Interference Analysis for mmWave Automotive Radar Considering Blockage Effect.

In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and post-processing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

Small cell enhancement has been considered as a promising way to cope with the rapid growth of need in mobile data traffic. However, it also leads to severe inter-cell interference, which means that some interference mitigation scheme is indispensable. Among lots of interference mitigation scheme, small cell on/off adaptation is thought to be an effective one. In this paper, small cells are modeled as a 2-D Poisson point process (PPP) based on stochastic geometry. And then the coverage probability of the small cell network can be derived as a tractable close-form expression. In addition, a Monte Carlo simulation is performed to verify the accuracy of the stochastic geometry model. Subsequently, the numerical results shows that small cell on/off adaptation can improve the receiving signal to interference plus noise ratio (SINR) of small cell downlink transmission by 4-6 dB. Meanwhile, the average ergodic transmission rate and power efficiency are both enhanced due to the improvement of the wireless environment.

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

Interference modeling and analysis in 3-dimensional directional UAV networks based on stochastic geometry

We propose an analytical framework based on stochastic geometry (SG) formulations to estimate a radar's detection performance under generalized discrete clutter conditions. We model the spatial distribution of discrete clutter scatterers as a homogeneous Poisson point process and the radar cross-section of each extended scatterer as a random variable of the Weibull distribution. Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section. We derive the optimum radar bandwidth for maximizing this metric under noisy and cluttered conditions. We also derive the peak transmitted power beyond which there will be no discernible improvement in radar detection performance due to clutter limited conditions. When both transmitted power and bandwidth are fixed, we show how the detection threshold can be optimized for best performance. We experimentally validate the SG results with a hybrid of Monte Carlo and full wave electromagnetic solver based simulations using finite difference time domain (FDTD) techniques.

Due to the increasing number of aerial radars and joint communication/sensing technologies, interference from uncoordinated radars will limit the target detection and ranging performance in the future. In this article, we investigate the interference behavior in an aerial radar network for sensing ground targets. We consider that the radars mounted on unmanned aerial vehicles (UAVs) that fly at a certain altitude are randomly distributed according to a two-dimensional homogeneous Poisson point process (HPPP), and that the propagation is modeled using a probabilistic line-of-sight channel model. For such a sensing network, we derive the distribution of the radar interference using a stochastic geometry based analysis. In particular, when Swerling I model is considered for radar cross-section area for the target, we derive the Laplace transform of the radar interference. To avoid a strong interference between neighboring radars, a guard zone is introduced within which the UAV radar transmission around the permitted active radar is inhibited. As the radar performance metric, we derive the successful ranging probability (SRP) of a given radar by exploiting the Laplace transform of radar interference. Using the analytic SRP, we show that we can optimize the radar network parameters such as the radius of the guard zone and the density of the active radars. In addition, we also discuss how the analytic SRP gives an insight into the spectrum utilization strategy for the UAV radar networks with the guard zones.

Experimental measurement results of automotive radar signal attenuation during various intensities of snowfall at the current automotive radar frequency (77 GHz) and low‐terahertz (THz) (100–300 GHz) frequencies are presented and compared in this study. The attenuation is characterised by measuring the ratio of the received power from a reference target through snow precipitation of various intensities and through the same path with no precipitation. Statistical analysis of the attenuation is presented. Higher attenuation is measured at a higher frequency, and also attenuation increases as snowfall rate and liquid water content in snowflakes increases. This study is fundamentally important to investigate the effect of adverse weather conditions on low‐THz radar performance in comparison with the current automotive radars operating in the traditional mm‐wave band. In addition, the effects of low‐THz wave attenuation and scattering due to typical contaminants formed on the radome of automotive radars and reflection from common objects on the road are presented.

In this article, we analyze the signal-to-interference ratio (SIR) coverage probability of a multi-cell downlink system with random traffic arrivals. Based on a theoretical model, in which the base stations (BSs) and devices are deployed as independent Poisson point processes (PPPs), we first present the sufficient and necessary conditions of the e-stable region for the queueing system. Then, by taking into account the impact of spatially queueing interactions among BSs, we focus on the steady status of the queues and introduce a two-queue-length approximated model for the system. Specifically, in the studied new model, the BSs are separated into two sets in the steady state: the long-queue BS set and the short-queue BS set. The locations of the former are modeled by a Neyman-Scott process and those of the latter are modeled as a residual hole process. By applying the first-order statistic approximation, we further approximate the hole process by a homogeneous PPP with the same density. To model the deployment of the BSs precisely, the related parameters in the approximated model are fitted. Using tools from stochastic geometry and queueing theory, we derive the SIR coverage probability in the steady state. An iterative algorithm is proposed to calculate the active probability of the BSs. To reduce the computational complexity, Beta distribution is applied to approximate the probability density function of the service rate in each iteration of the algorithm. Finally, the effect of the fitting parameters and the accuracy of our analysis are presented via Monte Carlo simulations.

