pith. sign in

arxiv: 2605.16144 · v1 · pith:45QZRB3Nnew · submitted 2026-05-15 · 📡 eess.SP · cs.MA· cs.NI

MAxLM: Multi-Agent Language Model-Based Scheduling and Resource Allocation in MU-MIMO-OFDMA-Enabled Wireless Networks

Adnan Quadri , Hongxiang Li This is my paper

Pith reviewed 2026-05-19 21:37 UTC · model grok-4.3

classification 📡 eess.SP cs.MAcs.NI
keywords multi-agent language modelscheduling and resource allocationMU-MIMO-OFDMAuplink scheduled accesswireless LANthroughput optimization
0
0 comments X

The pith

A multi-agent system using pretrained language models optimizes scheduling and resource allocation to increase uplink throughput in MU-MIMO-OFDMA wireless networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper sets out to demonstrate that a multi-agent framework driven by openly available pretrained language models can handle user scheduling and resource allocation for uplink scheduled access in joint MU-MIMO-OFDMA wireless LANs. The approach matters because effective assignment of channels and spatial streams to multiple stations can raise overall network throughput without requiring hand-crafted optimization routines or channel-specific math models. The authors introduce the WiSER platform to let the language-model agents operate autonomously and then evaluate the system in scenarios that vary the number of stations and the antenna count at the access point. Numerical comparisons show that the resulting uplink throughput exceeds that of standard benchmark schedulers.

Core claim

The MAxLM method frames uplink scheduling and resource allocation as a multi-agent task solved by pretrained small or medium language models, and the decisions produced by this system deliver higher uplink scheduled access throughput than benchmark techniques across tested station counts and access-point antenna configurations.

What carries the argument

A multi-agent framework in which each agent employs a pretrained language model to generate autonomous scheduling and resource allocation decisions for the uplink within the WiSER platform.

If this is right

  • Uplink scheduled access can support more simultaneous stations while maintaining or increasing total throughput.
  • The same language-model agents remain effective when the access point changes its number of antennas.
  • Scheduling decisions can be generated without feeding explicit channel-state equations into a traditional optimizer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Language models may serve as drop-in replacements for classical schedulers in other dynamic wireless resource problems.
  • Real hardware deployments could reveal latency or robustness issues not visible in the reported simulations.

Load-bearing premise

A general pretrained language model without domain-specific fine-tuning or explicit mathematical modeling of the wireless channel can still produce reliable near-optimal scheduling decisions across different numbers of stations and antenna setups.

What would settle it

Apply the MAxLM scheduler and the benchmark schedulers to a simulated network with a substantially larger number of stations or a different antenna configuration than those reported and measure whether the uplink throughput advantage disappears.

Figures

Figures reproduced from arXiv: 2605.16144 by Adnan Quadri, Hongxiang Li.

Figure 1
Figure 1. Figure 1: The (a) MU-MIMO-OFDMA-enabled WLAN where the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The MAxLM-optimized SRA: (a) workflow illustrated using [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Assignment errors made by the xLMs performing the BCQ [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Modular structure of the prompts showing the role, task, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of Gemma-optimized SRA’s MIMO user group size [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: CDF of the UL-SA rate-sum utilizing the MAxLM-optimized [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Wireless networks support multi-user (MU) communication with multiple-input multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) technologies. In the joint MU-MIMO-OFDMA-enabled transmission mode, network throughput can be significantly increased by effectively utilizing the multi-channel resources to schedule numerous wireless users/stations (STAs) simultaneously. In this paper, we study ways to optimize the user scheduling and resource allocation (SRA) for the UL scheduled access (UL-SA) of a joint MU-MIMO-OFDMA-enabled wireless local area network (WLAN). In particular, we propose a multi-agent (MA) framework that utilizes an openly available pretrained small/medium-sized Language Model (xLM) to perform SRA for the UL-SA. To facilitate autonomous SRA using our proposed technique, we introduce the AI-assisted Wireless Systems Engineering and Research (WiSER) platform. We evaluate the performance of MAxLM-optimized SRA for network scenarios with a varying number of STAs and antenna settings on the WLAN Access Point. Numerical results confirm that our proposed technique achieves higher UL-SA throughput than the benchmark techniques.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes MAxLM, a multi-agent framework that employs an openly available pretrained small/medium-sized language model to perform user scheduling and resource allocation (SRA) for uplink scheduled access (UL-SA) in MU-MIMO-OFDMA-enabled WLANs. It introduces the WiSER platform to support autonomous execution of these decisions and presents numerical results claiming higher UL-SA throughput than benchmark techniques across scenarios with varying numbers of stations (STAs) and access-point antenna configurations.

Significance. If the reported throughput gains prove robust, the work would be significant for demonstrating that general-purpose pretrained language models can be applied in a multi-agent setting to wireless resource allocation without domain-specific fine-tuning or explicit channel modeling. This offers a flexible, potentially scalable alternative to conventional optimization-based SRA methods. The introduction of the WiSER platform for autonomous execution adds a practical systems contribution.

