WINNER D5.4 v. 1.4 where t is the time, the full polarimetric (2x2) transfer matrix, κ ( t) , includes the losses <strong>and</strong> depolarisati<strong>on</strong> of all physical processes (reflecti<strong>on</strong>, diffracti<strong>on</strong>, scattering, etc) of each multipath r r comp<strong>on</strong>ents, xT, s is the positi<strong>on</strong> of the antenna element s of transmit antenna array, x Ru , is the positi<strong>on</strong> of the antenna element u of the receive antenna array u, <strong>and</strong> ν l is the Doppler comp<strong>on</strong>ent. Note that all parameters are in general time variant, which is not shown for simpler presentati<strong>on</strong>. 4.1.4.1 Inter-segment modeling The radio <strong>channel</strong> is in general not stati<strong>on</strong>ary. Nevertheless, over short periods of time <strong>and</strong> space, <strong>channel</strong> parameters vary very little, <strong>and</strong> the assumpti<strong>on</strong> of short-term stati<strong>on</strong>arity is often a very good approximati<strong>on</strong>. The parameters characterizing our <strong>channel</strong> model are called bulk parameters. The time durati<strong>on</strong>s, over which these bulk parameters are c<strong>on</strong>stant, are denoted <strong>channel</strong> segment a.k.a. drops in the nomenclature of the SCM. Over time <strong>and</strong> space, bulk parameters change <strong>and</strong> we characterize this variability statistically. For simulati<strong>on</strong> purposes, the first goal typically is to experience the range of variability of the <strong>channel</strong> rather than the medium-term evoluti<strong>on</strong> behaviour. Thus, the initial focus is mainly <strong>on</strong> the joint distributi<strong>on</strong>s of bulk parameters. Between <strong>channel</strong> segments, i.e. realizati<strong>on</strong>s of these r<strong>and</strong>om variables, independence is assumed. The physical interpretati<strong>on</strong> is that <strong>channel</strong> segments are relatively short <strong>channel</strong> observati<strong>on</strong> periods that are significantly separated from each other in time or space. Each term of the matrix <strong>channel</strong> is a sum of L multipath comp<strong>on</strong>ents that can be described by the time htτφθϕϑ , , , , , , given as: varying double-directi<strong>on</strong>al delay spread functi<strong>on</strong> (D 3 SF), ( ) L ht (, τφθϕϑ , , , , ) = ∑ αn() t δφ ( −φn, θ −θn, ϕ −ϕn, ϑ−ϑn, τ −τn) . (4.2) n= 1 The instantaneous power double-directi<strong>on</strong>al-delay spectrum (PD 3 S) can be written as: L 2 PI(, t τφθϕϑ , , , , ) = ∑ αn() t δφ ( −φn, θ −θn, ϕ −ϕn, ϑ−ϑn, τ −τn) , (4.3) n= 1 where ⋅ is the absolute value of the argument. The per <strong>channel</strong> segment (local) average PD 3 S, which represents <strong>channel</strong> characteristics per <strong>channel</strong> segment, can be defined as: P ( τφθϕϑ , , , , ) = E { P ( t, τφθϕϑ , , , , )} s t I L ∑ = p δφ ( −φ , θ −θ , ϕ −ϕ , ϑ−ϑ ) δτ ( −τ ) n= 1 n n n n n n In (4.4), the variables {L, p n , φ n , θ n , ϕ n , ϑ n , τ n } are the bulk parameters introduced above. The L paths are characterized by the orientati<strong>on</strong> of the last-bounce scatterer as seen from transmitter <strong>and</strong> receiver, as well as the total delay. This approach stems from superresoluti<strong>on</strong> parameter estimati<strong>on</strong> techniques (e.g., MUSIC, ESPRIT, SAGE) which decompose a measured <strong>channel</strong> resp<strong>on</strong>se based <strong>on</strong> the above model. The D 3 SF characterizes the dispersive behaviour of the mobile radio <strong>channel</strong> in delay domain <strong>and</strong> directi<strong>on</strong> domain seen either at transmitter or receiver sides. The equati<strong>on</strong>s are valid regardless of which terminal is the transmitter, either BS or MS <strong>and</strong> which terminal is the receiver either MS or BS. All parameters in (4.4) are r<strong>and</strong>om variables, since scatterers’ locati<strong>on</strong>s change with movement of the MS. Hence, the D 3 SF is a r<strong>and</strong>om process, which