The combination of simultaneous wireless information and power transfer (SWIPT) and non-orthogonal multiple access (NOMA) is a potential solution to improve spectral efficiency (SE) and energy efficiency (EE) of the upcoming fifth generation (5G) networks, especially in order to support the functionality of the Internet of things (IoT) and the massive machine-type communications (mMTC) scenarios. In this paper, we investigate joint power allocation and time switching (TS) control for EE optimization in a TS-based SWIPT NOMA system. Our aim is to optimize the EE of the system whilst satisfying the constraints on maximum transmit power budget, minimum data rate and minimum harvested energy per-terminal. The considered EE optimization problem is neither linear nor convex involving joint optimization of power allocation and time switching factors, and thus is extremely difficult to solve directly. In order to tackle this problem, we develop a dual-layer algorithm where Dinkelbach method is employed both in the inner-layer to optimize the power allocation and in the outer-layer to control the time switching assignment. Furthermore, a simplified but practical special case with equal time switching factors in all terminals is considered. Numerical results validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access (OMA) scheme in terms of EE can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Covert communication can hide the legitimate transmission from unauthorized eavesdropping. Benefiting from the deployment flexibility, unmanned aerial vehicles (UAVs) can be utilized to enhance communication confidentiality. In this correspondence, we consider a covert communication network with the aid of a full-duplex UAV relay, which is employed to help the transmission and confuse the warden. The warden adopts a radiometer to detect the covert transmission. We first find the optimal detection threshold and calculate the minimum detection error probability. Furthermore, a closed-form expression of outage probability via UAV relaying is derived over Rician fading. Then, a power optimization problem is formulated to maximize the effective convert throughput with covertness constraint. Numerical results illustrate that the cooperative jamming can disrupt the warden, and the optimal power tradeoff can guarantee the covert transmission effectively.

Effective recognition of communication jamming is of vital importance in improving wireless communication system’s anti-jamming capability. Motivated by the major challenges that the jamming data sets in wireless communication system are often small and the recognition performance may be poor, we introduce a novel jamming recognition method based on distributed few-shot learning in this paper. Our proposed method employs a distributed recognition architecture to achieve the global optimization of multiple sub-networks by federated learning. It also introduces a dense block structure in the sub-network structure to improve network information flow by the feature multiplexing and configuration bypass to improve resistance to over-fitting. Our key idea is to first obtain the time-frequency diagram, fractional Fourier transform and constellation diagram of the communication jamming signal as the model-agnostic meta-learning network input, and then train the distributed network through federated learning for jamming recognition. Simulation results show that our proposed method leads to excellent recognition performance with a small data set.

In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of things in this paper. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances and effectively protects the privacy of industrial information.

Transmission rate and harvested energy are wellknown conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multiobjective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. When the eavesdropping channel state information (CSI) is available at BS, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. When the CSI of the eavesdropping channel are not available, we first consider the case that the secure user is not the farthest from BS, and the transmit power of the farther users is maximized via joint precoding optimization to guarantee its security. Then, we consider the case when the farthest user from BS requires secure transmission, and the modified successive interference cancellation order and joint precoding optimization can be adopted to ensure its security. Similar method can be exploited to solve the two non-convex problems when the CSI is unknown. Simulation results demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively, with and without eavesdropping CSI.