基于SINR的动态无线网络分布式链路调度

无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够真实反映无线信号之间的干扰,提出一种在动态无线网络中基于SINR模型的常数近似因子的在线分布式链路调度算法(OLD_LS).在线的意思是指,在算法执行的过程中任意节点可以随时加入网络,也可以随时离开网络.节点任意加入网络或者从网络中离开体现了无线网络的动态变化的特性. OLD_LS算法把网络区域划分为多个正六边形,局部化SINR模型的全局干扰.设计动态网络下的领导者选举算法(LE),只要网络节点的动态变化速率小于1/ε, LE就可以在O(log n+log R)ε≤5(1-21-α/2)/6,α表示路径损耗指数, n是网络节点的规模, R是最长链路的长度.根据文献调研,所提算法是第1个用于动态无线网络的在线分布式链路调度算法.

异构无线网络中基于SINR和层次分析法的SAW垂直切换算法研究

B3G或者4G无线网络体系致力于集成各种异构无线接入网络,其中一个主要的设计课题是支持垂直切换的研究。该文将多属性QoS考虑在内,针对各业务特点,提出一种基于SINR(信干噪比)和层次分析法(AHP)的SAW(简单加权法)垂直切换算法(SASAW)。它综合考虑SINR的影响以及要获得同等数据速率情况下,目标网络需要的等效SINR数值、用户通信代价、网络可用带宽等来构造属性矩阵做切换判决。为了评估性能,考虑3GPP定义的4类业务,利用层次分析法中的特征向量法来决定各个QoS属性之间的权重关系,构造比较判决矩阵并检验其一致性;根据判决矩阵,利用特征根法获得权重向量;最后根据属性矩阵和权重向量,利用SAW垂直切换算法进行判决。通过对算法的通过率、丢话率、垂直切换次数、平均用户代价等的性能比较,结果表明该文提出的算法能够根据各业务特点综合考虑各属性间关系,获得优良的系统性能。

基于分式二次规划的互模糊函数赋形方法

在开展认知雷达波形设计时,由于发射波形与接收滤波器的非匹配体制,互模糊函数赋形相比传统模糊函数赋形优化自由度更高。该文针对强杂波条件下微弱运动目标检测问题,以最大化信干噪比为优化准则,提出了一种联合发射相位编码序列与接收滤波器设计的互模糊函数赋形方法。在恒模约束下,优化问题被建模为二次分式规划形式;然后通过引入辅助变量,并利用共轭梯度法求解Stiefel流形空间上的最小化问题,非凸优化据此转化为恒模约束二次优化问题;通过交替循环和类幂迭代算法求得最优解。此外考虑到发射波形受硬件限制而难以实现严格恒模,该文构建了一种低峰均比约束二次优化问题模型,并利用最近邻向量法求得最优解。最后,不同参数下的仿真与实测数据实验表明,该文赋形方法相较于传统方法具有较高的信干噪比增益和收敛速度。

无线传感网络中SINR模型下MIS算法的研究

在SINR模型上对MIS算法进行研究.首先通过理论分析得出MIS算法节点的度受SINR模型下路径损耗指数和信噪比值的约束,然后利用NetLogo和Matlab软件进行模拟和分析,最后通过实验得出当路径损耗指数和信噪比的值越小,MIS算法运行时间越短;当路径损耗指数和信噪比的值越大,MIS算法运行时间越长.

酉阵和非酉阵预编码的输出SINR分析

在LTE(Long Term Evolution)的预编码技术中,预编码矩阵存在着使用酉阵和非酉阵的选择。本文分析了酉阵预编码的输出SINR的形式,提出了一种简化计算输出SINR计算的方法,计算4个矩阵(8个矢量)的输出SINR操作复杂度下降到原始方法的18%。同时分析了非酉阵预编码时的输出SINR相比于使用酉阵时的损失。仿真结果表明,非酉阵预编码相比于酉阵预编码在输出SINR上会有损失,这种损失从统计上可以用线性关系近似描述。

SINR下基于PNC的无线网络容量研究

物理层网络编码(PNC)作为网络编码的延伸,可以通过降低无线网络节点的干扰提升无线网络的容量。在协议干扰模型下研究基于PNC无线网络的容量增益,只考虑发送节点临近的一个节点的影响,没有考虑全局节点的影响,且参数Δ单一、抽象,不能良好表达真实网络环境。针对这些缺点,提出一种物理干扰模型(SINR)下基于PNC的无线网络容量的评测方法。在试验仿真结果中,证实全局节点影响下PNC方案无线网络容量在SINR各参数下的性能指标优于其他编码方案,并给出所有参数下网络容量的上界以及容量上界随参数变化特性。

