A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point Networks

Internet Exchange Point(IXP)is a system that increases network bandwidth performance.Internet exchange points facilitate interconnection among network providers,including Internet Service Providers(ISPs)andContent Delivery Providers(CDNs).To improve service management,Internet exchange point providers have adopted the Software Defined Network(SDN)paradigm.This implementation is known as a Software-Defined Exchange Point(SDX).It improves network providers’operations and management.However,performance issues still exist,particularly with multi-hop topologies.These issues include switch memory costs,packet processing latency,and link failure recovery delays.The paper proposes Enhanced Link Failure Rerouting(ELFR),an improved mechanism for rerouting link failures in software-defined exchange point networks.The proposed mechanism aims to minimize packet processing time for fast link failure recovery and enhance path calculation efficiency while reducing switch storage overhead by exploiting the Programming Protocol-independent Packet Processors(P4)features.The paper presents the proposed mechanisms’efficiency by utilizing advanced algorithms and demonstrating improved performance in packet processing speed,path calculation effectiveness,and switch storage management compared to current mechanisms.The proposed mechanism shows significant improvements,leading to a 37.5%decrease in Recovery Time(RT)and a 33.33%decrease in both Calculation Time(CT)and Computational Overhead(CO)when compared to current mechanisms.The study highlights the effectiveness and resource efficiency of the proposed mechanism in effectively resolving crucial issues inmulti-hop software-defined exchange point networks.

Efficient Time Synchronization Approach for Wireless Communication Systems on GPP-Based Software-Defined Radio Platform

General purpose processer （GPP） based software-defined radio （SDR） platforms provide wireless communication system engineers with maximal architecture flexibility and versatility to construct a wideband wireless communication system. Nevertheless, the lack of hardware real-time timing control makes it difficult to achieve time synchronization between the base station and the terminals. In this paper, a software-based time synchronization （STS） method is proposed to realize the time synchronization of time division multiple access （TDMA） based wireless communication systems. A high precision software clock source is firstly constructed to measure the elapse of processing time. The Round-Trip Delay （RTD） algorithm is then presented to calculate timing advance values and achieve time synchronization. An example TDMA system is implemented on Microsoft Sora platforms to evaluate is effective to enable time synchronization for wideband the performance. Experiments show that the proposed mechanism wireless communication systems on GPP-based SDR platforms.

A 4th-order reconfigurable analog baseband filter for software-defined radio applications

This paper presents a 4th-order reconfigurable analog baseband filter for software-defined radios.The design exploits an active-RC low pass filter（LPF） structure with digital assistant,which is flexible for tunability of filter characteristics,such as cut-off frequency,selectivity,type,noise,gain and power.A novel reconfigurable operational amplifier is proposed to realize the optimization of noise and scalability of power dissipation.The chip was fabricated in an SMIC 0.13μm CMOS process.The main filter and frequency calibration circuit occupy 1.8×0.8 mm;and 0.48×0.25 mm;areas,respectively.The measurement results indicate that the filter provides Butterworth and Chebyshev responses with a wide frequency tuning range from 280 kHz to 15 MHz and a gain range from 0 to 18 dB.An IIP3 of 29 dBm is achieved under a 1.2 V power supply.The input inferred noise density varies from 41 to 133 nV/（Hz）;according to a given standard,and the power consumptions are 5.46 mW for low band（from 280 kHz to 3 MHz） and 8.74 mW for high band（from 3 to 15 MHz） mode.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,flexibility,and reduce network maintenance costs,a new Software-Defined Network(SDN)technology must be used in this infrastructure.Despite the various advantages of combining SDN and IoT,this environment is more vulnerable to various attacks due to the centralization of control.Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service(DDoS)attacks,but they often lack mechanisms to mitigate their severity.This paper proposes a Multi-Attack Intrusion Detection System(MAIDS)for Software-Defined IoT Networks(SDN-IoT).The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms.First,a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets:the Network Security Laboratory Knowledge Discovery in Databases(NSL-KDD)and the Canadian Institute for Cyberse-curity Intrusion Detection Systems(CICIDS2017),to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems.The algorithms evaluated include Extreme Gradient Boosting(XGBoost),K-Nearest Neighbor(KNN),Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR).Second,an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems(IDS)was developed to enable effective comparison between the datasets used in the development of the security scheme.The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system,with average accuracies of 99.88%and 99.89%,respectively.Furthermore,the proposed security scheme reduced the false alarm rate by 33.23%,which is a significant improvement over prevalent schemes.Finally,tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset,making it the best for IDS compared to the NSL-KDD dataset.

Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.

The Tianma 65 m radio telescope antenna

The Tianma 65 m radio telescope(TMRT)at Shanghai is a fully steerable single-dish radio telescope in China,operating at centimeter to millimeter wavelengths(1.25 GHz to 50 GHz).This paper presents details on the main specifications,design,performance analysis,testing,and construction of the telescope antenna.The measured total efficiency is better than 50%over the whole elevation angle range,first sidelobe levels are less than−20 dB,antenna system noise temperatures are less than 70 K at 30°elevation angle,and pointing accuracy is less than 3″.The measured and calculated results are in good agreement,verifying the effectiveness of the design and analysis.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

基于GNU Radio的软件化雷达中心机设计与实现

基于开源式软件无线电平台GNU Radio设计软件化雷达中心机架构,实现相控阵搜跟一体雷达中心机的快速开发和组件可重构。介绍了中心机接口、任务组成和工作流程,通过多线程并行处理多任务提高系统实时性,通过时序控制及优化保证仿真准确性,采用Message实现各分机通信。半实物测试表明设计的中心机满足雷达仿真要求,且能复用和重构。

Between the City and Images:An Analysis of Mainstream Media’s Paths of Constructing the Cultural Memory of a City:Taking Chengdu Radio and Television’s“Hi Chengdu”as an Example

Mainstream media play a crucial role in constructing the cultural memory of a city.This study used 319 short videos released by“Hi Chengdu,”a new media product of Chengdu Radio and Television,as samples.Based on the grounded theory,a research framework encompassing“content,technology,and discourse”was established to explore the paths through which mainstream media construct the cultural memory.Regarding content,this paper emphasized temporal and spatial contexts and urban spaces,delving deep into the themes of the cultural memory and vehicles for it.In terms of technology,this paper discussed the practice of leveraging audio/visual-mode discourse to stitch together the impressions of a city and evoke emotional resonance to create a“flow”of memory.As for discourse,this paper looked at the performance of a communication ritual to frame concepts and shape urban identity.It is essential to break free from conventional thinking and leverage local culture as the primary driving force to further boost a city’s productivity,in order to excel in cultural communication.

A Machine Learning Made Catalog of FR-Ⅱ Radio Galaxies from the FIRST Survey

We present an independent catalog(FRIIRGcat)of 45,241 Fanaroff–Riley TypeⅡ(FR-Ⅱ)radio galaxies compiled from the Very Large Array Faint Images of the Radio Sky at Twenty-centimeters(FIRST)survey and employed the deep learning method.Among them,optical and/or infrared counterparts are identified for 41,425 FR-Ⅱs.This catalog spans luminosities 2.63×10^(22)≤L_(rad)≤6.76×10^(29)W Hz^(-1)and redshifts up to z=5.01.The spectroscopic classification indicates that there are 1431 low-excitation radio galaxies and 260 high-excitation radio galaxies.Among the spectroscopically identified sources,black hole masses are estimated for 4837 FR-Is,which are in 10^(7.5)■M_(BH)■10^(9.5)M_(⊙).Interestingly,this catalog reveals a couple of giant radio galaxies(GRGs),which are already in the existing GRG catalog,confirming the efficiency of this FR-I catalog.Furthermore,284new GRGs are unveiled in this new FR-I sample;they have the largest projected sizes ranging from 701 to1209 kpc and are located at redshifts 0.31<z<2.42.Finally,we explore the distribution of the jet position angle and it shows that the faint Images of the FIRST images are significantly affected by the systematic effect(the observing beams).The method presented in this work is expected to be applicable to the radio sky surveys that are currently being conducted because they have finely refined telescope arrays.On the other hand,we are expecting that further new methods will be dedicated to solving this problem.

