Analysis of multi ray propagation for dsrc communication on street with riss on sidewalls

Analysis of Multi Ray Propagation for DSRC Communication on Street with RISs on Sidewalls Analysis of Multi ray Propagation for DSRC Communication on Street with RISs on Sidewalls Guilu Wu1,2,3 1 Stat[.]

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Analysis of Multi-ray Propagation for DSRC Communication on Street with RISs on Sidewalls Guilu Wu1,2,3 State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China School of Internet of Things Engineering, Jiangnan University, Wuxi, China {wugl}@jiangnan.edu.cn Abstract—Each element in the Reconfigurable Intelligent Surfaces (RISs) can independently change the phase (or/and) amplitude of the incident signal, so the RISs can be used to enable users to better receive the signal transmitted by the transmitter In this paper, a Multi-ray propagation model for RISs-assisted dedicated short-range communication (DSRC) system is presented and analyzed on street The 3-D geometry and image techniques in ray tracing are combined to build the proposed model This method could enhance the ray-tracing algorithm in RISs-assisted communication networks In addition, the envelope strength of the received signal is analyzed and verified in our experiments by regulating phase of RISs in two cases Different reflection times on transmitted signal wave bring different envelope strength of the received signal The simulation results show that different reflection times and adjustment angles directly affect the received signal strength Index Terms—Multi-ray propagation, Signal strength, DSRC, RIS, Mobile communication I I NTRODUCTION Ray tracing technology has been widely used to analyze electromagnetic (EM) wave propagation in wireless communication, especially for indoor environment or multi-obstacle scenario The brute force ray tracing technology [1] and the image technology [2] are the most widely used typical ray tracing technologies The brute force ray tracing method involves the rays which will or will not be transmitted to the receiver And the image method is well adopted to analyze radio propagation with low complexity geometry and low number of reflections [3] Many significant scenarios have a lot of reflections which occur between two parallel planes, such as between the ceiling and the floor in indoor communication, between two walls in a street and so on Taking intelligent transportation system (ITS) as an example, the Dedicated Short-Range Communication (DSRC) can well support non-safe applications in the Internet of vehicles Meanwhile, the data information is developing as a speed of explosion in vehicular networks The millimeter Wave (mmWave) communication technology can provide a large bandwidth to ensure the transmission of big data under the premise of transmission link reliability for vehicular networks [4] However, the mmWave communication has relative large path loss, and the transmission distance is not far The phenomenon of multiple reflections caused by obstacles 978-1-6654-1001-4/21/$31.00 ©2021 IEEE exacerbates this problems affected by materials of reflecting obstacles These restricts the applications of mmWave-based vehicular networks Reconfigurable Intelligent Surfaces (RISs) have recently emerged as a promising technology that enable novel and effective functionalities [5] And RISs comprised of tunable unit cells could mitigate against the path loss through regulating their reflection coefficients in manipulating electromagnetic waves Besides, the design process of an RIS make it easy to deploy on the obstacle surface, and the production cost is cheap Apparently, the significant multiple reflections often occur inside urban area between two parallel plane walls Due to short-distance transmission of mmWave communication, the signal from transmitter can be transmitted to the receiver through multiple reflection, as shown in Fig Hence, the traditional image technology is no longer suitable for solving complex ray tracing problems Combining with 3-D geometry technique, the shortcomings of the image technology in tracing a complicated signal ray reflected between two parallel surface can be solved partly When the fixed reflection angle is small, the complex calculation process on 3-D geometry and image techniques has not been effectively improved This paper introduces RISs to deploy on the two parallel surface of wall to reduce complex ray tracing The RISs technique is applied for enabling the expected reflection angle between two parallel plane surfaces Our main contributions can be summarized as follows • • • The RISs technology is introduced to ray tracing for solving complicated computation in multiple reflection process RISs are deployed in the two parallel surface of wall in urban scenario The effect of RISs technology on ray tracing is analyzed on the strength of the received signal Two cases which include even and odd reflection numbers are discussed in the receiver, respectively Simulation results reveal that the performance of RISsassisted ray tracing on