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Abstract The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) is considered as a promising approach to tackle soaring computing requirements. The broadcast nature of air‐to‐ground (A2G) links makes UAV communications vulnerable to eavesdroppers, so secure UAV communications remain an open question. This paper proposes a secure communication scheme for reconfigurable intelligent surface (RIS)‐assisted UAV‐MEC systems, in which the RIS assists the user in offloading data to the legitimate UAV, and the legitimate UAV provides computing services to the user. To fully expand the security computing capacity of the system, the communication link is improved by introducing RIS, and the jammer interferes with the eavesdropper. The secure computing capability of the system is maximized by optimizing communication resources and trajectories. Since the proposed problem is non‐convex, successive convex approximation (SCA) technique and block coordinate descent (BCD) technique is combined to solve the problem. The simulation results show that the proposed scheme in this paper can effectively improve the system secure computing bits compared with the benchmark scheme.

Abstract This paper introduces the short packet transmission in non‐orthogonal multiple access (NOMA)‐based unmanned aerial vehicle (UAV) assisted relay communication system. Short packet transmission has considerable potential to decrease the transmission latency of UAV communication, and NOMA can effectively enhance the spectrum efficiency and fairness. To improve the reliability of the system, an effective packet error rate (PER) minimization problem within short packet transmission is proposed by jointly optimizing the packet length, UAV placement, and power allocation under the reliability requirement and total power constraints. To address the intricate PER minimization problem, the optimization problem is firstly decomposed into three sub‐problems, and the corresponding monotonicity and convexity are analyzed, respectively. Then, an overall iterative optimization algorithm for PER minimization based on alternating direction method of multipliers algorithm and optimal solution algorithm is formulated by solving the three sub‐problems in an iterative manner. Simulation results validate the effectiveness and convergence of the proposed NOMA scheme and overall iterative optimization algorithm, respectively.

Abstract Owing to the advantages of low cost, flexibility, and the transmission characteristics of air–ground (AG) channels, using the unmanned aerial vehicle (UAV) as mobile relay to assist wireless communication has received significant interest recently. A green non‐orthogonal multiple access (NOMA)‐based multi‐UAV‐enabled relaying system with different rate requirements is proposed here. To improve energy efficiency (EE) of the UAV relays, an optimization problem for user grouping, UAV trajectory design and resource allocation under the constraints of information causality constraint, UAVs' maximum service capacity, maximum transmit power constraint, and different communication rate requirements of the mobile users (MUs) is formulated. According to the grouping results, the UAVs' trajectory and power allocation by Dinkelbach method, successive convex approximation, and condensation algorithm are alternately optimized. To solve this highly coupled non‐linear mixed integer programming problem, a graph‐based grouping algorithm is proposed to reduce the relative distance between the UAV relay and the MU. Simulation results show that the proposed optimization algorithm can effectively improve the EE performance of this NOMA‐based multi‐UAV‐enabled relaying system.

Abstract This paper studies unmanned aerial vehicle (UAV)‐assisted cognitive network, where the UAV can improve the communication quality of edge users. Short packet communication (SPC) is widely used due to its low delay transmission characteristic. Unlike long packet communication in conventional wireless networks, SPC has a non‐negligible packet error rate and its data transmission rate is less than Shannon capacity. Considering the fact that the UAV is usually powered by battery, the energy efficiency (EE) maximisation problem is investigated based on short packet transmission in the UAV‐assisted cognitive network. Firstly, the closed‐form expression of EE is analysed, and then the optimisation problem is formulated by jointly optimising the spectrum sensing time, packet error rate, the flight speed, and the coverage range of UAV. Secondly, the optimisation problem is solved by dividing it into four subproblems. Then, an efficient iterative algorithm is proposed to tackle this problem. Simulation results show that the proposed optimisation scheme can evidently improve the EE performance compared with other benchmark schemes. In addition, the proposed joint optimisation algorithm not only has better convergence than exhaustive method, but also has higher stability than PSO algorithm.

The rapid development of urbanization and urban land-use intensification have substantially reduced biodiversity and ecosystem services. Wildflower mixtures can improve regional biodiversity and habitat heterogeneity by establishing flower-rich habitats and providing multiple ecosystem services, which were considered one of the most favorable landscape ecological restoration technologies approaches. How to design wildflower mixtures to achieve optimal eco-efficiency is currently unclear. In China, wildflower mixture is one of landscape architecture’s most popular methods for establishing herbaceous vegetation. Currently, there is a notable lack of systematic research regarding species selection, and concerns have been raised about the design approach that introduces convergent species, which may potentially become invasive. This research integrated a wildflower mixture design database currently published in China and discovered community composition and construction methods. Based on this, we compared the species composition of wildflower mixtures in China and assessed the potential invasive species. Additionally, we attempted to explore the distribution over time and space, their potential invasion risk, and the main factors influencing species selection through modeling approaches. The results highlighted that species selection of mixture converged with different spatiotemporal attributes and design intentions, despite designed wildflower mixtures in China having gradually increased in recent years. A large number of species from North America and Europe have been causing an increase in potential invasion annually. Furthermore, the research confirmed that the convergence contributes to the homogenization of urban landscape vegetation communities, which leads to biological invasion from the design level, and revealed the possibility that ineffective ecological restoration techniques may cause adverse environmental impacts.

Unmanned aerial vehicles (UAVs) are considered an important component of 6G wireless technology. However, there are many challenges to the employment of UAVs, one of which is spectrum scarcity. To address this challenge, non-orthogonal multiple access (NOMA) and cognitive radio (CR) techniques are employed in UAV short-packet communication systems. In this paper, we consider a NOMA-based cognitive UAV short-packet communication system. Firstly, a mathematical expression for the effective throughput of the secondary users is derived. Then, we aim to maximize the effective throughput of the far secondary user by optimizing the sensing time, power allocation, and information bits under the constraints of the transmission power and effective decoding error probability. A joint optimization algorithm is used to solve this problem, where the bisection method and the one-dimensional linear search algorithm are used to solve the subproblem. The simulation results show that the proposed algorithm has low complexity and similar performance compared to the exhaustive method. In addition, the necessity of joint optimization is shown in the simulation results.

