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In recent years, unmanned aerial vehicles (UAVs) have attracted wide attention in both military and civilian fields because of their versatility, flexibility, and low-cost characteristics. In this letter, a UAV network architecture oriented to the virtual cloud processing center (VCPC) is considered, where the highly dynamic UAV network can transmit real-time data from sensors on the ground as well as data observed by the UAV nodes to the VCPC. Due to the high dynamic characteristics of UAVs, the topology of networks and traffic load within the coverage of the VCPC change frequently, especially when the priority of the information is different, and it is necessary to design a novel media access control (MAC) protocol to reduce the transmission delay of high priority and improve throughput of UAV networks. Therefore, an information priority oriented adaptive (IPOA-MAC) protocol is presented in this letter for high dynamic UAV networks. The relative length of control channel interval and service channel interval can be adaptively adjusted to improve network throughput in high dynamic application scenarios. Also, priority flag bits are set in data to ensure real-time transmission of high-priority information. Finally, the simulation results show the proposed IPOA-MAC can improve network performance effectively than other existing protocols.

Since the antennas on UAVs may have slight vibrations or the interference source is in the state of rapid movement in practice, the interference suppression performance and robustness of the traditional methods may suffer a decline. In this paper, we propose a flexible asymmetric null widening technique, which allows flexible adjustment of the null width to accommodate the variation of the interference source. This method has a good effect of spreading zero trap on the two-dimensional array and can effectively reduce the waste of degrees of freedom. Firstly, the flexible asymmetric null widening method is extended to two-dimensional arrays to accommodate 2D array antennas of UAVs. Secondly, when the SMI algorithm is applied in adaptive beamforming, the desired signal appears in sampling snapshots or using data samples, resulting in a model mismatch. To solve the model mismatch problem of UAV antenna arrays, this paper applies a sparsity-based interference plus noise covariance matrix reconstruction technique. Finally, for the application scenario that the UAV may receive signals from multiple directions, we apply the linear constrained minimum variance criterion (LCMV) to achieve the main beam gain formation in multiple directions. The simulation results show that we can generate a wide null and adjust the null width asymmetrically. The results also show that the model mismatch problem is avoided, and the performance of the adaptive beamforming is almost optimal. For the UAV antenna, we also implemented multiple beams to receive multiple signals .

In this paper, a new approach without iteration is proposed for direct position determination (DPD) with Doppler shifts in the presence of unknown direction-dependent mutual coupling (DDMC). The special banded symmetric Toeplitz structure of mutual coupling matrix and Doppler shift containing location information can be exploited for constructing a space-time transformation matrix, and the spectral function which is utilized to obtain the locations of sources is established via the orthogonality between space-time transformation matrix and noise subspace. Simulation results show that our solution can accurately determine the emitter position in a single-step without a priori information of the DDMC.

Deep learning has been widely investigated for radio applications. The classification performance of the deep learning greatly depends on the quality of dataset. However, the deficiency of the training data is a critical issue limiting the classification accuracy in practical scenarios. In this paper, we proposed to use the generative adversarial network (GAN) as a data augmentation tool to solve the problem of inadequate training issue under the lack of sufficient data samples. The data augmentation process could be realized by Nash equilibrium of generator and discriminator. The result shows that the accuracy of the classifier is increased by nearly 4 percentage in the signal to noise ratio range of 0 to 20 dB after data augmentation.

Recently, in order to increase the degrees of freedom, sparse arrays are widely studied for circular signals. However, there are many applications of non-circular signals in practice, but the characteristics of non-circular signals are rarely considered in array design. A new extended array configuration is designed for non-circular signals in this paper. The configuration is designed by incorporating the extended subarray into the maximum inter-element spacing constraint (MISC) to enlarge the number of continuous virtual array elements in the sum-difference co-array. The closed form expressions of the structure and the number of uniform DOFs are given in this paper. Simulation results show that the proposed geometry provide better accuracy for DOA estimation in the presence of unknown mutual coupling while preserving high degrees of freedom.

This paper is devoted to the angle estimation problem for bistatic multiple-input multiple-output (MIMO) radar under strongly correlated targets and spatially colored noise scenes. Firstly, the cross-covariance matrix is established by using the independent noise vector after matched filtering in time domain, and the components of spatial colored noise are eliminated. Then, the cross-covariance matrix is rearranged into a third-order tensor, and the reconstructed tensor can be obtained by concatenating two third-order tensors. Finally, the Parallel Factor (PARAFAC) decomposition is utilized to resolve angle estimates. Owing to the reconstructed third-order tensor, angle estimation by the proposed method is more accurate than existing methods and has stronger robustness even though there is a strong correlation between targets. Simulation results verify the advantages of our solutions over its cutting-edge counterparts.

In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) estimation of coherent signals under the direction-dependent mutual coupling (DDMC) based on weighted subspace fitting. DOAs are determined by applying the least square fitting between signal space and the modified array manifold at first. Subsequently, we put forward an approach to calculate the DDMC matrices and the complex fading coefficients by utilizing the estimated DOAs. Without any iterations, the proposed algorithm can identify the angular information of the coherent signals in a single step in the presence of DDMC. Numerical simulation results show the effectiveness of the proposed algorithm.

Full duplex radio acts as an important role in the future wireless networks since it can transmit and receive data simultaneously over the same channel. However, there are just a few medium access control (MAC) protocols designed for asymmetric full duplex radio networks, most of existed protocols are only suitable for fixed uplink and downlink nodes, which leads to a waste of channel resource. In this paper, we propose a novel MAC protocol for full duplex radio networks depend on the different transmission characteristics of uplink and downlink. To make an efficient use of channel and improve the network throughput, the proposed protocol is composed of two parts, uplink initiated link (UIL) based and downlink initiated (DIL) link based, respectively. In UIL model, access point (AP) can transmit data to different stations according to the UIL transmission slot design, vice versa in DIL model. The analysis and simulation results indicate that the proposed MAC protocol achieves throughput gains compared to existing protocol.

With the rapid proliferation of mobile wireless sensors networks (WSNs), which are widely deployed in the Internet of things, the Internet of vehicles, and flying ad hoc networks, more and more researchers are making the effort to improve energy efficiency and throughput of WSNs. In recent years, many protocols have been proposed to solve the energy shortage problem. Some of the protocols are based on cooperative or allocation schemes, some are based on contending schemes, and others are based on the foundation of hybrid schemes. However, it is still difficult to improve the energy efficiency and networks throughput simultaneously. In this article, we present a novel carrier sense multiple accesscollision avoidance and time division multiple access hybrid scheme protocol medium access control (MAC) on the MAC layer to improve the throughput and reduce energy consumption at the same time for mobile WSN with position prediction algorithms in 3-D space. Compared to existing protocols, the proposed protocol can efficiently reduce the energy consumption and significantly improve the throughput, especially for high-speed mobile WSN in the many-to-one communication paradigm. Extensive simulation results prove the performance of the protocol.