Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

Covert Communication with a Spectrum Sharing Relay in the Finite Blocklength Regime

In this paper,we investigate the feasibility and performance of the covert communication with a spectrum sharing relay in the finite blocklength regime.Specifically,the relay opportunistically forwards the source's messages to the primary receiver or conveys the covert messages to its own receiver via the sharing spectrum,while the warden attempts to detect the transmission.First,we derive a lower bound on the covertness constraint,and the analytical expressions of both the primary average effective covert throughput(AECT)and sum AECT are presented by considering the overall decoding error performance.Then,we formulate two optimization problems to maximize the primary and sum AECT respectively by optimizing the blocklength and the transmit power at the source and the relay.Our examinations show that there exists an optimal blocklength to maximize the primary and sum AECT.Besides,it is revealed that,to maximize the primary AECT,the optimal transmit power of each hop increases as its channel quality deteriorates.Furthermore,in the optimization for maximizing the sum AECT,the optimal transmit power at the source equals to zero when the channel quality from relay to the secondary receiver is not weaker than that from relay to the primary receiver.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

Improved spectrum sharing algorithm based on feedback control information in cognitive radio networks

In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.

Proactive Spectrum Monitoring for Suspicious Wireless Powered Communications in Dynamic Spectrum Sharing Networks

This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamming-assisted spec-trum monitoring scheme via spectrum monitoring data(SMD)transmission is proposed to maximize the sum ergodic monitoring rate at SM.In SWPC,the suspi-cious communications of each data block occupy mul-tiple independent blocks,with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster(symmetric scene)or randomly distributed(asymmet-ric scene)and the remaining blocks used for the in-formation transmission from suspicious transmitters(STs)to suspicious destination(SD).For the sym-metric scene,with a given number of blocks for SMD transmission,namely the jamming operation,we first reveal that SM should transmit SMD signal(jam the SD)with tolerable maximum power in the given blocks.The perceived suspicious signal power at SM could be maximized,and thus so does the correspond-ing sum ergodic monitoring rate.Then,we further reveal one fundamental trade-off in deciding the op-timal number of given blocks for SMD transmission.For the asymmetric scene,a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance.Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better perfor-mance than conventional passive spectrum monitor-ing,since the proposed schemes can obtain more accu-rate and effective spectrum characteristic parameters,which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.

Opportunistic spectrum sharing in software defined wireless network

Over the past few decades, the world has witnessed a rapid growth in mobile and wireless networks（MWNs） which significantly change human life. However, proliferating mobile demands lead to several intractable challenges that MWN has to face. Software-defined network is expected as a promising way for future network and has captured growing attention. Network virtualization is an essential feature in software-defined wireless network（SDWN）, and it brings two new entities, physical networks and virtual networks. Accordingly, efficiently assigning spectrum resource to virtual networks is one of the fundamental problems in SDWN. Directly orienting towards the spectrum resource allocation problem, firstly, the fluctuation features of virtual network requirements in SDWN are researched, and the opportunistic spectrum sharing method is introduced to SDWN. Then, the problem is proved as NP-hardness. After that, a dynamic programming and graph theory based spectrum sharing algorithm is proposed.Simulations demonstrate that the opportunistic spectrum sharing method conspicuously improves the system performance up to around 20%–30% in SDWN, and the proposed algorithm achieves more efficient performance.

Performance Analysis of Wireless Network with Opportunistic Spectrum Sharing via Cognitive Radio Nodes

Cognitive radio （CR） is found to be an emerging key for efficient spectrum utilization. In this paper, spectrum sharing among service providers with the help of cognitive radio has been investigated. The technique of spectrum sharing among service providers to share the licensed spectrum of licensed service providers in a dynamic manner is considered. The performance of the wireless network with opportunistic spectrum sharing techniques is analyzed. Thus, the spectral utilization and efficiency of sensing is increased, the interference is minimized, and the call blockage is reduced.

