A Survey on Air-Ground Networks of Unmanned Aerial Vehicles
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摘要:
无人机具有快速部署、成本低廉等优势. 无人机空地网络通过将基站设备部署至升空无人机平台,能从空中快速构建对地覆盖网络,因而在应急救灾、偏远覆盖、智能交通、智慧城市等方面具有广阔的应用前景,近年来受到广泛关注. 面向无人机空地网络应用场景,结合无人机的机动、组网、载荷等特点,围绕无人机空地网络覆盖性能提升、无人机空地网络通感算一体化设计、智能反射面技术辅助的无人机空地网络、鲁棒无人机空地网络四个维度,从网络场景、关键技术挑战、性能优化控制方法等几方面梳理无人机空地网络的研究现状,并探索优化提升无人机空地网络性能的未来研究方向.
Abstract:Unmanned aerial vehicles (UAVs) can be rapidly and cost-effectively deployed. By deploying the base station equipment to the launching UAV platform, the air-ground networks of UAVs can quickly build ground coverage network from the air, so it has broad application prospects in emergency relief, remote area coverage, intelligent transportation, smart city, and other aspects and has received wide attention in recent years. Based on the application scenario of air-ground networks of UAVs, the characteristics of UAVs’maneuver, network, and load were considered. From the four dimensions of coverage performance improvement of air-ground networks, integrated communication-sensing-calculation design of air-ground networks, reconfigurable intelligent surfaces (RIS)-assisted air-ground networks, and robust air-ground networks of UAVs, the research status of air-ground networks of UAVs was reviewed in terms of network scenarios, key technical challenges, and performance optimization control methods. In addition, the future research direction of optimizing the performance of air-ground networks of UAVs was explored.
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随着人类涉足的空间不断拓展,火灾对城市及人类威胁越来越大[1]. 由于声波不会产生二次污染与次生灾害,声波扑灭小型火灾是近年来提出的新兴灭火技术[2-3]. 这种技术在很多场景都被提到了应用的可能性,如太空中失重环境、小空间管道火焰和小型厨房火灾等[4-5].
学者们对声波和火焰之间相互作用进行了深入研究[6]. 在声波对火焰结构影响方面,Hauser等[7]研究发现横向声波能够影响火焰中漩涡结构产生从而导致火焰结构失去对称性. Davis等[8]采用纹影仪测量了声波作用下同轴扩散火焰周期性结构变化. Kim等[9]研究发现声速和当量比之间相位差是决定部分预混火焰线性或非线性特征主要因素. 此外,声波也会对燃烧化学反应速率产生影响. Demare等[10]将中频高振幅的声波作用于气体射流火焰,发现燃烧机理发生了变化,原来黄色长火焰缩短并且变成蓝色. Fachini[11]研究了环境温度高于绝热火焰温度条件下,声波扰动对液滴燃烧影响,发现声波作用下液滴蒸发速率随着达姆科勒数(Da数)增加而增大. Okai等[12]研究发现低频,低−中强度的声波能够增大液滴蒸发强度,且燃烧速率常数近似与频率成正比. Kim等[13]研究发现火焰应变率与声波频率位于同一数量级时,声波诱导化学反应速率和燃烧组分输运过程中流场参变量波动是影响反应活化能两个重要机制. 以上研究主要是针对燃烧室热声不稳定性,即声波与火焰之间影响是耦合的,声波与火焰热释放相位存在一个相互锁定机制[14].
美国国防高级研究计划局(Defense Advanced Research Projects Agency,DARPA)启动了快速灭火(instant flame suppression,IFS)研究项目,提出了声波灭火构想[15]. Niegodajew等[2]进行了横向低频声波扑灭燃烧器火焰的实验,结果发现声波扑灭小型火灾是可行的. Friedman等[16]研究了低频声波扰动线性火焰燃烧与熄灭特性,建立了声波熄灭火焰临界Da数,进一步证实了声波扑灭小型火灾可行性. Xiong等[17]以电线及外墙的熔滴火灾为背景,研究了声波扑灭快速移动的熔滴火焰,实验中的声压级范围为80 ~ 114 dB,声频率范围为90 ~ 110 Hz;此外,还建立了基于Damköhler数的火焰熄灭判据,以描述声波扑灭滴落火焰的潜在机制.
