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Quantitative analysis of the impact of rapid urbanization on hydrologic cycle is important for optimizing water resources management. The rapid development of small- and medium-sized cities is one of the most significant characteristics of global urbanization today and in the future. This study, therefore, was to evaluate the impact of rapid urbanization on the hydrologic cycle in a typical medium urbanizing city (Yiwu in China) using the Soil and Water Assessment Tool (SWAT) model. For this purpose, scenario analyses with four land uses from different years (i.e., 1990, 1995, 2000, and 2005) were conducted. The hydrological outputs from the watershed with rapid urbanization were compared with those from the surrounding watersheds with less degree of urbanization. Observed monthly surface runoff data during 1972–1988 and 1989–1993 were used for model calibration and validation, respectively. The results showed that the rapid urbanization from 1980 to 2005 in Yiwu led to increased surface runoff and reduced groundwater recharge, evapotranspiration, and soil water storage. More obvious changes in hydrological components were usually observed near the urban areas and in rainy seasons. This study provided important information for managing the rapid urbanization of small- and medium-sized cities in developing countries.

The design of dynamic wireless networks assisted by reconfigurable intelligent surfaces (RIS) mounted on aerial platforms (RIS‐APs) is conceived, where the connection status among users and RIS‐APs are selected according to the average channel quality dynamically and timely. Taking into account the time‐varying selection status and the mobility of users, we construct a long‐term dynamic process. The goal is to minimize the time‐averaged power consumption under the requirements of the time‐averaged minimum rate for users as well as the constraint of the maximum transmit power for the base station (BS), via jointly optimizing the active beamforming at the BS and passive beamforming at RIS‐APs. With the aid of Lyapunov concept‐based drift‐plus‐penalty (DPP) algorithm, the long‐term optimization problem is transformed into short‐term sub‐problems related to each other at each frame. Subsequently, the fractional programming method based on Lagrangian dual theory is applied to derive the solutions for active‐passive beamforming in a closed form. Finally, simulation results validate the convergence and effectiveness of the proposed algorithm.

In this paper, we consider a multi-channel cognitive radio network （CRN） where each secondary user （SU） can only choose to sense a subset of channels. We formulate a joint optimization problem of sensing- channel selection, sensing time and power allocation under the constraints of average transmit power budget and average interference power budget, which maximizes the CRN＇s total throughput. We propose a greedy algorithm to solve the joint optimization problem, which has much less computational complexity. Moreover, it is shown that the search space of the greedy algorithm can be further pruned. Finally, numerical results demonstrate that the greedy algorithm has comparable performance to the exhaustive search algorithm.

Wireless personal communication has become popular with the rapid development of 5G communication systems. Critical demands on transmission speed and QoS make it difficult to upgrade current wireless personal communication systems. In this paper, we develop a novel resource allocation method using deep learning to squeeze the benefits of resource utilization. By generating the convolutional neural network using channel information, resource allocation is to be optimized. The deep learning method could help make full use of the small scale channel information instead of traditional resource optimization, especially when the channel environment is changing fast. Simulation results indicate the fact that the performance of our proposed method is close to MMSE method and better than ZF method, and the time consumption of computation is smaller than traditional method.

Cooperative spectrum sensing (CSS) can improve the spectrum sensing performance by introducing spatial diversity in cognitive radio networks (CRNs). However, such cooperation also introduces the delay for reporting sensing data. Conventional cooperation scheme assumes that the cooperative secondary users (SUs) report their local sensing data to the fusion center sequentially. This causes the reporting delay to increase with the number of the cooperative SUs, and ultimately affects the performance of CSS. In this article, we consider the reporting delay and formulate the optimization problem of CSS with sensing user selection to maximize the average throughput of the CRN in both the additive white Gaussian noise (AWGN) environment and the Rayleigh fading environment. It is shown that selecting all the SUs within the CRN to cooperate might not achieve the maximal average throughput. In particular, for the AWGN environment, the sensing user selection scheme is equivalent to selecting the optimal number of cooperative SUs due to all the SUs having the same instantaneous detection signal-to-noise ratio (SNR). For the Rayleigh fading environment, the maximal average throughput is achieved by selecting a certain number of cooperative SUs with the highest instantaneous detection SNRs to cooperate. Finally, computer simulations are presented to demonstrate that the average throughput of the CRN can be maximized through the optimization.

