基于D2D链路的测量与建模

基于D2D链路的测量与建模

Abstract: Channel measurements and modelling have been long considered as the foundation for effective and efficient wireless communication system designs. Recently, there has been an explosive growth of research work dedicated to the so-called device-to-device (D2D) communications. In the mean time, however, measurements and modelling of D2D channels seem to somewhat fall behind. To promote research on these aspects, in this study, the authors provide a critical overview of the current state of research on D2D channels, and comprehensively discuss future trends and research directions.

摘要：信道测量与建模被长期作为无线通信系统设计实用性和高效性的基础。近年，对于所谓device-to-device（D2D）通信的研究工作呈现出爆发式增长，但同时关于D2D信道的测量与建模看起来没有同步进行。为了加深对于这些方向上的研究，本文中作者对D2D信道的当前研究现状做出客观的综述，并概要地讨论了未来趋势和研究方向。

1.introduction

1.简介

In recent years, device-to-device (D2D) communications have gained much attention to meet the increasing demand of wireless data service in spite of the increasingly scarce frequency spectrum [1]. The D2D communications are defined as the information exchange via a direct link between the end users, as opposed to transmitting and receiving signals through the cellular base station (BS). D2D communications behave as an underlay to the cellular network, which can achieve cellular controlled short-range direct data transmission for local area services by reusing the cellular spectrum, leading to both spectral efficiency improvement and cell throughput enhancement [2]. In traditional cellular networks, the radio resource in a cell can be allocated to only one cellular user equipment (UE) link. However, in cellular networks with D2D capability, multiple D2D links can share a resource block by spatially reusing the resource block within a cell. As a matter of fact, the D2D concept has been proposed long time ago for ad hoc networks, for example, WLAN and vehicular networks. Recently, the roadside infrastructure controlled D2D concept has been first proposed for vehicular communications [3].

近年来，随着频谱资源越来越稀缺，为满足无线数据业务的增长需要，device-to-device（D2D）通信被广泛关注[1]。D2D通信指的不是通过蜂窝基站（BS）发送和接收信号，从而在终端用户间建立一个直接相连的信息交换信道。D2D通信作为蜂窝网络的底层可以通过复用蜂窝网络频谱资源，实现多小区控制下本地短距离直接数据传输服务，从而提高了频谱效率，增加了小区吞吐量[2]。在传统的蜂窝网络中，一个小区的频率资源只能分配给一个用户设备（UE）连接，但是，在应用终端直联（D2D）技术的蜂窝网络中，多个D2D连接可以共享一个小区内空间复用的资源块，事实上，终端直联的概念曾在早起的移动自组网（ad hoc）中作为无线局域网（WLAN）和车载网络的例子被提出，近期，由路边基础设施控制下的终端直联概念在车载通信领域被首次提出[3]。

Not surprisingly, the D2D concept is embraced by various mainstream communication systems [long-term evolution (LTE), LTE advanced and beyond, and vehicular ad hoc networks] [4], thanks to its potential of boosting system throughput and improving spectral efficiency, both of which consist of the most important system development objectives [5]. Although D2D communication technologies are very promising, many research challenges have to

be addressed before their wide deployment. This paper will concentrate on one of the most important challenges, namely the characterisation of D2D communication channels. Reliable knowledge of the propagation channel and a corresponding realistic channel model serve as the enabling foundation for flexible and practical design and testing of D2D systems. This underlines the importance of developing physically meaningful yet easy-to-use methods to mimic D2D channels.Therefore much research attention has been attracted to D2D channel measurements, for understanding the underlying physical phenomenon in D2D propagation environments; and to D2D channel modelling, for facilitating the analysis and design of D2D communication systems.

毫无疑问，D2D的概念由于其可能增强系统吞吐量和提高频谱利用率而受到众多主流通信系统【长期演进技术版(LTE), 长期演进技术升级版(LTE-A)和超越长期演进技术版(LTE-B)，以及车载无线自组网】的推崇[4]，这两者被看作为最重要的系统开发目标[5]。尽管D2D通信技术非常有前途的,但是在研究工作广泛展开前应当克服众多研究面临的挑战。本文将重点关注其中一个最重要的挑战，即对D2D通信信道的描述。可靠的信息传输信道和一个相应的实际信道模型可作为D2D系统灵活实用研究和测试的理论基础。本文强调利用发展中自身意义深远并且易用的理论来模拟D2D信道非常重要。因此大部分研究的关注点放在D2D信道测量，D2D传播环境中底层物理现象的理解，D2D信道建模和D2D通信系统分析和设计的优化。

In this paper, we consider the general D2D concept that can involve any D2D communications, for example, human-to-human, vehicle-to-vehicle (V2V), vehicle-to-human, machine-to-machine,as shown in Fig.1.This is because,from the perspective of channel measurements and modelling, different D2D types will result in different communication scenarios, and thus leading to different D2D channel models. In order to give a comprehensive overview of current D2D channel measurements and modelling ,The D2D concept in this paper is a more general one. In this paper,provide the overview and perspective of D2D channel measurements and modelling.

本文中，我们认为大体上终端直联的概念可包含任何终端直联通信的过程，例如人与人、车辆与车辆（V2V）、车辆与人、机器与机器，如图1所示。这是因为从信道测量与建模的角度看，不同的D2D类型作用在不同的通信场景将会产生不同的D2D信道模型。为了对当前D2D信道测量和建模作出全面的综述，本文中D2D的概念更为宽泛。文中我们会提出D2D信道测量和建模的综合性观点。

The remainder of this paper is outlined as follows. In the next section, we will give an overview of recent advances in D2D channel measurements，We will then review the state-of-the-art D2D models,Some future challenges of D2D channel Measurements and modelling will then be addressed, followed by conclusions in the last section.

本文的其余部分介绍如下。在下一节中，我们将概述D2D信道测量的最新进展。然后我们将回顾D2D信道模型当前发展状况，一些D2D信道测量与建模的未来挑战将会被解决，接下来在最后一节中提出结论。

2.D2D channel measurements 2.D2D信道测量

The complete process of channel measurements and modeling includes the following steps: (i) channel measurements; (ii) raw data post-processing; and (iii) channel modelling and simulations.These steps are intimately interlaced. For example, the last step consisting of channel modelling and simulation may gain useful information in revising measurement campaigns in the first step.For raw data post-processing, spatial alternating generalized expectation-maximisation [6] has been widely adopted as one of the most popular channel estimation algorithms, thanks to its high accuracy, capability of estimating channel parameters and applicability to almost every type of antenna array. In this section,we will summarise and report some recent D2D channel measurements, followed by the overview of D2D channel modelling efforts in the next section.

