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The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV’s movement will be increased. In this article, the sensing performance, the power allocation and the UAV’s position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.

The deployment of unmanned aerial vehicle (UAV)-enabled cognitive radio system in the area covered by primary network is studied in this letter. The UAV shares the spectrum with primary user (PU) and aims to maximize the transmitted bits with limited battery capacity. When the UAV flies towards the primary transmitter, the sensing performance and throughput of the UAV system will be improved. However, the energy consumption increases linearly with the flying distance. The goal of this letter is to find the optimal position of the UAV and optimize the sensing performance as well as the power allocation such that the transmitted bits is maximized while the PU is sufficiently protected. An iterative optimization algorithm is proposed to obtain the system parameters. Simulation results show the efficiency of the proposed approach in terms of transmitted bits as compared to other schemes.

Relay selection for Relay Assisted (RA) networks is an economical and effective method to improve the spectrum efficiency. Relay selection performs especially well when the source node has accurate and timely Channel State Information (CSI). However, since perfect CSI knowledge is rarely available, research of relay selection with partial (statistical) CSI is of paramount importance. In this paper, relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision (MD) problem. And, the cost of obtaining the CSI is also considered in the formulated problem. Two relay selection schemes, Maximal Selection Probability (MSP) and Maximal Spectrum Efficiency Expectation (MSEE), are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory. The MSP scheme maximizes the probability that the Best Assisted Relay Candidate (BARC) can be selected, whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency. Experimental results show that the proposed schemes effectively improve the spectrum efficiency, and the MSEE scheme is more suitable for stable communication cases. Meanwhile, the MSP scheme is more suitable for burst communication cases.

In this study, the authors investigate the performance of an energy harvesting cognitive cooperative multiple relays network with spectrum sensing, where secondary user accesses the licensed spectrum according to the spectrum sensing results. Particularly, if the spectrum hole is detected, the secondary nodes harvest energy from the ambient environment, otherwise, the secondary source and relays transmit with the energy collected from the primary signals. Taking the imperfect spectrum sensing results into account, exact closed-form expressions for the total outage probability and capacity of the secondary network are derived under the context of Rayleigh fading channels, where the proposed modified selective cooperative decode-and-forward relaying transmission schemes are adopted. Compared with the conventional cognitive relay network, the authors must undertake the correlations among the received signal-to-noise ratios or signal-to-interference-plus-noise ratios caused by both the presence of primary user's interference and energy harvesting for the system understudy. It is shown that spectrum sensing phase and data transmission phase are mutual influence and mutual restraint.

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.

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

In view of the serious multi-access interference in traditional transform domain communication systems, which can suppress the performance of users and bit error rate, a partition multiple access interference reduction method is proposed, which is suitable for large-scale transform domain multiple access communication. A multi-user method is proposed to divide users into users within the region and users outside the region for reducing interference, and the formula of bit error rate is deduced. The partition vector calculation method is proposed and CAN algorithm is introduced to improve the autocorrelation of the partition vector. The overall performance of the method is analyzed from the aspects of spectrum efficiency and bit error rate under gaussian channel, and the optimal length of the partition vector is given. Simulation results show that this method can greatly expand the number of users, ensure the bit error rate at low level, and the spectrum efficiency is stable.

To solve the problem of massive multi-source data under the background of multi-sensor big data, a data fusion method which is easy to realize is used to fuse and mine the massive big data, and the difference between the fusion data and the real data is used as the stability judgment method. Compared with the measurement errors of single sensor and average estimation, it is proved that the heterogeneous sensor using weighted least square data fusion has higher measurement accuracy. The numerical examples are consistent with the theoretical derivation, which further verifies the effectiveness of the proposed method and improves the accuracy and mining effect of big data fusion.

Aiming at the problem of low search efficiency of traditional methods, this paper proposes a method to quickly and accurately estimate the parameters of multi-component linear frequency modulation (LFM) signal. Using the rotation equivalent relationship between the fractional Fourier transform (FRFT) and the Wigner distribution (WD), the calculation method of the optimal rotation angle of the LFM signal in the FRFT is deduced. Considering the problem that mutual interference between multi-component signals affects the estimation accuracy, an efficient search method is proposed to improve the estimation accuracy under the condition of less calculation amount. The simulation results show that this method can achieve fast and accurate estimation of multi-component LFM signal parameters.