combining 3-D geometry and image technologies is improved on mitigating transmission fading of a signal The rest of this paper is organized as follows In Section II, a series of related research works are elaborated on channel 481 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) propagation aspects In Section III, we consider a simple traffic scenario and revisit multiple reflection problem with RISs Section IV deals with the strength of received signal with even and odd reflections with RISs-assisted communications In Section V, the performance analysis as well as the theoretical validation are discussed in our simulation results Finally, we conclude the paper in Section VI II R ELATED WORKS The evaluation of radio transmission is increasing on wireless communication in past decade And ray tracing techniques have been widely proposed to focus on radio propagation in wireless networks, especially indoor environments [6], [7] Both the image method and the “brute force” method are discussed adequately in different two-dimensional (2-D) and three-dimensional (3-D) ray-tracing radio propagation environments Specifically, the “brute force” method considers the transmitted rays that will or will not reach the receiver In this process, a bundle of data is required to reach ray tracing The image method is applied for reflections which both plane surfaces are non-parallel and have low complexity on radio propagation with geometries Although the 3-D geometry technology can partially overcome the shortcomings of the image method on reducing computational complexity for reflections between two parallel plane surfaces, it is only suitable for a few of multiple reflected rays Two methods bring high complexity in data processing [8], [9] Apparently, the ray-tracing technology is closely related to the environment in which it is applied Reshaping the natural environment has opened up a new world for researchers to study this issue Enabling technologies such as intelligent device, smart network infrastructure, embedded systems will create new interfaces, new services, new products by creating smart environments and smart spaces with improvement of traditional applications [10], [11] Meanwhile, the new network architecture and radio access interface can be introduced to improve the performance of networks while the complexity increases without significantly in data processing A promising reflective wireless technology is required to reshape wireless environments in a smart way [12] RISs can be implemented on many novel applications and the corresponding performance metrics, such as capacity analysis, spectral resource optimizations, channel estimation, reliability analysis, are studied in wireless networks [13], [14] Among them, the analysis of channel transmission has always been a challenging task A recent study, in [15] where the far-field path-loss model is derived using physical optics techniques [16] develops free-space path loss models for different RISs-assisted communication scenarios by analyzing the physics and electromagnetic nature of RISs These works developed accurate models to depict channel characteristics for RISs-assisted wireless communication However, complex analysis methods are difficult to widely promote and apply [17] proposes a simple but sufficiently accurate model to analyze RISs-assisted wireless networks by leveraging scalar theory and the Huygens-Fresnel principle And a simple physical model for RISs-enabled transmission model based on physical parameters is presented in [18] But scatter incident signals and single reflection scenario are lack of consideration for practical application scenarios To the best of our knowledge, a few researches focus on the practical application scenario for RISs-assisted communication, such as vehicular networks [19], [20], [21] Hence, the jointed framework of RISs-assisted DSRC system has drawn significant attentions due to its superior capability in ITS In this paper, we propose a multi-ray propagation model for DSRC communication on street with RISs on sidewalls in mmWave environment In addition, the analysis of the proposed model which adopts 3-D geometry and image techniques is provided to display the process of tracing multiple reflection signal rays from the sending vehicle to the receiving vehicle For different reflection times, the reflection paths are different Furthermore, the received signal strength is also affected in this case III S YSTEM MODEL In this section, a specific application with RISs-assisted communication system is introduced in ITS To get the transmission distance between the transmitter and the receiver after multiple reflection, the equivalent transmission distance representation method is given out by 3-D geometry and image technique Furthermore, the received signal strength at the receiver is analyzed specifically for different cases At a traffic intersection with tall buildings, the millimeter wave communication happens between the vehicle S and the vehicle D The vehicle D is waiting on left side of the intersection as of traffic light