ProxySelect: Frequency Selectivity-Aware Scheduling for Joint OFDMA and MU-MIMO in 802.11ax WiFi

TL;DR

ProxySelect addresses the challenge of frequency-selectivity-aware scheduling for joint OFDMA and MU-MIMO in 802.11ax by introducing a scalable ILP framework that uses a proxy rate to approximate ZFBF performance. The key ideas are alpha-based semi-orthogonality screening to prune candidate user groups and a sampling-based method to generate up to T near-orthogonal groups per RU, keeping the ILP size practical. Experiments with ray-tracing channel models show ProxySelect achieves near-optimal sum rates with substantially lower computational cost compared to baseline approaches. The work demonstrates the practical viability of frequency-selectivity-aware scheduling for large-scale MU-MIMO/OFDMA WiFi deployments and provides a foundation for real-time implementations.

Abstract

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) to WiFi to support concurrent transmissions to a larger number of users. As bandwidth continues to grow, WiFi channels exhibit increased frequency selectivity, which poses new challenges for MU-MIMO user selection: the optimal user set varies across frequency and is interleaved over subbands (called resource units, or RUs). This frequency selectivity, coupled with the complex subband allocation pattern, renders conventional narrowband user selection algorithms inefficient for 802.11ax. In this paper, we propose \emph{ProxySelect}, a scalable and frequency selectivity-aware user scheduling algorithm for joint OFDMA and MU-MIMO usage in 802.11ax under zero-forcing beamforming (ZFBF). The scheduling task is formulated as an integer linear program (ILP) with binary variables indicating user (group)-RU associations, and linear constraints ensuring standard compatibility. To reduce complexity, we introduce a novel proxy rate--a function of individual channel strengths and their correlations--that approximates the ZFBF rate without requiring cubic-complexity matrix inversion. Additionally, we develop a sampling-based candidate group generation scheme that selects up to $T$ near-orthogonal user groups for each RU, thereby bounding the ILP size and ensuring scalability. Simulations using realistic ray-tracing-based channel models show that ProxySelect achieves near-optimal rate performance with significantly lower complexity.

Figures (6)

• Figure 1: OFDMA resource units for the 20 MHz channel. MU-MIMO is only allowed on RUs at levels 1 and 2 (shaded).
• Figure 2: Channel correlation versus frequency.
• Figure 3: Conference room with dimensions of $23\times 18 \times 2.8$ meters. 4 AP antenna heads (red) are placed at the corners at a height of $2.5$ meters.
• Figure 4: Impact of $\alpha$ and $T$.
• Figure 5: Comparison of sum rates.

Theorems & Definitions (2)

• Definition 1: $\alpha$-Compatibility
• Definition 2: $\alpha$-Compatibility on RUs