Relay assisted (RA) network with energy harvesting (EH) is a promising method to realize the green communication. Meanwhile, the relay selection (RS) is an effective method to exploit the performance advantage of EHRA network. However, the performance of RS depends on the perfect channel state information (CSI) deeply. And, there is an obvious performance gap between the RS strategy with perfect CSI and partial CSI. In order to improve the performance of EH-RA network with partial CSI, a RS strategy is proposed to optimize the spectrum efficiency (SE) under an EH constraint in this paper. The upper bounder (UB) of the proposed RS strategy is also analyzed under the perfect CSI assumption. Moreover, the statistical dynamic program (SDP) is investigated to cooperate with the proposed RS strategy by optimizing threshold under the partial CSI assumption. Numerical results show that the SE performance of the proposed RS strategy with SDP is much better than the greedy algorithm and approaches to the performance UB effectively.

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.

The performance of an underlay cognitive radio network equipped with multiple decode-and-forward relays is investigated in the presence of multiple primary transmitters and multiple primary receivers. The allowed maximum transmission powers for the secondary nodes are obtained according to the outage constraint of multiple primary users (PUs). The authors study selective cooperation scheme for the scenarios of non-direct relay transmission, direct and relay transmission with selection combining, direct and relay transmission with maximum ratio combining, and incremental relay transmission. Considering the dependence among the received signal-to-interference-plus-noise ratios caused by multiple PUs, exact and asymptotical expressions for the outage probability of the proposed selective cooperation schemes are derived over Rayleigh fading channels under underlay spectrum sharing constraints. It is shown that the diversity order for the underlay cognitive relay network with multiple primary transmitters and multiple primary receivers is zero. Finally, simulation results are presented to verify the correctness of the authors’ theoretical derivations.

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 ${\mathcal{ S}}\text {-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.

In this paper, we explore the cognitive cloud radio access networks with cooperative spectrum sensing in the presence of hardware impairment. Multiuser diversity technique is considered in order to enhance system capacity and improve the reliability of transmission. To avoid interfering with the primary user's communications, the BS intends to transmit messages towards the secondary user only when the spectrum hole is detected. Exact closed form expression for the outage probability of the system understudy is derived over Rayleigh fading channels. It is shown that the hardware impairment degrades the performance of the secondary system. Finally, simulation results are presented to verify the correctness of our analytical derivations.

Relay selection is a practical and effective way to improve the spectrum efficiency. However, most of the existing research about the relay selection is developed under the perfect instance channel state information (CSI) assumption. This premise limited the real application of relay selection. Fortunately, with the development of optimal stopping (OS) theory, we have a new way to overcome this shortage. In this paper, a polling relay selection strategy (PRSS) for the amplify-and-forward (AF) relay assisted (RA) network is proposed and analyzed. And, the OS theory is also investigated to cooperate with PRSS under the statistical CSI assumption. The simulation results show that PRSS with OS theory can improve the performance of AF RA network effectively.

Relay assisted (RA) network and the assisted relay (AR) selection is considered as an efficient technology to improve the communication performance since it is easy to use and do not require extra hardware cost. However, most of the previous research did not consider the imperfect state information of the AR candidate sufficiently since it is hard to be formulated. This shortage limited the application of the existing research in practice. In order to solve this problem, an AR selective (ARS) strategy is proposed and analyzed. Moreover, a threshold determination algorithm based on the stochastic dynamic program (SDP) is also developed to make the ARS strategy more practical. Numerical results show that performance of ARS strategy far superior to the performance without AR.

Relay assisted (RA) network with the assisted relay selection is a kind of effective method to improve the channel capacity and converge performance. However, most of the existing research about the assisted relay selection did not consider the statical channel information and the selective cost. This shortage limited the performance and application of RA network in practical scenario. In order to overcome this drawback, a sequential assisted relay selective scheme (ARSS) is proposed. And the performance upper bound (UB) of ARSS is also analyzed. Furthermore, in order to make ARSS more practical, a determination algorithm of the assisted relay selective threshold based on the stochastic dynamic program (SDP) is given to work with ARSS. Numerical results indicate that the performance of ARSS with SDP is close to the performance UB of ARSS effectively.