以上研究论证了声波扑灭火焰的可行性,但是由于声波灭火技术是新提出概念与技术,在声波未扑灭火灾情况下,采用声波控制火焰行为特性是需要关注的,而声波扰动下未熄灭火焰响应方式还未被详细研究. 本文研究了低频声波扰动乙醇池火燃烧特性,分别分析了实验声场分布、火焰的形态及破碎特征、火焰高度与宽度参数及其周期性脉动特性. 本研究对理解声波扰动下火焰响应特性及火焰动力学机制以指导声波灭火技术有重要意义.
1. 实验装置和方法
声波扰动下火焰由稳定转变为不稳定状态,为研究声波扰动下火焰转变过程,采用自主搭建声波扰动池火火焰燃烧实验台. 图1为声波扰动火焰燃烧实验台示意图,图中:Ln和Lw分别为声波导流管的长度和距离;Hf和Wf分别为火焰的高度和宽度. 实验装置包括3部分:声波控制与发射装置、池火燃烧器和参数测量装置. 声波控制与发射装置包括:信号产生器(JDS6600)产生正弦的声波信号,功率放大器(菱声DB3)将产生的声波信号放大并传输给扬声器,扬声器可以产生低频的声波. 为了能够产生较强的声波,参考之前研究,采用声波导流装置对扬声器发出的声波进行约束[2]. 燃烧器为直径4 cm圆柱形不锈钢杯,可以产生稳定层流扩散火焰. 常见无水乙醇(C2H5OH)作为实验过程中燃料. 采用声学测量装置对声波扰动下池火火焰位置处声学信息进行测量,此外,采用高速摄像机拍摄了声波扰动下火焰的形态特性. 分析方法如图1右下角所示,Hf与Wf都选择火焰概率云图为0.5位置处对应值[18].
实验中改变Ln,测量了Ln对于声波扰动火焰燃烧作用. 此外,对于火焰与Lw也进行研究. 表1为实验采用的参数. 为方便标记,各个实验条件简称见表1. 每次实验都保持池杯内乙醇燃料质量相同,当池火火焰位于稳定阶段[19],开启声波控制与发射装置,采用固定频率与声压声波对火焰进行扰动. 实验中保持稳定环境条件,即没有外界强噪声和风.
表 1 实验采用的参数Table 1. Experimental parameterscm 实验简称 Ln Lw 实验简称 Ln Lw N2-W5 2 5 N10-W0 10 0 N2-W10 2 10 N10-W10 10 10 N5-W0 5 0 N15-W0 15 0 N5-W10 5 10 N15-W10 15 10 2. 结果与讨论
2.1 实验整体阐释
本文实验装置与方案参考了文献[16]的策略,采用声波导流装置对声波进行约束,为进一步探索Ln与Lw对声波扰动火焰效果,基于文献[16]的研究与本实验环境所约束,设置8种匹配模式,从而确定Ln与Lw数值. 采用的声频率与压力值也是基于当前研究得到. 直径4 cm圆柱形燃烧器产生的乙醇池火是较为稳定并且是光学薄的,可以采用高速摄像机精确测量其特征. 首先,测量火焰当地声学参数,点燃池火之后静待池火燃烧稳定,每次实验尽最大可能保持燃烧器内燃料质量一致,采用高速摄像机测量时,都是选择池火稳定燃烧阶段进行. 相同的实验重复2 ~ 5次以保证实验结果可靠性. 采用高速摄像机测量火焰时序图像,所分析火焰响应参数都是基于MATLAB操作的.
2.2 实验声场
不同声波发射参数下,火焰位置处声学信息不同,作为声波扰动火焰燃烧的基础条件,采用声学测量装置测定了声波扰动下池火火焰位置处声压信息. 图2为不同实验条件下声压变化,实验中采用了3个声功率等级,从小到大分别表示为Level 1、Level 2和Level 3. 由图2可知:随着声波导流管长度(Ln)的增加,由于声学传播特性及声学反射影响,各频率下的声压大体呈波动形式变化. 随火焰与声波导流管距离(Lw)增加,各频率下声压值减小. 此外,火焰位置处声压值与声发射频率关系也是大体呈波动变化.
2.3 声波扰动下火焰形态及破碎特征
很多火焰相关研究都采用火焰概率云图描述火焰行为. 图3为自由火焰与N2-W5实验条件下火焰概率云图. 图3(a)、(b)和(c)分别对应声功率Level 1、Level 2和Level 3扰动下各频率火焰概率云图. 由图3可知:本实验中火焰为层流火焰,火焰脉动主要集中于火焰尖端,故自由火焰边缘较为规则,高概率火焰面积较大,较低概率火焰部分主要位于火焰尖端.
