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The communications between two driving vehicles along a narrow street may be limited by the presence of a third vehicle blocking the transmission. In this work, we investigate radio wave propagation at 28 GHz in an urban street canyon scenario by conducting channel measurements, where the vehicle(s) occlude(s) the line-of-sight path. We quantify th...
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... is foliage on the right side of the considered street canyon scenario (see Fig. 5), which might potentially become the source of signal attenuation and scattering; it should be taken into account in ray-tracing simulations. The foliage can be represented in two ways: (i) as a continuous surface of leaves, meaning that the diffuse scattering effects take place (in this case, the model developed in [18] can be used), ...
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... we create the 3D model of the environment (Fig. 5) consisting mostly of large objects in a CAD software, such as PTC Creo. Dimensions and locations of the surrounding objects were measured by tape-meter. The 3D models of cars (see Table I), which act as blockers and reflectors in our simulations, are constructed in the same software. The shape and the outline size of the car models ...
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... power equals to −88.2 dBm, which is 1 dB less than that obtained in the measurements. More accurate investigation of reflection by further simulations demonstrated two reflections in the square#2 with almost equal received power but the taps having 2.1 ns difference in terms of delay. This is caused by irregularities of the left wall visible in Fig. 5. There is a protruding part of the brick wall on the left side, which creates two propagation paths. Both of these taps in square#2 are well captured by our ray-based ...
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... more relatively weak MPC was observed in simulations at the same AoA as the LOS component but with the delay of 65 ns (Fig. 7, square#3). The source of this tap is a car (car N o 3 in Fig. 5) parked behind the Tx at a certain angle, which creates a diffracted incoming path. Based on our simulations, this path was found to contribute −100 dBm of the total received power, which has a 4-dB discrepancy with the measured value (−104 dBm). The time difference between the measured and the simulated data is negligible (i.e. 0.5 ...
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... reflection. The presence of diffraction explains high losses (over 20 dB) over a relatively short propagation time. Despite that, simulation results are well aligned (under 3 dB of difference) with the measured data. Square#6 is produced by higher-order reflected MPCs from the parked cars #1 and #2 as well as the open door on the left side (see Fig. 5). In the process of modeling, this path was the most challenging, since it is very sensitive to the slope of the glass in a passenger car parked near the wall. The power difference between the measured and simulated results is 4.5 dB (−105.3 dBm is the measured power, while −100.8 dBm corresponds to the simulated ...
Citations
... Considering the foreseen massive number of IoT devices, UAVs are an attractive solution for energy efficiency and QoS improvements due to the enhanced coverage resulting from their high mobility and ability to hover as discussed in [13]. In fact, it is challenging to obtain Line of Sight (LOS) using terrestrial BSs in urban canyon environments [14], [15], and it is hard to envision smart cities without the assistance of UAVs [13], [16]. Furthermore, by equipping the UAVs with reconfigurable antennas [17], more degrees of freedom could be attained since it is possible to adjust the beam footprint of the UAV by means of electrical, optical, mechanical, and material change techniques to boost even more the coverage with QoS guarantees 2 . ...
... Notice that this may be possible since the optimization problem determined by the two GP subproblems operates over a feasible set that is a subset of the original feasible set given by (9b)-(9c). We perform this in line 10 of the optimization algorithm by solving P1-3, which is nothing but P1 with a fixed input {h, θ B , x uav , y uav } and optimization Algorithm 1 Optimum UAV position and IoT nodes' transmit power 1: Input: {x k , y k , c k } ∀k∈K , γ 0 , p min , p max , h min , ξ 2: it = 0 (iteration index) 3: Set t (0) = ∞ and x (0) uav , y (0) uav according to (15) 4: repeat 5: it ← it + 1 6: Solve P1-1 given x ...
The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of unmanned aerial vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-signal-to-average-interference-plus-noise ratio (
) Quality-of-Service (QoS) constraints. We minimize the worst case average energy consumption of the latter, thus targeting the fairest allocation of the energy resources. The problem is nonconvex and highly nonlinear; therefore, we reformulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst case average energy consumption. Furthermore, we show that the target
is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.
... Probably, this may happen due to the limited accuracy of the 3D model or utilized GO and UTD methods. Misalignment between the measured and modelled results in vehicular deployments may easily be 4 dB or even higher [28]. However, investigation of individual multipath components to determine the source of the divergence in such an unfavourable scenario is, in principle, quite difficult. ...
Wireless communication and radars will play a crucial role for autonomous vehicles in the nearest future. However, the blockage caused by surrounding cars can degrade communication performance, while automotive radars are never aimed to operate in such conditions. Therefore, in this paper, the authors propose the concept of near‐ground propagation, reducing the blockage effect in the road traffic conditions. Specifically, the radio waves may freely propagate under the blocking car's bottom if the antennas are placed as low as possible to the road. Based on the measured and modelled results presented in the paper, it may be claimed that near‐ground communication and radar sensing are feasible and may combat even heavily obstructed cases. Nevertheless, some challenges associated with antenna locations were encountered. For example, it was discovered that antenna height at 0.5 m acts less effectively against blockage than at 0.3 m. Next, the 27 dB excess loss at the 0.5 m antenna height in the radar deployment is larger than 17 dB at 0.3 m. In its turn, the higher ground clearance of the blocking vehicle positively affects the near‐ground performance. Additionally, the signal propagation at the grazing angle crucially reduces the relevant losses.
