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Recently, as data demand has increased owing to the rapidly increasing demand for wireless devices and the influence of data traffic, various technologies are being developed to support it. Among them, millimeter-wave (mmWave) frequencies with rich spectra and high data-transmission rates suffer from the problem of large path loss. Accordingly, there is a growing interest in unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which can be utilized advantageously to reconstruct wireless communication environments. Therefore, this work considers a large-scale system comprising a number of users and Flying RISs, combining UAVs and RISs to increase algorithm utilization. We propose a deep neural network-based algorithm that places Flying RISs in an appropriate location so that they can support as many users as possible. Simulation results confirmed that the proposed technique could place Flying RISs in an efficient location with higher accuracy and speed in large-scale systems compared to existing techniques.

With the recent expansion of 5th generation mobile communication services, data demand is increasing. Correspondingly, the use of high-frequency millimeter waves and unmanned aerial vehicles (UAVs), which have features of high-altitude operation and mobility, and ease of deployment, is growing. However, for millimeter waves, narrow beams are used to compensate for large path losses, and appropriate beam allocation algorithms are essential. Therefore, in this paper, we propose an algorithm that utilizes a Gaussian process to predict the position of UAV-user equipment (UAV-UE). We then calculate the codebook based on the predicted position information and track the optimal beam in UAV-enabled communication systems. The results of a performance analysis confirm that the proposed algorithm can predict the UAV-UE position with high accuracy, and it has a high spectral efficiency. In addition, in the process of tracking the optimal beam, the complexity of codebook calculation is reduced by using the previously predicted UAV-UE position information.

Reconfigurable intelligent surfaces (RISs) represent a highly promising technology that enhances the capacity and coverage of wireless networks by intelligently reconfiguring the wireless propagation environment in highly advanced wireless communications. The objective of this study is to solve the problem of secrecy rate maximization for multiple RIS-aided millimeter-wave communications by jointly optimizing the active RISs and the RIS phase shifts of the considered system. For this nonconvex problem, we propose multitask learning in a deep neural network to predict the RIS phase shift and active RISs. Numerical results based on realistic, three-dimensional, ray-tracing simulations show that the proposed solution can predict the RIS phase and active RIS with an accuracy rate > 96%. These results confirm the viability of RIS-aided secure wireless communications.

As the majority of data traffic is generated in indoor environments, millimeter-wave (mm-wave) communications are essential. However, owing to their high directivity and high penetration loss, indoor mm-wave communication is vulnerable to blockages caused by users’ bodies and ambient obstacles. In this study, we investigate an online learning-based method that achieves efficient beam and blockage prediction for indoor mm-wave. The proposed method takes advantage of the fact that the optimal beam index and blockage status depend on the user’s position and corresponding data traffic demand. Simulation results based on 3GPP’s new radio channel and blockage models revealed that the proposed scheme could predict mm-wave blockages with an accuracy exceeding 90%. These results confirmed the viability of the proposed deep neural network (DNN) model for predicting optimal mm-wave beam and spectral efficiencies.

This paper presents a simplified joint synchronization scheme for the integer carrier frequency offset and physical cell identity using a primary synchronization signal (PSS) in 5G new radio (NR) communication systems. We demonstrate the efficiency of our proposed NR-PSS synchronization scheme by deriving its simplified implementation, which exploits the near-zero autocorrelation feature between cyclically shifted NR-PSS symbols. As a figure of merit, we compute the probability of detection failure of the proposed NR-PSS synchronization scheme and validate its accuracy via simulations. To illustrate the benefits and limitations of the proposed NR-PSS synchronization scheme, we compare it with the conventional NR-PSS synchronization scheme, considering factors such as detection performance and computation complexity. Numerical results indicate that, regardless of the channel environment, the proposed NR-PSS synchronization scheme achieves a significant reduction in arithmetic complexity while maintaining the same detection capability as existing NR-PSS synchronization schemes.

This paper presents a novel formulation for detecting the secondary synchronization signal in a narrowband Internet of Things communication system. The proposed approach is supported by a noncoherent algorithm that eliminates the need for channel information. A robust joint synchronization scheme is developed by decoupling the estimations of the physical cell identity and the carrier frequency offset. We derive the detection probability of the proposed physical cell identity detector and the mean squared error of the carrier frequency offset estimator, demonstrating their accuracy through simulation results. The performance of the proposed detection scheme is compared with that of existing detection schemes in terms of both estimation accuracy and computational complexity. Experimental results confirm that the proposed synchronization method exhibits superior performance while maintaining relatively lower complexity compared with benchmark methods.

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network technologies that aim to support enormous connection, deep coverage, low power consumption, and low cost. Therefore, low cost of implementation and maintenance is one of the key challenges of NB-IoT terminals. This paper presents a low-complexity formulation for narrowband secondary synchronization signal (NSSS) detection in the NB-IoT system, supported by a coherent algorithm that requires a priori knowledge of the channel. By exploiting a symmetric conjugate feature of the NSSS sequence, a joint physical cell ID and radio frame number detection method with low complexity is proposed for coherent detection. The probability of erroneous detection of the presented NSSS detection method is computed, and the analytical model is verified by means of simulation. Numerical experiments will demonstrate that the proposed detection scheme remarkably reduces the computational complexity with almost the same detection ability compared to the existing detection scheme.