The natural gradient descent is an optimization method for real-valued neural networks that was proposed from the viewpoint of information geometry. Here, we present an extension of the natural gradient descent to complex-valued neural networks. Our idea is to use the Hermitian extension of the Fisher information matrix. Moreover, we generalize the projected natural gradient (PRONG), which is a fast natural gradient descent algorithm, to complex-valued neural networks. We also consider the advantage of complex-valued neural networks over real-valued neural networks. A useful property of complex numbers in the complex plane is that the rotation is simply expressed by the multiplication. By focusing on this property, we construct the output function of complex-valued neural networks, which is invariant even if the input is changed to its rotated value. Then, our complex-valued neural network can learn rotated data without data augmentation. Finally, through simulation of online character recognition, we demonstrate the effectiveness of the proposed approach.

We propose a distributed blind equalization method for wireless sensor networks, in which a source sends data and each node performs time-domain equalization to estimate the data from a received signal that is affected by inter-symbol interference. The equalization can be performed distributively based on the mutually referenced equalization principle. Even if the nodes in the network are not fully connected to each other, the average consensus technique enables us to perform the equalization of all channels.

Compared with acoustic microphone (AM) speech, bone-conducted microphone (BCM) speech is much immune to background noise, but suffers from severe loss of information due to the characteristics of the human-body transmission channel. In this letter, a new method for the speaker-dependent BCM speech enhancement is proposed, in which we focus our attention on the spectra restoration of the distorted speech. In order to better infer the missing components, an attention-based bidirectional Long Short-Term Memory (AB-BLSTM) is designed to optimize the use of contextual information to model the relationship between the spectra of BCM speech and its corresponding clean AM speech. Meanwhile, a structural error metric, Structural SIMilarity (SSIM) metric, originated from image processing is proposed to be the loss function, which provides the constraint of the spectro-temporal structures in recovering of the spectra. Experiments demonstrate that compared with approaches based on conventional DNN and mean square error (MSE), the proposed method can better recover the missing phonemes and obtain spectra with spectro-temporal structure more similar to the target one, which leads to great improvement on objective metrics.

In this paper, we explore how to enhance the physical layer security performance in downlink cellular networks through cooperative jamming technology. Idle user equipments (UE) are used to cooperatively transmit jamming signal to confuse eavesdroppers (Eve). We propose a threshold-based jammer selection scheme to decide which idle UE should participate in the transmission of jamming signal. Threshold conditions are carefully designed to decrease interference to legitimate channel, while maintain the interference to the Eves. Moreover, fewer UE are activated, which is helpful for saving energy consumptions of cooperative UEs. Analytical expressions of the connection and secrecy performances are derived, which are validated through Monte Carlo simulations. Theoretical and simulation results reveal that our proposed scheme can improve connection performance, while approaches the secrecy performance of [12]. Furthermore, only 43% idle UEs of [12] are used for cooperative jamming, which helps to decrease energy consumption of network.

In this paper, we investigate a novel joint multi-relay and jammer selection (JMRJS) scheme in order to improve the physical layer security of wireless networks. In the JMRJS scheme, all the relays succeeding in source decoding are selected to assist in the source signal transmission and meanwhile, all the remaining relay nodes are employed to act as friendly jammers to disturb the eavesdroppers by broadcasting artificial noise. Based on the more general Nakagami-m fading channel, we analyze the security performance of the JMRJS scheme for protecting the source signal against eavesdropping. The exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the JMRJS scheme over Nakagami-m fading channel are derived. Moreover, we analyze the security-reliability tradeoff (SRT) of this scheme. Simulation results show that as the number of decode-and-forward (DF)relay nodes increases, the SRT of the JMRJS scheme improves notably. And when the transmit power is below a certain value, the SRT of the JMRJS scheme consistently outperforms the joint single-relay and jammer selection (JSRJS) scheme and joint equal-relay and jammer selection (JERJS) scheme respectively. In addition, the SRT of this scheme is always better than that of the multi-relay selection (MRS) scheme.

For an odd prime q and an integer m≤q, we can construct a regular quasi-cyclic parity-check matrix H_I(m,q) that specifies a linear block code C_I(m,q), called an improper array code. In this letter, we prove the minimum distance of C_I(4,q) is equal to 10 for any q≥11. In addition, we prove the minimum distance of C_I(5,q) is upper bounded by 12 for any q≥11 and conjecture the upper bound is tight.

In this paper, we propose an improved weighted least square (IWLS) method to estimate and compensate phase variations utilizing pilots, for Orthogonal Frequency Division Multiplexing (OFDM) based very high throughput wireless local area networks (WLANs). The remaining phase is composed of the common phase error (CPE) and the sampling time offset (STO). For IWLS, the CPE maximum likelihood (ML) estimation is proposed to improve the CPE estimation accuracy, while the STO fitting is proposed to enhance the estimation of STO. With these two mechanisms, IWLS can improve phase estimation performance. Simulation results show that, compared to weighted least square (WLS) scheme, a better pocket error rate (PER) is achieved by using the proposed method, but with a comparable complexity.

In the large-scale multi-UAV systems, the direct link may be invalid for two remote nodes on account of the constrained power or complex communication environment. Idle UAVs may work as relays between the sources and destinations to enhance communication quality. In this letter, we investigate the opportunistic relay selection for the UAVs dynamic network. On account of the time-varying channel states and the variable numbers of sources and relays, relay selection becomes much more difficult. In addition, information exchange among all nodes may bring much cost and it is difficult to implement in practice. Thus, we propose a decentralized relay selection approach based on mood-driven mechanism to combat the dynamic characteristics, aiming to maximize the total capacity of the network without information exchange. With the proposed approach, the sources can make decisions only according to their own current states and update states according to immediate rewards. Numerical results show that the proposed approach has attractive properties.