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We first formulate subchannel allocation as an optimization problem with the assumption that all subchannels are allocated equal power distribution in each PRN, i.e., p ∼, n i = 1 N i P total i ∀ i (14).
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For fairness, the traffic flows sharing a link will be allocated equal resources, that is, equal packet transmission opportunity on the link.
When all nodes use the same fairness constraints that is equal to ρ fair = ρ i = 1/N, each node will be allocated equal transmission rate.
In these protocols, since the relay either amplifies the received signal or decodes it but uses the same codebook as the source when forwarding, source and relay should be allocated equal time slots in the cooperation phase.
Assuming homogeneous channel conditions, all concurrent flows are allocated an equal share of downlink transmission slots regardless of flow types and locations.
Here, we concentrate on the performance of a user to which 48 data subcarriers are allocated, which is equal to one subchannel in the FUSC permutation mode of WiMax [31].
As a simple example, the pulses are allocated to four targets with equal proportion in Fig. 3, e.g., the probability of being illuminated for each target is equal.
When substantial quantization intervals are constructed or a large number of bits are allocated to each feature dimension, equal width (EW) quantization offers an approximate continuous-to-discrete mapping.
By and large, our general findings can be summarized in the following three aspects: When substantial quantization intervals are constructed or a large number of bits are allocated to each feature dimension, equal width (EW) quantization offers an approximate continuous-to-discrete mapping.
Considering that the final performance evaluation measure is the digital transmission characteristic in terms of BER or spectral efficiency, i.e., often referred to as channel capacity, we evaluate the channel capacity performance (in bps/Hz), where equal powers are allocated to each input port.
See Appendix 2. If we assume orthogonal transmissions where equal resources are allocated to each user in a round-robin scheduling manner, the ergodic capacity C of a typical user in the K-tier heterogeneous network is given as: C = {frac{{{t_{mathrm{s}}}}}{T}{p_{1}}{C_{0}} + frac{{T - {t_{mathrm{s}}}}}{T}sumlimits_{k = 1}^{K} {{p_{k}}{C_{k}}} }. (7).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com