Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Intelligent Spectrum Management Based on Radio Map for Cloud-Based Satellite and Terrestrial Spectrum Shared Networks

Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.

Solving Dynamic Spectrum Management Problem Based on Cloud Computing Using Genetic Algorithm

With the rapid development of wireless sensor network （WSN）, the demands of limited radio frequency spectrum rise sharply, thereby dealing with the frequency assignment of WSN scientifically and efficiently becomes a popular topic. To improve the frequency utilization rate in WSN, a spectrum management system for WSN combined with cloud computing technology should be considered. From the optimization point of view, the study of dynamic spectrum management can be divided into three kinds of methods, including Nash equilibrium, social utility maximization, and competitive economy equilibrium. In this paper, we propose a genetic algorithm based approach to allocate the power spectrum dynamically. The objective is to maximize the sum of individual Shannon utilities with the background interference and crosstalk consideration. Compared to the approach in [1], the experimental result shows better balance between efficiency and effectiveness of our approach.

Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput,lower latency,and wider coverage.However,spectrum scarcity places obstacles in the sustainable development.To accommodate the expanding network within a limited spectrum,spectrum sharing is deemed as a promising candidate.Particularly,cognitive radio(CR)has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically.However,the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability.In this paper,we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence(AI).Specifically,SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and interoperability.AI is further used to make predictive environment perception and to configure the network for optimal resource allocation.Briefly,the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

Spectrum Resource Management of Cognitive Radio

As the most important technology of CR, the wireless spectrum resource management technology is the key to CR performance improvement. By introducing the concept of resource space to describe wireless spectrum resource management in the field of CR technology, a data system of wireless resource management is formed that covers wireless spectrum resource space, resource grid and available resource atlas. Besides, the corresponding lamination distributional management structure and the resource management database are constructed. The resources description system and the management structure will become the theoretical concept foundation and reference of the CR spectrum resources management technology.

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Inter-operator spectrum sharing scheme for reconfigurable system

Bargaining based mechanism for sharing spectrum between radio access networks （RANs） belonging to multioperators is studied, to improve spectrum utilization efficiency and maximize network revenue. By introducing an intelligent agent, each RAN has the ability, which includes trading information exchanging, final decision making, and so on, to trade the spectrum with other RANs. The proposed inter-operator spectrum sharing mechanism is modeled as an infinite-horizon bargaining game with incomplete information, and the resulting bargaining game has unique sequential equilibrium. Consequently, the implementation is refined based on the analysis. Simulation results show that the proposed mechanism outperforms the conventional fixed spectrum management （FSM） method in network revenue, spectrum efficiency, and call blocking rate.

Competitive Communication Spectrum Economy and Equilibrium

Consider a competitive“spectrum economy”in a communication system where multiple users share a common frequency band and each of them,equipped with an endowed“monetary”budget,will“purchase”its own transmit power spectrum(taking others as given)in maximizing its Shannon utility or pay-off function that includes the effects of interference and subjects to its budget constraint.A market equilibrium is a price spectrum and a frequency power allocation that independently and simultaneously maximizes each user’s utility.Furthermore,under an equilibrium the market clears,meaning that the total power demand equals the power supply for every user and every frequency.We prove that such an equilibrium always exists for a discretized version of the problem,and,under a weak-interference condition or the Frequency Division Multiple Access(FMDA)policy,the equilibrium can be computed in polynomial time.This model may lead to an efficient decentralized method for spectrum allocation management and optimization in achieving both higher social utilization and better individual satisfaction.Furthermore,we consider a trading market among individual users to exchange their endowed power spectrum under a price mechanism,and we show that the market price equilibrium also exists and it may lead to a more socially desired spectrum allocation.