The vehicle S is driving slowly along the street from west to east while it transmits a signal to the waiting vehicle D adopting to DSRC protocol The connectivity of wireless networks is modeled as a quasi-static wireless channel In this process, the limited communication capabilities achieve liberation with the help of RISs, which are deployed on the wall facade, as shown in Fig This scenario can be modelled in a block form of parallel and perpendicular planes in a 3-D coordinate system The wall is completely covered by multiple large RISs The structure in a traffic intersection of communication environment is modeled as plane surface The EM wave from the vehicle S arrive at the vehicle D through multiple reflections on RISs In addition, Fig also gives out the corresponding geometry legend The coordinate origin is at the dark spot symbol (•) Then, the location of the vehicle S and the vehicle D are specified as a point in a 3-D coordinate system, denoted as (xS , yS , zS ) and (xD , yD , zD ), respectively Hence, the distance taken by the line of sight path is given by DLOS = p (xS − xD )2 + (yS − yD )2 + (zS − zD )2 (1) where xS ∈ (−∞, −L/2], yS ∈ [0, L], xD ∈ [−L/2, L/2] and yD = L Besides, we assume that the antenna of vehicles have same height and level, zS = zD Specially, the height of 482 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) where, ELOS is the signal strength at the receiver, and ET is the signal strength of the transmitter, and Γ is transmission coefficient of obstacle, and DLOS represents the distance of LOS between transmitter and receiver, and λ is wavelength of T M transmission signal M M M When the signal is transmitted on channel through multiple reflection, the multiple reflection transmission is equivalent to one LOS transmission by 3-D geometry and image techniques for reducing computational complexity Hence, the received strength of a multiple reflected signal is given as RIS D  RIS 2πDref S Fig Model of DSRC communication on street with RISs the transmitter and the receiver are same and can be ignored in this case In DSRC environment, a signal ray at the receiving terminal D is multiple reflection signals by RISs Referring to Fig 1, the multiple reflections between two parallel walls are happen to relay and reflection a transmitted signal According to Fig the transmission distance by a series of reflection signals is expressed as Dref = l1 + l2 + · · · + ln+1 (2) where, li is path length for each segment, l1 , l2 , · · · , ln+1 , respectively The number of n reflections are determined by deployment in practice Generally, n should not be too large as of transmission path loss From Fig and Fig 2, a signal undergoes n reflections between two parallel walls In order to facilitate the analysis and design, we assume all angles, ψ0 = ψ1 = ψ2 = · · · = ψn−1 = ψe/o , are equal through adjusting the corresponding phase of an RIS on the wall It is noted that the geometry angle of forward reflection wave satisfies ψe/o ∈ [0, π/2] and cos(ψe/o ) ∈ [0, 1] Based on 3-D geometry technique [3], the multiple reflection paths with eq (2) can be rewritten as, specifically, ( D −yS Dref (even) = (n+1)y cos(ψe ) (3) D +yS Dref (odd) = ny cos(ψo ) and ( tan−1 (ψe ) = tan−1 (ψo ) = xS −xD (n+1)yD −yS xS −xD nyD +yS (4) where, n is the number of reflections (even order or odd order) According to the above analysis and geometric relationship, the possible values of Dref (even) and Dref (odd) are {Dref (even) , Dref (odd) } ∈ [nL, +∞) IV A NALYSIS OF THE RECEIVED SIGNAL STRENGTH The received strength of the line of sight (LOS) signal can be expressed as [22] 2πDLOS ELOS =  ET Γe−j[ λ DLOS ] (5) ET Γe−j[ λ ] (6) ER = Dref Pn+1 where the sum of path length, i=1 li , can be obtained by Dref in eq (3) We assume that the transmission signal which has unit power is an unmodulated carrier signal u(t), u(t) = A cos(2πfc t+ Φ), where fc is the carrier frequency and Φ is the initial phase We adopt the low-pass complex equivalent of this signal by dropping the carrier term to represent as, u(t) = A exp(jΦ) As of the Doppler spectrum with the moving vehicle S, we consider all reflected signals have unit constant amplitudes, such as A = 1, for rapidly varying phase terms We assume the time-varying reflection coefficient of an RIS is β(t) = ejψ(t) , which has unit gain amplitude and the phase term is constantly changing Then, the n reflection coefficients at each reflection point, R1 , R2 , · · · , Rn , on the parallel street constitute a set of vectors [β1 , β2 , · · · , βn ] According to eq (6), the received strength of this signal can be rewritten as ER = ejΦ Qn −j i=1 βi e Pn+1 i=1 Pn+1 2π( l ) i=1 i λ li (7) In Fig and Fig 2, the transmission signal is transmitted from the vehicle S to the vehicle D through multiple reflections on RISs Submitting β(t) into eq (7), the received strength of this signal can be rewritten as 2π(l1 +l2 +···+ln+1 ) λ ejΦ ej(ψ0 +ψ1 +···+ψn ) e−j ER = l1 + l2 + · · · + ln+1 2πDref λ ej(Φ+$− = Dref (8) ) Let us suppose $ = ψ0 + ψ1 + · · · + ψn It is well known that, if 2π Φ+$ = Dref + 2kπ, k ∈ Z, (9) λ then, the received complex envelop of signal has maximum strength for the minimum Dref Otherwise, if 2π Dref + (2k + 1)π, k ∈ Z, (10) λ the received complex envelop of signal has minimum strength for the maximum Dref Against this background, the different total phase values $ can be achieved through controlling each phase value ψi of different RISs for satisfying eq.