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From cognitive radio to artificial intelligence, explain the development process of dynamic spectrum management technology

Views:3     Author:Site Editor     Publish Time: 2020-02-06      Origin:Site


With the rapid development of radio technology, limited frequency resources have been unable to meet the increasing demand for frequency. In order to improve the utilization efficiency of frequency resources, comprehensive management of frequency resources has received widespread attention. During the development of traditional static management to dynamic management, new technologies such as cognitive radio and TDOA positioning have been developed rapidly. However, the most advanced artificial intelligence technology at present will complete the conversion of data to decision-making more efficiently and complete comprehensive tasks more intelligently.


Cognitive Radio


At present, the wireless network generally adopts a fixed spectrum allocation method, and almost all wireless terminals work under the spectrum allocated by some spectrum management agencies (such as the International Telecommunication Union and the spectrum management agencies of various countries). Research shows that under this method, most of the allocated spectrum is often not fully used in many areas, and its utilization rate ranges from 15% to 85%. Dynamic spectrum sharing is an important subject in the research of cognitive wireless networks, which aims to improve the utilization efficiency of wireless spectrum resources. Driven by opportunity, cognitive radio allows wireless terminals to automatically sense, identify, and utilize any available spectrum resources. Once an authorized user on the used spectrum segment appears, the wireless terminal will actively give up the corresponding spectrum and switch to another available spectrum.


In order to solve the problem of dynamic spectrum utilization, cognitive radio technology has developed rapidly in recent years. The research of cognitive radio mainly includes: the Next Generation Wireless Communication (xG) project funded by the US Defense Advanced Research Projects Agency (DARPA), a National Natural Science Foundation project on cognitive wireless technology being performed by the winlab laboratory of Rutgers University Research on adaptive radio frequency technology in the UK's Mobile Telecommunications Technology Virtual Center, DRIVE, OverDRiVE, and TRUST funded by the European Communications Association, collaboration and cross-layer design techniques, spatial signal detection and analysis, and QoS in the cognitive radio system of the national "863" plan Guarantee mechanism, etc.


Cognitive radio is a radio technology that can change parameters at the transmitting end according to environmental changes. It can use this frequency band when no user uses the authorized frequency band, which greatly improves the spectrum utilization rate and makes up for the shortcomings of fixed spectrum allocation. It is the key technology of the next generation network. From a technical point of view, cognitive radio can change transmission-end parameters according to environmental changes. It is used for adaptive spectrum management and the development of subsystems—smart antennas, sensors and receivers, adaptive modulation and waveform technologies, etc., and is the key to the dynamic use of spectrum in next-generation networks. Cognitive radio can use this frequency band when no user uses the authorized frequency band, which greatly improves the spectrum utilization rate and makes up for the shortcomings of fixed spectrum allocation.


Cognitive radio has two main capabilities. One is cognitive ability. Cognitive ability is the ability to obtain perceptual information from the environment. Use complex techniques to obtain instantaneous spatial variables of the environment and avoid interference with other users. The other is the ability to reset. Cognitive ability perceives the spectrum, and reset ability allows the radio to dynamically configure hardware parameters so that it can send and receive on different frequencies, and it can also use different transmission accesses supported by hardware devices. Therefore, cognitive radios can implement multiple functions in dynamic spectrum management. The first is spectrum sensing, determining which spectrum is available, and detecting whether an authorized user exists when the user works on an authorized frequency band. The second is spectrum management, selecting the best available channel to use. Third is spectrum sharing, adjusting channel access with other users, and providing users with appropriate spectrum arrangement methods. The last is spectrum shift. After detecting the space-time channel of authorized users, it moves to other frequency bands.


The disadvantage of cognitive radio technology is that the complexity of the system is greatly increased, and the communication quality cannot be completely guaranteed. Therefore, a new system is proposed internationally, namely, a dynamic authorization management system. In other words, companies such as radio and television are allowed to lease idle spectrum to users in hot spots. At present, the main representatives of international research fronts are LSA of the European Union and SuperWiFi of the United States. Although cognitive radio partially solves the problem of dynamic spectrum management in a certain frequency band from the perspective of radio users, there is still a lack of effective solutions for the dynamic management of the full spectrum by radio management agencies. In the technical analysis process of radio spectrum management, it is often necessary to identify important signals and abnormal signals. When there are many types of signals in the frequency band and many transmitting stations, multiple signals will overlap in the same frequency band, which brings great challenges to spectrum management. The spectrum sensing in cognitive radio technology can quickly identify the idle spectrum and establish an idle spectrum pool, but it cannot solve the dynamic spectrum allocation problem for changing radio spectrum conditions. The spectrum dynamic management needs to solve not only the dynamic sensing of the spectrum environment, but also the close integration of autonomous decision-making to achieve dynamic management. The rise of artificial intelligence may bring development opportunities for dynamic spectrum management.


With the development of technology, artificial intelligence has been applied in e-commerce, finance and medical treatment. The term artificial intelligence was first proposed by cognitive scientist John McCarthy in his research. His interpretation of artificial intelligence is a conjecture of this research, that is, any learning behavior or other intellectual characteristics can be accurately described in principle, so that it can be manufactured Make a machine to simulate it. With the development of technology, artificial intelligence has been applied in e-commerce, finance and medical treatment. At the same time, machine learning and deep learning are often mentioned along with artificial intelligence. Machine learning is a pathway or subset of artificial intelligence that emphasizes learning rather than computer programs. A machine uses sophisticated algorithms to analyze large amounts of data, identify patterns in the data, and make predictions. Deep learning is a subset of machine learning. It uses large amounts of data and computing power to simulate deep neural networks. These three concepts are closely linked, but each has its own emphasis.