A novel SINR and mutual information based radar jamming technique

The improvements of anti-jamming performance of modern radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio(SINR)-based and mutual information(MI)-based jamming design techniques were proposed. To interfere with the target detection, the jamming was designed to minimize the SINR of the radar seeker. To impair the estimation performance, the mutual information between the radar echo and the random target impulse response was used as the criterion. The spectral of optimal jamming under the two criteria were achieved with the power constraints. Simulation results show the effectiveness of the jamming techniques. SINR and MI of the SINR-based jamming, the MI-based jamming as well as the predefined jamming under the same power constraints were compared. Furthermore, the probability of detection and minimum mean-square error(MMSE) were also utilized to validate the jamming performance. Under the jamming power constraint of 1 W, the relative decrease of the probability of detection using SINR-based optimal jamming is about 47%, and the relative increase of MMSE using MI-based optimal jamming is about 8%. Besides, two useful jamming design principles are concluded which can be used in limited jamming power situations.

最大化SINR的MIMO雷达发射协方差矩阵设计

对于由实际阵元功率放大器非线性特性引起的波形畸变问题，现有多输入多输出（MIMO）雷达发射协方差矩阵设计方法大都不能在解决的同时保证较高的输出信干噪比（SINR）。鉴于此，文中以最大化输出SINR和保证MIMO雷达高自由度为设计目标，结合二相编码（BPSK）波形满足恒模约束的特性，给出了一种满秩发射协方差矩阵，验证了提出的协方差矩阵可以用于生成BPSK波形，并推导了其可获得的最大输出SINR。仿真结果表明，当干扰位置已知时，文中方法拥有较高的输出SINR，同时能够抑制更多的干扰。

LOW-COMPLEXITY JOINT BEAMFORMING AND POWER ALLOCA-TION FOR SINR BALANCING IN COGNITIVE RADIO NETWORKS

We consider the Signal-to-Interference plus Noise Ratio(SINR) balancing problem in-volving joint beamfoming and power allocation in the Cognitive Radio(CR) network,wherein the Single-Input Multi-Output Multiple Access Channels(SIMO-MAC) are assumed.Subject to two sets of constraints:the interference temperature constraints of Primary Users(PUs) and the peak power constraints of Cognitive Users(CUs),a low-complexity joint beamforming and power allocation algo-rithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection(SDMCPA-CP) for SINR balancing is proposed.Compared with the existing algorithm,the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions signifi-cantly,especially when large numbers of PUs and CUs are active,while still providing the optimal balanced SINR level for all the CUs.

Performance Analysis on Output SINR of Robust Two-Stage Beamforming

In this paper, we present a theoretical analysis of the output signal-to-interference-plus-noise ratio (SINR) for eigen-space beamformers so as to investigate the performance degradation caused by large pointing errors. For the sake of reducing such performance loss, a robust scheme, which consists of two cascaded signal processors, is proposed for adaptive beamformers. In the first stage, an algorithm possessing time efficiency is developed to adjust the direc-tion-of-arrival (DOA) estimate of the desired source. Based the achieved DOA estimate, the second stage provides an eigenspace beamformer combined with the spatial derivative constraints (SDC) to further mitigate the cancellation of the desired signal. Analysis and numerical results have been conducted to verify that the proposed scheme yields a better robustness against pointing errors than the conventional approaches.

Probability density analysis of SINR in massive MIMO systems with matched filter beamformer

This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This distribution of SINR can be used to make an analysis of average sum-rate,outage probability,and symbol error rate of massive MIMO downlink with MF at BS.From simulation,it is found that the derived approximate analytical expression for PDF of SINR is consistent with the simulated exact PDF from the definition of SINR in medium-scale and large-scale MIMO systems.

USER SELECTION FOR SINR-BASED UPLINK MULTIUSER MIMO SYSTEMS

To avoid the exhaustive search, we propose a fast user selection algorithm for Signal-to-Interference-plus-Noise-Ratio (SINR)-based multiuser Multiple-Input Multiple-Output (MIMO) systems with Alamouti Space-Time Block Code (STBC) transmit scheme. A locally optimal selection criterion is proposed at first. Then, the incremental selection approach is applied, which selects one among the residual available users to maximize the minimum user SINR step by step. Simulation results show that the fast algorithm gains over 90% of the diversity benefit achieved by the exhaustive search selection, and that the fast algorithm has much lower computational burden than the exhaustive search one, for the scenario where the number of all the available users is much greater than that of the selected users.