... Considering the foreseen massive number of IoT devices, UAVs are an attractive solution for energy efficiency and QoS improvements due to the enhanced coverage resulting from their high mobility and ability to hover as discussed in [13]. In fact, it is challenging to obtain Line of Sight (LOS) using terrestrial BSs in urban canyon environments [14], [15], and it is hard to envision smart cities without the assistance of UAVs [13], [16]. Furthermore, by equipping the UAVs with reconfigurable antennas [17], more degrees of freedom could be attained since it is possible to adjust the beam footprint of the UAV by means of electrical, optical, mechanical, and material change techniques to boost even more the coverage with QoS guarantees 2 . ...
... Notice that this may be possible since the optimization problem determined by the two GP subproblems operates over a feasible set that is a subset of the original feasible set given by (9b)-(9c). We perform this in line 10 of the optimization algorithm by solving P1-3, which is nothing but P1 with a fixed input {h, θ B , x uav , y uav } and optimization Algorithm 1 Optimum UAV position and IoT nodes' transmit power 1: Input: {x k , y k , c k } ∀k∈K , γ 0 , p min , p max , h min , ξ 2: it = 0 (iteration index) 3: Set t (0) = ∞ and x (0) uav , y (0) uav according to (15) 4: repeat 5: it ← it + 1 6: Solve P1-1 given x ...
The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of Unmanned Aerial Vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-Signal-to-average-Interference-plus-Noise Ratio Quality of Service (QoS) constraints. We minimize the worst-case average energy consumption of the latter, thus, targeting the fairest allocation of the energy resources. The problem is non-convex and highly non-linear; therefore, we re-formulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst-case average energy consumption. Furthermore, we show that the target average-Signal-to-average-Interference-plus-Noise Ratio is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.
... Considering the foreseen massive number of IoT devices, UAVs are an attractive solution for energy efficiency and QoS improvements due to the enhanced coverage resulting from their high mobility and ability to hover as discussed in [13]. In fact, it is challenging to obtain Line of Sight (LOS) using terrestrial BSs in urban canyon environments [14], [15], and it is hard to envision smart cities without the assistance of UAVs [13], [16]. Furthermore, by equipping the UAVs with reconfigurable antennas [17], more degrees of freedom could be attained since it is possible to adjust the beam footprint of the UAV by means of electrical, optical, mechanical, and material change techniques to boost even more the coverage with QoS guarantees 2 . ...
... Notice that this may be possible since the optimization problem determined by the two GP subproblems operates over a feasible set that is a subset of the original feasible set given by (9b)-(9c). We perform this in line 10 of the optimization algorithm by solving P1-3, which is nothing but P1 with a fixed input {h, θ B , x uav , y uav } and optimization Algorithm 1 Optimum UAV position and IoT nodes' transmit power 1: Input: {x k , y k , c k } ∀k∈K , γ 0 , p min , p max , h min , ξ 2: it = 0 (iteration index) 3: Set t (0) = ∞ and x (0) uav , y (0) uav according to (15) 4: repeat 5: it ← it + 1 6: Solve P1-1 given x ...
The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of Unmanned Aerial Vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-Signal-to-average-Interference-plus-Noise Ratio Quality of Service (QoS) constraints. We minimize the worst-case average energy consumption of the latter, thus, targeting the fairest allocation of the energy resources. The problem is non-convex and highly non-linear; therefore, we re-formulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst-case average energy consumption. Furthermore, we show that the target average-Signal-to-average-Interference-plus-Noise Ratio is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.
... When concentrating on the 28 GHz band, the authors of [19] performed detailed measurements in an urban street canyon scenario and focused on evaluating the prospective impact of the car blockages. In particular, the presence of car(s) between the transmitter and receiver has been evaluated. ...
... This is probably the effect of the two-ray propagation, where one path travels below cars and reflects from the ground (i.e. it travels in the tunnel between the ground and the bottom of the car). However, other effects (such as diffraction) may have also impact on the observed results, as discussed e.g. in [19]. Finally, interesting observations can be made while analyzing the average attenuation at each distance. ...
Platooning is considered to be one of the possible prospective implementations of the autonomous driving concept, where the train-of-cars moves together following the platoon leader's commands. However, the practical realization of this scheme assumes the use of reliable communications between platoon members. In this paper, the results of the measurement experiment have been presented showing the impact of the blocking cars on the signal attenuation. The tests have been carried out for the high-frequency band, i.e. for 26.555 GHz. It has been observed that on one hand side, the attenuation can reach even tens of dB for 2 or 3 blocking cars, but in some locations, the impact of a two-ray propagation mitigates the presence of obstructing vehicles.
... The mmWave band will be used in V2V communication in which two antennas in the front and rear bumper of vehicles are installed. In such cases, the Tx and Rx antennas are located close to the ground [145,146]. Furthermore, The internet of things (IoT) and sensors application operating on mmWave and propagate near the ground will open a new direction for future research [147]. ...
