  In wireless communications, the cooperative communication (CC) technology
promises performance gains compared to traditional Single-Input Single Output
(SISO) techniques. Therefore, the CC technique is one of the nominees for 5G
networks. In the Decode-and-Forward (DF) relaying scheme which is one of the CC
techniques, determination of the threshold value at the relay has a key role
for the system performance and power usage. In this paper, we propose
prediction of the optimal threshold values for the best relay selection scheme
in cooperative communications, based on Artificial Neural Networks (ANNs) for
the first time in literature. The average link qualities and number of relays
have been used as inputs in the prediction of optimal threshold values using
Artificial Neural Networks (ANNs): Multi-Layer Perceptron (MLP) and Radial
Basis Function (RBF) networks. The MLP network has better performance from the
RBF network on the prediction of optimal threshold value when the same number
of neurons is used at the hidden layer for both networks. Besides, the optimal
threshold values obtained using ANNs are verified by the optimal threshold
values obtained numerically using the closed form expression derived for the
system. The results show that the optimal threshold values obtained by ANNs on
the best relay selection scheme provide a minimum Bit-Error-Rate (BER) because
of the reduction of the probability that error propagation may occur. Also, for
the same BER performance goal, prediction of optimal threshold values provides
2dB less power usage, which is great gain in terms of green communicationBER
performance goal, prediction of optimal threshold values provides 2dB less
power usage, which is great gain in terms of green communication.
