and heterogeneous network (HetNet) and published some of the landmark papers and books on these topics, widely used by both academia and industry. Since 2010, he and his team have also developed ground-breaking work in modelling and designing smart built environments. His Google scholar citations are in excess of 7800 with an H-index of 38.
Prior to his current appointments, he studied and worked at Imperial College London, Oxford University, University of Bedfordshire, and East China University of Science and Technology (PhD in 1995), reaching the rank of a Lecturer, Reader and Professor in 2002, 2005 and 2006, respectively.