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The simulations of Figures 12 are13 are provided to verify the multicast gain of the proposed algorithm in terms of the average PSNR, where randomly distributed users in the square of Figure9 request an MBMS of test video "Football".
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Unfortunately, such a best-user (BU) approach does not make use of the multicast gain, which can yield low utility of resources, especially for a large multicast group size.
From the previous discussion, it can be seen that if we try to take advantage of multicast gain by using WU approach, the BS needs to send multicast packets only once but the consequence is that the transmission rate must be chosen as the lowest rate of all the users.
Inspired by WU and BU as extreme cases of multicast gain and multiuser diversity and threshold- scheme, if the base-station transmitter has full knowledge of the instantaneous channel gains, 's, of all users in every times-lot, the BS can sort users in the descending order of their instantaneous channel gains, that is,, and selects a subgroup of users that have the highest channel gains and as.
It is because that the proposed algorithm benefits from both multicast gain and the diversity gin of the subchannel qualities by considering the configurations of the MCPs.
In other words, correlation in channel responses will decrease the benefits of combining multicast gain and multiuser diversity.
The throughput comparison illustrates that with the ability of combining multicast gain and multiuser diversity, the proposed scheme outperforms both BU and WU for a wide range of SNR.
Then performance evaluation and comparisons are discussed to illustrate the trade-off between multicast gain and multiuser diversity and the significance of full and partial channel knowledge.
Combining both multicast gain and multiuser diversity, the throughput performance of ECOMP is superior to both BU and WU in the considered SNR range and asymptotically converges to WU at high SNR.
Hence, and accordingly, is likely to be very low when is large, which may lead to inefficient use of available resource (bandwidth) although multicast gain is exploited (As to be shown in Section 2.3.1, asymptotically converges to zero as increases).
In addition, numerical results illustrate that multiuser diversity is most pronounced at low SNR region since the difference in supportable rates of various users is large while multicast gain is superior at high SNR region where the difference in channel gain is compressed by the log-function that results in small difference in supportable rates among the users.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com