A Spectrum Sharing Model that Counters Eavesdropping:An Energy Efficiency View

Wireless transmission is subject to eavesdropping.When wireless transmission ceases,the assigned frequency channel is unused,wasting the spectral opportunity given.In this study,a spectrum sharing model that reduces spectral wastage and protects against eavesdropping is proposed.First,cognitive radio(CR)shares the channel access with primary user(PU).When the CR senses that the channel is idle,CR can seize the unoccupied channel for its own use.If the channel is detected to be occupied by PU,CR transmits artificial noise to jam any potential eavesdropper.To what extent is this operation beneficial to the CR?The main concern of this study is the energy efficiency(μ)of CR,i.e.,the ratio of channel throughput to its energy consumption.The relationship betweenμand the percentage of frame duration allocated for sensing(τ)was investigated.This study contributes a novel theoretical expression that allows us to find the optimalμandτvalues,denoted byμ∗andτ∗.With the availability of this expression,the relationships between(μ∗,τ∗)and other important system parameters can be understood thoroughly.Our investigation reveals that strong CR signal will result in highμ∗without the need of increasingτ∗.On the other hand,a strong primary signal allows a shortτ∗and it improvesμ∗.High sampling rate for sensing may be unnecessary,as it does not improveμ∗significantly.A more demanding target probability of detection requires a higher sensing duration,but it has insignificant impact onμ∗.

Joint utility optimization algorithm for secure hybrid spectrum sharing

As the rapid development of wireless communication networks has resulted in better user experiences,the spectrum resources occupied and energy consumption have increased considerably and resulted in great costs.To address the energy consumption and cost problems of spectrum sharing in cognitive radio networks,a hybrid spectrum sharing model combining the free spectrum of authorized users and the leased spectrum of mobile network operators is given.Based on the hybrid model,a function of throughput and costs,including energy consumption and transaction costs,is constructed,and a joint utility optimization problem is analyzed.The transactions between secondary users and primary users are performed on the consortium blockchain on which users can directly trade spectrum and the transaction information is recorded.In order to improve the joint utility,the Lagrange multiplier method is used to achieve the optimal solution for the sensing time,the number of secondary users involved in sensing,and the transmission power.The simulation results show that the joint utility optimization algorithm proposed in this paper can achieve higher joint utility under the constraints of the minimum throughput requirement and maximum transmission power.

IRS-Enabled Spectrum Sharing:Interference Modeling,Channel Estimation and Robust Passive Beamforming

Intelligent reflecting surface(IRS),with its unique capability of smartly reconfiguring wireless channels,provides a new solution to improving spectrum efficiency,reducing energy consumption and saving deployment/hardware cost for future wireless networks.In this paper,IRS-enabled spectrum sharing is investigated,from the perspectives of interference modeling,efficient channel estimation and robust passive beamforming design.Specifically,we first characterize the interference in a spectrum sharing system consisting of a single primary user(PU)pair and a single secondary user(SU)pair,and extend it to the large-scale network by leveraging the Poisson point process(PPP).Then,we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels.Moreover,the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy.Finally,we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts.It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

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.

Optimal energy-efficiency capacity and sensing tradeoff for hybrid spectrum sharing in CRN

This paper investigates the tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model, where the spectrum sharing method is based on sensing results of secondary user （SU）. The metric ＇bits per joule＇, which captures the effect of energy overhead in spectrum sensing, is adopted to evaluate energy-efficiency capacity. We first formulize the tradeoff between energy-efficiency capacity and spectrum sensing as an optimization problem with mixture constraint of sensing time and detection threshold. Under some certain condition on the domain of detection threshold, i.e. in which we can＇t improve energy-efficiency capacity through increasing the detection probability, the original optimization problem can be reduced to a new unconstrained one, which only relates to sensing time. Then the existence and uniqueness of optimal sensing time to achieve maximum energy-efficiency capacity are discussed and a low-complexity algorithm is proposed to obtain the optimal solution. Finally, numerical simulation is performed to verify the theoretical analysis results. The simulation results indicate that hybrid spectrum sharing is remarkably beneficial to energy-efficient transmission in cognitive radio networks （CRN）. And the proposed algorithm can quickly converge to the optimal solution.

