Most existing deep learning (DL) based direction-of-arrival (DOA) estimation methods treat direction finding problem as a multi-label classification task and the output of the neural network is a probability spectrum where the peaks indicate the true DOAs. These methods essentially belong to grid-based methods and may encounter grid mismatch effect. In this paper, we focus on gridless DL based DOA estimation under generalized linear array which can be regarded as a uniform linear array (ULA) with/without “holes”. By using the Toeplitz structure, a deep convolutional neural network (CNN) is proposed to estimate the noiseless covariance matrix of the aforementioned ULA with “no holes,” based on which the DOAs can be retrieved by using root-MUSIC. To increase the generalization, the parameters of the CNN with respect to different number of sources are pre-trained and stored in a database. We then propose another CNN for source enumeration in order to choose suitable parameters from the database. Our method can find more sources than sensors and do not suffer from the grid mismatch effect.

In this paper, we propose a relay cooperation scheme for backscatter communication systems for performance enhancement, in which one user backscatters incident signals from a power beacon (PB) to a relay and a receiver simultaneously, and then the relay decodes the received signals and forwards the decoded signals to the receiver. We consider two cases that the relay is with/without an embedded energy source. In particular, if the relay does not have an energy source, an energy harvesting phase is required, during which the relay harvests energy from the PB while the user backscatters information to the receiver. We first formulate system throughput maximization problems for both cases by finding the optimal time allocation schemes, from which some useful insights are provided. Then, with a given amount of information required to be delivered, the transmission time minimization problems for both cases are also formulated, and the optimal solutions are derived in closed-form. Numerical results reveal the proposed scheme can significantly enhance the system throughput and transmission time.

In this paper, we propose a novel deep learning (DL)-based gridless direction-of-arrival (DOA) estimation method for generalized linear arrays using residual attention network (RAN) and transfer learning (TL). The proposed method can improve the DOA estimation performance in both low and high signal-to-noise ratio (SNR) regions by focusing on the important features in the input and avoiding the problems of gradient vanishing and network degradation. Moreover, we introduce the idea of TL to reduce the complexity and costs of training. The experimental results demonstrate the effectiveness and superiority of our method compared with existing methods.

Physical layer security and full duplex have been two widely applied techniques in wireless communications community, where the physical layer security can prevent eavesdropping without the upper layer data encryption, while the full duplex can improve spectrum efficiency through simultaneous transmission and reception. In this paper, we jointly consider full duplex and security by investigating the cross-layer optimization problem to maximize the secrecy rate in the multi-hop wireless network. This combination aims to improve both spectrum efficiency and security of conventional wireless systems at the same time. We first construct a cross-layer secrecy rate model based on full duplex constraints, secrecy capacity constraints, and secrecy flow balance. Then, we formulate it into a mixed integer and non-linear programming problem and reformulate it with reformulation-linearization technique and convex hull relaxation into the linear form. Finally, we validate our proposed optimization method and algorithm through comparing it with half duplex and jamming to demonstrate that the proposed combination of security and full duplex achieve the significant improvement of spectrum efficiency and security.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.