In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed
compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint
sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in
each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The
reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements
by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger
than the sparsity of the sampled sensor data is needed for successful recovery.
This paper describes a new method utilizing dual-frequency GPS data to improve the accuracy of Ground-based
Augmentation System (GBAS). With this so called ionosphere-isolating method, two dual frequency carrier-smoothing
filters are used to estimate differential correction and ionosphere refraction separately. By dual frequency filtering, the
filter in ground and airborne can be decoupled. By utilizing both of the two estimations, dual-frequency airborne user can
eliminate residual differential correction error imposed by ionosphere spatial gradient. A new GBAS architecture that
exploits ionosphere-isolating method to improve the accuracy of code differential systems is presented.
This paper presents a selection and grouping algorithm based on proposed network coded cooperative strategy in
aeronautical communications. In such a practical scenarios, communications between high-speed mobile nodes are
troubled with high reliability and capacity demands of the emerging aeronautical applications. The idea of network
coded cooperative strategy is combining information at the relay and joint decoding at the destination, which could
improve reliability and capacity without much loss of throughput of the whole networks. Then, we consider the selection
and grouping algorithms in this context. An adjacency matrix is defined to describe the connection of the nodes in one
radio contact disk, so that, the selection and grouping algorithm is equal to construct this adjacency matrix avoiding girth
4 and minimize the system outage probability. At the end of the paper, simulations and results will be given to
demonstrate the algorithms.
KEYWORDS: Orthogonal frequency division multiplexing, Satellites, Satellite communications, Statistical analysis, Modulation, Signal to noise ratio, Mobile communications, Telecommunications, Systems modeling, Statistical modeling
This paper presents the performance analysis and optimization of LDPC coded OFDM system which provides high signal quality in LEO satellite communications. A three-state statistical channel model of LEO satellite channel is adopted including Rayleigh, Rician and shadowed Rician distributed submodels. The Irregular LDPC codes without loops of length 4 are optimized by the differential evolution algorithm under this LEO satellite channel. Simulations of BPSK, QPSK, 16QAM modulated systems are also conducted to analyze performances of theses systems.
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