An Overview of Key Technologies on 6G-Enabled Communication and Computing Integration for 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。第六代(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 physical conditions such as size, power consumption, and cost, high energy consumptions have become key bottleneck for the large-scale application of new intelligent terminals. In contrast to Fifth-Generation (5G) networks, Sixth-Generation (6G) will achieve profound architectural enhancement of the RAN, sink computing capabilities toward the RAN side, and enable the RAN to perform part of tasks originally executed by end devices. With the end-edge collaboration, new intelligent terminals are expected to realize lightweight, low-cost and long-endurance evolution, which is of great significance for supporting the large-scale deployment of ubiquitous intelligence in 6G networks. Progress Current advancements in terminal energy consumption optimization with 6G end-edge collaboration are discussed, focusing on three primary offloading modes: local execution, full offloading, and partial offloading. Local execution requires the terminal to process all tasks, leading to high computational energy consumption, while full offloading shifts all tasks to the RAN, reducing terminal energy use but increasing transmission energy costs, particularly in poor channel conditions. Partial offloading combines the advantages of both modes, optimizing energy consumption based on real-time network conditions. For partial offloading, existing research has introduced several optimization techniques to enhance energy efficiency. (1) Feature extraction and filtering: Through semantic encoding and information extraction approaches, feature extraction is performed at the UE to transmit only task-relevant data to the RAN. This reduces the amount of redundant or unnecessary data sent, minimizing transmission energy consumption (2) Model partitioning for offloading: This technique divides a large deep learning model into different layers based on its network structure, with simpler layers processed at the UE and more complex ones offloaded to RAN. By leveraging end-edge collaborative reasoning, this method optimizes energy consumption by balancing the computational load between the terminal and RAN. (3) Model lightweighting: By reducing model complexity through techniques like pruning, quantization, and knowledge distillation, this method lowers computational overhead while maintaining performance. (4) Incremental reasoning: This method focuses on the changes in data or features, performing localized reasoning only on updated portions and reusing historical computations, significantly reducing redundant calculations. The above optimization techniques collectively enhance the performance and energy efficiency of terminal devices within the 6G end-edge collaboration framework. Conclusions This paper provides a comprehensive discussion of terminal energy consumption optimization with 6G end-edge collaboration. It summarizes the functional evolution of enhanced RAN, constructs an end-edge collaborative service framework for communication-computation integration, and establishes a theoretical model including terminal computing energy consumption and transmission energy consumption. The composition and influencing factors of energy consumption under different offloading modes are clarified. Key technologies for energy optimization based on end-edge collaboration are further discussed, including feature extraction and filtering, model partitioning for offloading, model lightweighting, and incremental reasoning. Given the energy consumption fluctuations caused by the dynamic nature of wireless channels, this paper introduces energy optimization mechanisms such as semantic compression, dynamic partitioned offloading, adaptive model pruning, and incremental reasoning to strike a dynamic balance between optimizing energy consumption and maintaining task performance. Taking intelligent robot video understanding as a typical application scenario, a test platform is developed to validate the effectiveness of the proposed optimization mechanisms. This paper also analyzes the challenges currently faced in the research and discusses future research directions. Prospects Although the end-edge collaborative energy-saving technologies have achieved initial progress, they still face many challenges in practical deployment, especially under real network environments, dynamic wireless channels, and large-scale user access. Future research should focus on the trade-off between optimization overhead and system robustness, and further investigate dynamic communication–computation resource substitution modeling in stochastic resource environments, as well as multi-user collaborative strategies and global energy efficiency optimization. Meanwhile, as the technology matures, the standardization and engineering implementation of end-edge collaborative energy-saving frameworks will become crucial for the large-scale adoption of 6G applications. Future studies should therefore promote deeper integration between algorithm design and network architecture, enabling the practical deployment of low-power, 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) 45.4 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) 44.7 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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