Decision-making scenarios for reducing the computational complexity in a green cognitive radio receiver

The idea that we highlight in this paper is how to reduce the computational complexity by limiting the processing in the receiver chain. For this we seek for limiting the use of the beamforming process and the equalization process according to the conditions. Indeed, with the cognitive features the receiver observes its environment and decides to either keep or turn off these processes without degrading its performances. The decision method that we developed is based on the statistical modeling of the radio environment, and the purpose of this approach is to minimize the percentage of bad decisions by considering the errors of observation. Furthermore, we first address the two decision scenarios separately and we seek to prove that the decisions to turn off the equalizer and the beamforming, when they are not necessary, lead to reduce the computational complexity of the receiver. Then we focus on the behavior of the receiver facing both decision scenarios. Thus, we formulate the decision problem in two different ways: in the first case we consider that the receiver performs joint decisions about the two operations of beamforming and equalization, in the second case we suppose that it handles the two decision scenarios sequentially. We compare then the performance of the receiver in the two cases. Key PointsDecision making by statistical modelingReducing the computational complexity for green radio