is described by joint distributi<strong>on</strong> of its r<strong>and</strong>om variables. The statistics of multipath comp<strong>on</strong>ents amplitudes, delays, <strong>and</strong> azimuth <strong>and</strong> elevati<strong>on</strong> angles at both ends are generally not separable. Hence, they have to be described in joint probability density functi<strong>on</strong>s (pdf). However, multidimensi<strong>on</strong>al joint pdf is not tractable mathematically. Therefore, simplificati<strong>on</strong>s are needed for simulati<strong>on</strong> purposes. As a result, <strong>on</strong>ly partial dependencies of distributi<strong>on</strong>s of different parameters are usually assumed. One of the most comm<strong>on</strong> assumpti<strong>on</strong>s is uncorrelated scattering (US). We assume independence of all parameters for different paths, i.e., different n. Therefore, (4.4) is characterized by the joint distributi<strong>on</strong> f( pn, φn, θn, ϕn, ϑn, τ n) , which is independent of n. The expectati<strong>on</strong> in (4.4) is over short periods of time, where <strong>channel</strong> parameters vary <strong>on</strong>ly slightly, <strong>and</strong> the assumpti<strong>on</strong> of short-term stati<strong>on</strong>arity is valid. The over segments (global) average PD 3 S is obtained by taking the expectati<strong>on</strong> of the per <strong>channel</strong> segment (local) average PD 3 S over all bulk parameters (4.4) Page 44 (167)
WINNER D5.4 v. 1.4 { } ( τφθϕϑ , , , , ) ( τφθϕϑ , , , , ) P = E P s (4.5) The average PD 3 S in (4.4) is the average per <strong>channel</strong> segment, which may differ from <strong>on</strong>e segment to another. In order to relate (4.4) to the average PD 3 S over segments (4.5), all bulk parameters except the r<strong>and</strong>om powers can be pulled out of the expectati<strong>on</strong> to arrive at: P ( τφθϕϑ , , , , ) { (, , , , )| ,..., , ,..., , ,..., , ,..., , ,..., } = ∫ E P τφθϕϑ φ φ θ θ ϕ ϕ ϑ ϑ τ τ s 1 L 1 L 1 L 1 L 1 L L ∏ i= 1 f( φ, θ , ϕ , ϑ, τ ) dφdθdϕ dϑdτ i i i i i i i i i i (4.6) L = ∑ E{ pi | τφθϕϑ , , , , } f( τφθϕϑ , , , , ) i= 1 { τφθϕϑ} = LE p| , , , , f(, τφθϕϑ , , , ). From (4.6), the over segments (global) average PD 3 S (i.e., ( , , , , ) P τφθϕϑ ) is equivalent to the c<strong>on</strong>diti<strong>on</strong>al expected power of the multipath comp<strong>on</strong>ents multiplied by the joint double-directi<strong>on</strong>al-delay probability density functi<strong>on</strong>. The power spectrum in each dimensi<strong>on</strong> is obtained by integrati<strong>on</strong> over other dimensi<strong>on</strong>s. Thus, power delay spectrum P ( τ ) , power azimuth-departure-angle spectrum P ( φ) , power elevati<strong>on</strong>-departure-angle spectrum P ( θ ) , power azimuth-arrival-angle spectrum P ( ϕ) , power elevati<strong>on</strong>-arrival-angle spectrum P can be derived as: ( ϑ) ( ) ( , , , , ) P τ P τφθϕϑ dφdθdϕdϑ = ∫∫∫∫ (4.7) ( ) ( , , , , ) P φ P τφθϕϑ dτdθdϕdϑ = ∫∫∫∫ (4.8) ( ) ( , , , , ) P θ P τφθϕϑ dφdτdϕdϑ = ∫∫∫∫ (4.9) ( ) ( , , , , ) P ϕ P τφθϕϑ dφdθdτdϑ = ∫∫∫∫ (4.10) ( ) ( , , , , ) P ϑ P τφθϕϑ dφdθdϕdτ = ∫∫∫∫ . (4.11) The corresp<strong>on</strong>ding marginal probability density functi<strong>on</strong>s (pdf) of parameters of each domain can be derived from: ( ) ( , , , , ) f τ f τφθϕϑ dφdθdϕdϑ = ∫∫∫∫ (4.12) ( ) ( , , , , ) f φ f τφθϕϑ dd τ θdϕdϑ = ∫∫∫∫ (4.13) ( ) ( , , , , ) f θ f τφθϕϑ dφdτdϕdϑ = ∫∫∫∫ (4.14) ( ) ( , , , , ) f ϕ f τφθϕϑ dφdθdτdϑ = ∫∫∫∫ (4.15) ( ) ( , , , , ) f ϑ f τφθϕϑ dφdθdϕdτ where f ( τ ), f ( φ) , f ( θ ), f ( ϕ) , f ( ϑ) , <strong>and</strong> ( , , , , ) = ∫∫∫∫ , (4.16) f τφθϕϑ are the pdf of path delays, the pdf of azimuth departure angles, the pdf of the elevati<strong>on</strong> departure angles, the pdf of the azimuth arrival angles, the pdf of the elevati<strong>on</strong> arrival angles, <strong>and</strong> the joint double-directi<strong>on</strong>al-delay probability density functi<strong>on</strong> of argument parameters, respectively. Similarly, the P ( τ ) , P ( φ) , P ( θ ) , P ( ϕ) , P ( ϕ) can be expressed as: ( ) { } P τ = LE p| τ f( τ) (4.17) Page 45 (167)