Then, we propose to serve a second layer of users, and we evaluate the achievable rate of the new layer depending on the broadcast strategy.
In the following proposition, we analyze this specific case and evaluate the minimum achievable ergodic SE of the system.
We apply the proposed scheme for each individual channel coefficient in the system and evaluate the resulting achievable uplink and downlink rates.
For the channel given by (37) and (38), we evaluate the achievable rate in (6) with the choice of the jointly Gaussian random variables U, Ur, SR, SD, XR, XD, and XR given by U 0 = X D, 0 + α 1 − α r S D (40) U r = 1 + ρ β / γ X r + α r S D (41) U = U 0 + ρ β / γ 1 + ρ β / γ U r (42) X D = X D 0 + ρ β / γ X r, (43).
This mathematical modelling is used to run different simulations for gear-shift control algorithm design during system development, allowing us to evaluate the achievable performance and its dependency on system properties.
These models can be useful for different purposes: (a) during system development, to evaluate the achievable performance and its dependency on system properties; (b) as simulation tools for gear shift control algorithm design.
We also evaluate the achievable performance averaged over randomly located node configurations and compare the proposed scheme with the mostly used nearest-node selection algorithm in terms of accuracy and cost.
Next, we evaluate the achievable uplink throughput of macrocell.
In this section, we numerically evaluate the achievable rate based on the lower bound in (10).
On uplink, we first evaluate the achievable uplink throughput of femtocell.
