A two-hop relay network is considered, in which an eavesdropper can overhear the relaying signal. To prevent the eavesdropper from decoding this signal, a destination transmits a jamming noise while a source transmits the data signal to the relay. At the same time, the relay can harvest energy from both the source signal and the jamming noise, and use this harvested energy to forward the received signal to the destination. In such a wireless-powered relay system with cooperative jamming, we propose two adaptive relaying schemes based on power splitting and time switching techniques. In the proposed power splitting-based relaying (PSR) and time switching-based relaying (TSR) schemes, the relay controls the power splitting ratio (ρ) and time switching ratio (α), respectively, in order to achieve a balance between signal processing and energy harvesting. We find analytically the optimal values of ρ and α in each scheme to maximize the secrecy capacity under the assumption of high signal-to-noise ratio. Interestingly, although the eavesdropper’s channel state information (CSI) is used in the derivation of the optimal control parameters (ρ and α), they are shown not to be affected by the eavesdropper’s CSI in a high SNR regime. This implies that the proposed schemes can be effective even for practical environments where there is no eavesdropper’s CSI. Furthermore, simulation results show that they well coincide with the exact solutions in practical environments even though the closed-form solutions are obtained with a high SNR assumption. Moreover, the comparisons of PSR and TSR in various scenarios show that the two relaying schemes have complementary performances depending on the network conditions. Specifically, PSR achieves greater secrecy capacity than TSR when the channel condition is unfavourable to the eavesdropper for wiretapping.

This paper considers a two-way relay network, in which two sources exchange data through a relay and a cooperative jammer transmits an artificial noise (AN) while a number of nearby eavesdroppers overhear to recover data from both sources. The relay harvests energy from the two source signals and the AN, and then, uses this harvested energy to forward the received signals to the two sources. Each source eliminates its own signal from the relaying signal by self-cancellation and then decodes the data signal received from the other source. For this wireless-powered two-way relay system, we propose two secure relay protocols based on power splitting and time switching techniques. The two protocols are power splitting-based two-way relaying (PS-TWR) and time switching-based two-way relaying (TS-TWR), in which the relay respectively controls the power splitting ratio (ρ) and time switching ratio (α), in order to achieve a balance between the data receiving and the energy harvesting. The optimal values of ρ and α for each protocol are found analytically to maximize the minimum guaranteed secrecy capacity (CminS) considering multiple eavesdroppers in high signal-to-noise ratio environments. Numerical results show that both the PS-TWR and TS-TWR protocols using the optimized values of ρ and α achieve the near-optimal CminS no matter how many eavesdroppers exist anywhere. Comparisons of the two protocols in various scenarios also show that PS-TWR achieves better CminS than TS-TWR because PS-TWR inherently has a shorter vulnerable time for eavesdropping than TS-TWR.

This paper considers a multiple unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In this WPCN, UAVs broadcast radio frequency (RF) signals to facilitate a wireless power transfer (WPT) during the downlink phase, and ground nodes (GNs) harvest energy from these RF signals and transmit data to their respective UAVs in the uplink phase. To maximize the minimum uplink throughput of GNs, we jointly optimize the scheduling, transmit power of GNs, and trajectory of UAVs, while satisfying the energy neutrality of GNs and the mobility constraints of UAVs. To solve this non-convex optimization problem, we apply a successive convex approximation to divide the original problem into subproblems and make each of them convex for each optimization variable. Subsequently, we propose an iterative algorithm based on a block coordinate descent technique and efficiently find the optimal solution for each convex subproblem. The simulation result reveals that resource allocation and the trajectory of UAVs are strongly influenced by the interference level within the network. Furthermore, the result verifies that the proposed optimization approach significantly outperforms existing baseline schemes by properly coordinating co-channel and cross-link interferences between distinct UAV networks.

In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

In multi-beam satellites, transmit power is a limited resource shared among beams, and allocating higher power to certain beams may cause more interference with others. Moreover, beam placement is the issue of determining the center position of each beam and potentially leads to inter-beam interference depending on the locations of ground nodes. Therefore, in this study, we investigate the joint optimization problem of beam placement and transmit power to maximize the sum spectral efficiency in multi-beam low-Earth-orbit satellite communication systems, taking into account the inter-beam interference and user distribution. We solve the optimization problem using the gradient ascent method and quadratic transform, and then propose an optimization-based algorithm that iteratively searches for the beam center positions and transmit power levels. To reduce the complexity of this iterative algorithm, we present a deep neural network (DNN) architecture and a training method for approximating optimal solutions, and propose a deep-learning (DL)-based algorithm that quickly infers the optimal values using the pre-trained DNN. Simulation results show that the two proposed algorithms have a clear trade-off in performance between the spectral efficiency and the computation time. In particular, the DL-based algorithm achieves 3% lower spectral efficiency than the optimization-based algorithm; however, the computation time can be significantly reduced. Furthermore, both schemes achieve at least 10% higher spectral efficiency than the benchmark schemes without joint optimization by optimally adjusting both the beam center position and transmit power according to the node distribution and satellite environments.

Spectrum sensing is a key enabling technology for cognitive radio. Since there is a tradeoff between the probability of missed detection and the probability of false alarm according to a value of sensing threshold, it is very important to determine the sensing threshold suitable for cognitive radio environments. In this paper, we propose a novel method to determine the sensing threshold in the cognitive radio system, in which the secondary user (SU) first decides its transmission power for the communication and then decides the sensing threshold for the coexistence with the primary user (PU). For the coexistence, the SU controls its sensing threshold adaptively according to its transmission power in order to guarantee the minimum decodable SINR for the primary receiver. The analysis results show that the adaptively controlled sensing threshold decreases both the missed detection and the false alarm simultaneously and so enables both SU and PU to coexist in the same channel without interfering each other.