(9) and eq.(10) 483 Φ+$ = 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) N RIS Left R1 l1 S j0 D R3 l3 j1 l2 Right j2 L/2 L j ln n -1jn ln +1 Rn R2 RIS v Fig Geometry digram of multi-ray propagation for DSRC communication with RISs $= when Dref 2π · nL − Φ + 2kπ, k ∈ Z, λ = nL The Envelope Strength of the Received Signal (dB) In other words, the maximum strength condition on the received complex envelop of signal is given as (11) and otherwise, the minimum strength condition on the received complex envelop of signal is also given as $ → 0, when Dref → +∞ (12) In this case, the controller (i.e., FPGA) adopts a centralized method to conveniently realize the appropriate phases on RISs in real-time [5], [23] The Even Number of Reflection, N 10-5 1.6 n=6 n=8 n=10 1.4 1.2 0.8 0.6 0.4 0.2 V S IMULATION R ESULTS In this section, the propagation model has been developed in the Matlab program The simulation scenario is build in Fig All walls is combination of bricks and RISs The signal is transmitted from the transmitter to the receiver through multiple reflections A Parameters Setting Having derived the received complex envelop of the multiple reflection signal in the vehicle D, we now consider the qualitative dependence of the number of reflection on the proposed model parameters Referring to eq (3), eq (7) and eq (8), we focus on the received complex envelop with different number of reflections for RISs-assisted DSRC networks We assume that the width of the street is L = m The vehicle S moves at a slowly speed v from the initial position xS = −1000 m The mmWave operating frequency is 30 GHz For ease of presentation, the amplitude of reflection coefficient with RISs is one unit and the transmission signal is an unmodulated radio frequency carrier signal B Results Analysis In order to capture the effects of the complex envelope strength in the received signal of RISs-assisted communication, we regulate the different number of reflections (Even and Odd) on sidewalls Fig and Fig display the envelope strength of the received signal with different phases, $, of 10 20 30 40 50 60 70 80 90 Fig The envelope strength of the received signal for the vehicle D at different phases of RISs when a transmission signal from the vehicle S undergoes even number of reflections RISs As a whole, the maximum and minimum envelope strength of the received signal are corresponding to $ = 0o and $ = 90o , respectively When 0o < $ < 90o , the transmission signal from the vehicle S bounces between the parrel sidewalls many times before arriving to the vehicle D Two extremes for $ = 0o or $ = 90o basically represent that a transmission signal travels in a line of sight or in blocking state, respectively Specifically, Fig shows the envelope strength of the received signal by different even number of reflections from 0o to 90o on phases of RISs and the results undergo a steady decline to At even number of reflections, the envelope strength of the received signal decreases as the number of reflections increase for the same phase of RISs The more number of reflections mean the longer transmission distance, the signal dropping off sharply with distance When the phase of RIS is 90o , there is no signal arrive at the receiver Hence, the envelope strength of the received signal is zero Similarly, the similar characteristics are displayed in Fig when the number of reflections are odd The envelope strength 484 The Envelope Strength of the Received Signal (dB) 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) The Odd Number of Reflection, N 10-4 3.5 R EFERENCES n=7 n=9 n=11 2.5 1.5 0.5 0 10 20 30 40 50 60 70 80 90 Fig The envelope strength of the received signal for the vehicle D at different phases of RISs when a transmission signal from the vehicle S undergoes odd number of reflections of the received signal will decreases sharply when the received signals undergo briefly and smoothly in the initial stage from 0o on phases of RISs However, it’s interesting that the envelope strength of the received signal from the odd number of reflections (n + 1) have significant improvements for comparing with even number of reflections (n) signal According to 3-D geometry and image methods in ray tracing, the extra distance is the reason for this result VI C ONCLUSION The proposed jointed propagation model for RISs-assisted DSRC system on sidewalls based on 3-D geometry method and image method offers a novel solution to the study of channel propagation characteristics In multiple reflections between two parallel plane, the 3-D geometry method improves the ray tracing technique for measurement of complexity and the image technique mirrors the signal source at a particular face