SUMMARYThe global spread of wireless devices with mobile Internet access and the increasing demand of multimedia‐based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built‐in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd.

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Massive Multiple-Input Multiple-Output (massive MIMO) system relies on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, transmission of massive MIMO system is subject to excessive feedback overhead. In this paper, we propose a Deep Learning (DL) approach-based channel estimation technique to enhance the performance of massive MIMO system. This technique is used to enhance recovery quality and improve trade-off between compression ratio (CR) and complexity of massive MIMO system. The proposed technique is based upon using the Channel State Information Network combined with gated recurrent unit (CsiNet-GRU). Moreover, the dropout method is used in the proposed technique to reduce overfitting during the learning process. The simulation results demonstrate that the proposed CsiNet-GRU technique results in a significant improvement in performance when compared with existing techniques used in conjunction with massive MIMO systems.

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