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The semi-greedy algorithm gives many more transmission opportunities to users with good channel quality (low loss probability).
As expected, for very low loss probability the gain in using any retransmission scheme is small, as when loss probability approaches 0 there are no retransmissions, neither in the uncoded nor in the coded schemes (hence no coding opportunities).
The greedy algorithm distributes the throughput quite evenly, giving only slight advantage to users with low loss probability, e.g., the users with 0.05 and 0.5 loss probabilities get throughput of 0.08 and 0.06, respectively.
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Photonic packet switching faces a major challenge in providing low packet loss probability under the constraint of shallow optical buffer depth.
The proposed method in [18] is adjusting the source traffic rates at the upstream nodes to achieve low packet loss probability.
At outcome, the factors were valence (gain, loss) probability (low, high) and magnitude (£3, £9).
PSNR gains of 5 dB or more are typical for packet loss probability as low as 5%.
Compared with the existing node architecture and scheduling algorithm, the simulation results show that the proposed architecture and the TFJSS can reduce the packet loss probability with low delay largely.
However, due to the preemptive service discipline, the mean number of low-priority customers in the queue, as well as the customer loss probability of the low-priority class are increasing at a greater rate than the corresponding ones for the high-priority class.
It is worth stating at this point that except for low input data rates, the tail probability tends to overestimate the packet loss probability and it can only be used as long as ϖ A ≤ ϖ S.
These negative influences, which could degrade the performance of low-priority classes with respect to some important metrics such as the packet loss probability and the packet delay, are often called the inter-class effects.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com