## 1 Introduction

The importance of reference channel models (RCMs) as a distinct kind of radio channel modeling has already been widely realized [*Almers et al*., 2007]. RCM's purpose is to adequately simulate typical radio environment properties and thus be used as a test platform for the development of new generations of access radios, exploring modulation and coding techniques, different smart antenna and multiple-input multiple-output (MIMO) system designs, etc. Most of the commonly used RCMs are stochastic, or more precisely, geometry-based stochastic channel models (GBSCMs). This means that GBSCM parameters are generated from some stochastic process [*Almers et al*., 2007; *Haneda et al*., 2011]. Therefore, these models suffer the risk of unrealistic channel realizations due to their random nature and of inaccuracies of the parameterization extraction approximation.

Current GBSCMs are mostly measurement based, so prior to parameterization, data from real-world measurements are needed. Besides the fact that measurements are time consuming and expensive, additional limitations are caused by antenna properties, phase synchronization, measurement errors, and random events that could be present only while specific measurement is taking place and especially if measurements have not been repeated on the same route or measurement set, which is shown in earlier studies [*Molisch et al*., 2006; *Asplund et al*., 2006; *Correia*, 2006; *Sirkova*, 2006].

Deterministic RCMs are suggested as a possibility in earlier paper [*Katalinić Mucalo et al*., 2012], but they are still not explored enough as an achievable option for RCMs, mainly due to their complexity and vast system requirements. The feasible alternative for feeding geometry-based deterministic RCM would be a set of ray tracing (RT) simulated environments. Ray tracing allows high-resolution simulations, thus providing a very detailed description of the radio environment and the propagation phenomena. However, RT is a very time-consuming process with extremely high demands for both CPU time and memory capacities, in order to store and manipulate all the data necessary for a very fine spatial resolution. Also, RT computational burden grows significantly with the number of considered receiver points. In this paper it is elaborated how to decrease stored RT data and enable interpolation of omitted receiver points while ensuring even higher resolution than the ones originally sampled. These, at first hand, contradictive aims are achieved by smart interpolation process using ray entity concept that in the end decreases needed computational time and complexity, while preserving the accuracy of the full ray-tracing model.

The paper analyzes the arrangement of the rays in an urban multipath environment and in particular, virtual sources in cases of reflection and diffraction propagation with up to two interactions. Similar work on ray dynamics in multipath environment has already been done in [*Katalinić Mucalo and Zentner*, 2011] and showed that due to the nature of diffraction, there is no common stationary virtual source of neighboring rays even when ending very close (below 1 m) to each other and in spite of undergoing identical multipath interactions. In that work, any considerable visibility length could be obtained only by approximation using tolerance, basically approximating VTx locus, which is part of a circle, by a point. The motivation for that was to obtain parameters for stochastic based reference channel models that incorporate stationary clusters, i.e., virtual sources, but no virtual sources that move in correlation with user movement. Further in the paper, ray entity definition will be given and will be different from one in [*Katalinić Mucalo and Zentner*, 2011].

Appreciating the finding that diffraction causes virtual source of rays to move in correlation to moving of receiver [*Zentner et al*., 2013], this paper analyzes a new method for detection of visibility area and virtual sources for moving receivers. The paper elaborates the method for determining trajectories of virtual sources and how those trajectories can be utilized for the interpolation of RT results. The paper is limited by taking into account only direct rays and reflected and diffracted rays up to two interactions per ray. Although a 3-D RT tool [*Degli-Esposti et al*., 2004; *Fuschini et al*., 2008.] used for feeding the interpolation engine is calculating both diffuse scattering and over-the-roof diffraction, in this paper these two propagation modes are not considered. However, the discussion and presented concepts are easily extensible to these propagation phenomena as well.

The paper is organized as follows: In section 2, the concepts of reflection and diffraction propagation phenomena, ray entities, and virtual sources will be shown as well as using those concepts for the interpolation of RT results. In section 3, ray entity detection will be explained for three examples in an urban scenario, and the statistics of ray entity lengths will be given. Section 4 will discuss receiver and virtual source trajectories that are needed so that interpolation of ray length, angles, and power can be interpolated for enhancing RT performance and as a building block of deterministic RCM. The paper ends with conclusions given in section 5.