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Quantum computing is a field of study aimed at developing technologies based on the principles of quantum theory. In this research area, enormous theoretical progress has been made, but at the current state, few quantum algorithms have been developed. It is very hard to efficiently construct new ones. The complexity is due to the non-intuitive nature of quantum operations. To overcome this obstacle, we opt for the integration of an automatic design approach. In this paper, we propose an adapted version of BackTracking algorithm allowing complete but optimal exploration of the quantum circuits space. For a given problem, the test of possible quantum gate sequences is optimized by using a constraint making it possible to ignore all unnecessary calculation in an efficient manner. In addition, solutions are generated automatically without resorting to mathematical formulas. Our approach allowed us to generate two new versions of controlled quantum teleportation. In the first version, the controller can switch the teleported Qubit between two possible distant receivers, while in the second version, this controller can choose between two different transmitters. [ABSTRACT FROM AUTHOR]

In this paper, the main research object is evolution algorithm applied for an ultra-wideband three-dimensional (3D) circular antenna array and the significance of elevation angles and their influence on the system performance are highlighted. The 3D channel is calculated by means of 3D shooting and bouncing ray/image techniques which specify multipath elevation angles and azimuth for horizontal plane angles. The capacities of the two-dimensional (2D) and three-dimensional transmitting antenna arrays have been presented. Beam-synthesizing techniques are applied at the transmitter to focus the transmitter energy for the sake of mitigating the multi-path effect and improving channel capacity. A combination of asynchronous particle swarm optimization (APSO) and dynamic differential evolution (DDE) methods are introduced to make adjustment to the length of the feed line on each array element for the maximum capacity. In comparison with 2D azimuth-only antenna arrays, a higher directive gain can be achieved from the 3D antenna arrays results in high channel capacity. Besides, it is discovered that the capacity for the 3D array improves by as much as over 10% when compared to that for the 2D array. As revealed by numerical results, APSO is capable of obtaining better beam-forming pattern to improve the channel capacity than DDE by about 9%. Not only does our research provide qualitative outcomes, it also presents quantitative results. [ABSTRACT FROM AUTHOR]

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramér–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy harvesting device-to-device (D2D) communication system, where D2D transmitter can use mode A to directly communicate with D2D receiver or use mode B as a relay to assist cellular communication while communicating with D2D receiver by adopting non-orthogonal multiple access technology. Firstly, the outage probability expression in two modes is derived, and the communication mode is determined according to outage performance. Then, assuming that the full system information is available, the channel allocation and relay selection are completed by Kuhn–Munkres algorithm, and the offline power allocation of D2D users is realized by reinforcement learning. Next, the offline optimization results are taken as the training data set to train the neural network, and the optimal model of the transmission power is obtained. Considering the transmission power constraint, the online power allocation optimization algorithm is further proposed. Numerical results demonstrate the accuracy of derived outage probability, and the proposed resource allocation algorithm can improve the performance of hybrid system. [ABSTRACT FROM AUTHOR]

In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to increase the number of connected devices and the spectrum efficiency. Both underlay and overlay spectrum access modes (SAMs) are considered. We introduce a dynamic user grouping (UG) concept as a practical constraint for SIC, in order to decrease the receiver complexity. Moreover, SIC constraints are introduced based on a minimum signal-to-noise-plus-interference ratio (SINR) at the receiver. We formulate a joint dynamic UG, power allocation, and resource block (RB) assignment problem, aiming at maximizing the total sum-rate of both CUs and MTC-Ds. We transform the obtained non-convex mixed-integer programming problem into a convex problem by using a quadratic fractional programming (FP). A heuristic method is also proposed to reduce complexity. Simulation results demonstrate that the proposed solution outperforms the conventional CN (C-CN) method and the results of the proposed optimization methods are close to optimal solution obtained by the exhaustive search (ES) algorithm in terms of total average sum-rate and network connectivity, while requiring less transmit power. The heuristic method decrease the computational complexity of the FP method at the expense of a small reduction in spectrum efficiency. [ABSTRACT FROM AUTHOR]

To balance the output stability and maximum efficiency of a system, an efficiency optimization method based on adaptive frequency control is proposed. In this paper, load transmission is carried out using a DC–DC converter at the receiving end. When the load changes, controlling the rectified output voltage is carried out to realize the maximum efficiency point tracking. Meanwhile, the duty cycle of the rear stage DC–DC converter is changed to keep the output voltage constant. The sampled rectified voltage is compared with the estimated value and the resulting error data are sent to the system transmitter through ASK modulation wireless communication. Then the transmitter demodulation communication information through PID algorithm control is used to achieve the frequency of the adaptive adjustment. The feasibility of the proposed method has been verified by the simulation and experimental results. When the output voltage is 5 V, the constant voltage accuracy is within ± 1%, the maximum efficiency is up to 79.2%, and the dynamic response time is only 160 ms. [ABSTRACT FROM AUTHOR]

Rendezvous control of multiple robots without losing network connectivity has important implications in multi-robot system including formation control, coordinated task assignments, and cooperative robotic missions. This paper introduces a new coordinate-free, bearing-based algorithm to enable rendezvous of distributed mobile robots at any designated leader robot node using hierarchical tracking of wireless network topology. An assumption is made that the robot can only sense and communicate with their neighbors (i.e., local sensing). The proposed approach preserves connectivity during the rendezvous task, adapts to dynamic changes in the network topology (e.g., losing or re-gaining a communication link), and is tolerant of mobility faults in the robots. We theoretically analyze the proposed algorithm and experimentally demonstrate the approach through simulations and extensive field experiments. The results indicate that the method is effective in a variety of realistic scenarios in which the robots are distributed in a cluttered environment. [ABSTRACT FROM AUTHOR]

This paper addresses the self-interference (SI) cancellation at baseband for full-duplex MIMO communication systems in consideration of practical transmitter imperfections. In particular, we develop a subspace-based algorithm to jointly estimate the SI and intended channels and the nonlinear distortions. By exploiting the covariance and pseudo-covariance of the received signal, we can increase the dimension of the received signal subspace while keeping the dimension of the signal subspace constant, and hence, the proposed algorithm can be applied to most of full-duplex MIMO configurations with arbitrary numbers of transmit and receive antennas. The channel coefficients are estimated, up to an ambiguity term, without any knowledge of the intended signal. A joint detection and ambiguity identification scheme is proposed. Simulation results show that the proposed algorithm can properly estimate the channel with only one pilot symbol and offers superior SI cancellation performance. [ABSTRACT FROM AUTHOR]

An algorithm for reconstructing images of reflectors from echo signals that propagate in a test object, which consists of several regions with different acoustic properties, is considered. The ray trajectories are calculated using the method of direct construction of the family of rays that escape from the point where a transmitter is located but not the Fermat variational principle. After the family of rays is constructed, their belonging to a certain acoustic scheme can be analyzed and the approximation of the calculated delays on the spatial mesh of the image reconstruction region (IRR) can be performed. This will allow the reconstruction of both the pulse travel time from the transmitter to any point in the IRR and back to the receiver and the attenuation of the pulse amplitude caused by the divergence, reflection, and refraction effects at the boundaries of the regions that constitute the test object. Numerical and model experiments show the working capacity of the proposed algorithm for reconstructing reflector images. [ABSTRACT FROM AUTHOR]