完整的信道测量和建模的过程包括以下步骤:(1)信道测量;(2)原始数据后处理;(3)信道建模和模拟。这些步骤紧密交错。例如,最后一步组成的信道建模与仿真可能获得有用的信息在第一步修改测量活动。对原始数据后处理、空间交替广义expectation-maximisation[6]被广泛采用为最受欢迎的信道估计算法,由于其精度高,能力估计信道参数和适用于几乎所有类型的天线阵。在本节中,我们将总结和报告最近的一些D2D信道测量,其次是概述D2D信道建模努力在下一节。

To build up proper channel measurement campaigns, the first step is to choose interesting and typical communication scenarios. This is because different communication scenarios will result in different channel statistics and thus different channel models [7, 8]. Therefore how to properly choose and partition typical D2D communication scenarios is significant for D2D channel measurements. Firstly,D2D communications are used in dense and crowded scenarios.Secondly, we choose specific application scenarios from modern society, such as indoor office. Thirdly, we classify the scenarios according to the link specifics, such as outdoor-to-outdoor (O2O).Based on current measurement campaigns and our understanding of D2D communications, this paper classifies D2D communication scenarios as the following ten propagation environments: urban macrocell, urban microcell, suburban, rural, indoor office, indoor shopping mall, stadium, open air festival, road traffic and highway,as shown in Table 1. Detailed explanations about the antenna height, the UE velocity, the UE density and the wave propagation on these scenarios are listed as follows.

建立适当的信道测量活动,第一步是选择有趣的和典型的沟通场景。这是因为不同的通信场景将导致不同的信道数据,因此不同的信道模型[7,8]。因此如何正确选择和分区典型D2D通信场景D2D信道测量具有重要意义。首先,D2D通信用于密集和拥挤的场景。其次,我们从现代社会选择特定的应用场景,如室内办公室。第三,我们将根据链接细节的场景,比如outdoor-to-outdoor(O2O)。根据目前的测量活动和我们理解D2D通信,本文将D2D通信场景接下来的十传播环境:城市宏单元,城市微蜂窝,郊区,农村,室内办公,室内购物中心,体育场,露天的节日,道路交通和高速公路,如表1所示。详细解释关于天线高度,速度问题,问题密度和波传播这些场景列出如下。

Urban macro-cell/micro-cell outdoor-to-outdoor(O2O) It is assumed that the UEs are located well below street level (1–3 m height). The UE velocity is assumed to be in the range of 0–15 km/h.In the urban macro-cell or micro-cell O2O scenarios, the UE density is higher than that in suburban and rural areas, and the radio wave propagation is mainly affected by the buildings and obstacles in the outdoor [9, 10].

城市macro-cell /微蜂窝outdoor-to-outdoor(O2O)假设问题位于远低于街面(1 - 3

米高度)。速度问题被认为是在0-15 km / h。城市macro-cell或微蜂窝O2O场景,问题密度高于郊区和农村地区的学生,和无线电波传播主要是在户外建筑的影响和障碍(9、10)。

2.1Urban macro-cell outdoor-to-indoor (O2I) 2.1城市微蜂窝户外到户内

The urban macro-cell O2I propagation environment is similar to the urban macro-cell O2O scenario. In O2O propagation scenario, UEs are typically located at street level, whereas in the O2I scenario, UEs may be more widely distributed in elevation domain as they may be located on different floors in buildings (1.5–15 m) [11]. The UE velocity is in the same range with the urban macro-cell scenario (0–15 km/h). The path loss and shadowing model should take different UE heights into account. The penetration loss through the wall depends on the wall thickness and material, and the propagation through the window plays an important role in this scenario [12]. Owing to these reasons, the type of buildings should be defined. In old buildings with normal glass windows, the window penetration loss may be remarkably lower than that in modern buildings with thick safety glass or metal-coated windows [13].

城市macro-cell O2I传播环境类似于城市macro-cell O2O场景。在O2O传播情况下,问题通常坐落在街道上,而在O2I场景中,问题可能更广泛分布在海拔域可能会在不同的楼层建筑(1.5 -15)[11]。问题速度与城市在同一个范围macro-cell场景(0-15 km / h)。路径损耗和阴影模型应该考虑不同问题高度。通过墙渗透损失取决于壁厚和材料,透过窗户和传播起着重要的作用在这个场景中[12]。由于这些原因,建筑物应该被定义的类型。在旧建筑与普通玻璃窗户,窗户穿透损失可能显著低于粗的现代建筑安全玻璃或金属包覆窗户[13]。

2.2Urban micro-cell O2I 2.2城市微蜂窝O2I

In this scenario, UEs are located below rooftops, typically at 1.5–15 m depending on the heights of surrounding buildings [14]. UEs may be located on different floors of the buildings. The UE velocity in this scenario is about 0–15 km/h. The radio wave propagation can be divided into outdoor propagation, penetration through the wall and indoor propagation. Since UEs can be located on different floors,the path loss and shadowing model should be determined in the channel model [15].

在这种情况下,问题都位于屋顶下面,通常在1.5 -15根据周围建筑物的高度[14]。问题可能会在不同的楼层的建筑。问题的速度在这个场景中0-15 km / h。无线电波传播可以分为户外传播、渗透穿过墙壁和室内传播。自问题可以在不同的楼层,路径损耗和阴影模型应该确定信道模型[15]。

2.3Suburban/rural O2O 2.3城郊/乡村 O2O

It is assumed that this scenario represents radio propagation in large areas (radius up to 10 km) with low building density, and the UE antenna height is typically in the range of 1–3 m, which is similar to the average building height. The UE velocity is assumed to be in the range of 0–20 km/h. In suburban outdoor scenarios, the UE density is lower than that in the urban scenario, and the wave propagation is affected by the natural environment such as forests and mountains, instead of the high buildings and intensive obstacles [16].

假设这个场景代表了无线电传播大面积(半径10公里)建筑密度较低,和问题天线高度一般在1 - 3米的范围,这是类似于普通建筑高度。速度问题被认为是在0-20 km / h。在郊区的户外场景中,问题密度低于在城市场景中,波传播是受自然环境影响,如森林和山脉,

而不是高的建筑物和密集的障碍[16]。

Suburban/rural O2I 城郊/乡村 O2I

In this scenario, the UE antenna height is in the range of 1.5–10 m,which depends on the average height of buildings in suburban or rural environments. The UE velocity is about 0–20 km/h that is higher than urban scenarios. Line-of-sight (LoS) probability from UEs to building walls is high because of wide open areas and low building heights [17]. However, depending on geographical location, there might be heavy vegetation (forest) and/or hilly terrain resulting in low probability of LoS. The penetration loss through the wall is assumed to be several tens of decibels owing to collapsed buildings [18].

在这个场景中,问题天线高度是1.5 -10米的范围内,这取决于建筑物的平均高度在郊区或农村环境。问题的速度大约是0-20 km / h,高于城市场景。视距(LoS)概率问题建筑墙很高,因为开放地区和低建筑高度[17]。然而,根据地理位置不同,可能会有沉重的植被(森林)和/或丘陵地形导致低概率的洛杉矶。渗透损失通过墙被认为是几十分贝的由于倒塌的建筑[18]。

Indoor office indoor-to-indoor (I2I) 室内办公室户内到户内 (I2I)

It is assumed that the UE antennas are located from 0.5 to 3 m up to the ceiling level. The UE velocity is assumed to be in the range of 0–10 km/h. The UEs may be distributed over the same floor in a single building or different floors in a single building and/or in different buildings, which results in different wave propagations. If the UEs are on the same floor, the propagation depends on the walls and obstacles around; if the UEs are on the different floors in a single building, the propagation should consider the penetration caused by the ceilings between different floors; if the UEs are in different buildings, the propagation is divided into outdoor propagation,penetration through the wall and indoor propagation.