Level 1声波条件下声压较小,导致声波对火焰扰动相对较小,声波驻波特性导致火焰横向周期性变化,因此,Level 1声波条件下的火焰概率云图中高概率火焰面积减少,较低概率的部位分布在火焰四周. 各个频率对应火焰高度是随着频率增加大体呈增长趋势,而火焰的宽度变化不明显. 对于Level 2和Level 3声波扰动情况,火焰位置处声波扰动较强,火焰形态出现较大幅度变化,相应概率云图中高概率面积位于火焰中心区域,较低概率面积分散在火焰核心四周. 云图中火焰概率形态表现为“类球状”,表明火焰受到了声波压迫导致平均高度降低,火焰平均宽度增加,火焰平均面积被压缩. 被压缩火焰的“类球状”形式可能是由于声波剪切作用导致边缘火焰无法维持燃烧,因此,火焰燃烧区域主要附着于池火上方中心位置.
为更加详细研究不同声压声波扰动池火火焰形态特性响应,采用34 Hz固定频率声波进行试验. 图4为火焰图像的时序分析,实验条件为N2-W5. 图4(a)、(b)、(c)和(d)分别对应自由火焰、0.36 Pa(34 Hz)、0.73 Pa (34 Hz)和1.06 Pa (34 Hz)声压扰动下火焰瞬时图像.
图4中,每个子图的火焰序列对应时间范围是火焰振荡一个周期,每个序列的每幅图对应时刻为火焰周期内等间隔时刻. 自由火焰脉动十分规律,主要是尖端闪烁,表现为明显流动涡扰动不是由火焰根部上升[20]. 流动涡升起是非对称形式,火焰表现为非对称蜿蜒型周期性脉动. 0.36 Pa声波作用使得火焰形态表现横向收缩,这是由于声波周期性振荡扰乱了升起涡,导致火焰被稳定. 0.36 Pa声波扰动火焰一个周期内,未出现明显火焰振荡,火焰夹断现象也没有发生,这是由于声波扰动火焰周围流动涡无法顺利上升,导致火焰燃烧区域整体发展. 以0.73 Pa声波扰动火焰时,明亮火焰区域变小,火焰被压缩,火焰边缘变得不规则. 对于更强1.06 Pa声波扰动,火焰明亮区域被拓展,火焰边缘变得更加杂乱. 这可能是由于声波卷吸了大量空气进入火焰区域,从而扩展燃料蒸汽与空气混合区域面积,使燃料与空气混合较充足.
相比自由火焰,声波扰动下火焰形态特征会出现较大变化,火焰细节结构需要被重建以理解火焰动力学相关机制. 图5为火焰细节结构特征分析示意图(图中序号①,②,③,④分别为图4中对应序号火焰形态). 由图5(a)可看出:燃烧器产生火焰是非对称流体涡诱导的蜿蜒型火焰,火焰顶部由于涡挤压而发生火焰夹断,此时,火焰两侧流动涡上升情况是不一致. 由图5(b)可得到:较低声波压力扰动下火焰边缘是呈小尺度周期性振荡,扰乱了火焰流体涡上升与发展,故此时火焰未出现火焰夹断现象,火焰边缘较平缓;在另一方面,周期性振荡导致火焰燃烧区域来回挤压,从而导致火焰宽度变小;声波周期性振荡也导致火焰燃烧区燃料分子以更快速度耗散在环境中,表现为火焰面积减小. 显然,0.73 Pa声波扰动火焰的情况是0.36 Pa声波扰动火焰的进一步发展,此处不再讨论. 对于更强烈声波扰动(1.06 Pa,图5(c))可知:火焰形态被强烈扭曲,空气与燃料被声波卷吸导致其混合的区域增大,表现为火焰横向上变宽,并且出现火焰下探现象. 声波导致不规则流动扰动下,火焰边缘变得更加杂乱扭曲,为保持稳定,火焰重心降低,火焰形状变为“类球状”附在池杯上方. 0.36 Pa声波扰动下,火焰表现为稳定状态,声波压力为1.06 Pa时,火焰表现为扰乱状态. 随着声压增加,火焰由“锥状”转变为“类球状”.
