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A steady-state filter gain was then designed for a constant-gain DUF to reduce the computations without much performance loss.
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When In,max is large, the performance loss becomes much smaller.
As the Gillespie level still integrates time much faster than the particle level, this performance loss is, however, not relevant for the overall performance of the simulation.
Compared to the Max-Log-LLR algorithm, it has much lower computational complexity at the expense of performance loss.
Compared with the iterative approach the simplified algorithm has much lower computational task at the cost of marginal performance loss.
It has been demonstrated that maximum-likelihood (ML) and Max-log a posteriori probability (APP) MIMO detection suffer from a substantial performance loss under the presence of weak transmitter noise, whereas linear ZF receivers are much less affected.
Simulation results show that the generated codes have much less cycles (length 6 cycles eliminated) and memory conflicts (75% reduction on idle clocks), while having no BER performance loss compared to WiMAX codes.
Where a 1percentt performance loss is a big deal?
However, the performance loss at low SNR is very small, and an optimal joint bit and energy allocation algorithm would require a much higher complexity.
However, it causes the performance loss.
Ignoring channel uncertainty may cause performance loss.
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