假设问题天线位于从0.5到3米的上限水平。速度问题被认为是清廉的km / h。问题可能分布在同一层在一个建筑或不同的层在一个单一的建筑和/或在不同的建筑物,导致不同的波传播。如果问题是在同一层,传播取决于周围的墙壁和障碍物;如果问题在不同的楼层在一个大楼,传播应该考虑不同层之间的渗透造成的天花板,如果问题在不同的建筑物,传播分为户外传播、渗透穿过墙壁和室内传播。

Indoor shopping mall I2I 户内购物中心I2I

The shopping mall propagation scenario consists of an open space,which is surrounded by smaller rooms, for example shops. The open space usually contains some obstructions, such as catering areas and escalators. Additionally, there may be multiple floors with shops located on different floors. The indoor UE height is assumed to be in the range of 0.5–3 m on each floor, 0.5–15 m on different floors and the distance between UEs is 1–30 m or longer depending on the mall size. In open space, it is assumed that there are reflected multi-paths with rather long delays compared to indoor closed space. The movement of UEs is quite slow and the speed varies from 0 km/h (catering area and inside shops) to 10 km/h (walking space and gallery). Additional loss caused by human body shadowing may need to be considered when the shopping centre is crowded. Body effects are, however, missing in the initial channel models. The penetration and diffraction loss through concrete walls as well as glass windows/doors may need to be considered, when interference between indoor and outdoor users has to be taken into account.

购物中心传播场景包括一个开放的空间,周围的小房间,例如商店。开放空间通常包含了一些障碍,比如餐饮领域,自动扶梯。此外,可能会有多个层商店在不同的楼层。室内问题高度的范围被认为是0.5每层3米,0.5 -15在不同的楼层和之间的距离问题外墙面米或更长时间根据商场的大小。在开放空间,它假定有反映归纳相当长的延迟相比,室内封闭的空间。问题的运动比较缓慢,从0公里/小时的速度变化(餐饮面积和内部店)到10公里/小时(步行空间和画廊)。额外的损失造成的人体跟踪时可能需要考虑拥挤的购物中心。身体影响,然而,失踪在最初的信道模型。通过混凝土墙渗透和衍射损失以及玻璃窗户/门可能需要考虑,当室内和室外之间的干扰用户必须考虑。

Urban road vehicle-to-vehicle (V2V) 城市道路车辆到车辆(V2V)

In the urban V2 V scenario, the road consists of two to four lanes, the houses are closer to the curb, and the traffic density is higher than the other scenarios. The V2 V distance is usually smaller than 300 m and the vehicle antenna height is in the range of 1–3 m. In the urban road, the velocity of vehicles is about 0–60 km/h because of the high traffic density [19]. The propagation includes the penetration caused by the cars, buildings and other obstacles along the roadside [20, 21].

在城市V2V场景中,道路由两个四车道,房子靠近路边,交通密度高于其他场景。V2 V通常小于300米,距离和车辆天线高度是1 - 3 m的范围。在城市道路,车辆的速度大约是0-60 km / h因为交通密度高[19]。传播包括渗透造成的汽车,沿着路边建筑物和其他障碍(20、21)。

Highway V2V 高速公路V2V

In the highway V2 V scenario, there are two to four lanes in each direction with few buildings and cars around, thus the velocity is much higher than that in the urban road. The V2 V distance is normally larger than 1 km or ranges from 300 m to 1 km, and the vehicle antenna height is in the range of 1–3 m. In the highway,the velocity of vehicles is 60–120 km/h, which is higher than the road traffic scenario. The propagation is mainly caused by the natural environment and metal fences on the highway side [22].

在高速公路V2 V的场景中,有两到四车道在每个方向上很少有建筑和汽车,因此速度远远高于城市道路。V2 V通常大于1公里距离或范围从300到1公里,和汽车天线高度是1 - 3 m的范围。在高速公路上,车辆的速度是60 - 120公里/小时,这是高于道路交通场景。造成的传播主要是自然环境和金属栅栏在高速公路上[22]。

Based on the aforementioned scenarios, several D2D channel measurement campaigns have been built up recently. These measurement campaigns only cover parts of the above-defined scenarios and more measurements are needed for better understanding of D2D channels. Compared with the conventional cellular link, the major characteristics of D2D channels can be observed in the following two aspects: (i) both the transmitter and the receiver have low elevation antennas in D2D links, whereas either the transmitter or the receiver is located at higher grounds (above rooftops/ceiling/close to rooftops) in the traditional cellular link; (ii) both the transmitter and the receiver could be moving in D2D links (dual mobility), while at most one is moving in a traditional cellular link. These factors contribute significantly to the specific channel propagation characteristics of D2D channels [23].

基于上述场景,几个D2D信道测量运动最近建立了。这些测量运动只覆盖部分上述定义场景和更多的测量需要更好地理解D2D频道。相比与传统的蜂窝连接,D2D渠道的主要特点可以观察到在以下两个方面:(i)发射机和接收机低海拔天线在D2D链接,而发射机或接收机位于更高的理由(上图屋顶天花板/接近屋顶)在传统的蜂窝联系;(2)发射机和接收机可以朝着D2D链接(双移动),而一个正朝着一个传统的蜂窝连接。这些因素显著的特定信道传播特性D2D频道[23]。

Symmetric and low UE antenna height: D2D path loss should reflect the fact that the nodes have nearly the same low antenna height, thus higher signal attenuation can be expected for the same distance between UEs and the probability of LoS against distance is expected to be lowered. The scattering environment on both sides of transmission link has similar statistical properties, such as the distributions of angle of departure (AoD) and angle of arrival(AoA).

对称天线高度和低问题:D2D路径损耗应该反映这一事实节点几乎相同的天线高度较低,因此可以预期更高的信号衰减相同的距离问题和洛杉矶的概率距离预计将降低。散射环境传播双方的链接也有类似的统计特性,如离去角的分布(AoD)和到达角(AoA)。

UE density/proximity: Nearby UEs have high probability of LoS propagation, thus stochastic system level analysis requires the introduction of LoS probability or some break point distance.Interlink dependency is expected to be higher owing to UE proximity. Effect of shadow fading correlation for D2D links and cellular links will be more noticeable.

问题密度/距离:附近的议题LoS传播的概率很高,因此随机系统级分析要求引入洛杉矶概率或一些断点的距离。连接依赖有望更高由于问题接近。影响D2D阴影衰落相关性的链接和蜂窝链接会更明显。

Mobility: Terminals and surrounding objects are moving, which results in large Doppler spread compared with cellular links, where the BS is static and the variation of large-scale channel propagation parameters is over long periods of time.