复杂干扰场景下的稳健自适应波束形成

针对复杂电磁环境中最小方差无畸变响应及干扰加噪声协方差矩阵重构波束形成器输出信干噪比下降的问题,本文基于均匀圆阵(uniform circular array,UCA)提出了一种适用于该场景的稳健自适应波束形成算法。所提算法利用稀疏重构技术得到的谱函数构造和改进干扰抑制矩阵,使其在干扰方位产生宽零陷,提高了算法的稳定性。考虑到UCA存在副瓣电平高及空间非均匀采样问题,利用二阶约束优化准则抑制UCA副瓣,使得所提算法在抑制干扰的同时具有较低的波束副瓣。当UCA半径选择合适时,所提算法的性能与最优波束形成器接近,仿真验证了所提算法的有效性。

A Deep Reinforcement Learning-Based Power Control Scheme for the 5G Wireless Systems

In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.

信号最优极化滤波及性能分析

研究了信号最优极化滤波问题,以信号干扰噪声比(SINR)为目标函数建立了带非线性约束的优化模型,利用变量代换将其转化为无约束的优化问题,利用极值必要条件将优化问题转化为一元二次方程求根问题,导出了最大SINR和最优接收极化的解析表达式。性能分析表明:信号和干扰极化状态差异越大,滤波性能越好,完全极化干扰容易被抑制,而部分极化干扰效果好。

极化轨道约束下的最优极化(一)

本文针对极化测量过程中干扰和噪声对接收效果的影响，提出一种极化轨道约束下的变极化接收方法，以改卷接收系统的SINR（信号干扰噪声比）。在极化轨道约束下的SINR最大化问题是一个寻求局部最优的问题，本文在任意的信号与干扰极化条件下，解决了任意给定的极化轨道上SINR的最大化问题，并对极化轨迈上最优极化与其他两种最优极化得到的SINR进行了比较。

基于遗传算法的多基站协作通信功率分配方案

将多基站协作通信的功率分配问题转换为信干噪比(SINR)均衡问题。通过设置路径损耗门限为各用户判决与其通信的协作基站,并考虑用户间接收SINR的公平性,给出基于遗传算法的多基站协作通信功率分配方案。数值分析表明,相对于传统蜂窝小区等功率分配以及多基站协作通信等功率分配,该功率分配方案使系统中各用户的平均SINR分别提高17.75dB和2.36 dB。

空天异构网络中基于Q学习的切换判决优化算法

小型空中飞行平台在接入空天异构无线网络时,现有切换算法未充分考虑下一时刻网络与用户状态以及用户对不同业务的传输需求。为此,提出一种支持空天异构无线网络的Q学习优化算法。在回报函数中考虑用户体验,将通过综合预测方法获得的当前与下一时刻网络的信干燥比、用户移动速度、网络切换代价、信息传输的时延及网络的拥塞程度作为综合评价参数,同时根据层次分析法确定不同业务类型下的评价参数权值。仿真结果表明,在用户到达率较高、环境干扰强度较强时,相比基于Q学习的切换判决算法,该算法可以有效提高网络切换成功率并降低切换次数,在传输不同类型的业务时,可以提供更优的网络切换策略并降低切换阻塞率。

基于信干噪比增益的自适应阵列抗干扰性能分析

针对自适应阵列现有的抗干扰性能评价指标存在权系数伸缩敏感,难以直观反映后续信号处理性能等缺点,在现有性能指标分析基础上,提出了信干噪比(SINR)增益指标,并给出了均匀线性阵列SINR增益的解析表达式.新指标具有明确物理含义,可方便比较不同空域滤波器的抗干扰性能,并定量反映抗干扰后续的信号处理性能.仿真实验结果验证了提出新方法的有效性.

阵列互耦对极化敏感阵列滤波性能的影响

在阵列互耦条件下,给出了极化敏感阵列接收信号模型;并在形式上把互耦误差分解为正交阵元接收因子误差和阵列接收因子误差,分别代表了阵列接收信号极化信息误差和空间信息误差.给出互耦条件下,信号、干扰空间匹配系数、极化匹配系数和最大SINR的数学表达式.在单干扰源情况下,分析了阵列互耦对极化敏感阵列滤波性能的影响.