计算机科学
吞吐量
贪婪算法
波束赋形
聚类分析
启发式
线性规划
整数规划
干扰(通信)
传输(电信)
自由度(物理和化学)
多输入多输出
信道状态信息
计算机网络
数学优化
帧(网络)
蜂窝网络
频道(广播)
最优化问题
物理层
电信线路
分布式计算
可靠性(半导体)
无线网络
光谱效率
复式(建筑)
异构网络
信道分配方案
算法
无线电资源管理
选择算法
网络规划与设计
访问控制
时分多址
物理
接入网
作者
Lyutianyang Zhang,Liu Cao,Zhengchuan Chen,Dongyu Wei,Mingzhe Chen,Vanlin Sathya,Shiwen Mao
标识
DOI:10.1109/twc.2026.3662793
摘要
Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.
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