In recent years, the development of wireless communication leads to an explosive growth of energy demand, widely application of smart devices and rapid emergence of services. So, the energy efficient communication is expected urgently to save power and prolong the lifetime of the resource-constrained terminal devices. Especially in 5G age, the excellent spectrum efficiency provides more opportunity to save power by adjusting the transmission power of the delay tolerable (DT) service. Meanwhile, although the tradeoff between energy efficiency and service delay plays a non-negligible role in the energy efficient communication, it is not exploited sufficiently due to the time variation and randomness of wireless communication channel. For this reason, the fundamental tradeoff between energy efficiency and delay of the DT service is investigated and analyzed. And, the optimal problem of energy efficient communication for DT service is formulated as a Markov Decision Process (MDP) which can be solved effectively by statistical dynamic programming (SDP) since the perfect channel state information (CSI) is hard to obtain. To improve the utility of research, the approximate SDP (ASDP) and Q-learning are also investigated to overcome the limitation of the curse of dimensionality and model-based algorithm respectively.

Green energy powered cognitive radio is proposed as a promising method to improve the energy efficiency (EE) and spectrum efficiency (SE). We consider the green power farms that harvest energy from solar and wind, which is used for the primary transmitter and the cognitive sensor network (CSN). The primary transmitter has priority to utilize the spectrum and the energy, and then the energy powered for the CSN may not be sufficient. Both the energy management and energy harvesting process will affect the throughput of the cognitive sensors (CSs). In this paper, we aim to design the system parameters in the CSN (including the sensing threshold, the sensing time, the final decision threshold in the fusion center, and the number of cooperating CSs) that can improve the utilization efficiency of the harvested energy and maximize the CSs' throughput. Since the parameters are intertwined with the energy causality constraint and the average throughput, we decouple the influence of final decision threshold on the throughput from the influence of the sensing time and sensing threshold. The optimization problem is divided into two sub-problems. Algorithm 1 and Algorithm 2 are proposed to solve sub-problem 1 and sub-problem 2, respectively. The simulation results show that the proposed scheme can improve the average throughput significantly, and is able to achieve the best tradeoff between SE and EE.

Unmanned Aerial Vehicle (UAV) aided communication has the potential to provide on-demand wireless services and improve the outdoor link throughput. Applications for UAVs are rapidly growing with the development of Internet of Things. Because of limited battery energy, the UAVs need time-limited spectrum access to complete data transmission. Hence there are two challenges for the UAV-based communication: 1) Spectrum-efficient design; 2) Energy-efficient design. In this paper, we investigate the optimization of spectrum efficiency (SE) and energy efficiency (EE) for cognitive UAV network based on location information. Because of high mobility, the cognitive radio (CR) based UAVs operate on different frequency bands that vary with time and space. Thus, one spectrum band that is available in one region may not be necessarily available in another region. Based on location information of the primary transmitter and the UAV, we propose a hybrid mode in which the sensing performance and UAV's transmit power can be adjusted simultaneously to satisfy the outage constraint of the primary user. The multi-objective optimization theory is used to solve the tradeoff between SE and EE. The UAV's transmit power, sensing time and sensing threshold are optimized jointly to solve the tradeoff problem. To further improve the SE and EE performance, we propose a multi-frame combined sensing scheme, in which multiple frames are bundled together. Simulation results are provided to show the SE-EE tradeoff design, to validate the effectiveness of the proposed hybrid mode, and to show the advantages of the multi-frame combined sensing scheme in EE performance.

This paper studies the secure transmit design for a downlink multiple-input-single-output cognitive radio network (CRN) with a practical energy harvesting (EH) model. Due to the open architecture of a CRN, the confidential information intended for the secondary receiver is prone to be eavesdropped by external eavesdroppers (Eves). Specifically, considering imperfect channel state information, our aim is to jointly design the transmit beamforming vector and the power splitting ratio, such that the outage-constrained secrecy rate is maximized under the constraints of total power consumption, secrecy rate outage probability, EH and quality of service requirements. The original problem is not convex and intractable in general, in order to tackle this non-convexity issue, we utilize semidefinite relaxation and Bernstein-type inequality to transform the outage constraints into a deterministic form. We show that the optimal solution can be obtained by alternately optimizing two convex subproblems. Also, we prove that the optimal solution of the relaxed problem will always be rank one. Furthermore, we consider a large deviation inequality (LDI)-based approach to obtain a sub-optimal solution, which can significantly reduce the computational complexity. Finally, simulation results have been provided to demonstrate the performance of our proposed designs.

Energy harvesting techniques are promising in next generation wireless communication systems. However, most of the existing works are based on an ideal linear energy harvesting model. In this paper, a multiple-input single-output cognitive radio network is studied under a practical non-linear energy harvesting model. In order to improve the security of both the primary network and the secondary network, a cooperative jamming scheme is proposed. A robust artificial noise aided beamforming design problem is formulated under the bounded channel state information error model. The formulated problem is non-convex and challenging to be solved. Using S-Procedure and the semidefinite relaxation method, a suboptimal beamforming can be obtained. Simulation results show that the performance achieved under the non-linear energy harvesting model may be better than that obtained under the linear energy harvesting model. It is also shown that the cooperation between the primary network and the secondary network can obtain a performance gain compared with that without this cooperation.

MicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates.A miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method.The superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/.