Design and Application of an S-band Fast Radio Bursts Search Pipeline for the Nanshan 26 m Radio Telescope

Fast radio bursts(FRBs)are among the most studied radio transients in astrophysics,but their origin and radiation mechanism are still unknown.It is a challenge to search for FRB events in a huge amount of observational data with high speed and high accuracy.With the rapid advancement of the FRB research process,FRB searching has changed from archive data mining to either long-term monitoring of the repeating FRBs or all-sky surveys with specialized equipments.Therefore,establishing a highly efficient and high quality FRB search pipeline is the primary task in FRB research.Deep learning techniques provide new ideas for FRB search processing.We have detected radio bursts from FRB 20201124A in the L-band observational data of the Nanshan 26 m radio telescope(NSRT-26m)using the constructed deep learning based search pipeline named dispersed dynamic spectra search(DDSS).Afterwards,we further retrained the deep learning model and applied the DDSS framework to S-band observations.In this paper,we present the FRB observation system and search pipeline using the S-band receiver.We carried out search experiments,and successfully detected the radio bursts from the magnetar SGR J1935+2145and FRB 20220912A.The experimental results show that the search pipeline can complete the search efficiently and output the search results with high accuracy.

The Impact of Network Topologies and Radio Duty Cycle Mechanisms on the RPL Routing Protocol Power Consumption

The Internet of Things(IoT)has witnessed a significant surge in adoption,particularly through the utilization of Wireless Sensor Networks(WSNs),which comprise small internet-connected devices.These deployments span various environments and offer a multitude of benefits.However,the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities.In response to this,the Internet Engineering Task Force(IETF)has developed the IPv6 Routing Protocol for Low-power and Lossy Networks(RPL)to address the unique requirements of such networks.Recognizing the critical role of RPL in maintaining high performance,this paper proposes a novel approach to optimizing power consumption.Specifically,it introduces a developed sensor motes topology integrated with a Radio Duty Cycling(RDC)mechanism aimed at minimizing power usage.Through rigorous analysis,the paper evaluates the power efficiency of this approach through several simulations conducted across different network topologies,including random,linear,tree,and elliptical topologies.Additionally,three distinct RDC mechanisms—CXMAC,ContikiMAC,and NullRDC—are investigated to assess their impact on power consumption.The findings of the study,based on a comprehensive and deep analysis of the simulated results,highlight the efficiency of ContikiMAC in power conservation.This research contributes valuable insights into enhancing the energy efficiency of RPL-based IoT networks,ultimately facilitating their widespread deployment and usability in diverse environments.

Cognitive Jammers Assisted Covert Communication in Cognitive Radio Networks

In this work,we investigate the covert communication in cognitive radio(CR)networks with the existence of multiple cognitive jammers(CJs).Specifically,the secondary transmitter(ST)helps the primary transmitter(PT)to relay information to primary receiver(PR),as a reward,the ST can use PT's spectrum to transmit private information against the eavesdropper(Eve)under the help of one selected cognitive jammer(CJ).Meanwhile,we propose three jammer-selection schemes,namely,link-oriented jammer selection(LJS),min-max jammer selection(MMJS)and random jammer selection(RJS).For each scheme,we analyze the average covert throughput(ACT)and covert outage probability(COP).Our simulation results show that CJ is helpful to ST's covert communication,the expected minimum detection error probability and ACT can be significantly improved with the increase of false alarm of CJ.Moreover,the LJS scheme achieves best performance in ACT and COP,followed by RJS scheme,and MMJS scheme shows the worst performance.

