摘要：

针对6G网络中超密集无线体域网(WBAN)所面临的计算资源匮乏与共信道干扰的问题，提出一种干扰感知的高效任务卸载策略。首先，设计面向WBAN医疗信息的软件定义网络(SDN)边缘计算架构，并建立考虑历史状态的优先级评分机制与优先级排队模型；其次，提出邻居节点感知算法(NNAA)，生成当前超帧节点的邻居节点矩阵；随后，提出干扰感知的卸载策略(IAOS)，该策略定义了考虑卸载收益、卸载开销、卸载状态的系统收益模型；接着，设计考虑系统收益与节点并发数量的目标函数，引入遗传算法中的交叉变异策略来跳出局部最优，并且对不可行解进行修正；最后，利用改进的二进制开普勒优化算法(IBKOA)解出使得目标函数最大化的卸载决策。实验结果表明：在数据量变化的环境中，IAOS策略的时延对比其他算法平均降低了74.5%；在患者数量变化的环境中，IAOS策略的节点干扰率、时延、能耗相较对比算法分别平均降低了61.43%、59.28%、58%，吞吐量与系统收益分别平均提升了149.5%、74.38%。

关键词: 6G网络, 无线体域网, 软件定义网络, 边缘计算, 共信道干扰, 任务卸载策略

Abstract:

Ultra-dense Wireless Body Area Networks (WBAN) integrated into 6G networks face the issues of scarce computational resources and co-channel interference. This study proposes an interference-aware efficient task offloading strategy to address these challenges. First, a Software Defined Network (SDN)-based edge computing architecture for WBAN medical information is designed, and a priority scoring mechanism and priority queuing model that consider historical states are established. Second, a Neighbor Node Aware Algorithm (NNAA) is proposed to generate a neighbor node matrix for the current superframe node. Subsequently, an Interference-Aware Offloading Strategy (IAOS) is introduced, which defines a system benefit model that considers offloading gains, offloading overheads, and offloading states. Next, an objective function that considers both the system benefits and the number of concurrent nodes is designed. Crossover and mutation strategies from genetic algorithms are incorporated to escape local optima, and corrections are made for infeasible solutions. Finally, an Improved Binary Kepler Optimization Algorithm (IBKOA) is used to derive the offloading decision that maximizes the objective function. Experimental results demonstrate that in environments with varying data volumes, the IAOS strategy reduces the latency by an average of 74.5% compared with other algorithms. In environments with varying numbers of patients, the IAOS strategy achieves average reductions of 61.43%, 59.28%, and 58% in node interference rate, latency, and energy consumption, respectively, compared with the comparison algorithms, while increasing the throughput and system benefits by average values of 149.5% and 74.38%, respectively.

Key words: 6G network, Wireless Body Area Networks (WBAN), Software Defined Network (SDN), edge computing, co-channel interference, task offloading strategy