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.

Compressive Wideband Spectrum Sensing Based on Random Matrix Theory

Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology,the spectrum resources are increasingly strained which needs optimal solutions.Cognitive radio(CR)is one of the key technologies to solve this problem.Spectrum sensing not only includes the precise detection of the communication signal of the primary user(PU),but also the precise identification of its modulation type,which can then determine the a priori information such as the PU’service category,so as to use this information to make the cognitive user(CU)aware to discover and use the idle spectrum more effectively,and improve the spectrum utilization.Spectrum sensing is the primary feature and core part of CR.Classical sensing algorithms includes energy detection,cyclostationary feature detection,matched filter detection,and so on.The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal,but it is easily affected by noise and the threshold is not easy to determine.The combination of multiple-input multiple-output(MIMO)with CR improves the spectral efficiency and multipath fading utilization.To best utilize the PU spectrum while minimizing the overall transmit power,an iterative technique based on semidefinite programming(SDP)and minimum mean squared error(MMSE)is proposed.Also,this article proposed a new method for max-min fairness beamforming.When compared to existing algorithms,the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio(SINR).Furthermore,the proposed algorithm effectively improved the system performance in terms of number of iterations,interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

Analysis of secondary throughput and optimization in cooperative spectrum sensing

In cooperative spectrum sensing, more secondary user makes more opportunity for detecting the vacant spectrums, which resulting the spectrum utilization improved, however much bandwidth would be occupied for sending the local observation results, inducing the reduced secondary throughput. In this paper, an adaptive algorithm for selecting detection threshold was proposed, where the maximized secondary throughput can always be achieved while assuring sufficient protection to primary user, regardless of the number of sensing users in cognitive radio networks. Theoretical analysis and simulation results validate the proposed scheme.

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.

Generation of orthogonal UWB shaping pulses based on compressed chirp signal

This study investigates a novel method to numerically generate orthogonal ultrawide band （UWB） shaping pulses based on compressed chirp signal. First, a pulse template with less than 1 ns duration time, which is used to construct a Hermitian matrix, is produced with a compressed chirp pulse. Sub-nanosecond orthogonal pulses are then generated for UWB by using the Hermitian matrix eigenvectors. The simulation results show that the power spectral density distribution of the UWB shaping pulses met the constraint of Federal communications commissions （FCC） spectral mask. The shaping pulses not only have higher spectrum utilization ratio and very short time duration but also have excellent autocorrelation and cross-correlation properties, which is an advantage to reduce the interference between multiusers. Especially, a method to produce sub-nanosecond orthogonal UWB shaping pulses by using a relatively longer duration chirp signal is presented.

A dual plasmonic core-shell Pt/[TiN@TiO_(2)]catalyst for enhanced photothermal synergistic catalytic activity of VOCs abatement

Volatile organic compounds(VOCs)are ubiquitous organic pollutants affecting atmospheric environment and human health.The development of new efficient and environmentally friendly materials utilizing photothermal synergistic catalysis for purification of VOCs is still challenging.Herein,we design and prepare a core–shell TiN@TiO_(2)nanostructure integrating with nanoscaled Pt(Pt/[TiN@TiO_(2)])by an attractive quenching method.The strong light-harvesting capability of Pt and TiN components improve light-to-heat utilization efficiency by their intrinsic surface plasmon resonance effect.The TiO_(2)component upon the surface and the coexisting coupling effect of Pt0 and Pt2+enhance the photocatalytic effect of the system.As a result,the catalytic performance is significantly improved with toluene(120 ppm)conversion of 100%under the gas hourly space velocity of 72,000 mL·g^(−1)·h^(−1)and light illumination of 500 mW·cm^(−2).The desired catalyst thus achieves highly efficient coupling effect of photocatalysis and light-to-heat conversion for promoting VOCs abatement.