Page 56 - ITU Journal: Volume 2, No. 1 - Special issue - Propagation modelling for advanced future radio systems - Challenges for a congested radio spectrum
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ITU Journal: ICT Discoveries, Vol. 2(1), December 2019




                                                               for the result of    and   . It is common in the existing lit-
                                                               erature that the authors start analyzing the channel from
                                                               the delay domain, and the azimuth spread has some cor-
                                                               relation with the delay [8, 9]. In this paper, we believe
                                                               that it is worthy of focusing more on the features of the
                                                               directionality of the wireless channel.











           Fig. 4 – The methodology of directional antenna channel modeling.
          5.  DIRECTIONAL        ANTENNA       CHANNEL
              MODELING

          In this section, we propose the directional antenna chan-
          nel model. Firstly, we will introduce the clusters of the
          rays at the receiver. Then we will present the channel    (a) Visualized clustered rays in the ray tracing simulator.
          model with three key parameters: 1) the number of clus-
          ters and its PMF; 2) the cluster center (in degrees) in the
          azimuth domain and its PDF; 3) the cluster power ratio
          (in dBm) and its PDF.

          5.1 Clusters of the incident rays
          The received signal propagated in a directional antenna
          channel model can be characterized by the summation of
          the clusters, and each cluster can be further characterized
          by the summation of all the rays within the cluster:


                              ℳ  ℛ m
              (   i, m ,    i, m ,    i, m ) = ∑ ∑    i, m (   i, m ,    i, m ,    i, m )  (2)  (b) Clustered rays in the delay domain.
                                ⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟
                             m=1 i=1
                                        m (   i, m ,   i, m ,   i, m )
          where   ,    and    are the delay, DOA and power ratio of
          the received signal respectively, the subscript m and i rep-
          resent the corresponding parameters of the m-th clus-
          ter and the i-th ray. The terms    i, m (   i, m ,    i, m ,    i, m ) and
             (   i, m ,    i, m ,    i, m ) represent the received signal of each
          m
          ray and each cluster, respectively. We de ine that at one
          receiver, the cluster number is ℳ, and the number of rays
          within each cluster is ℛ .
                              m
          Fig. 5 shows an example given by one MS located 600 m          (c) Clustered rays in the azimuth domain.
          away from the BS in a virtual random city environment.  Fig. 5 – An example of the rays in clusters with respect to    and   .
          In particular, Fig. 5(a), Fig. 5(b) and Fig. 5(c) illustrate
          the cluster classi ication considering clustered rays in the  To further characterize the clusters of   , we de ine the
          ray tracing simulator, the delay domain and the azimuth  cluster gap,   , as the shortest angular distance between
          DOA domain, respectively. As expected, the received rays  two clusters in degree. In other words, if the DOA of two
          are clustered in both delay domain and angular domain.  neighboring rays has a gap greater than   , these two rays
          The clusters in both domains have some similarities to a  fall into two clusters. Noting that in our system model in
          certain degree. However, the cluster division in two do-  section 3, we consider MS located at random positions.
          mains does not always share the exact consistency since  However, the  inal results show that the distance between
          multiple complex factors in the scenario are responsible





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