f I F 2 = f I i I L center ∗ ⋯ ∗ f I i I L center ⏟ N − th order convolution (11).
As we know, the joint distribution functions shown in (28) can be calculated by P ( L center, k < K ) = P ( L center ) P ( k < K L center ) P ( L edge, k < K ) = P ( L edge ) P ( k < K L edge ) (29).
Similarly, the conditional m th moment of Y can be calculated as E [ Y m L center ] = ∫ y m f Y ( y | L center ) dy = α m M 0 ( mβ, γ ) γ 2 R mβ (19).
The distribution function of K can be expressed as P Set − B ( k < K ) = P ( L center, k < K ) + P ( L edge, k < K ) (28).
Figure 2 presents an example of a real trajectory matrix (left), the representations coefficients L (center), and the singular value distribution of the coefficients (right).
Hence, it is easy to derive the expectation of I i as E [ I i L center ] = P t, 0 E [ X ] E [ Y L center ] E [ H ] = P t, 0 α M 0 μ γ 2 R β e λ 2 σ 2 2 (23).
The interference from the interior UEs in a neighboring cell is I i =P t,0 X Y H, and its PDF is derived as f I i ( I L center ) = 1 P t, 0 ∫ 0 ∞ 1 h f H ( h ) ∫ 0 ∞ 1 x f X ( x ) f Y I / P t, 0 xh L center dxdh (10).
With (12), (19), (21), and (22), the conditional m th moment of I i is E I i m L center = P t, 0 m E X m E Y m L center E [ H m ] = P t, 0 α m M 0 ( mβ, γ ) m !
Then, the expectation of interference from a cell in Set-B can be calculated as E [ I i ( Set − B ) ] = E [ K ( Set − B ) ] E [ X ] E [ H ] = P ( L center ) P t, 0 E [ Y L center ] + P ( L edge ) P t, 1 E [ Y L edge ] E [ X ] E [ H ] = P t, 1 α M 1 + ( P t, 0 − P t, 1 ) α M 0 μ R β e λ 2 σ 2 2 (45).
For the scenario that interfering UEs are in the cells of Set-A, the corresponding PDF of I i is f I i ( Set − A ) ( I ) = f I i I L center.
