Partially Overlapping Channels Dynamic Allocation Method for UAV Ad-hoc Networks in Emergency Scenario
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摘要: 飞行自组网(FANETs)因具有高机动、自组织等特点,被广泛应用于应急救援场景。在应急场景中,大量用户寻呼请求造成局部流量激增与有限频谱资源之间产生难以协调的矛盾,FANET中面临严重的信道干扰问题,亟需将频谱利用率高的部分重叠信道(POCs)扩展到应急场景中。然而,POCs的邻信道特性,导致干扰复杂难以刻画。因此,该文研究了FANET部分重叠信道分配方法,通过几何预测重构时变干扰图和无干扰最小信道间隔矩阵刻画POCs干扰模型,在此基础上提出一种基于上界置信区间的POCs动态分配算法(UCB-DAL),通过分布式决策求解近似最优信道分配方案。仿真结果表明,该算法实现了网络干扰和信道切换次数之间性能折中,具有较好的收敛性能。Abstract: The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
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表 1 干扰范围
δ(t) 0 1 2 3 4 5 IR(δ(t)) 132.6 90.8 75.9 46.9 32.1 0 1 构建干扰图和无干扰最小信道间隔矩阵算法
输入:节点进行交互,获取位置信息及建立通信链路的节点信息。 输出:预测干扰图GIN(t)和无干扰最小信道间隔矩阵K_C(t)。 (1) 初始化:干扰图GIN(t)、矩阵K_C(t)。St(ui), Sr(uj)为节点
集合的子集。(2) for $ \forall $ui$\in $St(ui)do (3) for $ \forall $uj$\in $Sr(uj)do (4) if ui与uj建立通信对 then (5) 进行下一次迭代; (6) end if (7) if ui与uj之间距离不大于预测干扰距离 (8) ui与uj之间存在干扰边; (9) end if (10) 根据式(14)求出ui与uj预测的信道间隔δ; (11) end for (12) end for 2 UCB的部分重叠信道动态分配学习算法(UCB-DAL)
输入:预测干扰图GIN(t)及无干扰最小信道间隔矩阵K_C(t),
信道分配矩阵C_U(t–k–1)。输出:信道分配方案C_U(t)。 (1) 初始化:所有玩家获取奖励R_M和累积奖励C_M。
Sv(un)为节点集合,Sn(un)为un邻居节点集合。(2) for epi =1:max_epi do (3) for $ \forall {u_n} \in {\text{Sv}}({u_n}) $do (4) 根据式(17)选取效益最大的动作; (5) for$ \forall {u_j} \in {\text{Sn}}({u_n}) $ do (6) if 玩家$ {u_n} $与邻居节点$ {u_j} $的信道间隔大于等于无干扰
最小信道间隔then(7) 对玩家un当前选择动作给予奖励并更新R_M和累积
奖励C_M;(8) else (9) 对当前动作根据式(18)给予惩罚并更新R_M和累积
奖励C_M;(10) end if (11) 更新玩家un所选信道及信道矩阵C_U(t); (12) end for (13) end for (14) if 所有玩家都找到收益最大动作 then (15) 结束循环; (16) end if (17) end for -
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