Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability; the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.

This letter explores how to apply a generative adversarial network (GAN) to the classification of hyperspectral images (HSIs) to obtain a smooth training process and better classification results. To this end, the ideas of the progressive growing GAN (PG-GAN) and Wasserstein generative adversarial network gradient penalty (WGAN-GP) are combined to propose a new method for HSI classification. PG-GAN is optimized from the training process of generating adversarial networks. It gradually increases the depth of the network and the size of the input image, making the training smoother. WGAN-GP is optimized in terms of the loss function. The gradient penalty method is used to solve the problems of vanishing gradient and exploding gradient, making the training more stable. Based on the combination of the two methods, a classifier is added to the model so that it can complete the HSI classification task. The proposed method is evaluated over two publicly available hyperspectral data sets, the Indian Pines and University of Pavia data sets. The results show that the proposed method can achieve good training results with only a small amount of labeled training data.

In this paper, we design a segment training based individual channel estimation (STICE) scheme in the classical three-node it amplify-and-forward (AF) one-way relay network (OWRN). The linear minimum mean-square-error (LMMSE) channel estimator is used to obtain a good initialization, and an iterative maximum a posteriori (MAP) channel estimator is developed to improve the estimation accuracy. We then investigate the underlying power allocation at the relay node both to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The closed-form Bayesian Cramér-Rao Bound (CRB) is also derived to evaluate the proposed algorithm. Finally, numerical results are provided to corroborate the proposed studies.

We propose a continuous-variable quantum secret sharing (CVQSS) scheme based on thermal terahertz (THz) sources in inter-satellite wireless links (THz-CVQSS). In this scheme, firstly, each player locally preforms Gaussian modulation to prepare a thermal THz state, and then couples it into a circulating spatiotemporal mode using a highly asymmetric beam splitter. At the end, the dealer measures the quadrature components of the received spatiotemporal mode through performing the heterodyne detection to share secure keys with all the players of a group. This design enables that the key can be recovered only by the whole group players’ knowledge in cooperation and neither a single player nor any subset of the players in the group can recover the key correctly. We analyze both the security and the performance of THz-CVQSS in inter-satellite links. Results show that a long-distance inter-satellite THz-CVQSS scheme with multiple players is feasible. This work will provide an effective way for building an inter-satellite quantum communication network.