This paper addresses the problem of sparse signal detection based on compressive sensing (CS) framework. In order to reduce the computational complexity of detection, we propose a sparse compressive detection approach by substituting sparse random projection matrix for conventional dense matrix, and develop a theoretical model that exactly characterizes the relationship of the detection probability with the number of measurements, the signal-to-noise ratio (SNR), as well as the degree of sparsity. Simulation results show that the performance of the proposed detector is comparable to the conventional CS detector in which Gaussian dense projections are employed.

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In large scale wireless sensor networks (WSNs) it is crucial for a truster node to find the optimal social trust path (OSTP) to the target trustee node subjecting to more than one end-to-end Quality of Trust (QoT) constraints to perform trust evaluation. In this letter, for the first time, quantum annealing based OSTP selection algorithms is applied to the NP-Complete OSTP selection from many social trust paths between the trustee and truster node pair in large scale WSNs. We conduct the experiments on the Enron email dataset and the experiment results show that QA based algorithms outperforms its heuristic opponents.

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In this letter, we propose a method that combines breadth first tree search Maximum Likelihood (ML) and Zero Forcing (ZF) detection for spatial multiplexing Multiple Input Multiple Output (MIMO systems utilizing the similarity property of the Quadrature amplitude modulation (QAM) to detect the symbol. The proposed method shows substantial reduction in complexity, in terms of both reducing the number of nodes and number of operation to be performed at each node of the tree. The simulation results show that this approach achieves 48% of reduction in the computational complexity compared to breadth first ML with reduced nodes for a 2×2 systems and 75% for the 4×4 systems without any degradation in the performance.

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This paper proposes an interference detection method in MIMO transmission, which utilizes periodical preamble signals in a frequency domain. In the propose method, second antenna receives the signals while first antenna transmits the preamble signals. Hence, the interference can be detected by observing the subcarriers which are not mapped short preamble signal using the received signal after the FFT processing. Moreover, we utilize the orthogonal polarized antennas to reduce the mutual coupling between the transmitter and receiver. By a computer simulation and measurement with the use of orthogonal polarized antenna, it is shown that the proposed method can successfully detect interference from the other system when the interfering power is greater than the noise power.

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The operational performance of radio navigation systems in ARNS (Aeronautical Radio Navigation Service: 960-1215MHz) band is required to be estimated whether or not they will harmfully interfere with each other by sharing the frequency band. The performance of these systems will be estimated by using some interference measures such as the received pulse power distribution. For example, the wide band signal of GPS-L5 will receive interference from DME (Distance Measuring Equipment) signals. In this paper, we propose an estimation method for the signal environment in ARNS band using recorded signals. And we compare the experimental result with conventional theoretical results in order to check the proposed method.

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In this letter, we develop closed-form analytical expressions for the false alarm and missed detection probabilities of a cooperative spectrum sensing (CSS) scheme using cognitive radio (CR) nodes that are equipped with improved energy detectors (IEDs), employ receive antenna diversity, and operate over Hoyt (Nakagami-q) or Rician (Nakagami-n) faded sensing channels. The derived expressions are validated through extensive simulations.

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