In this paper, a novel directional modulation with distributed receiver selection (DM-DRS) scheme is proposed for secure wireless communications. In DM-DRS, a particular subset of receivers is activated and part of the information bits are modulated by the index of the activation pattern, in addition to traditional digital modulation. Especially, the scrambling matrix is introduced for the sake of preventing the eavesdropping. Moreover, the performances of bit error rate (BER) in terms of the union bound for both the legitimate user and eavesdropper are respectively derived in the context of an optimal joint maximum likelihood (ML) detector, and the theoretical BER upper bounds are demonstrated to be tight by the numerical results. In the context of the discrete-input and continuous-output system, the ergodic secrecy rates of the legitimate user and the eavesdropper are obtained, the secrecy rate is also quantified. Furthermore, our numerical results exhibit that DM-DRS can achieve an increased transmission rate compared to its traditional directional modulation with cooperative receivers (DM-CR) and spatial and direction modulation (SDM) counterparts, while guaranteeing an improved BER performance.

In this article, we consider a cognitive radio (CR) relay network where one source secondary user (SU) communicates with its corresponding destination SU with the help of relay SUs. Conventionally, equal bandwidth and/or power are allocated to each relay SU, which may not be efficient for the CR with limited bandwidth and power. Therefore, this article presents bandwidth and power allocation with amplify-and-forward (AF) or decode-and-forward (DF) relaying protocol to (1) maximize the sum network throughput; (2) minimize the total transmit power of the CR network with considering the fairness of power drain of relay SUs; (3) maximize the energy efficiency of the CR network. It is shown that DF relaying protocol can achieve better performance when the decoding rate constraint is not considered. In contrast, when considering the decoding rate constraint in DF relaying protocol, we propose the hybrid relaying protocol that combines AF and DF relaying protocols. We formulate the joint bandwidth and power allocation problem with hybrid relaying protocol to maximize the sum network throughput. A greedy algorithm is developed to solve the joint optimization problem, which has much less computational complexity. It is shown that the greedy algorithm has comparable performance to the exhaustive search algorithm. Finally, numerical results are provided to endorse our proposed algorithms.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint trasceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.

In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation and wideband beamsplit, an uncharted domain in existing literature. Our approach to maximizing secrecy rates involves formulating an optimization problem for joint power allocation and analog beamformer design, employing a two-stage process encompassing a semi-digital solution and analog approximation. This problem is efficiently solved through a combination of alternating optimization, fractional programming, and block successive upper-bound minimization techniques. Additionally, we present a low-complexity beamsplit-aware beamfocusing strategy, capitalizing on geometric insights from near-field wideband propagation, which can also serve as a robust initial value for the optimization-based approach. Numerical results substantiate the efficacy of the proposed methods, clearly demonstrating their superiority over TTD-free approaches in fortifying wideband PLS, as well as the advantageous secrecy energy efficiency achieved by leveraging low-cost analog devices.

Artificial noise (AN) is a key physical-layer security scheme for wireless communications over multiple-input multiple-output wiretap channels. Recently, artificial noise elimination (ANE) has emerged as a strategy to mitigate the impact of AN on eavesdroppers. However, the influence of ANE on the secrecy rate when counteracting AN has not been investigated. In this paper, we address this issue by establishing scaling laws for both average and instantaneous secrecy rates in the presence of AN and ANE. Based on the scaling laws, several derived corollaries provide insights into the mutual constraints between the number of transmit antennas, receive antennas, and antennas at eavesdroppers, revealing the interplay between these factors. A key corollary reveals that when the eavesdropper possesses more than twice as many antennas as the transmitter, secure communication may no longer be guaranteed. Additionally, by comparing scenarios where ANE counteracts AN with those where AN is not employed, this study identifies sufficient conditions under which AN remains effective. Finally, the derived secrecy rates provide guidelines for system design, even in the presence of advanced ANE countermeasures implemented by the eavesdropper.