In this work we present a case for dynamic spectrum sharing between different operators in systems with carrier aggregation (CA) which is an important feature in 3GPP LTE-A systems. Cross-carrier scheduling and sensing are identified as key enablers for such spectrum sharing in LTE-A. Sensing is classified as Type 1 sensing and Type 2 sensing and the role of each in the system operation is discussed. The more challenging Type 2 sensing which involves sensing the interfering signal in the presence of a desired signal is studied for a single-input singleoutput system. Energy detection and the most powerful test are formulated. The probability of false alarm and of detection are analyzed for energy detectors. Performance evaluations show that reasonable sensing performance can be achieved with the use of channel state information, making such sensing practically viable.
We consider the problem of sensing in the presence of a desired signal in the context of future 3GPP LTE-A based cognitive cellular systems employing multiple-input multipleoutput (MIMO) transmission. Energy detection (ED) based on equal gain combining and beamforming are investigated. Receive beamformers for energy detection (ED) are designed according to the Neyman-Pearson criterion to maximize the probability of detection for a given probability of false alarm. Suitable suboptimum solutions to the maximization problem with a good tradeoff between performance and complexity are identified. Furthermore, we also formulate the likelihood ratio test (LRT) for this scenario. Performance simulations indicate that a significant performance gain is achieved in ED if the receive beamformer is chosen properly. This work has been performed in the framework of the Cognitive Mobile Radio (CoMoRa) project, which is partly funded by the Federal Ministry of Education and Research (BMBF) of Germany.
Abstract-In this paper we will derive an algorithm, which estimates the channel blindly exploiting the statistical dependencies of the transmitted signal caused by channel coding. An additional feature of this algorithm is that in contrast to most blind deconvolution algorithms phase correct estimates can be obtained. The error performance of the proposed algorithm depends on the characteristics of the channel code. If the code has appropriate properties, which is true for some convolutional codes as well as for several block codes, e.g. especially low-density parity check codes (LDPC), the proposed algorithm performs similarly or slightly better in comparison to higher order statics based algorithms.
Within this paper graph based non-coherent decoding algorithms for LDPC encoded systems are proposed. We study a class of LDPC codes suitable for non-coherent detection. On the basis of the related graphs a non-coherent decoding algorithm with variable trade-off between computational complexity and BER performance is derived. The proposed scheme is also capable to deal with pilot symbols if available. Finally, the excellent performance of the proposed methods is verified by simulations.
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