2.4 声波扰动下火焰高度及宽度演化
火焰高度是描述火灾危险性的重要标度,声波扰动下,由于当地流体周期性运动,火焰高度会发生较大变化. 对不同响应距离和声学参数作用下火焰相对高度(Hf/Hf0)进行研究,Hf为各声波条件下火焰高度;Hf0为自由火焰高度. 图6为声波扰动下火焰相对高度变化,由图可知:随着声波压力增加,在各条件下相对火焰高度大体是减小的. 声压与频率对火焰相对高度影响规律不强,呈现一种上升波动变化. 这表明较高声波压力扰动下火焰高度会发生降低,而较低声波压力扰动下火焰高度变化较缓. 声波频率越高,声波对火焰高度抑制效果越弱.
火焰附近流体流动特性会受到声波扰动影响,对声波引起的当地流体运动可以由声学雷诺数(ReA)表示:
ReA=UAlυair, (1) 式中:
$ {U_{\text{A}}} = \sqrt {P{\text{/}}\rho } $ ,为声波扰动空气当地速度幅度(m/s),其中,P为当地声压(Pa),ρ为当地流体密度(kg/m3);l为特征长度(m);$ {\upsilon _{{\text{air}}}} $ 为火焰处流体动力黏度(Pa·s).对于声波驱动特征长度可以表示为声波循环均方根位移:
l=UAω, (2) 式中:ω为声波角频率,
$ \omega = 2{\text{π }} f $ , f为声波频率(Hz).图7为火焰相对高度随声学雷诺数变化,由图可知:随着声学雷诺数增加,相对高度呈减小趋势. 因为随着声雷诺数增加,火焰位置处当地流体流动更为强烈,所以ReA越大,火焰相对高度越小. 除N2-W5实验情况外,其他实验条件下火焰相对高度随ReA减小情况更明显. N2-W5与其他实验条件展示出来的差别可能是声波导流管长度(Ln)及扬声器与火焰距离(Lw)太短,火焰与扬声器之间短距离导致火焰位置处当地空气运动状态受扬声器影响而与其他实验情况不一致. 此外,声波扰动下火焰相对高度基本都呈现小于0情况,由于横向声波诱导了横向当地空气运动,从而使火焰燃料在横向上被消耗,造成火焰高度降低.
声波扰动下火焰宽度会受到横向速度扰动,导致火焰在横向上发生挤压或拓展,因此,对火焰相对宽度(Wf/Wf0)进行研究,其中:Wf为各声波条件下火焰宽度;Wf0为自由火焰宽度. 图8为声波扰动下火焰相对宽度变化,由图可知:在各个声波频率下,随声波压力增加,火焰相对宽度大体呈增加趋势. 对于Ln小于5 cm情况,声波扰动下火焰相对宽度值存在大于1.0与小于1.0情况,即存在一个使Wf/Wf0等于1临界值,而对于Ln大于5 cm情况,火焰相对宽度都小于1.0. 与之前分析所一致,弱声波扰动会导致火焰受到空气振荡挤压,从而宽度降低,但强声压扰动会扰乱火焰区域,火焰宽度会增加.
图9为火焰相对宽度随声学雷诺数的变化,由图可知:雷诺数较低时,火焰相对宽度主要集中于小于1.0范围之内,随声雷诺数增加,火焰相对宽度由小于1.0区域移动到大于1.0区域. 这表明小声雷诺数扰动下火焰宽度是挤压状态,大声雷诺数扰动下火焰宽度是拓展状态.
根据图7与图9相关研究发现,火焰高度与宽度存在一定联系. 图10为无量纲火焰高度与火焰宽度关系,由图10可见:声波扰动下火焰宽度与高度呈现线性关系,即火焰高度随宽度增加而增加.
声波扰动下火焰宽度与高度关系模型为
HfHf0ReA=0.77WfWf0ReA+0.00257. (3) 2.5 声波扰动下火焰周期性
火焰面积在一定程度上可以代表火焰放热情况. 火焰脉动特性对于火灾蔓延起着很大作用. 为得到声波扰动火焰响应特性,对火焰面积周期性进行研究. 通过对火焰面积无量纲化,火焰面积归一化为
I′(t)=A(t)ˉA, (4) 式中:A(t)为火焰瞬时面积(m2),t为时刻;
$ \bar A $ 为自由火焰的平均面积(m2).图11为自由及0.36 Pa声波扰动下火焰参数Iʹ(t)随时间变化,由图可知,在自由火焰振荡阶段,火焰面积脉动表现一定周期性,但是由转折Iʹ(t)光滑线发现,自由火焰振荡存在一定不稳定特征. 12.0 s后对火焰进行0.36 Pa声波扰动,由火焰面积振荡Iʹ(t)光滑线可看出,火焰面积周期性变得更加稳定. 如2.3节分析,较低压力声波可将火焰流动涡扰乱,横向流体被压缩与扩展,火焰纵向不稳定振荡被消除.