Anti-Jamming Null Space Projection Beamforming Based on Symbiotic Radio

With the development of information technology,more and more devices are connected to the Internet through wireless communication to complete data interconnection.Due to the broadcast characteristics ofwireless channels,wireless networks have suffered more and more malicious attacks.Physical layer security has received extensive attention from industry and academia.MIMO is considered to be one of the most important technologies related to physical layer security.Through beamforming technology,messages can be transmitted to legitimate users in an offset direction that is as orthogonal as possible to the interference channel to ensure the reception SINR by legitimate users.Combining the symbiotic radio(SR)technology,this paper considers a symbiotic radio antijamming MIMO system equipped with a multi-antenna system at the main base station.In order to avoid the interference signal and improve the SINR of the signal received by the user.The base station is equipped with a uniform rectangular antenna array,and using Null Space Projection(NSP)Beamforming,Intelligent Reflecting Surface(IRS)can assist in changing the beam’s angle.The simulation results show that NSP Beamforming could make a better use of the null space of interference,which can effectively improve the received SINR of users under directional interference,and improve the utilization efficiency of signal energy.

The characteristics of negative corona discharge and radio interference at different altitudes based on coaxial wire-cylinder gap

The corona discharge from transmission lines in high-altitude areas is more severe than at lower altitudes. The radio interference caused thereby is a key factor to be considered when designing transmission lines. To study the influence of altitude on negative corona characteristics, an experimental platform comprising a movable small corona cage was established: experiments were conducted at four altitudes in the range of 1120-4320 m, and data on the corona current pulse and radio interference level of 0.8-mm diameter fine copper wire under different negative voltages were collected. The experimental results show that the average amplitude, repetition frequency and average current of the corona current pulse increase with increasing altitude. The dispersion of pulse amplitude increases with increase in altitude, while the randomness of the pulse interval decreases continuously. Taking the average current as an intermediate variable,the relationship between radio interference level and altitude is obtained. The result of this research has some significance for understanding the corona discharge characteristics of ultra-highvoltage lines.

Ambulatory Radio Iodine Therapy in the Management of Hyperthyroidism in Africa: African Systematic Review and Perspectives in Burkina Faso

Introduction: In hyperthyroidism, selective irradiation of the thyroid gland with radioactive iodine is a radical treatment and an alternative to surgery. The aim of this review is to assess the medium-term efficacy of outpatient treatment of hyperthyroidism with iodine-131 in Africa. Methods: We identified the studies carried out in Africa on outpatient radiation therapy between 2016 and 2020. For each article included, we noted the country concerned and the year of publication, the numbers studied, the socio-demographic characteristics of the patients, the indications for radio iodine therapy, the dose administered, the results of the hormonal dosage 6 months after radiation. Results: 13 retrospective studies were included to constitute a total population of 925 patients. The average age was 40.77 years, the sex ratio of 1/5.4 with a clear female predominance. The 3 main etiologies of hyperthyroidism justifying outpatient radio iodine therapy were Graves’ disease (55.89%), toxic multinodular goiter (22.70%) and toxic adenoma (21.40%). The average dose of iodine 131 administered per course is 13.7 mCi. No short-and medium-term complications were reported. The radio iodine therapy was effective in 86.08% (n = 796) of the patients with extremes of 72% and 100%. Conclusion: Radio iodine therapy is effective in Africa. It is simple, inexpensive on an outpatient basis and well tolerated. The introduction of outpatient radio iodine therapy could improve the management of patients with hyperthyroidism in Burkina Faso.

The Tianma 65 m radio telescope at Shanghai

The Shanghai Tianma 65 m radio telescope(TMRT)is a large,fully rotatable radio telescope with multiple scientific purposes.The main body of the telescope and four low-frequency receiving systems,including L,C,and S/X bands,were completed between 2008 and 2012.From 2013 to 2017,four high-frequency receiving systems,including Ku,K,Ka,and Q bands,were constructed and their performance was comprehensively tested.There are three main innovations.(1)A fully movable large radio telescope system with advanced performance and complete functions has been built.(2)An advanced,reliable main reflector adjustment system has been completed,overcoming gravity deformation and creating a large antenna with a main reflective surface accuracy of 0.28 mm(root mean square)for any elevation.(3)Five innovative technologies have been developed to achieve high-precision pointing in any direction within 3″.The TMRT has made a crucial contribution to the orbital measurement and positioning of China’s lunar and deep space probes.Significantly enhancing China's ability to participate in international VLBI observations and radio astronomy,this has facilitated a series of achievements in observational radio astronomical research,in areas such as VLBI,spectral lines,and pulsars.