流动性:终端和周围的物体移动,导致大的多普勒扩散与蜂窝连接相比,b是静态和大规模的信道传播参数的变化在很长一段时间。

All the aforementioned factors have impact on both D2D small-scale and large-scale channel propagation characteristics and should be observed and discussed by channel measurements.Currently, most D2D channel measurements have concentrated on the discovery of the following significant D2D channel characteristics, as shown in Table 2.

所有上述因素影响D2D小规模和大规模的信道传播特性,应该观察和讨论信道测量。目前,大多数D2D信道测量集中在以下重要的发现D2D信道特性,如表2所示。

Large-scale parameter 大数据

The path loss and shadow fading are the main large-scale channel factors that have major impact on D2D channel modelling. One of the WINNER II models [24] is from indoor office measurement,where the access point is located in corridor, and the UE is randomly dropped within the indoor office. The antenna height for both nodes ranges from 1 to 2.5 m. Besides, 3GPP indoor pico model [25] is applicable for indoor hotspot evaluation. This model assumes the access point is located on the ceiling, and the UE is uniformly dropped within the indoor area. One shortcoming is that the antenna is modelled as being installed on the ceiling. These models enable performance comparisons between D2D and other technical solutions and ensure a consistency in simulation results.

路径损耗和阴影衰落是主要的大型渠道因素D2D频道造型产生重大影响。获胜者之一二

世从室内办公室测量模型[24],访问点位于走廊,室内办公室内的随机下降问题。两个节点的天线高度范围从1到2.5。此外,3gpp室内pico模型[25]适用于室内热点评估。该模型假设访问点位于天花板,问题是在室内面积均匀下降。一个缺点是天线建模是安装在天花板上。这些模型支持D2D之间的性能比较和其他技术解决方案和保证仿真结果的一致性。 LoS/non-LoS (NLoS)

Most measurement models include LoS and NLoS models. For NLoS, WINNER II model [24] introduces penetrations of indoor–outdoor propagation, such as light walls, heavy walls and floor loss. So the total path loss depends on the type of link (LoS or NLoS), the type of walls, the number of walls and the number of floors. The propagation model [25] includes the LoS and NLoS components depending on the distance between nodes. Owing to the low antenna heights and I2I scenarios, its formulas for path loss calculation are simple and easy to use for D2D communication evaluation, and the measurements from these LoS and NLoS models are suitable for D2D channel modelling.

大多数测量模型包括洛杉矶和仿真结果模型。对于仿真结果,赢家II模型[24]介绍了室内外传播、渗透等光墙,沉重的墙壁和地板上的损失。所以总路径损耗取决于类型的链接(洛杉矶或仿真结果),类型的墙壁,墙壁和地板的数量。传播模型[25]包括洛杉矶和仿真结果组件根据节点之间的距离。由于较低的天线高度和I2I场景,其路径损耗的公式计算简单和易于使用的D2D沟通评估,这些洛的测量和仿真结果模型是适合D2D信道模型。

K-factor K-因子

For the ITU UMi and InH channel models [25], the Ricean K-factor (in dB) is modelled as a Gaussian random variable with fixed mean and standard deviation values for LoS. It should be noted that, while most works reported in the literature confirm the choice of a fixed mean K-factor, there are some works (e.g. [26, 27]) that propose linear distant-dependent models for the mean K-factor for LoS scenarios. Unfortunately, no other measurement-based results are available in the literature to validate the sensitivity suggested by the model in [26]. Local scatterers around the transmitter and the receiver (because of low elevation antennas) in D2D channel create different K-factor characteristics compared with traditional communication links.

ITU UMi和异烟肼信道模型[25],Ricean的增殖系数(dB)建模为高斯随机变量与洛杉矶的固定的平均值和标准偏差值。应该注意的是,虽然大多数工作报告在文献中确定一个固定的选择意味着增殖系数,有一些作品(例如(26、27)),提出线性distant-dependent模型为洛杉矶的情况意味着增殖系数。不幸的是,没有其他文献中出现的计量结果来验证提出的灵敏度模型[26]。当地散射在发射机和接收机(因为低海拔的天线)在D2D信道创建不同的增殖系数特征与传统的通信链路。

Delay spread 延迟扩散

For the power delay profile (PDP), the most relevant prior works in the literature have adopted single-slope exponential PDP, including the 3GPP SCM models [28]. This has been confirmed by results from different measurement campaigns. However, dual-slope exponential models would be more accurate to represent scenarios with large number of scatterers [29]. One shortcoming of these models is that they assume that the first path is always the strongest path which may not be true in general, especially for O2I and even I2I scenarios. In [30], the authors propose a model to accommodate the case when the first path may not be the strongest path. However, further independent measurement-based results would be necessary to verify the

validity and the choice of the model parameters.

电力延迟(PDP),之前最相关的文献中采用斜坡指数PDP工作,包括3 gpp SCM模型[28]。这已经被从不同的测量结果证实运动。然而,双斜率指数模型更准确代表场景与大量的散射[29]。这些模型的一个缺点是,他们假设第一个路径永远是最强的路径可能不是真的,特别是对于O2I甚至I2I场景。在[30]中,作者提出一个模型来适应情况下当第一个路径可能不是最强的路径。然而,进一步独立计量的结果将是必要的来验证模型的有效性和选择参数。

Doppler spread 多普勒传播

Dual mobility causes different Doppler characteristics compared to the traditional cellular links. Therefore Doppler spectrum for link level simulations [31] and 3GPP SCM [28] for system level simulations should be modified to reflect the changing of Doppler conditions. The D2D links experience larger Doppler spread which is caused by mobility of UEs at both sides and by the moving surrounding objects. In the literature, V2V channels are commonly characterised by means of RMS delay and Doppler spreads [32],and the Doppler power spectral density. Regarding the RMS delay spread, the smallest value is obtained in rural environments [33],and the largest value is obtained in urban environments [34].

双流动导致不同的多普勒特征相比,传统的蜂窝连接。因此多普勒频谱对链路级仿真[31]和3 gpp SCM系统级模拟[28]应该被修改,以反映变化的多普勒条件。D2D链接经历较大的多普勒扩散引起的流动性问题的双方和周围的移动对象。在文献中,V2V渠道通常为特征的RMS时延和多普勒扩散[32],和多普勒功率谱密度。关于RMS时延扩展,获得的最小值是在农村环境[33],在城市环境中获得最大的价值[34]。

Angle spread 角传播

In D2D communications, the link is symmetric, so both transmitter and receiver see a similar environment, and should have similar distributions for all parameters [35]. In typical cellular scenarios,the statistical angular properties of AoD (BS side) and AoA (UE side) are different [36]. Typically, AoD has smaller angle spread to account for the fact that the BS has higher antenna height and is further away from scatterers surrounding the user terminal [28, 37,38]. In D2D studies, UEs have similar antenna height and thus are likely to have similar scattering environments. To account for this fact, the D2D link in terms of AoD and AoA statistics is symmetrical and uses the parameters recommended for AoA, that is UE side, at both sides of the link [23].

