An Overview of Key Technologies for 6G-Enabled Communication-Computing Integration and Energy-Efficiency Optimization
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摘要: 随着以智能网联新能源汽车、人工智能终端、智能机器人为代表的新型智能终端快速普及，多模态大模型等新兴应用不断向端侧落地，泛在智能业务持续拓展。在此背景下，终端算力开销急剧增长。受制于体积、功耗与成本，智能终端设备普遍面临能耗过高、续航不足的问题。第6代(6G)无线通信网络通过无线接入网(RAN)架构增强与算力下沉，有望承接终端侧高能耗、高算力任务，赋能新型终端实现轻量化、低成本、长续航升级。该文首先阐述了增强型RAN赋能的6G端–边协同服务框架，并建立终端计算与传输能耗理论模型。然后，该文系统梳理了面向端–边协同推理的典型节能技术，并提出面向动态无线环境的通算联合自适应优化机制，实现通信与计算能耗的协同优化。最后，以具身智能终端为典型场景验证所提机制的节能效果，并总结当前研究在资源协同、全局优化与标准化落地等方面的挑战与未来方向。Abstract:
Significance Constrained by size, power consumption, and cost, emerging intelligent terminals often face excessive energy consumption and limited battery life. These limitations have become major bottlenecks to large-scale deployment. Compared with Fifth-Generation (5G) wireless networks, Sixth-Generation (6G) wireless networks are expected to enhance the Radio Access Network (RAN) architecture and move computing capability toward the RAN side. High-energy and compute-intensive Artificial Intelligence (AI) tasks that are originally executed by end devices can therefore be processed by the network. Through End-Edge Collaboration, emerging intelligent terminals can be upgraded toward lightweight design, low cost, and long battery life, thereby supporting the large-scale deployment of ubiquitous intelligence in 6G networks. Progress Current progress in Terminal Energy Consumption Optimization through 6G End-Edge Collaboration is reviewed, with emphasis on local execution, full offloading, and partial offloading. In local execution, User Equipment (UE) processes all tasks locally, which leads to high computing energy consumption. In full offloading, all tasks are transferred to the RAN. This reduces terminal-side computing energy consumption but can increase transmission energy consumption, especially under poor channel conditions. Partial offloading combines the benefits of both modes and optimizes energy consumption according to real-time network conditions. For partial offloading, four representative optimization techniques are summarized. (1) Feature Extraction and Filtering. Semantic encoding and information extraction are performed at the UE, and only task-relevant data are transmitted to the RAN. This reduces redundant data transmission and lowers transmission energy consumption. (2) Split Offloading. A large Deep Neural Network (DNN) is divided into layers according to its structure. Simpler shallow layers are processed at the UE, whereas more complex deep layers are offloaded to the RAN. This method balances terminal-side and RAN-side computational loads through End-Edge Collaborative Inference. (3) Model Lightweighting. Model complexity is reduced through pruning, quantization, and knowledge distillation, which lowers computational overhead while maintaining task performance. (4) Incremental Inference. Only changed data or updated features are processed, while historical computations are reused. This reduces redundant computation. Together, these techniques improve terminal performance and energy efficiency within the 6G End-Edge Collaboration framework. Conclusions This paper systematically reviews Terminal Energy Consumption Optimization for 6G End-Edge Collaboration. It summarizes the functional evolution of enhanced RAN, constructs an End-Edge Collaborative service framework for Communication-Computing Integration, and establishes a theoretical model of terminal computing energy consumption and transmission energy consumption. The composition and influencing factors of energy consumption under different offloading modes are clarified. Key energy optimization technologies, including Feature Extraction and Filtering, Split Offloading, Model Lightweighting, and Incremental Inference, are then discussed. To address energy consumption fluctuations caused by dynamic wireless channels, the paper proposes energy optimization mechanisms based on Adaptive Semantic Compression, Dynamic Split Offloading, Adaptive Model Pruning, and Incremental Inference. These mechanisms maintain a dynamic balance between energy optimization and task performance. Using embodied intelligent robot video understanding as a typical application scenario, a test platform is developed to verify the effectiveness of the proposed