Unmanned aerial vehicles (UAVs) are prominent to modern wireless networks but are severely limited by onboard energy, making continuous operation of a large swarm infeasible. To address this, this paper proposes a probabilistic activation scheme in which UAVs from a larger candidate pool can enter sleep states to conserve energy. Leveraging a tractable probabilistic activation strategy, each UAV is independently switched on with an optimized probability that can be easily derived in closed form. This differentiates our work from earlier heuristic sleep-mode techniques which rely on traffic-threshold rules without analytical guarantees. We thus develop a tractable analytical framework using stochastic geometry to evaluate this scheme, modeling the active UAVs as a 3D Poisson Point Process (PPP) under a probabilistic Line-of-Sight (LoS)/Non Line-of-Sight (NLoS) propagation conditions. Novel analytical expressions are derived for the coverage probability, average achievable rate, and network energy efficiency, with their accuracy rigorously validated by Monte Carlo simulations. Our analysis reveals a fundamental trade-off between network performance and power consumption, demonstrating the existence of an optimal activation probability that maximizes the system performance. This analytical framework enables network operators to optimize activation and transmit power profiles across the entire network. The goal of this optimization is to improve outage probability, ergodic rate, and energy efficiency.

To satisfy the requirement of increasing mobile traffic, heterogeneous networks (HetNets) will be deployed for the fifth generation (5G) mobile communications systems. Since the deployment of base stations (BSs) in HetNets is generally dense and random, stochastic geometry has increasingly been used to analyze the performance of HetNets. Most of the existing work is based on Poisson point processes (PPPs), which lead to some closed-form expressions for local delay and energy efficiency. Considering the geographical distribution of BSs, PPP may not be accurate enough. In contrast, a Poisson cluster process (PCP) proves more realistic for the deployment of BSs. In this paper, by using the discontinuous transmission (DTX) mode, the local delay and energy efficiency based on PCP have been analyzed. It is found that the local delay is larger and the energy efficiency is lower under PCP because of the strong intra-cluster interference. However, the local delay under PCP becomes similar to that under PPP if the radii of clusters are sufficiently large.

In this paper we analysed a special kind of wireless ad hoc networks. The elements of this network are distributed according to marked Poisson point processes in the plan so as to derive the characteristics of this network we use stochastic geometry. We assume that the channel between receivers and transmitters varies according to a gamma distribution and based on this assumption we model our network. The main topic of study in this issue is transmission capacity so we focus on it as well using some other related terms we try to find some bounds for it. Our work shows that different assumptions of channel variation in wireless ad hoc networks must be carried by precise calculation because of the complication of transmission capacity.

This paper presents a novel approach for mutual interference mitigation in automotive radars based on advanced waveform design by imposing time and frequency diversity to frequency modulated continuous wave (FMCW) chirp signal. Performance of the proposed waveform has been investigated using software-defined radio transceiver, and measurement results are provided. The system concept is validated by developed automotive radar channel model considering realistic 3D road traffic scenario using a ray-tracing tool. Theoretical analysis and simulation examples of mutual interference scenarios in automotive environment are provided to evaluate the effectiveness of the proposed method. Performance of both conventional FMCW waveform and proposed waveform under mutual interference environments has been provided for fair comparison. Results show that the proposed system is able to detect the targets of interest successfully while mitigates the false targets in mutual interference environments.

The critical issue of mutual interference in automotive radar leads to a growing number of processing algorithms for mitigation. Analyses of their performance have been mostly based on either simulations or on a small number of exemplary measurements. In this contribution, a corpus of real-world radar measurements is used, to which strongly varying simulated interferences are added. A statistical analysis framework is then applied to a set of state-of-the-art mitigation algorithms, studying the impact of highly complex object environments on their performance.

Spatial Poisson process models are popular partly because of their mathematical tractability based on a simple and powerful calculus whose main ingredients, besides Campbell’s formula, are the covariance formula and the exponential formula (also know as Campbell’s second formula). These formulas extend immediately to marked Poisson processes with independent (or location dependent) IID marks as a consequence of the fact that such marked Poisson processes can be described as unmarked Poisson processes in a higher-dimensional space. This slight modification of point of view will be applied to the study of the point processes obtained by meas of elementary transformations of a basic Poisson process, such as thinning, translation or clustering. All the results concerning Poisson processes find straightforward extensions concerning Cox processes. The calculus of spatial marked Poisson or Cox processes will be applied to a particular type of stochastic geometry (featuring the Boolean model) and to the exact sampling of cluster point processes.

Effects of directional antennas on outband D2D mmWave communications in heterogeneous networks