Artificial intelligence was officially proposed at the Dartmouth Conference in 1956, and entered a period of rapid development in 2006. With the breakthrough of deep learning algorithms in speech and image recognition, the commercialization of artificial intelligence has achieved rapid development. In 2016, AlphaGo defeated Li Shishi, and artificial intelligence received unprecedented attention in the world. Artificial intelligence products and services continue to be launched, such as Amazon Echo smart speakers, Facebook to use artificial intelligence to improve user experience, have been widely recognized in the market, BAT is also actively promoting artificial intelligence projects.


In terms of policy assistance, the government vigorously supports the artificial intelligence industry. In the "New Generation Artificial Intelligence Development Plan" issued in July this year, it is proposed that by 2020, the overall technology and application of artificial intelligence in China will be synchronized with the world's advanced level; The basic theory of artificial intelligence has achieved major breakthroughs, and some technologies and applications have reached the world-leading level; by 2030, China's artificial intelligence theory, technology and applications will generally reach the world-leading level and become the world's major artificial intelligence innovation center.


The Global Summit on Artificial Intelligence for Humanity, held in Geneva from June 7 to 9, 2017, aims to accelerate the development and popularization of artificial intelligence (AI) solutions to tackle poverty, hunger, health, education, equality and environmental protection And other global challenges. As the United Nations specialized agency in charge of information and communication technology, ITU aims to guide the continuous innovation of artificial intelligence to ultimately achieve the UN's sustainable development goals. ITU Secretary-General Houlin Zhao said: "We are providing a neutral Platform to reach consensus on the capabilities of emerging artificial intelligence technologies. "


"In many public competitions we organize, we can see teams using artificial intelligence as a basic tool in many fields, from creating personalized learning experiences for Tanzanian children who do not have formal education, to empowering consumers to use medical triple-recording devices Equipment makes medical decisions, and then guides advanced, autonomous robotic vehicles to explore the deep sea or find paths on the moon ’s surface, "said Xcuse Foundation CEO (CEO) Marcus Shingles." We recognize that with the With the accelerated advancement and popularization of artificial intelligence, a new generation of problem solvers faces great opportunities in meeting global challenges. "This event is the first meeting of a series of activities of the annual conference of artificial intelligence. There are government, industry, and UN agencies. Representatives from civil society and the artificial intelligence research community participated to discuss the latest developments in artificial intelligence and its impact on regulatory, ethical, and security and privacy issues.


In order to solve the shortage of electromagnetic spectrum resources, artificial intelligence will focus on intelligent decision-making. Professor Wu Qihui, a spectrum research expert, spoke at the "2017 Global Future Network Development Summit" and said that traditional spectrum decision-making is a manual method, mainly because the scenario is relatively simple, and decision-making may not be necessary, or even just prediction. But now spectrum operations are conducted in a complex electromagnetic spectrum environment, and the complexity is mainly reflected in diversity, intensive, large-scale, high dynamics and high confrontation. We study intelligent spectrum decision-making or autonomous spectrum decision-making. From the operational point of view, we mainly deal with pre-war rapid planning, wartime self-coordination, and confrontation with the enemy. The intelligent decision-making method of man-machine hybrid is mainly used for advance decision and temporary decision.


Driven by the mobile Internet, the Internet of Things, and the integrated information network of the heavens and the earth, the future wireless network will develop in the direction of higher speed, more access, and wider coverage, posing more challenges to spectrum resources. In order to meet these three challenges, we need to carry out three changes in the spectrum. These three changes also reflect the Internet +, artificial intelligence +, and spectrum transformation.


In order to solve the shortage of electromagnetic spectrum resources and promote the transformation of spectrum resources from static monopoly to dynamic sharing, artificial intelligence will focus on intelligent decision-making and promote the transition from isolated monitoring to grid-based monitoring and analysis. Decision-making shifts to autonomous decision-making. Radio spectrum machine learning system is a technical application of artificial intelligence in radio frequency management.


The Radio Defense Machine Learning System funded by the Defense Advanced Research Projects Agency (DARPA) consists of four major technologies:

1. Feature learning: identify signals from signal data and classify them according to user settings.

2. Intelligent monitoring: intelligently focus on the key frequency bands or frequency points in the spectrum from the massive data collected in real time. Predict and adjust to the corresponding key monitoring frequency band or frequency point according to the rules set by the user.

3. Automatic perception recognition: Automatically adjust monitoring settings based on user task needs.

4. Signal synthesis: Digitally synthesize signals according to user needs and improve the quality of synthesized signals.


In the technical analysis process of radio spectrum management, it is often necessary to identify important signals and abnormal signals. This usually depends on the experience of monitoring facilities and engineers. Once it encounters emergencies such as black broadcasts and pseudo base stations, it often requires a lot of manpower. Time inspection positioning. In addition, in order to improve the frequency use efficiency, the management department hopes to improve the frequency band sharing technology and predict the use of the frequency band in order to perform frequency reuse without causing interference.


It is true that the application of artificial intelligence in radio management also faces many challenges. For example, the application of artificial intelligence in dynamic spectrum management is based on big data. The data required is not only large but complex. Radio monitoring data, frequency data, and station data have their own focuses but are inseparable. On the other hand, if deep learning achieves autonomous decision-making, it also requires a set of rigorous research and judgment rules, and a quantifiable assessment of the frequency spectrum. "National Radio Management Plan (2016-2020)" points out: During the "Thirteenth Five-Year Plan" period, the primary task is to innovate spectrum management, establish a scientific and reasonable spectrum use assessment and frequency recovery mechanism, and form an administrative approval and market-oriented configuration management system. Therefore, on the one hand, we lay a solid foundation, on the other hand, we must also keep abreast of forward-looking technology development trends, use artificial intelligence technology to serve dynamic spectrum management, and serve radio management in the new situation.


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