mechanisms. Current challenges and future research directions are also analyzed. Prospects Although End-Edge Collaborative energy-saving technologies have achieved initial progress, practical deployment still faces challenges in real network environments, dynamic wireless channels, and large-scale user access. Future research should examine the trade-off between optimization overhead and system robustness. It should also study dynamic communication-computing resource substitution modeling in stochastic resource environments, multi-user collaboration strategies, and global energy-efficiency optimization. As the technology matures, standardization and engineering implementation of End-Edge Collaborative energy-saving frameworks will become critical to the large-scale adoption of 6G applications. Future studies should further integrate algorithm design with network architecture, enabling practical deployment of low-power and high-efficiency intelligent communication systems. -
表 1 常见神经网络的计算模型参数
神经网络类型 核心计算模块 关键影响参数 计算复杂度
核心表达式内存访问次数
核心表达式典型适用场景 全连接网络
(Fully Connected Network, FCN)全连接层矩阵乘法 批大小$ B $、全连接层数$ L $、特征维度$ d $ $ O(B\times L\times {d}^{2}) $ $ 2Bd+{d}^{2} $ 简单特征分类、模型输出层 卷积神经网络-标准卷积
(Convolutional Neural Network, CNN)卷积运算 卷积核大小$ K\times K $、输入通道$ {C}_{\text{in}} $、输出通道$ {C}_{\text{out}} $、输入大小$ W\times H $、输出大小$ W'\times H' $、批大小$ B $ $ O(K\times K\times {C}_{\text{in}}\times {C}_{\text{out}}\times W\times H) $ $ \begin{array}{ll} BHW{C}_{\text{in}}+{K}^{2}{C}_{\text{in}}{C}_{\text{out}}+\\ BH'W'{C}_{\text{out}}\end{array} $ 图像分类、目标检测、视觉特征提取 CNN-深度可分离卷积(MobileNet) 深度卷积+逐点卷积 $ \begin{array}{l} O(K\times K\times {C}_{\text{in}}\times W\times H)+\\ O({C}_{\text{in}}\times {C}_{\text{out}}\times W\times H)\end{array} $ $ \begin{array}{l} 2BHW{C}_{\text{in}}+{K}^{2}{C}_{\text{in}}+\\ BHW{C}_{\text{in}}+{C}_{\text{in}}{C}_{\text{out}}\\ +BH'W'{C}_{\text{out}}\end{array} $ 端侧轻量级视觉任务、移动设备AI推理 CNN-分组卷积(ResNeXt) 分组卷积运算 分组数$ g $ $ O\left(\dfrac{K\times K\times {C}_{\text{in}}\times {C}_{\text{out}}\times W\times H}{g}\right) $ $ \begin{array}{l}BHW{C}_{\text{in}}+{K}^{2}{C}_{\text{in}}{C}_{\text{out}}/g+\\ BH'W'{C}_{\text{out}}\end{array} $ RAN侧视觉任务、多维度视觉特征提取 循环神经网络
(Recurrent Neural Network, RNN)递归矩阵乘法 序列长度$ T $、隐藏层维度$ H $、输入维度$ X $、批大小$ B $ 单向：$ O(T\times H\times (H+X)) $
双向：$ O(2\times T\times H\times (H+X)) $单向：$ BTH $
双向：$ 2BTH $短序列文本处理、时序信号简单预测 图神经网络
(Graph Neural Network, GNN)邻居聚合+稀疏矩阵乘法 层数$ L $、图中边的数量$ E $、节点特征维度$ d $ $ O(L\times E\times d) $ $ 2Nd+Ed $ 社交网络分析、推荐算法、知识图谱推理 长短期记忆网络
(Long Short-Term Memory, LSTM)多门控矩阵乘法 时间步数$ \mathrm{T} $、输入大小$ I $、隐藏层大小$ H $、批大小$ B $ $ O(\mathrm{T}\times (I\times H+H\times H)) $ $ BTH $ 长序列文本分析、时序数据预测、语音识别 Transformer-标准 自注意力机制+前馈网络 注意力层数$ L $、序列长度$ T $、模型维度$ {d}_{\mathrm{m}} $、前馈网络维度$ {d}_{\text{ff}} $、批大小$ B $ $ O(L\times T\times (T\times {d}_{\mathrm{m}}+{d}_{\mathrm{m}}\times {d}_{\text{ff}})) $ $ B{T}^{2}+BT{d}_{\text{ff}} $ 自然语言处理、长序列时序分析、多模态融合 Transformer-轻量(MobileBERT/Transformer-Tiny) 近似/线性注意力+前馈网络 轻量化模型注意力层数$ {L}_{\text{s}} $、序列长度$ T $、轻量化模型维度$ {d}_{\text{s}} $、批大小$ B $ $ O({L}_{\text{s}}({T}^{2}\times {d}_{\text{s}}+T\times d_{\text{s}}^{2})) $ $ B{T}^{2}+BT{d}_{\text{s}} $ 意图识别、事件抽取、文本分类、情感分析 Vision Transformer(ViT) 图像分块+自注意力+前馈网络 图像分块数$ {N}_{\text{clip}} $、批大小$ B $ $ \begin{array}{l} O({N}_{\text{clip}}\times L\times T\times (T\times {d}_{\mathrm{m}}\\+{d}_{\mathrm{m}}\times {d}_{\text{ff}}))\end{array}$ $ B{N}_{\text{clip}}{T}^{2}+B{N}_{\text{clip}}T{d}_{\text{ff}} $ 细粒度视觉识别、复杂场景
理解
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表 2 传输能耗测试仿真参数配置
属性 值 载波频率 7 GHz 带宽 20 MHz 子载波间隔 30 kHz TTI 0.5 ms TDD帧结构 DDDSU(3DL:1GP:1UL) 信道模型 UMa in TR 38.901 UE最大发射功率 23 dBm 业务流量模型FTP 3 Case 1: File size = 4 MB, arrival rate λ = 0.004 Case 2: File size = 40 MB, arrival rate λ = 0.001
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表 3 两种Case对应的SINR分布及单包传输能耗仿真结果(概率分布)
类别 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Case 1
(4MB)SINR(dB) 45.40 41.27 39.59 38.29 37.08 35.72 33.97 31.80 28.69 24.91 能耗(mJ) 1.2×103 4.1×103 1.1×104 2.5×104 5.6×104 1.1×105 2.5×105 4.4×105 4.5×105 2.1×106 Case 2
(40MB)SINR(dB) 44.70 40.90 38.67 37.56 36.13 34.50 32.21 30.01 27.31 21.74 能耗(mJ) 1.5×104 4.8×104 1.0×105 2.3×105 7.7×105 1.7×106 4.2×106 4.4×106 5.1×106 2.2×107
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