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The scaling laws of average sum rate and of average worst case delay are derived.
We compare the scheduling algorithms in terms of average sum rate (SR) and complexity requirements.
By using a NUM framework, the gains were evaluated in terms of average sum rate and average network congestion.
Figure 3 demonstrates the relation of average sum rate versus number of relays under the algorithm in [2, 4] and the proposed one, respectively.
The gains are evaluated in terms of average sum rate and average network congestion by using a network utility maximization framework.
The scenarios were assessed in terms of average sum rate of the cluster, and traditional single cell transmission served as a baseline for comparison.
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Figure 6 Comparison of average sum-rates for i.ni.d. and i.i.d.i.d
Figure 8 Comparison of average sum-rate performance of proposed scheduling scheme with direct transmission for i.i.d.i.d
It can now be shown easily that the lower bound of average sum-rate is maximized when α ≈ 0.414 (as for outage minimization), whereas the upper bound is maximized when α ≈ 0.618 (as in [9]).
We provide an analytical description of the impact of nonlinear degradation on average sum rate performance of cooperative network.
Figure 3 Impact of relay positions on average sum rate.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com