A2.1.8 Time-spatial profile in cluster a) Delay profile in the cluster ith An empirical profile model normalized by the first arrival path’s power in the cluster is ith given as follows: (A2-28) where and denote the average excess path distance and the maximum excess path distance lI lImax in the cluster, respectively. On the other hand, the excess path distance (m) can be changed to the l access delay time  (s) by using the relation of . The delay profile model in the cluster, , can then be determined: (A2-29) where and denote the excess average delay time and the maximum excess delay time in  I Imax the cluster, respectively. b) Departure angular profile in the cluster ith An empirical departure angular profile (ik) with departure angle in the cluster AODI ik ith normalized by its central angular path’s power is given as: (A2-30) c) Arrival angular profile in the cluster ith An empirical arrival angular profile model (ik) with arrival angle in the cluster AOAI AI ith normalized by its central angular path’s power is given by: (A2-31) where denotes the average arrival angle in the cluster. 180º is a typical value. AI AI A2.2 Generation of reduced variability models based on TSP model A2.2.1 NLOS scenario The reduced variability is created by setting its key parameters. Each channel can be realized by the following procedure: Set test environments and key parameters Step 1: Set the Frequency conditions, City structure and Cell conditions on Table AA2-1. Set the simulation conditions Step 2: Set the number of clusters for NLOS and LOS, level difference between the peak path’s power and cut off power in NLOS scenario Lth and LOS scenario LSth. Generation of long-term TSP Step 3: Generation of delay time Step 3.1: a) Calculation of maximum delay time Convert excess distance (m) into excess delay time (s) by equation (A2-32):  l (A2-32) The maximum delay time can be calculated by considering Lth as follows:
A2.1.8 Time-spatial profile in cluster a) Delay profile in the ith cluster An empirical profile model normalized by the first arrival path’s power in the ith cluster is given as follows: (A2-28) where lI and lImax denote the average excess path distance and the maximum excess path distance in the cluster, respectively. On the other hand, the excess path distance l (m) can be changed to the access delay time  (s) by using the relation of . The delay profile model in the cluster, , can then be determined: (A2-29) where  I and Imax denote the excess average delay time and the maximum excess delay time in the cluster, respectively. b) Departure angular profile in the ith cluster An empirical departure angular profile AODI (ik) with departure angle ik in the ith cluster normalized by its central angular path’s power is given as: (A2-30) c) Arrival angular profile in the ith cluster An empirical arrival angular profile model AOAI (ik) with arrival angle AI in the ith cluster normalized by its central angular path’s power is given by: (A2-31) where AI denotes the average arrival angle in the cluster. AI 180º is a typical value. A2.2 Generation of reduced variability models based on TSP model A2.2.1 NLOS scenario The reduced variability is created by setting its key parameters. Each channel can be realized by the following procedure: Step 1: Set test environments and key parameters Set the Frequency conditions, City structure and Cell conditions on Table AA2-1. Step 2: Set the simulation conditions Set the number of clusters for NLOS and LOS, level difference between the peak path’s power and cut off power in NLOS scenario Lth and LOS scenario LSth. Step 3: Generation of long-term TSP Step 3.1: Generation of delay time a) Calculation of maximum delay time Convert excess distance l (m) into excess delay time  (s) by equation (A2-32): (A2-32) The maximum delay time can be calculated by considering Lth as follows:
R15-SG05-C-0057!R1!MSW-E.docx