D2D通信的链接是对称的,所以发射机和接收机都看到类似的环境中,并且应该有相似的分布参数[35]。在典型的蜂窝场景中,大气气溶胶的统计角度特性(BS)和AoA(问题)是不同的[36]。通常,大气气溶胶有小角蔓延至占BS具有更高的天线高度和远离周围散射用户终端(28日37、38)。在D2D研究中,问题也有类似的天线高度,因此可能会有类似的散射环境。考虑到这一事实,D2D链接在大气气溶胶和AoA统计信息是对称的,并使用参数推荐AoA,问题方面,双方的联系[23]。 3 D2D channel models

3 D2D信道模型

According to the analysis in Section 2, compared with conventional cellular channels, D2D channels have unique characteristics. This results in that modelling approaches and corresponding channel models for cellular systems cannot be directly used for D2D systems. Unlike a rich and fascinating history of the research in cellular channels, the investigation of D2D channel

modelling is still in its infancy.

根据第二部分的分析，与传统的蜂窝信道相比，D2D信道有其独特的特点。这导致建模方法和相应的信道模型不能直接用于D2D蜂窝系统。蜂窝信道有着丰富多彩的历史研究，与它不同的是，D2D信道造型仍处于起步阶段。

Based on the understanding of D2D propagation characteristics via either theoretical analyses and/or channel measurements, we can develop accurate yet easy-to-use channel models. We classify existing channel models in terms of their respective modelling approaches. The majority of existing channel models in the literature can be categorised as deterministic models and stochastic models, whereas the later ones can be further categorised as geometry-based stochastic model (GBSM) and measurement-based pseudo-geometric model (MBPGM) (Table 3).

基于对D2D传播特性的理解，通过理论分析和信道测量,我们还可以开发精确的易于使用的信道模型。根据各自的建模方法我们对现有的信道模型进行分类。著作中现有的大多数信道模型可以归类为确定性模型和随机模型，而后来的可以进一步归类为几何随机模型(GBSM)和伪几何计量模型(MBPGM)(表3)。

Deterministic channel models characterise D2D channelparameters in a purely deterministic manner, for example, ray-tracing approach [39]. This modelling approach needs an accurate database, high computation time and use approximations of the Maxwells equation for its solution [40]. Therefore deterministic channel model typically has high complexity and cannot be easily generalised.

确定的信道模型以确定性的方式描述D2D信道参数，例如射线跟踪方法。这种造型方法需要一个准确的数据库、大量的计算时间以及使用麦克斯韦方程的近似解决方案。因此确定的信道模型通常具有较高的复杂性并且不易被普及。

The MBPGM is entirely based on channel measurements. Examples of MBPGM include the widely used SCM and WINNER models, as well as the recently developed COST 2100 model [41]. More recently, by considering D2D communication environments, 3GPP proposes D2D MBPGMs [42] based on the modifications of WINNER model [24]. However, owing to the nature of MBPGM, the 3GPP D2D channels model cannot easily mimic some important characteristics owing to the dual mobility, for example, scatter drifts and transitions between different propagation environments or between LoS and NLoS scenarios. Note that the widely used SCM, COST 2100 and WINNER models are often mistakenly referred to as GBSMs.Such misunderstanding is largely due to the fact that the main channel parameters are all related to scatterers/clusters, for example, AoA/ AoD and angel spread. However, note that all these scatterer/ cluster-related parameters are actually obtained solely based on measurements, instead of predefined stochastic distributions of the scatterers/clusters. Therefore all the SCM, COST 2100 and WINNER models are more properly classified as stochastic parameter models. In the following, we will give a brief introduction of those widely used MBPGMs.

伪几何计量模型完全基于信道测量。MBPGM的例子包括广泛使用SCM和WINNER模型以及最近发展的COST2100模型。最近,通过考虑D2D通讯环境,3 gpp基于修正后的WINNER模型提出D2D伪几何计量模型。然而,由于MBPGM双重流动特性,3 gpp D2D信道模型无法轻易模仿一些重要特征，例如,不同的传播环境之间以及仿真结果之间的散射漂移和转换。值得注意的是，广泛使用的SCM,COST2100和WINNER模型经常被错误地称为几何随机模型。这种误解很大程度上源于这样的事实,主要信道参数都与例如AoA /AoD以及角差这样的散射/集

群有关。然而,需要注意的是,所有这些散射体相关的参数实际上完全是通过测量获得的，而不是预先定义的随机分布的散射/集群。因此，所有的SCM,COST2100和WINNER模型归类为随机参数模型更合适。接下来,我们将对广泛使用的伪几何计量模型做一个简要介绍。

WINNER channel model: In WINNER channel models [24], the basic principle is that for every link the large-scale parameters, fox example, angular spreads, are taken from a map. In that way the correlation properties of those parameters are matched with those observed in measurements. However, the small-scale parameters,for example, AoA and AoD, are randomly drawn from a distribution, independently for each link. This means that even close-by links have independent values for for example AoA and AoD, which is of course not the case in reality. This spatial inconsistency is not problematic with the quasi-stationary modelling of WINNER (drop concept), but it has an impact on performance with for example multi-user multi-input multi-output (MIMO) case. The spatial inconsistency also means that the WINNER approach does not handle time evolution very well. New set of parameters are randomly drawn at each location of a mobile, and there is no smooth transition between two locations. This means that dynamic simulations are problematic. Interpolation between two locations is of course possible. The interpolation can be done by drawing random small-scale parameters, such as cluster delays, powers, directions and so on, to two UE locations and linearly interpolating parameter values in between locations. A problem may result because interpolated values are always between the original values, and thus with interpolation all distributions become narrower. WINNER models also do not specify transitions between different propagation environments (urban, rural, outdoor, indoor and so on) or between LoS and NLoS, which also create spatial inconsistency and unrealistic transients.

WINNER 信道模型：在WINNER 信道模型中每一个环节的基本原则是,大规模的参数,例如角差,都来自映射。那些参数的相关属性以这种方式与观测值相匹配。然而,每一个独立链接的小规模参数,例如AoA和AoD,都是随机分布的。这就意味着即使靠近链接也有独立的值,例如AoA和AoD,当然现实中并非如此。这种不一致不影响似稳的WINNER模型,但它对有些情况下的性能有影响,例如多用户多输入多输出(MIMO)的情况。这种不一致也意味着WINNER模型不能很好地处理时间演化。新设置的参数在每个位置随机移动，而且在两个位置之间没有平稳的过渡。这意味着动态模拟是有问题的。两个位置之间插值是可以实现的。两位置之间的线性插值可以通过规划如集群延迟和力的方向等小规模随机参数来实现。由于总是在两个原始数据之间插值,因此可能导致插值分布变得越来越窄。WINNER模型没有指出不同传播环境(城市、农村、户外、室内等)之间以及仿真结果之间的转换，这也导致了空间不一致以及不切实际的转变。

3GPP D2D channel model: In December 2012,a study item on LTE Device to Device Proximity Services was established in 3GPP[42].Several channel models Were proposed for D2D in 3GPP RAN1 meetings in Malta, Chicago,and Fukuoka during the first half of 2013 and the agreement was as follows: symmetric angular spread distribution and dual mobility corrections; direction of travel (velocity vector) independent and random; Doppler is determined by path AOA/AOD; uniform AoA spread of 104°.

