The device-to-device (D2D) communication is viewed as an attractive technique to increase the spectrum efficiency and the data transmission rate in the wireless network. In this paper, we investigate the joint resource allocation and power control problem for cooperative D2D users (DUs) which multiplex cellular users (CUs) in downlink cooperative D2D heterogeneous networks. The studied resource allocation problem contains the spectrum resource block allocation and the selection of an idle user which works as a relay to assist the D2D links communication, while the power control aims to reduce the interference between users and improve the communication quality of service (QoS). To efficiently maximize the total throughput of all the DU links and the CU links on the premise of guaranteeing the communication QoS for CUs, we propose a quantum coral reefs optimization algorithm (QCROA) to obtain the optimal joint resource allocation and power control scheme. The simulation results demonstrate that the proposed QCROA achieves an excellent performance for different network communication scenarios.
The wireless energy harvesting (EH) technique is regarded as a new way to provide an energy supply for energy-constrained cognitive relay networks (CRNs). A novel wireless EH cognitive multiuser relay network (CMRN) for the underlay protocol is investigated in this paper. In this system, there are multiple primary users (PUs) and multiple secondary users (SUs). The SUs can share the licensed spectrum and harvest energy from ambient signals. The problems of multiple relay selection by the SUs and of finding the optimal EH ratio are considered. We analytically derive the exact expression of the throughput of a secondary network. In it, there are four constraints: for the permitted peak interference with each primary transmitter (PT); for the sum interference for each PT; that the transmit power of secondary source nodes (SSNs) and secondary relays (SRs) should be less than the energy harvested; and that each secondary source node/secondary destination node (SSN-SDN) pair can only choose one SR. To obtain the optimal performance of the secondary network's throughput, we should optimize the multiple relay selection scheme and the EH ratio. Actually, it is a classic integer optimization problem to design an optimal multiple relay selection scheme. However, the selection of the optimal EH ratio is a continuous optimization problem. The joint multiple relay selection and time slot allocation is a classical hybrid optimization problem. So, we propose a novel quantum sine cosine algorithm (QSCA) for resolving the difficulty with optimization of multiple relay selection and the EH ratio. Our simulation results verify our proposed solution by showing the influence of different parameters for the proposed model and by demonstrating good performance under the QSCA.
Energy harvesting (EH) technology is considered to be a promising approach to provide enough energy for energy-constrained Internet of Things (IoT). In this paper, we propose an energy harvesting and information transmission mode for the spectrum sharing system with cooperative EH-abled IoT applications in beyond 5G networks. Different from most existing IoT spectrum-sharing research studies, in our system, both primary user (PU) and IoT devices (IDs) collect energy for their information transmission. In addition, for all IDs, they should realize two communication functions: working as relays to help the information transfer process of PU and completing their own information transmission. We analytically derive exact expressions for the throughput of the primary system and IoT system and then formulate two objective functions. It is easy to see that power splitting ratio, dynamic EH ratio, power sharing ratio, and relay selection should be optimized to get the best performance for different communication circumstances. Actually, it is a hybrid NP-hard problem to optimize these parameters and traditional algorithms cannot solve it well. Therefore, a novel algorithm-quantum whale optimization algorithm (QWOA) is proposed to obtain the best performance. Simulation results show the good performance of QWOA in different simulation scenarios.
With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.
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