In this letter, we study the performance of multi-antenna relay networks with limited feedback beamforming in decode-and-forward (DF) relaying. Closed-form expression for both outage probability and symbol error rate are derived by using the moment generation function (MGF) of the combined signal-to-noise ratio (SNR) at the destination. Subjected to a total power constraint, we also explore adaptive power allocation between source and relay to optimize the performance. Simulations are given to verify the correctness of our theoretical derivations. Results show that the proposed adaptive power allocation solution significantly outperforms the uniform power allocation method.

Transfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multi-similarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the “negative transfer” and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.