3GPP D2D信道模型：2012年12月,一项关于LTE服务设备方面的研究项目成立了3GPP。2013年上半年在马耳他、芝加哥以及福冈RAN1会议上，提出了几种3GPP D2D信道模型,协商结果如下：对称角的扩散分布以及双重流动性修正;运动(速度矢量)的独立性和随机方向;多普勒由AOA /AOD路径决定;相同的AOA都以104°传播。

COST 2100 channel model: The COST modelling approach is as such not necessarily aimed

to D2D, because COST channel models are designed with one end of the link fixed [41]. The channel model could be composed of a set of randomly drawn clusters with all the parameters drawn from probability distributions which are extracted from channel measurements. Cluster would have visibility regions as in the COST model. Each cluster would be coupled to a subset of other clusters. If two radios enter visibility regions of coupled clusters the radio signal propagates interacting with the clusters. A consistent model has a proper number of `active' clusters with proper characteristics for each possible set of locations of transceivers. Some clusters can also be moving [43].

COST2100信道模型:COST模型方法不一定旨在D2D,因为COST模型设计的一端固定连接。信道模型可以由一系列集群和所有从信道测量中提取出来的概率分布参数随机组成。在COST信道模型集群有它的可见区域。每个集群将被耦合成其他集群的一个子集。如果两个收音机输入可见区域耦合的集群，无线电信号的传播将与集群交互。一致的模型在每个收发器的位置有适当数量的“活跃”特色集群。其中一些集群也是可以移动的。

The GBSM is derived from some predefined stochastic distribution of the scatterers/clusters by applying the fundamental laws of wave propagation. Such models can be easily adapted to diverse scenarios by modifying the stochastic distribution and properties of scatterers/clusters and the shape of the scattering region. GBSMs can be further classified into regular-shaped GBSMs (RS-GBSMs) [44一9] and irregular-shaped GBSMs (IS-GBSMs) [50, 51] depending on whether scatterers/clusters are placed on regular shapes, for example, two-sphere and two-cylinder, or irregular shapes. Its direct involvement of scatterers/clusters renders GBSM, one of the most promising candidates for D2D channel modelling. However, compared with MBPGM, GBSM is a bit more complicated, which blocks the development of D2D GBSM.

GBSM来源于一些遵从应用波传播基本规律而预定义的随机分布的散射/集群。这样的模型可以通过修改随机分布和散射的特性/集群以及散射区域的形状后很容易地适应不同的情况。几何随机模型根据散射/集群放置的常规形态，进一步分为形状规则的几何随机模型和形状不规则的几何随机模型，例如，二维球、二维圆以及不规则形状。直接参与的散射/集群呈现出GBSM,这是D2D信道模型最有前景的模型之一。然而,与MBPGM相比,GBSM有些复杂,这阻碍了D2D GBSM的发展。

4 D2D channel simulation and analysis 4 D2D信道的模拟与分析

In this section, we choose GBSM as an example to show some important channel properties for various D2D scenarios. Both RS-GBSM and IS-GBSM are considered in these simulations. Basic parameters used in simulations for RS-GBSM are as follows: both transmitter and receiver have two antenna elements, the carrier frequency is set to 5.25 GHz, because that some of traditional measurements on D2D channels for vehicular systems are in this band, and the scatterers are symmetrical around the transmitter and receiver, while basic parameters used in simulations for IS-GBSM are as follows: both transmitter and receiver have two antenna elements in the form of uniform antenna array, the centre frequency is 5.25 GHz, and the distance of the antenna is O.SA, different antenna distances are compared in the simulation of RS-GBSM, whereas in the simulation of IS-GBSM, we choose its mid-value to make more reasonable compansons.

在本节中,我们选择GBSM作为一个例子来展示D2D场景一些重要模型属性。这些模拟同时考虑了RS-GBSM和IS-GBSM。用于模拟RS-GBSM的基本参数如下:发射机和接收机都有两

个天线元素,载波频率设置为5.25 ghz,因为在D2D中用传统方法测量车辆系统也在这个分支中,散射对称地分布在发射机和接收机周围,而IS-GBSM用于模拟的基本参数如下:发射机和接收机都有两个形式统一的天线阵,中心频率为5.25 ghz,天线的距离是0.5 l,不同的天线距离在RS-GBSM模拟中被拿来相互做比较,而在模拟IS-GBSM时,为了做更合理的比较，我们选择它的中值。

4.1 D2D channel properties for RS-GBSM 4.1 D2D RS-GBSM信道属性

Based on Figs. 2-6, we can observe that the spatial correlation decreases with the increase of the distance between the transmitter and the receiver. As shown in Fig. 2a, the spatial correlation of three-dimensional (3D) two-sphere model is stronger than that of the two-dimensional two-ring and 3D two-cylinder models, which caused by the rich scatterers around the transceiver. In Fig. 2b, the spatial correlation of LoS case is stronger than the NLoS case, and the spatial correlation increases with the Ricean K-factor. From Fig. 3, it can be noted that the increasing of the scatterers radius around the transceiver directly causes the decreasing of spatial correlation. Because under the same scatterers distribution, when the scatterers radius is expanded, the scatterers density is decreasing and leads to low correlations. As illustrated in Figs. 4a and b, the distribution of elevation angle significantly influences the spatial correlation properties. Compared with the case of uniform distribution, because of the truncated Gaussian and Laplace distribution concentrate on the elevation angle in a narrow range, the spatial correlation decreases rapidly, and gradually levelling off, meanwhile, the spatial correlation of cosine distribution is relatively large. As demonstrated in Figs. 4c and d we can observe that when the angle spread is large, because of the multipath reflection and scattering, the angle spread at the receiving antenna is broadened, which caused the spatial correlation decreasing. As shown in Fig. 5a, the speed of the transceiver has significantly influences on the power spectrum density (PSD). Fig. 5b clearly proves that the high velocity of UEs causes obvious spread of PSD. While as Fig. 6 illustrates, the other factors such as the horizontal and elevation angle distribution and angle spread have less influence on the PSD properties.

