Energy-Efficient Secure Communications for Wireless-Powered Cognitive Radio Networks

In this study, we investigate energy-efficient secure communications for wireless-powered cognitive ratio networks, in which multiple secondary users (SUs) share the same frequency band with primary users (PUs) and energy harvesting (EH) nodes harvest energy from the transmitted signals, even though information decoding is not permitted. To maximize the average secrecy energy efficiency (SEE) of SUs while ensuring acceptable interference on PUs and the required amount of energy for the EH nodes, we propose an energy-efficient transmit power control algorithm using dual decomposition, wherein suboptimal transmit powers are determined in an iterative manner with low complexity. Through extensive simulations in various scenarios, we verify that the proposed scheme has a higher average SEE than conventional schemes and a considerably shorter computation time than the optimal scheme.

Low-Complexity Transmit Power Control for Secure Communications in Wireless-Powered Cognitive Radio Networks

In this study, wireless-powered cognitive radio networks (WPCRNs) are considered, in which N sets of transmitters, receivers and energy-harvesting (EH) nodes in secondary networks share the same spectrum with primary users (PUs) and none of the EH nodes is allowed to decode information but can harvest energy from the signals. Given that the EH nodes are untrusted nodes from the point of view of information transfer, the eavesdropping of secret information can occur if they decide to eavesdrop on information instead of harvesting energy from the signals transmitted by secondary users (SUs). For secure communications in WPCRNs, we aim to find the optimal transmit powers of SUs that maximize the average secrecy rate of SUs while maintaining the interference to PUs below an allowable level, while guaranteeing the minimum EH requirement for each EH node. First, we derive an analytical expression for the transmit power via dual decomposition and propose a suboptimal transmit power control algorithm, which is implemented in an iterative manner with low complexity. The simulation results confirm that the proposed scheme outperforms the conventional distributed schemes by more than 10% in terms of the average secrecy rate and outage probability and can also considerably reduce the computation time compared with the optimal scheme.

Distributed Transmit Power Control for Energy-Efficient Wireless-Powered Secure Communications

In this study, we consider energy-efficient wireless-powered secure communications, in which N sets of transmitter, receiver, and energy harvesting (EH) nodes exist; each EH node is allowed only to harvest energy from the transmitted signals but is not to permitted to decode information. To maximize the sum secrecy energy efficiency (SEE) of the node sets while ensuring minimum EH requirement for each EH node, we propose a distributed transmit power control algorithm using a dual method, where each transmitter adjusts its transmit power iteratively until convergence without sharing information with the other node sets. Through simulations under various environments, we show that the proposed scheme surpasses conventional schemes in terms of the sum SEE and has significantly reduced computation time compared with the optimal scheme, which suggests the effectiveness and applicability of the proposed distributed method.

Cost-Effective Optimization for Blockchain-Enabled NOMA-Based MEC Networks

Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.