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The neural controller uses a self-organizing Hermite-polynomial-based neural network (SHNN) to approximate an ideal feedback controller.
The supervisor compensator is designed to eliminate the approximation error between the neural controller and ideal feedback controller without chattering phenomena.
Nevertheless, our results demonstrate that, when the preprocessing is based on the quantized-CIRs obtained through ideal feedback channels, the resultant achievable symbol-error-rate and sum-capacity remain close to that obtained with the perfect CIRs based design.
As a result, we have an ideal feedback channel.
In this approach of this study, an ideal feedback path is considered.
We restrict our investigation purely to the closed loop part, focusing on the algorithm and thus assuming ideal feedback.
Similar(40)
Instead, FF-NDMA is not affected by the non-ideal feedback repetition process.
Non-ideal feedback repetition processes (i.e., dretx>0) have a detrimental effect over NDMA and H-NDMA.
When non-ideal feedback processes for repetitions are considered, FF-NDMA can boost the delay and energy performance of NDMA and H-NDMA owing to the feedback-free repetition procedure.
The non-ideal feedback process also cause a reduced energy consumption with FF-NDMA as compared to NDMA and H-NDMA because devices spent less time in the waiting state to successfully complete a packet transmission.
When non-ideal feedback for the repetition process is considered (e.g., dretx=4), the FF-NDMA protocol obtains a significantly reduced delay and lower energy consumption as compared to NDMA and H-NDMA schemes.
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