학술논문
IRS辅助的感知增强认知无线电网络资源分配方案 / Resource Allocation for IRS-assisted Cognitive Radio Network with Sensing-enhanced Spectrum Sharing
Document Type
Academic Journal
Source
信号处理 / Journal of Signal Processing. 40(2):326-335
Subject
Language
Chinese
ISSN
1003-0530
Abstract
在过去的几十年里,大部分可用频谱已被用于高速通信服务.这导致了无线通信系统中的频谱资源稀缺问题.另一方面,大量可用频谱的利用率并不高.为提升通信系统的频谱利用率,学界提出了基于共享的认知无线电技术.认知无线电允许次用户与主用户共享频谱资源,对于提升无线通信网络的频谱效率十分重要,然而目前基于认知无线电的通信网络存在着低信噪比情况下感知性能低下、严苛干扰条件下性能提升受限等缺点.最近,智能反射面(Intelligent Reflecting Surface,IRS)被广泛应用在无线通信的各个领域,可以智能调控无线传输环境以满足频谱和能效的高要求.为了进一步提升认知无线电感知性能与频谱效率,提出了一种新颖的IRS辅助的感知增强频谱共享认知无线电网络.针对此网络,建立了次用户和速率最大化优化问题,联合优化次基站波束赋形向量、智能反射面相移矩阵、感知时间.因存在变量耦合,所构建的优化问题是非凸的,提出一种基于块坐标下降(Block Coordinate Descent,BCD)的迭代算法以交替优化波束形成和智能反射面相位以及感知时间.以逐次凸逼近(Successive Convex Approximation,SCA)求解非凸目标和约束函数,并利用半正定松弛(Semidefinite Relaxation,SDR)解决秩一条件约束,采用一维搜索来寻找最佳感知时间.通过仿真结果证明提出的方法能够快速收敛且对认知无线电网络的感知性能以及频谱效率有较大提升作用.
Over the past few decades,most of the available spectrum has been used for high-speed communication ser-vices.This has led to a scarcity of spectrum resources for wireless communication systems.By contrast,a large portion of the available spectrum has a low utilization rate.In order to improve the spectrum utilization of communication sys-tems,the academic community has proposed sharing-based cognitive radio technology.Cognitive radio allows second-ary users to share spectrum resources with primary users,which can significantly improve the spectrum utilization effi-ciency of wireless communication networks.However,the current communication networks based on cognitive radio have some shortcomings such as a low perception performance under a low signal-to-noise ratio and limited performance improvement under severe interference conditions.Recently,an intelligent reflecting surface(IRS)has been widely used in various wireless-communication fields.It can intelligently regulate the wireless transmission environment to meet the high spectrum and energy efficiency requirements.Specifically,an IRS is a planar array consisting of a large number of reconfigurable passive component units,each of which can independently modulate the amplitude and phase of the incident signal(controlled by a central intelligent controller).This improves the signal transmission environment between the transmitting and receiving ends,assists in signal transmission,and improves the signal-to-noise ratio on the receiving end.In order to further improve the sensing performance and spectral efficiency of cognitive radio,a novel IRS-assisted sensing enhanced spectrum sharing cognitive radio network is proposed.For this network,an optimization problem that involved maximizing the secondary users and rates was established,with the goal of jointly optimizing the beamforming vector of the secondary base stations,intelligent reflection surface phase shift matrix,and perception time.Because of variable coupling,the constructed optimization problem was non-convex.An iterative algorithm based on block coordinate descent was proposed to alternately optimize the beamforming,intelligent reflector phase,and per-ception time.The non-convex objectives and constraint functions were solved using successive convex approximation,and rank-one conditional constraints were solved using semidefinite relaxation.A one-dimensional search was used to de-termine the optimal perception time.The simulation results showed that the proposed method could quickly converge and greatly improve the sensing performance and spectral efficiency of a cognitive radio network.
Over the past few decades,most of the available spectrum has been used for high-speed communication ser-vices.This has led to a scarcity of spectrum resources for wireless communication systems.By contrast,a large portion of the available spectrum has a low utilization rate.In order to improve the spectrum utilization of communication sys-tems,the academic community has proposed sharing-based cognitive radio technology.Cognitive radio allows second-ary users to share spectrum resources with primary users,which can significantly improve the spectrum utilization effi-ciency of wireless communication networks.However,the current communication networks based on cognitive radio have some shortcomings such as a low perception performance under a low signal-to-noise ratio and limited performance improvement under severe interference conditions.Recently,an intelligent reflecting surface(IRS)has been widely used in various wireless-communication fields.It can intelligently regulate the wireless transmission environment to meet the high spectrum and energy efficiency requirements.Specifically,an IRS is a planar array consisting of a large number of reconfigurable passive component units,each of which can independently modulate the amplitude and phase of the incident signal(controlled by a central intelligent controller).This improves the signal transmission environment between the transmitting and receiving ends,assists in signal transmission,and improves the signal-to-noise ratio on the receiving end.In order to further improve the sensing performance and spectral efficiency of cognitive radio,a novel IRS-assisted sensing enhanced spectrum sharing cognitive radio network is proposed.For this network,an optimization problem that involved maximizing the secondary users and rates was established,with the goal of jointly optimizing the beamforming vector of the secondary base stations,intelligent reflection surface phase shift matrix,and perception time.Because of variable coupling,the constructed optimization problem was non-convex.An iterative algorithm based on block coordinate descent was proposed to alternately optimize the beamforming,intelligent reflector phase,and per-ception time.The non-convex objectives and constraint functions were solved using successive convex approximation,and rank-one conditional constraints were solved using semidefinite relaxation.A one-dimensional search was used to de-termine the optimal perception time.The simulation results showed that the proposed method could quickly converge and greatly improve the sensing performance and spectral efficiency of a cognitive radio network.