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The effectiveness of laser chaos decision-makers in facilitating ultrafast decision-making makes it possible for a real-time process of channel allocation scheme for the non-orthogonal multiple access (NOMA) technology in the next generation of wireless communications. However, managing the increasing number of users is challenging as the complexity of channel allocation increases significantly. To resolve this challenge, this paper proposes a novel approach to address scalability problems by introducing a parallel bandit architecture using an array of laser chaos decision-makers. In the proposed method, each user is allocated a specific channel by a dedicated laser chaos decision-maker, thereby reducing the number of available options compared with the conventional approach. This parallel bandit architecture enables the system to efficiently manage increasing users while maintaining scalability and ultrafast channel allocation in NOMA. Additionally, fairness is considered by incorporating a logarithmic utility function for design compensation. Numerical simulation results demonstrate that the proposed method achieves higher data rate and enhanced fairness than conventional NOMA approaches such as minimum distance-NOMA (MD-NOMA), conventional-NOMA (C-NOMA), and uniformed channel gain difference-NOMA (UCGD-NOMA). Moreover, the system performance is evaluated on a larger scale, accommodating a significant number of users, with the study considering up to 64 users, surpassing the limitations of the conventional approach of one laser chaos decision-maker, which is constrained to 10 users.

The Internet of Things (IoT) has revolutionized the way we interact with everyday objects by connecting sensors/actuators to the Internet to monitor and control various aspects of our environment. As the number of IoT devices continues to grow, efficient medium access control protocols are needed to ensure that communication takes place in a reliable and scalable manner. In this paper, we investigate the performance of the Asynchronous Pulse Code Multiple Access (APCMA) protocol in massive IoT scenarios through simulations and analysis. We show that APCMA is able to outperform CSMA/CA in terms of the number of successfully transmitted messages in scenarios with up to 30,000 transmitting sensor nodes while also being able to utilize the channel more efficiently. We also examine the sensitivity of APCMA’s performance with respect to its parameters.

Unmanned aerial vehicles (UAVs) are used widely in wireless communications to enhance the service experience of mobile users on the ground. This study investigates a dynamic resource management problem in a large-scale UAV-aided wireless network, where multiple UAVs operate as aerial base stations to serve ground users. To enhance communication efficiency for all users in the presence of co-channel interference, we propose the use of an Ising model-based optimization method for fast and accurate UAV-user association and resource allocation. First, we formulate the dynamic user association and resource allocation problem as a combinatorial optimization problem. Subsequently, we transform the objective function in the formulated problem into the energy function of a quadratic unconstrained binary optimization (QUBO) model and applied coherent ising machine (CIM), which has been proven to be robust for solving dense Ising problems. We evaluate our proposed method through simulations and the real CIM for optimization. Additionally, we compared our method with simulated annealing, steepest descent, and exhaustive search. Performance evaluation results indicate that our proposed method is superior in terms of computational time and data rate.

With the increasing requirements for location-based services for Internet of things (IoT) applications, ultrawideband (UWB) technology provides accurate indoor positioning capabilities. However, indoor environments contain various obstacles leading to significant signal propagation effects. This results in errors in the time-of-arrival-based UWB positioning system. Specifically, a non-line-of-sight (NLOS) signal induces additional distance and position errors owing to the path delay compared to a line-of-sight (LOS) signal. Therefore, UWB signal classification is essential for improving positioning accuracy. Recently, various approaches have successfully classified UWB signals, including machine-learning-based methods such as convolutional neural networks (CNNs) and long short-term memory (LSTM). This study proposes a hybrid quantum CNN (HQCNN) inspired by a CNN for UWB signal classification. HQCNN employs a classical layer before a quantum embedding circuit and variational quantum circuits for the convolutional filter. These structures enable efficient training and implementation. We used UWB channel impulse response data to demonstrate the performance of the proposed algorithm and compared the benchmarks with HQCNN using the evaluation metrics. The results showed that the HQCNN outperformed the others.

Generating paired sequences with maximal compatibility from a given set is one of the most important challenges in various applications, including information and communication technologies. However, the number of possible pairings explodes in a double factorial order as a function of the number of entities, manifesting the difficulties of finding the optimal pairing that maximizes the overall reward. In the meantime, in real-world systems, such as user pairing in non-orthogonal multiple access (NOMA), pairing often needs to be conducted at high speed in dynamically changing environments; hence, efficient recognition of the environment and finding high reward pairings are highly demanded. In this paper, we demonstrate an efficient pairing algorithm to recognize compatibilities among elements as well as to find a pairing that yields a high total compatibility. The proposed pairing strategy consists of two phases. The first is the observation phase, where compatibility information among elements is obtained by only observing the sum of rewards. We show an efficient strategy that allows obtaining all compatibility information with minimal observations. The minimum number of observations under these conditions is also discussed, along with its mathematical proof. The second is the combination phase, by which a pairing with a large total reward is determined heuristically. We transform the pairing problem into a traveling salesman problem (TSP) in a three-layer graph structure, which we call Pairing-TSP. We demonstrate heuristic algorithms in solving the Pairing-TSP efficiently. This research is expected to be utilized in real-world applications such as NOMA, social networks, among others.

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channel-selection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of −15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of −15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.

Interference management is one of the most critical issues in underlaying device-to-device (D2D) communication due to the coexistence of D2D pairs and cellular users that operate under the same spectrum. In this paper, we provide the interference management algorithm to maximize the performance of the D2D communication while satisfying the quality-of-service requirements of the cellular communications in both uplink and downlink phases. The proposed algorithm includes: 1) the admission control and power allocation to ensure that the interference from D2D communication does not affect to the cellular communications and 2) the shared channel assignment to maximize the total throughput of the D2D communication. We prove that our proposed algorithm can achieve at least half of the performance of optimal algorithm. The simulation results validate the feasibility, convergence, and optimality of our algorithm: it cannot only closely approximate the optimal throughput of D2D communication but also outperform existing algorithms.