Unlicensed Spectrum Sharing Game Between LEO Satellites and Terrestrial Cognitive Radio Networks

By cognitive radio,the low Earth orbit（LEO） satellites may prefer to operate in the unlicensed spectrum which is open to all the users,and compete for the limited resources with terrestrial cognitive radio networks（CRNs）.The competition can be regarded as a game and analyzed with game theory.This particular unlicensed spectrum sharing problem is modeled here,and the special properties of ＂spatially-distinguished-interference＂ and the short period of the interactions between satellites and terrestrial CRNs are explored.Then,the problem is formulated as a ＂partially-blind＂ finitely repeated prisoner＇s dilemma by game theory.Finally,we begin with two promising spectrum sharing schemes,which can be used to enforce the frequency reuse among the remotely located terrestrial CRN players as well as to overcome the observation noise.By analysis and comparison,it is proposed that the novel refreshing-contrite-tit-for-tat（R-CTFT） is the optimal spectrum sharing scheme.Simulation results verify that it can be used to utilize the spectrum most efficiently.

Spectrum Sensing Using Optimized Deep Learning Techniquesin Reconfigurable Embedded Systems

The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.

Mutual Information Maximization via Joint Power Allocation in Integrated Sensing and Communications System

In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.

Multi-band spectrum auction framework based on location information in cognitive radio networks

Cognitive radio（CR） technology is considered to be an effective solution to allocate spectrum resources,whereas the primary users of a network do not fully utilize available frequency bands.Spectrum auction framework has been recognized as an effective way to achieve dynamic spectrum access.From the perspective of spectrum auction,multi-band multi-user auction provides a new challenge for spectrum management.This paper proposes an auction framework based on location information for multi-band multi-user spectrum allocation.The performance of the proposed framework is compared with that of traditional auction framework based on a binary interference model as a benchmark.Simulation results show that primary users will obtain more total system revenue by selling their idle frequency bands to secondary users and the spectrum utilization of the proposed framework is more effective and fairer.

Joint power and spectrum allocation algorithm in cognitive radio networks

The spectrum sharing problem between primary and cognitive users is mainly investigated. Since the interference for primary users and the total power for cognitive users are constrained, based on the well-known water-filling theorem, a novel one-user water-filling algorithm is proposed, and then the corresponding simulation results are given to analyze the feasibility and validity. After that this algorithm is used to solve the communication utility optimization problem subject to the power constraints in cognitive radio network. First, through the gain to noise ratio for cognitive users, a subcarrier and power allocation algorithm based on the optimal frequency partition is proposed for two cognitive users. Then the spectrum sharing algorithm is extended to multiuser conditions such that the greedy and parallel algorithms are proposed for spectrum sharing. Theory and simulation analysis show that the subcarrier and power allocation algorithms can not only protect the primary users but also effectively solve the spectrum and power allocation problem for cognitive users.

Spatio-Temporal Population Density and Spatial Dynamic Spectrum Allocation

A realistic population density distribution scenario in conjunction with the spatial dynamic spectrum allocation （DSA） is taken into account to mitigate the spectrum wastage in terms of extra guard bands. For the insertion of the extra guard bands, an efficient strategy based on self-assessment is applied to each victim cell individually and independently. Consequently, it is no more required to spread the extra guard band over the whole DSA region. Simulation results StlOW an improvement of 3% -4% in percentage of satisfied users for Universal Mobile Telecommunications System （UMTS） network and 4%-5% for Digital Video Broadcasting Terrestrial （DVB-T） network.

Spectrum Allocation and Protocol Based on Cognitive Acoustic Technique in Underwater Networks

In this paper, a strategy is developed for spectrum sharing among multiple cognitive users in underwater environment. This strategy requires all nodes to negotiate and reallocate the channels before sending data, and Hungarian method is used to maximize the sharing rewards. Simulation results show that the proposed strategy can avoid collisions between source-destination node pairs, and guarantee that the communication system gets maximum sharing rewards. Both the parameters of POMDP model and the number of available channels have influence on the system sharing rewards, and the rewards will increase when the channels have larger transition probabilities or more channels are available for communication. However, the channels with larger bandwidths can attract more nodes to access, and thus will lead to more collisions.