图12为自由及0.36 Pa声波扰动下火焰参数Iʹ(t)周期和相位图(实验条件N2-W5,声频率34 Hz). 以Iʹ(t)、Iʹ(t-2τ)和Iʹ(t-τ)为x、y和z坐标轴得到的图像可以表示无量纲火焰面积相位特征,其中,τ为延滞时间,τ值小于半个周期..
图12(a)为自由火焰参数Iʹ(t) 的周期变化,火焰参数Iʹ(t) 变化周期性十分明显,在一个火焰振荡周期内,随流动涡上升,火焰被抬起,当流动涡发展到临界值,火焰被流动涡夹断,之后,火焰形态变化进入下一周期. 图12(b)为火焰参数Iʹ(t) 相位图,自由火焰相位变化较规则,但部分相位存在线性变化趋势,这是由于火焰在提升之后被流动涡夹断,因此,参数Iʹ(t) 会发生迅速减小,在相位图上表现为线性现象. 图12(c)为0.36 Pa声波扰动下火焰参数Iʹ(t) 周期变化,与自由火焰相比,0.36 Pa声波扰动下火焰参数Iʹ(t) 的周期性更加规则,火焰周围流动涡运动和火焰边缘变化不明显. 图12(d)为0.36 Pa声波扰动下火焰参数Iʹ(t) 相位图,相比自由火焰,0.36 Pa声波扰动下火焰相位图变得更加规则,火焰相位线性现象变得不明显,更倾向于圆形. 这是由于声波调制了火焰相位变化,导致火焰相位稳定并且更加光滑. 这验证之前分析,较低声压会导致火焰周期性更加明显,火焰被声波调制而稳定.
图13为0.73 Pa声波扰动下火焰参数Iʹ(t) 随时间变化(实验条件N2-W5,声频率34 Hz). 在9.2 s时,采用声波对火焰进行横向扰动,火焰参数Iʹ(t) 由稳定振荡转变为杂乱形式. 由于较强声波会导致火焰周围流体流动不再稳定,火焰面积被横向速度压缩或拓展,火焰稳定流动特性被声波干扰变得紊乱. 此外,火焰参数Iʹ(t) 平滑线在声波作用下降低且呈转折形式,因声波导致流体流动虽然增强了空气与燃料混合,但声波也加强了燃料分子扩散,故火焰参数Iʹ(t) 平滑线值变小.
图14为0.73 Pa声波扰动下火焰参数Iʹ(t) 周期和相位图(实验条件N2-W5,声频率34 Hz). 图14(a)表示0.73 Pa声波扰动下火焰参数Iʹ(t) 周期变化,与自由火焰和0.36 Pa声波扰动火焰参数Iʹ(t) 变化规律相比较,0.73 Pa声波扰动会导致火焰失去稳定振荡特性,火焰周期性变为混乱状态,表明火焰会受到声波诱导空气流动压迫与提升,火焰形态变化不再受流动涡控制. 图14(d)为0.73 Pa声波扰动下火焰参数Iʹ(t) 相位图,与自由火焰和0.36 Pa声波扰动火焰参数Iʹ(t) 相位图比较,0.73 Pa声波扰动会导致火焰失去规则的环形相位特性,相位形式变为离散型,呈混沌特征.
3. 结 论
为深入了解声波扰动下火焰失稳特性与火焰动力学机制,采用横向声波对乙醇池火进行扰动. 得到结论如下所示:
1) 随火焰与声波导流管距离增加,声压大体呈波动形式变化. 随声波导流管长度增加,声压值减小. 火焰位置处声压值与声发射频率关系也是大体呈波动变化的.
2) 自由火焰脉动十分规律,表现为尖端闪烁. 0.36 Pa声波作用使得火焰形态变得稳定,更强的1.06 Pa声波扰动导致火焰边缘变得更加杂乱. 随着声压增加,火焰概率云图由“锥状”转变为“类球状”.