Introduction and design of RISs further promote the performance of DSRC system The multi-ray propagation for RISs-assisted DSRC communication can be analyzed deeply on the different entry and exit angle of signal for each RIS deployment position in future work ACKNOWLEDGMENT The authors wishes to thank the anonymous reviewers for their helpful comments that have significantly improved the quality of the presentation This work has been supported in part by the Jiangsu Planned Projects for Post-doctoral Research Funds project under grant 2020Z113, the Open Foundation of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education under grant 2019FF09 [1] Seidel, S Y and Rappaport, T S., ‘A ray tracing technique to predict path loss and delay spread inside buldings’, IEEE Global Commun conf., Orland, FL, 1992 [2] Valenzuela, R A., ‘A ray tracing approach to predicting indoor wireless transmission’, IEEE 43rd Vehicular technology conf., NJ, 1993 [3] Tam, W K and Tran, V N., ‘Multi-ray propagation model for indoor wireless communications’, Electron Lett., 1996, 32, (2), pp 135-137 [4] Wu, G L and Xu, P P., ‘Link QoS analysis of 5G-enabled V2V network based on vehicular cloud’, Science China Information Sciences, 2018, 61, (10), pp 272-278 [5] Alexandropoulos, G C., Lerosey, G., Debbah, M., et al.,‘Reconfigurable intelligent surfaces and metamaterials: The potential of wave propagation control for 6G wireless communications’, arXiv: 2006.11136, 2020 [6] McJown, J W., ‘Ray-tracing as a design tool for radio networks’, IEEE Network Magazine, 1991, pp 27-30 [7] Reza, A W., Noordin, K A., Islam, M J et al., ‘Investigation of a New 3D Intelligent Ray-Tracing Model’, Wireless Personal Communication, 2014, 77, 691C717 [8] Erricolo, D and Uslenghi, P L E., ‘Propagation path loss-a comparison between ray-tracing approach and empirical models’, IEEE Transactions on Antennas and Propagation, 2002, 50, (5), pp 766-768 [9] Geok, T K., Hossain, F., Kamaruddin, M N., et al., ‘A comprehensive review of efficient ray-tracing techniques for wireless communication’, International Journal on Communications Antenna and Propagation, 2018, 8, (2), pp 123-136 [10] Hutton, D M., ‘Smart Environments: Technology, Protocols and Applications’, Kybernetes, 1972 [11] Srividhya, ‘Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems’, International Journal of Trend in Research and Development (IJTRD), 2017 [12] Liang, Y., Long, R., Zhang, Q., et al., ‘Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart’, Journal of Communications and Information Networks, 2019, 4, (2), pp 40-50 [13] Zhao, J and Liu, Y., ‘A survey of intelligent reflecting surfaces (IRSs): Towards 6G wireless communication networks’, arXiv: 1907.04789, 2020 [14] Wu, Q and Zhang, R., ‘Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network’,IEEE Commun Mag., 2020, 58, (1), pp 106-112 [15] Ozdogan, O., BjOrnson, E and Larsson, E G., ‘Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling’, IEEE Wireless Commun Lett., 2020, 9, (5), pp 581-585 [16] Tang, W K , Zheng, M., Chen, X Y., et al., ‘Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement’, IEEE Trans Wireless Commn., early access, 2020 [17] Di Renzo, M., Habibi Danufane, F., Xi, et al., ‘Analytical modeling of the path-loss for reconfigurable intelligent surfaces C anomalous mirror or scatterer ?’, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020, pp 1-5 [18] Ellingson, S W., ‘Path loss in reconfigurable intelligent surface-enabled channels’, arXiv: 1912.06759, 2019 [19] Makarfi, A U., Rabie, K M., Kaiwartya, O., et al., ‘Physical Layer Security in Vehicular Networks with Reconfigurable Intelligent Surfaces’, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020, pp 1-6 [20] Chen, Y., Wang, Y., Zhang, J., et al, ‘QoS-Driven Spectrum Sharing for Reconfigurable Intelligent Surfaces (RISs) Aided Vehicular Networks’, IEEE Transactions on Wireless Communications, 2021,20, (9), pp 59695985 [21] Al-Hilo, A., Samir, M., Elhattab, M., et al., ‘Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamforming’, arXiv:2101.12247, 2021 [22] Fontan, F P and Espineira, P M., ‘Modeling the wireless propagation channel: A simulation approach with MATLAB’, United Kingdom: Wiley, 2008 [23] Matthiesen, B., Bj?rnson, E., De Carvalho, E., et al., ‘Intelligent Reflecting Surface Operation Under Predictable Receiver Mobility: A Continuous Time Propagation Model’, IEEE Wireless Communications Letters, 2021, 10, (2), pp 216-220 485 ... framework of RISs- assisted DSRC system has drawn significant attentions due to its superior capability in ITS In this paper, we propose a multi- ray propagation model for DSRC communication on street with. .. NAFOSTED Conference on Information and Computer Science (NICS) propagation aspects In Section III, we consider a simple traffic scenario and revisit multiple reflection problem with RISs Section IV... DSRC system The multi- ray propagation for RISs- assisted DSRC communication can be analyzed deeply on the different entry and exit angle of signal for each RIS deployment position in future work