We propose a bi-level bargaining framework for distributed user association and resource allocation. In the followerlevel game, UE competition occurs in a non-cooperative manner. In contrast, full coordination is assumed among the BSs in the leader-level game. Congestion factors are introduced to balance the loads on small BSs with different capacities  We consider two fundamental limitations of HetNets: the backhaul bottleneck and UE capability constraints. Accordingly, constraints are introduced on the wireless resources and maximum number of serving UEs in small BSs to reflect the backhaul bottleneck, and minimum rate requirements for UE devices are introduced to represent the UE capability constraints.  Potential game theory is used to decouple the backhaul bottleneck constraints. The existence, uniqueness and convergence of the Nash equilibrium (NE) of the follower-level game are also verified. Finally, the resident-oriented GS algorithm is used to obtain a stable single-BS association.翻译

我们提出了一个分布式用户关联和资源分配的双层谈判框架。在追随者层次博弈中，UE设备之间以非合作的方式进行竞争。相反，在领导者层次博弈中，假设基站之间完全协调。为了平衡不同容量的小型基站的负载，引入了拥塞因素。我们考虑了HetNets的两个基本限制：后向链路瓶颈和UE能力约束。因此，引入了无线资源和小型基站能够服务的最大UE数量的约束，以反映后向链路瓶颈，并引入了UE设备的最低速率要求来表示UE能力约束。我们使用潜在博弈理论来解耦后向链路瓶颈约束。还验证了追随者层次博弈的纳什均衡（NE）的存在性、唯一性和收敛性。最后，使用面向居民的GS算法来获得稳定的单基站关联。

相关问题

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ，建立了什么模型

这段文字并没有建立具体的模型，而是介绍了LyDROO算法的伪代码以及解决问题(P1)和(P4)的方法。该算法是一种在线学习算法，利用深度神经网络来近似最优策略，并通过解决最优资源分配问题来求解目标函数。具体而言，算法主要分为两个部分：第一个部分是通过DNN生成松弛的卸载决策，然后使用NOP方法对其进行量化；第二个部分是针对量化后的决策，通过解决最优资源分配问题来选择最优的决策。整个算法的复杂度主要在第七行，即计算最优资源分配问题的部分，但该问题是一个凸优化问题，可以高效地解决。

翻译：Visible-Infrared Image Alignment for UAVs: Benchmark and New Baseline。With the extensive use of multisensors in uncrewed aerial vehicles (UAVs), multimodality information processing has become the research focus. In academic research pertaining to object detection and tracking tasks in UAVs, researchers often align visible-infrared image pairs as a preprocessing step. However, in actual tasks, the dual-modality image pair acquired by UAVs is unaligned, which significantly limits the application of downstream tasks. At present, there are no publicly available multimodality image alignment datasets for UAVs. In this article, we present a large-scale benchmark for the dual-modality image alignment task in UAVs, including 81000 training image pairs and 15000 testing image pairs. Meanwhile, we propose a transformer-based dual-modality image alignment network as the baseline for this benchmark. First, the algorithm extracts multiscale features for image representation to address unaligned image pairs with varying resolutions. Second, a transformer-based alignment network is proposed to improve the fusion of features from heterogeneous modalities. Finally, deformable attention is adopted to alleviate the problem of memory explosion. Numerous experiments on this dual-modality image alignment benchmark are conducted to demonstrate the effectiveness of our algorithm. Source codes are available at https://github.com/gaozhinanjiu/UAVmatch.

关于无人机可见光-红外图像对齐的研究
研究背景与意义
在无人机(UAV)应用领域，可见光和红外成像技术被广泛用于环境监测、目标检测等多种场景。然而，由于传感器特性和拍摄条件的不同，这两种模态的图像存在显著差异，给后续处理带来了挑战。因此，研究如何有效地实现可见光-红外图像配准具有重要价值。
论文概述
针对这一问题，《VisIRNet: Deep Image Alignment for UAV-taken Visible and Infrared Image Pairs》提出了基于深度学习的方法——VisIRNet框架[^2]。该方法旨在解决由不同时间戳采集所得异质影像间的精准匹配难题，并试图建立新的性能评估标准以及提供改进的基础解决方案。
方法论介绍
VisIRNet采用卷积神经网络(CNN)作为核心组件，设计了一套端到端训练机制来自动提取特征并完成空间变换估计任务。具体而言：

数据预处理：考虑到实际应用场景复杂多变的特点，在输入阶段会对原始图片实施一系列增强措施；

特征映射构建：利用编码器结构捕捉局部细节的同时保留全局语义信息；

仿射变换参数预测模块：通过解码路径生成描述两幅图相对位姿关系的一组向量，进而指导像素级对应关系的学习过程；

损失函数定义：综合考虑几何一致性和平滑度约束等因素制定优化目标，确保最终输出具备较高的鲁棒性。

实验验证与分析
为了全面检验所提方案的有效性，作者精心挑选了多个公开可用的数据集开展对比实验。结果显示，在多种评价指标下（如重叠率、角点误差等），VisIRNet均能取得优于现有技术水平的表现。更重要的是，这项工作还首次尝试建立了专门面向UAV平台的测试基准，为未来同类课题提供了宝贵参考资料。
新基线模型特点
新提出的基线模型不仅继承了传统算法的优点，而且融入了现代AI工具箱里的先进技术成果。特别是借助大规模标注样本库的支持，使得整个系统能够在更广泛的条件下保持稳定运行状态。此外，通过对内部架构持续迭代升级，预期可以在不久将来达到更高的精度水平。
import torch.nn as nn

class VisIRNet(nn.Module):
def __init__(self):
super(VisIRNet, self).__init__()
# 定义CNN层和其他必要组件

def forward(self, visible_img, ir_img):
# 处理流程逻辑
pass