The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond.
... Multi-scenario setting of resilience of an urban tourism environment system Drawing on relevant research results (Solomitckii et al. 2020;Ioannou et al. 2019), the resilience of the regional tourism environment system is investigated in multiple scenarios: maintaining the status quo (scenario 1), extensive growth (scenario 2), and sustainable growth (scenario 3). Table 2 shows the simulation parameters for the resilience of the system. ...
The rapid development of the urban economy in China and the accompanying income growth experienced by urban residents have increased demand for tourism and leisure, which has brought pressure on the urban tourism environment system (UTES), making the contradiction between tourism economic development and the ecological environment increasingly acute. While seeking to rationalize the economic, social, and ecological benefits of tourism, reducing the fragility of the UTES and improving its anti-interference and recovery capabilities have become attracted significant attention from scholars in China and elsewhere. This paper establishes a definition of resilience for an UTES and constructs an evaluation index system for it in terms of the social, economic, and ecological environments. It also establishes an entropy weight-TOPSIS resilience evaluation model to measure resilience in regional systems, using ArcGIS to analyze the standard deviation ellipse and center of the gravity track of the resilience. System dynamics was used to construct diagrams of causal relationships and stock flow for the constituent elements of UTES to show the mechanisms that promote its resilience. This paper investigates 14 cities of Gansu Province in particular to simulate the resilience model of a regional system.
... 1) First, monostatic σ(φ 2 , θ 2 ) of the the accurate detectable car model is precalculated and stored. 2) Then an adapted version of the image-based RT tool utilized in [15] is employed to calculate the angles φ 1 , θ 1 , φ 2 , θ 2 and the lengths of the paths R 1 and R 2 shown as dashed line in Fig. 1. 3) Then, G t (φ 1 , θ 1 ) and G r (φ 1 , θ 1 ) radar gains are calculated at estimated angles by formula (2). (1). ...
... The dominating reflection mechanism is specified by reflection coefficient, expressed as Γ = (η − η 0 )/(η + η 0 ), where surface impedance η is one of the two components. Following this, it can thus be stated that the difference should be in the order of Γ, varying in the above-mentioned range [15]. ...
The employment of passive reflectors enables the millimeter-wave automotive radars to detect an approaching vehicle in non-line-of-sight conditions. In this paper, the installation of such reflectors above the sidewalk at an intersection is proposed and studied, avoiding pedestrians' blockage and road dust effect at ground level. Through the analysis of the backscattering power, it is shown that the suggested scheme may detect an approaching vehicle in the blind zone at distances of 30,łdots,50 m to the intersection point. Additionally, the analysis shows that efficient operation is highly dependent on the spatial orientation and size of the reflector. Even a few degrees rotation may change the detecting range by several meters. In turn, the larger area of the reflector may cover longer detecting distances, improving the radar scheme's overall performance. It is also shown that further performance enhancement can be achieved by employing a C-type radar, contributing an extra 5 dB to the backscattering power relative to an A-type radar. However, despite these improvements, the strongest scattering centre of the detectable vehicle is systematically identified to the bumper zone.
... Thus, the relevant question emerges: Is there a way to keep the vehicles connected in the LOS-obstructed conditions without infrastructure assistance? During the ray-tracing simulations in [7], occasionally, it was found that if the transceiver antenna is placed close to the ground, the signal may propagate under the obstructing vehicle and road (waveguide-like propagation) without experiencing much losses. Primarily, this effect might potentially keep the communicating vehicles connected and expand the radar sensing capabilities even in an obstructed LOS scenario. ...
... Critical parameters, such as path loss and delay spread in different environments, have been reported [236,237]. The vehicle types in use and the antennas' positioning call for major concerns [238]. In the first place, there are several hundreds of heavy-duty trucks and buses plowing the highways, but much has not been reported about such vehicles. ...
The field of wireless communication networks has witnessed a dramatic change over the last decade due to sophisticated technologies deployed to satisfy various demands peculiar to different data-intensive wireless applications. Consequently, this has led to the aggressive use of the available propagation channels to fulfill the minimum quality of service (QoS) requirement. A major barometer used to gauge the performance of a wireless communication system is the spectral efficiency (SE) of its communication channels. A key technology used to improve SE substantially is the multiple input multiple output (MIMO) technique. This article presents a detailed survey of MIMO channel models in wireless communication systems. First, we present the general MIMO channel model and identified three major MIMO channel models, viz., the physical, analytical, and
standardized models. The physical models describe the MIMO channel using physical parameters. The analytical models show the statistical features of the MIMO channel with respect to the measured data. The standardized models provide a unified framework for modern radio propagation architecture, advanced signal processing, and cutting-edge multiple access techniques. Additionally, we examined the strengths and limitations of the existing channel models and discussed model design, development, parameterization, implementation, and validation. Finally, we present the recent 3GPP-based 3D channel model, the transitioning from 2D to 3D channel modeling, discuss open issues, and highlight vital lessons learned for future research exploration in MIMO communication systems.