Relay selection is a crucial method that can improve the spectrum efficiency and converge performance of relay assisted (RA) network. However, most of the existing research about relay selection did not consider the effect of imperfect channel state information (CSI) and the CSI obtaining cost. Few of the study about the relay selection with imperfect CSI focused mainly on the decode-and-forward (DF) protocol. In order to overcome these shortage, a sequence relay selection strategy (SRSS) for the amplify-and-forward (AF) RA network is proposed and analyzed in this paper. Furthermore, in order to make SRSS more practical, the optimal stopping (OS) theory is investigated to work with SRSS. Numerical results indicate that the performance of SRSS with OS theory can achieve the upper bound performance of SRSS effectively.

Multiple input multiple output (MIMO) and pre-coder are the effective methods to overcome the influence of the time-varying channel since it is a key factor of the communication performance. However, the existing researches of MIMO and pre-coder focus on the spectrum efficiency under the Gaussian input assumption. This drawback limits the pre-coder technology application in the green communication. In order to overcome this shortage, we studies the energy efficiency of MIMO system in the time-varying channel based on the time-delay ability of the communication service under the equal probability finite-alphabet input assumption which is more likely to the digital communication signal. Furthermore, a feasible pre-coder algorithm based on the stochastic dynamic program is proposed. The numerical results show that the proposed algorithm performance approaches the energy efficient up bounder of the MIMO system effectively.

Binary Exponential Backoff (BEB) algorithm is utilized to resolve the channel contention for IEEE 802.11 wireless networks. However, BEB algorithm does not meet the user expectation due to the large collision probability and the poor network fairness when the number of stations is large. To overcome the problems, a novel synchronized contention windows-based backoff algorithm is proposed, i.e., SCW. In SCW algorithm, each station (STA) actively tracks the transmission cases of the network and when the channel state is changed, the contention window (CW) of each station which participates in the competition is synchronized by resetting the CW, which makes each station get the medium access grant with the same probability in next channel contention. The experimental results show that with the increase of the number of the stations, the fairness index of the SCW algorithm is nearly equal to 1 and always keeps stable, and when the number of stations is large (nearly 36 STAs), the throughput and delay of the network are respectively increased and reduced by nearly 11% and 6% than the conventional BEB algorithm. The proposed algorithm can effectively improve the performance of throughput and delay, and it has an excellent fairness of network. Therefore, it is especially suitable the wireless networks with the dense stations.

Motivation: A number of methods have been reported that predict protein–protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary surfaces. Are the reported high accuracies realistic? Results: We find that the reported accuracies of the predictions are significantly over-estimated, and strongly dependent on the structure of the training and testing datasets used. The choice of which protein pairs are deemed as non-interactions in the training data has a variable impact on the accuracy estimates, and the accuracies can be artificially inflated by a bias towards dominant samples in the positive data which result from the presence of hub proteins in the protein interaction network. To address this bias, we propose a positive set-specific method to create a ‘balanced’ negative set maintaining the degree distribution for each protein, leading to the conclusion that simple sequence-based features contain insufficient information to be useful for predicting PPIs, but that protein domain-based features have some predictive value. Availability: Our method, named ‘BRS-nonint’, is available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/. All the datasets used in this study are derived from publicly available data, and are available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html Contact: maozuguo@hit.edu.cn; d.r.westhead@leeds.ac.uk

Relay-assisted (RA) network with relay node selection is a kind of effective method to improve the channel capacity and convergence performance. However, most of the existing researches about the relay selection did not consider the statically channel state information and the selection cost. This shortage limited the performance and application of RA network in practical scenarios. In order to overcome this drawback, a sequence relay selection strategy (SRSS) was proposed. And the performance upper bound of SRSS was also analyzed in this paper. Furthermore, in order to make SRSS more practical, a novel threshold determination algorithm based on the stochastic dynamic program (SDP) was given to work with SRSS. Numerical results are also presented to exhibit the performance of SRSS with SDP.

Background 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. Methodology/Principal Findings 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. Conclusions 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/.