In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.
In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.
In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.
In this paper, we study the secrecy throughput in a massive Multiple-Input-Multiple-Output (MIMO) full-duplex wireless powered communication network (WPCN). The network consists of a massive MIMO base station (BS) and two groups of single-antenna sensor nodes which harvest energy from the BS. The first group, referred to as information transmitters (ITs), use the harvested energy to transmit information back to the BS; the second group, referred to as energy receivers (ERs), use the harvested energy for non-transmission related operations. We consider a two-slot protocol. In the first time slot, all nodes harvest energy from the BS. In the second slot, ITs transmit information to the BS, while BS use one set of its antennas to receive the signals and the other set of antennas to transmit artificial noise (AN) to the ERs. The AN serves two purposes: wireless power transfer to the ERs, and information security for the ITs with ERs, which are considered as potential eavesdroppers. We aim to maximize the total secrecy throughput of ITs subject to the ERs' received energy constraints. The problem is shown to be non-convex. To tackle the problem, we propose a twostage suboptimal approach, referred to as Maximizing Received AN aided Secrecy Throughput Maximization (MRAN-STM). In the first stage, the transmitted AN is optimized to maximize the minimum received AN of all the ERs. Then, in the second stage, the power allocation and time slot duration are optimized to maximize the total secrecy throughput. Numerical results show the improvement of the proposed algorithm.
Nowadays, the Internet of Things (IoT) acts as a key enabler for smart cities, intelligent transportation systems, precision medicine, smart grids, etc. However, the computing power of IoT nodes is weak, and encryption algorithms cannot be used to ensure the secure transmission of data because of the high complexity. In recent years, the emerging technologies of wireless powered communication network (WPCN) and physical layer security (PLS) are regarded as potential solutions to allow IoT nodes to harvest energy from radio frequency (RF) and ensure secure data delivery. The secrecy rate, which is defined as the difference of main channel capacity and eavesdropping channel capacity, represents the secure data delivery ability. How to maximize the secrecy rate with the rather limited energy for IoT network is a challenging problem. In this paper, we formulate the secrecy rate maximization problem as an optimization problem. We solve this problem by two steps: using one-dimension research to find the optimal energy harvesting duration and find the closed-form solution for transmission power and transmission duration for every node. Compared with the heuristic algorithm, the proposed algorithm can obtain better performance from secrecy rate perspective.
This paper investigates a wireless powered secure communication network (WPSCN), in which the legitimate users have no constant energy supply and need to be wireless powered for information transmitting. The harvest-then-transmit protocol is adopted in the WPSCN, i.e., the energy access point (EAP) first transfers energy to legitimate users through energy beamforming, and then, the legitimate users transmit their data to the information access point (IAP) by time-division multiple access. Since an eavesdropper intercepts users’ uplink information, the EAP creates artificial noise to cripple the interception capability of the eavesdropper. For providing a secrecy throughput balancing across different legitimate users, we aim to maximize the minimum secrecy throughput of all users via time allocation, energy beamforming, and artificial noise design. To this end, a low-complexity two-stage algorithm is proposed. In the first stage, without taking into account the eavesdropper's wiretapping, we design time allocation and energy beamforming by solving the problem of minimum throughput maximization, which is convex and can be easily solved. In the second stage, two easily implementable strategies are proposed for artificial noise design so as to strengthen the noise power at the eavesdropper and simultaneously reduce the noise power at the IAP. Simulation results show that the performance of the proposed approach is close to the upper bound and not sensitive to scenario settings, which confirms the effectiveness of the proposed method.
This paper investigates secure communication from a wireless-powered transmitter to a desired receiver with multiple eavesdroppers in the wireless powered communication networks (WPCNs). Considering the non-linear energy harvesting (EH) model, we propose a secure two-phase communication protocol with the help of a hybrid base station (HBS). First, in the power transfer (PT) phase, the HBS transfers wireless power to the transmitter. Then in the subsequent secure information transmission (SIT) phase, the transmitter sends the secret information using the energy harvested in the PT, under the protection of artificial noise (AN) generated by the HBS. First, based on this communication protocol, we maximize the secrecy throughput with perfect channel information state (CSI) under the transmit power constraint at the HBS. The secrecy throughput maximization (STM) problem is non-convex, and hence we reformulate it by exploiting the primal decomposition method (PDM) to obtain tractable forms. The PDM-based transmit scheme (PDM-TS) is proposed for the STM. In addition, considering the imperfect CSI of wiretap channel, we further design the robust transmit scheme for the worst-case secrecy throughput maximization (wSTM) problem. Since the wSTM shows high non-convexity, we extend the PDM by combining it with the S -procedure, and the PDM-based robust transmit scheme (PDM-RTS) is proposed for the wSTM. Finally, the numerical simulations are provided to show the effectiveness of the proposed transmit schemes.
In this paper, we study a robust beamforming design for multi-user multiple-input multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet of Things devices under the non-Linear energy harvesting model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve energy harvesting fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method outperforms the traditional AN-aided method and CJ-aided method.