基于图2 - 6,我们可以观察到空间相关性随着发射机和接收机之间距离的增加而增加。正如在图2中显示的那样,三维双球体模型的空间相关性比二维的双环和三维的两缸模型强,这种现象是由于收发器周围有丰富的散射。在图2 b中,视距情况下空间相关性的影响由于莱斯K因子系数的增加而强于非视距情况下。图3可以指出收发器周围散射半径增加直接导致空间相关性的减少。因为在相同的散射分布下，当散射半径扩大时散射密度减少,这导致相关性的降低。图4a和b说明仰角的分布显著影响空间相关性属性。与均匀分布的情况相比,由于在仰角方向上截断高斯拉普拉斯集中分布的范围，空间相关性迅速减小,并逐渐趋于平缓,与此同时,余弦分布有较大的空间相关性。图4 c和d表明，正如我们所观察到的那样，接收天线的多路径反射和散射导致空间相关性降低。图5a表明收发器的速度大大影响功率谱密度(PSD)。图5b显然证明了高速度会明显影响PSD的传播。图6说明其他因素如土地卧式锻机仰角分布和角度扩散对PSD属性影响较小。 4.2 D2D channel properties for IS-GBSM 4.2 D2D IS-GBSM信道属性

In IS-GBSM, the effect of the parameters on the statistical properties is limited. Owing to the randomness of the scatterers, we compare the results based on the same scatterers distribution. From Fig. 7a, it is clear that if we select a relatively large update time, the PSD would be changed

significantly. This is caused by the randomness of the scatterers and the time-varying modelling method. If we choose a small update time, the channel can be seen as wide sense stationary in the whole update time. We can know from Fig. 7b that the speed of the transceiver has obvious effect on the PSD properties. When the speed of the transceiver is faster, the impact on the Doppler correspondingly becomes larger, which will change the shape of the Doppler spectrum. As illustrated in Fig. 8a, the fixed scatterers have no significant effect on the PSD properties, the fixed scatterers randomly distribute around the transceiver, the distribution of the fixed scatterers does not substantially change the Doppler properties. As demonstrated in Figs. 8b and c, the density of the scatterers does not effect the PSD, because the impulse response is obtained in a small update time, the increase of the scatterers density is equivalent to doing the interpolation on the existing impulse response. Then the spatial correlation properties are illustrated in Figs. 9 and 10. The density of the moving scatterers has the most significant impact on the spatial correlation whereas the influence of other parameters such as the transceiver speed, the fixed scatterers distribution and density is not so obvious.

在IS-GBSM中，参数对统计特性的影响是有限的。由于散射的随机性,我们对有相同的分布散射结果作比较。从图7a中可以很明显看出,如果我们选择一个相对跨度较长的更新时间,PSD将显著改变。这是由散射的随机性以及时变模型导致的。如果我们选择一个小更新时间,信道可以被视为在整个更新时间内几乎是固定不变的。我们可以从图7 b知道收发器的速度对PSD的特性有明显的影响。当收发器的速度变快,对多普勒的影响也相应变大,这将改变多普勒频谱的形状。如图8中所示,固定散射PSD性质没有显著的影响,在固定散射随机分配在收发器周围，,固定散射的分布没有显著改变多普勒特性。图8 b和c说明,散射的密度不影响PSD,因为在一个小更新时间内获得脉冲响应，散射增加的密度相当于做现有脉冲响应的插值。接下来，空间相关性的属性在图9和10也展示出来。移动散射的密度对空间相关性的其他参数有很重要的影响,如收发速度、固定散射分布和密度不是那么显而易见。 4.3 Comparison between RS-GBSM and IS-GBSM 4.3 RS-GBSM和IS-GBSM的比较

From the simulation results, we can make a comparison between RS-GBSMs and IS-GBSMs. RS-GBSM can reflect the spatial correlation caused by the angle difference between the signal and the antenna array in the MIMO system, as well as the Doppler change caused by the speed of the transceiver, the horizontal angle distribution and angle spread. The IS-GBSM is based on the most real scatterers environment, which can also reflect the MIMO spatial correlation properties. The biggest advantage of IS-GBSM method is that it enables the analysis of the PSD properties caused by the speed of the transceiver, the horizontal angle distribution and angle spread. In general, RS-GBSMs are used for theoretical analysis of channel statistics and theoretical performance evaluation of D2D communication systems. Different from RS-GBSMs, IS-GBSMs intend to reproduce the physical reality and thus need to modify the location and properties of the effective scatterers of RS-GBSMs. Therefore IS-GBSMs are actually a greatly simplified stochastic version of ray-tracing method but still suitable for a wide variety of D2D scenarios by properly adjusting the statistical distributions of effective scatterers.

根据仿真结果,我们可以对RS-GBSMs和IS-GBSMs做个比较。RS-GBSM可以反映MIMO系统中由天线阵的信号和分布式天线系统引起的空间相关性的变化，以及由收发机的速度、水平角分布和角扩散改变引起的多普勒变化。IS-GBSM基于最真实的散射环境,从而也反映了天线系统的相关性属性。IS-GBSM方法的最大优点是,它能分析由收发机的速度,水平角分布

和角扩散改变引起的PSD属性。一般来说,RS-GBSMs用于D2D通信系统的频道统计和绩效评估理论分析。不同于RS-GBSMs ，IS-GBSMs打算再现现实,因此需要修改RS-GBSMs有效散射的位置和属性。因此IS-GBSMs实际上是大大简化后的随机版本的射线追踪方法,但通过适当调整有效散射的统计分布仍适合各种D2D场景。

5 Future challenges in D2D channel measurements and modelling 5 D2D信道测量和建模未来挑战

Recently, in many channel modelling related forums, workshops and conferences, for example, COST IC1004 forum, D2D channel measurements and modelling are very hot topics and attract more and more research interests. Currently, one common understanding is that none of the existing channel models comprehensively covers the D2D scenario with adequate accuracy in all dimensions.More importantly, current modelling approaches are not sufficient in describing the unique D2D channel characteristics. The challenges discussed in this section can be considered as guidelines for setting up future measurement campaigns and proposing more realistic D2D channel models.

最近,在例如COSTIC1004论坛等这样的与造型相关的论坛、研讨会上,D2D信道测量和建模都是非常热门话题,引起人们越来越多的研究兴趣。目前已有的共识是，现有的信道模型都不能全面涵盖在所有维度上有足够精度的D2D场景。更重要的是,目前的建模方法在描述D2D信道模型的特征时是不足够。本节中讨论的挑战可以视为建立未来测量活动和提出更现实的D2D信道模型的指南。

The first challenge is how to develop a general yet easy-to-use channel model for various D2D scenarios. As shown in Table 1,in general D2D communications have ten scenarios. In fact, the scenarios can be well beyond the listed ones if detailed propagation environments are taken into account. Currently, no channel model has the ability to cover all these scenarios. For better design of D2D systems and fair comparison of different D2D technologies, it is desirable to propose general D2D channel models for various scenarios. To this end, more measurement campaigns should be built up for different D2D scenarios, discovering unique D2D channel characteristics. Based on the observation and analysis from huge measurement data, one can develop a general D2D channel model by using geometry-based modelling approach or measurement-based pseudo-geometric modelling approach. Recently, 3GPP has made their efforts on the development of D2D channel models according to the modifications of WINNER model.