3) 较高声波压力扰动下火焰高度降低,较低声波压力扰动下火焰高度变化较缓. 随声学雷诺数增加,火焰相对高度减小. 较高声波压力导致火焰相对宽度增加. 小声雷诺数扰动下火焰宽度是被挤压状态,大声雷诺数扰动下火焰宽度是被拓展状态.
4) 较低声压会调制火焰导致其周期性变得更稳定,相位变得规则,较高声压会扰乱火焰周期性,使得火焰脉动紊乱,相位变得混沌.
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表 1 无人机空地网络国内外研究现状汇总
Table 1. Research summary of air-ground UAV networks
场景
描述优化算法 控制手段 性能指标 来源 待进一步解决问题 无人机空地网络覆盖 启发式算法 无人机部署位置、
无人机轨迹用户覆盖百分比 文献[1,13] 现有研究通常针对用户静止场景下,采用传统优化算法实现轨迹与资源控制,以提高网络性能. 当地面用户数量增加且用户出现高移动特性时,如何进一步优化空地双移动约束下的对地网络覆盖性能还需进一步分析 覆盖率 文献[22,24] 凸优化 无人机部署位置、
无人机轨迹、功率覆盖用户数量 文献[14-15] 中断概率 文献[27-30] 聚类算法 无人机基站的三维位置、用户对路径损失的补偿因子 覆盖用户数量 文献[16-17,31] 回声状态网络、深度强化学习 无人机初始部署位置、轨迹、功率控制、带宽 吞吐量(移动用户覆盖问题) 文献[20] 覆盖时间与覆盖率 文献[21] 覆盖效率(覆盖率与覆盖时间比值) 文献[23-26] 基于网络的异构粒子群优化算法 无人机轨迹、带宽 覆盖面积 文献[33] 无人机空地网络通感算一体化 凸优化、聚类 射波束成型、无人机位置、轨迹、飞行速度、用户关联、发射波束成型、感知频率/时间、无人机飞行轨迹 平均用户速率 文献[4,13-15] 现有研究主要围绕通感一体化或通算一体化场景,未充分研究通感算一体化网络设计与优化控制. 此外,在网络控制时所采取的传统优化算法复杂度较高导致其很难适应高度变化的信道场景. 后续研究可针对无人机通感算场景,基于机器学习技术设计低复杂度的无人机通感算一体化网络管控方案 用户关联、功率分配、无人机位置 效用函数 文献[18] 任务卸载比、网络资源、无人机轨迹 能耗 文献[48-49] 深度强化学习 无人机飞行方向、飞行距离、功率分配、信道分配 感知有效性和公
平性文献[19] 任务卸载比、网络资源、无人机轨迹 时延 文献[51] 用户关联、无人机轨迹规划、功率分配 加权频谱效率 文献[52] 无人机轨迹、时隙总数、感知调度 感知目标的平均信息年龄 文献[8] RIS 辅助的无人机空地网络 深度强化学习
算法无人机飞行轨迹、频谱资源、计算资源、智能反射面系数、设备发射功率等 数据新鲜度 文献[61] 当前面向 RIS 辅助的无人机空地网络控制研究通常具有极高的控制复杂度和极大的信息反馈量,如何进一步降低网络控制复杂度并降低网络控制时的信息反馈量,使得其能够更好地适配无线环境变化 能耗 文献[62] 凸优化算法、块坐标下降法 无人机飞行轨迹、频谱资源、计算资源、智能反射面系数、设备发射功率等 能耗 文献[63] 能效 文献[12,65] 鲁棒无人机空地网络 联邦学习 设备选择、无人机部署、子信道分配、传输功率控制 最小化联邦学习模型的收敛时间和学习精度损失 文献[69-71] 当前研究考虑的外部电磁环境相对简单,当面对外部复杂电磁环境与有意干扰情况,如何提升无人机空地网络鲁棒性还需要进一步研究 深度优先搜索算法、连续凸近似、拉格朗日对偶、块坐标下降算法、遗传算法和粒子群算法 用户调度,带宽分配、功率控制、轨迹控制、半径和圆心 能耗效率、最大化每个用户的平均最小传输速率、最大化系统容量、最大化服务用户数 文献[19,72,75-77] 多智能体深度强化学习 飞行轨迹、子载波分配、通信/干扰功率 能耗效率、最大化每个用户的平均最小安全传输速率、最大化每个用户的安全容量 文献[78-81] -
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