第一个挑战是对各种D2D场景如何开发一个通用而易用的信道模型。如表1所示,一般来说D2D通信有十个场景。事实上,如果考虑详细的传播环境，场景可以不止列出的这些。目前,没有信道模型能涵盖所有这些场景。为了更好地设计D2D信道系统以及对不同的D2D技术进行公平的比较,应该对各种场景提出更通用的D2D信道模型。为此,对于不同D2D场景应该建立更多的测量活动，以发现其独特的信道特点。根据对庞大测量数据的观察和分析，我们可以采用线性几何造型方法或伪几何计量造型的方法开发一个通用D2D信道模型。最近,3gpp根据WINNER的修改模型努力发展D2D信道模型。

Followed up by the above-mentioned challenge, another important challenge is how to implement a channel model that can run multiple D2D scenarios simultaneously. As shown in Table 1, D2D channels should rely on different scenarios to characterise different links. So far, 3GPP D2D channel model is the only model officially announced by 3GPP group and follows the basic modelling approach of WINNER model. Therefore the current implementation of the 3GPP D2D channel model does not allow simultaneous simulation of

multiple links in different scenarios. Instead, to evaluate the performance of a network containing different scenarios, multiple drops should be run, one for each scenario, and merged afterward. This approach can be justified for conventional point-to-point cellular systems, where an end-to-end transmission involves only one link. However, this approach cannot be used in D2D systems, where an end-to-end transmission involves multiple heterogeneous links simultaneously. By introducing the UE's moving track and the location of clusters/scatters, COST2100 channel model seems to be one possible solution for this challenge. However, COST2100 is not designed for D2D channels and thus significant modifications are necessary.

紧接着上述挑战的另一个重要的挑战是，如何实现一个可以同时运行多个D2D场景的信道模型。如表1所示,D2D模型应该依靠不同的场景来描述不同的链接。到目前为止,3GPP D2D信道模型是唯一由3 GPP集团正式宣布遵循WINNER模型的基本建模方法。因此当前实现的3gpp D2D信道模型不允许同时在不同的场景中模拟多个链接。相反,为了评估包含不同场景的网络性能,应该运行多个单独以及合并后的场景。这种方法可以判断传统的从一个端到另一端的传输只有一个链接的点对点蜂窝系统是否合理的。然而,这种方法不能用于从一个端到端的传输同时涉及多个异构链接的D2D系统。通过对问题的跟踪以及引入散射/集群的移动位置,COST2100信道模型似乎是这一挑战一种可能的解决办法。然而,COST2100不是专为D2D信道设计的,所以大量的修改是必要的。

Actually, an ideal channel model would be closer to ray-tracing-based model, for example, deterministic ray-tracing channel model or GBSM. Considering the complexity issue and the ability to cover various communication scenarios, GBSM expresses obvious advantages over deterministic ray-tracing model. As mentioned in Section 3, COST2100 actually belongs to MBPGM. Therefore the development of D2D GBSMs may be better solution of the aforementioned two challenges. Taking into account the complexity aspect and the nature of propagation channels, a new D2D channel modelling approach is desirable. The new D2D channel modelling approach should combine the advantages of both MBPGM and GBSM. The development of such new D2D modelling approach is possible but challenging. COST 2100 model can be considered as an attempt which properly incorporates the UE's moving track and the location of clusters/scatters into conventional MBPGM. To better design this new modelling approach, it is important to find a proper way of introducing the scatter/cluster location model and the transition model among different propagation scenarios into current MBPGM.

实际上,一个理想的信道模型接近基于射线追踪的模型,例如,确定性射线跟踪模型或GBSM。考虑涵盖各种通信场景的复杂性问题时,GBSM表达明显优于确定性射线跟踪模型。正如在第3部分提及的,COST2100模型实际上属于MBPGM模型。因此发展D2D GBSMs可能是解决前面提到的两个挑战较好的方案。考虑到传播渠道的复杂性以及本质,可以采取一个新的D2D信道建模方法。新的D2D信道建模方法应结合MBPGM和GBSM的优点。这种新的D2D造型方法的发展是可能的但具有挑战性。COST2100模型尝试把跟踪到的问题以及集群/散射移动的位置与传统MBPGM的正确合并。更好地设计这种新的建模方法,重要的是在不同传播场景下找到一种引入散射/集群位置的模型并且把这种模型过渡到当前MBPGM。

To solve the aforementioned challenges, we have to face some other key challenges regarding channel measurement and channel data analysis and post-processing. For example, how to properly build up measurement campaign to measure various D2D scenarios while keeping the spatial consistency, how to measure multiple D2D pairs communication scenarios and

the impact to each other, how to obtain the accurate location of clusters/ scatterers from measured data etc. All these challenges necessitate innovation and breakthrough in the fields of channel measurements and channel data analysis and post-processing.

为了解决上述挑战,我们不得不面对一些关于信道测量、信道数据分析和后期处理的关键挑战。例如,如何在保持空间一致性的同时正确测量各种D2D场景,如何衡量多个D2D通信场景以及它们的相互影响,如何从测量数据中获得集群/散射的精确位置等。所有这些挑战需要在信道测量、信道数据分析和后期处理领域有创新和突破。 Conclusions 结论

In this paper, we put together a comprehensive overview of state-of-the-art D2D channel research, in order to facilitate channel-oriented D2D communication system design and optimisation. We first summarised D2D channel measurements in more than ten application scenarios, and provided in-depth discussion on their associated key parameters. Then, we critically reviewed and compared a number of prevalent channel models and their feasibility in D2D scenarios. We also used GBSM as an example to demonstrate certain channel characteristics for various D2D environments. Future challenges in this research area are also discussed in detail.

在本文中,我们建立一个最先进的综合概述D2D信道研究,为了方便channel-oriented D2D通信系统设计和优化。我们首先总结D2D信道测量在十多个应用程序场景,并提供深入讨论相关的关键参数。然后,我们回顾并比较严重的普遍在D2D场景中信道模型及其可行性。我们也使用GBSM作为一个例子来演示某些信道特性对各种D2D环境。在这个研究领域未来的挑战也详细讨论。 Acknowledgments 鸣谢

This work was jointly supported by the National 973 project (Grant no. 2013CB336700), the National Natural Science Foundation of China (Grant no. 61101079, 61222105, and 61471268), the Science Foundation for the Youth Scholar of Ministry of Education of China (Grant no. 20110001120129), the National 863 Project (Grant No.2014AA01A706), the State Key Laboratory of Rail Traffic Control and Safety (RCS2014K008, CS2014ZT11 and RCS2011ZZ002), Beijing Jiaotong University, and the project [13510711000] of the Science and Technology Commission of Shanghai Municipality “System design and demo-construction for cooperative networks of high-efficiency 4G wireless communications in urban hot-spot environments”.

这项工作是共同支持的国家973项目(批准号2013 cb336700),中国国家自然科学基金(批准号61101079,61101079,61471268),青年学者的科学基金会中国教育部(批准号20110001120129)、国家863项目(格兰特No.2014AA01A706),轨道交通控制与安全国家重点实验室(RCS2014K008 CS2014ZT11和RCS2011ZZ002),北京交通大学,项目[13510711000]上海市科委的“系统设计和demo-construction合作高效4 g无线通信网络在城市热点环境”。

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