major comments (2)
  1. [Numerical Results] Numerical Results section: the central claim that MAxLM achieves higher UL-SA throughput than benchmarks is load-bearing for the paper's contribution, yet the manuscript provides no details on experimental setup, number of trials, statistical significance testing, exact definitions of the benchmark techniques, or how LLM-generated decisions are validated for feasibility under MU-MIMO-OFDMA constraints (e.g., power, spatial-stream, and subcarrier allocation limits).
  2. [Method] Method / System Model: the assumption that a general pretrained language model (without fine-tuning or explicit mathematical modeling of the wireless channel) can reliably produce feasible and near-optimal SRA decisions across varying STA counts and antenna configurations is not accompanied by any analysis of constraint satisfaction or convergence properties, which is required to substantiate superiority over benchmarks.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'openly available pretrained small/medium-sized Language Model (xLM)' could be clarified with the specific model name or parameter count to aid reproducibility.
  2. [Numerical Results] The manuscript would benefit from a table summarizing the key simulation parameters (e.g., bandwidth, MCS, noise power, number of antennas) used in the numerical evaluation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below, providing the strongest honest responses possible. Where the comments correctly identify gaps in the original submission, we have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: [Numerical Results] Numerical Results section: the central claim that MAxLM achieves higher UL-SA throughput than benchmarks is load-bearing for the paper's contribution, yet the manuscript provides no details on experimental setup, number of trials, statistical significance testing, exact definitions of the benchmark techniques, or how LLM-generated decisions are validated for feasibility under MU-MIMO-OFDMA constraints (e.g., power, spatial-stream, and subcarrier allocation limits).

    Authors: We agree that these details were insufficient in the original manuscript. In the revised version, we have substantially expanded the Numerical Results section with: a full description of the experimental setup (including channel models, noise figures, and WLAN parameters); the number of Monte Carlo trials performed per scenario; statistical significance testing via paired t-tests confirming throughput gains (p < 0.05); explicit definitions and parameter settings for each benchmark (e.g., greedy SNR-based selection, proportional-fair scheduling, and a convex-relaxation baseline); and a new paragraph detailing the feasibility validation step inside the WiSER platform, where LLM outputs are parsed and corrected to respect power limits, spatial-stream counts, and subcarrier orthogonality before execution. These additions directly address the reproducibility and rigor concerns. revision: yes

  2. Referee: [Method] Method / System Model: the assumption that a general pretrained language model (without fine-tuning or explicit mathematical modeling of the wireless channel) can reliably produce feasible and near-optimal SRA decisions across varying STA counts and antenna configurations is not accompanied by any analysis of constraint satisfaction or convergence properties, which is required to substantiate superiority over benchmarks.

    Authors: We acknowledge that the original manuscript contains no formal analysis of constraint satisfaction rates or convergence properties. Our approach is intentionally empirical and relies on prompt engineering plus multi-agent interaction rather than explicit channel modeling or theoretical guarantees. In the revised manuscript we have added a dedicated discussion subsection explaining the practical mechanisms (iterative agent refinement and WiSER post-processing) that encourage feasible outputs, along with empirical observations of decision feasibility across the tested STA and antenna configurations. A rigorous theoretical convergence proof for black-box LLM agents lies outside the scope of this work; we therefore provide only the empirical evidence already present in the numerical results. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The manuscript proposes an empirical multi-agent LLM framework (MAxLM) for user scheduling and resource allocation in MU-MIMO-OFDMA WLANs, evaluated via numerical simulations against benchmarks across varying STA counts and antenna configurations. No closed-form derivation, first-principles result, or mathematical chain is claimed that reduces to fitted parameters or self-citations by construction. Performance claims rest on direct throughput comparisons using the introduced WiSER platform, with no evidence of self-definitional loops, renamed known results, or load-bearing self-citations that substitute for independent validation. The approach is self-contained as an applied AI scheduling method whose validity is assessed externally through simulation outcomes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach implicitly assumes the language model can map network state descriptions to valid scheduling actions without additional mathematical constraints.

pith-pipeline@v0.9.0 · 5744 in / 1098 out tokens · 30548 ms · 2026-05-19T21:37:24.551513+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Zhang, K

    R. Zhang, K. Xiong, Y. Lu, B. Gao, P. Fan, and K. B. Letaief, ‘‘Joint coordinated beamforming and power splitting ratio optimization in mu-miso swipt-enabled hetnets: A multi- agent ddqn-based approach,’’ IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 677–693, 2021

    work page 2021

  2. [2]

    Quadri and H

    A. Quadri and H. Li, ‘‘Resource allocation using deep learning in uplink 802.11 ax networks,’’ in GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023, pp. 4020–4025

    work page 2023

  3. [3]

    Balakrishnan, K

    R. Balakrishnan, K. Sankhe, V. S. Somayazulu, R. Van- nithamby, and J. Sydir, ‘‘Deep reinforcement learning based traffic-and channel-aware ofdma resource allocation,’’ in 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019, pp. 1–6

    work page 2019

  4. [4]

    Y. S. Nasir and D. Guo, ‘‘Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,’’ IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2239–2250, 2019

    work page 2019

  5. [5]

    Naderializadeh, J

    N. Naderializadeh, J. J. Sydir, M. Simsek, and H. Nikopour, ‘‘Resource management in wireless networks via multi-agent deep reinforcement learning,’’ IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3507–3523, 2021

    work page 2021

  6. [6]

    Chatzistefanidis, A

    I. Chatzistefanidis, A. Leone, and N. Nikaein, ‘‘Maestro: Llm- driven collaborative automation of intent-based 6g networks,’’ IEEE Networking Letters, vol. 6, no. 4, pp. 227–231, 2024

    work page 2024

  7. [7]

    Jiang, B

    S. Jiang, B. Lin, Y. Wu, and Y. Gao, ‘‘Links: Large language model integrated management for 6g empowered digital twin networks,’’ in 2024 IEEE 100th Vehicular Technology Confer- ence (VTC2024-Fall). IEEE, 2024, pp. 1–6

    work page 2024

  8. [8]

    X. Wu, J. Farooq, Y. Wang, and J. Chen, ‘‘Llm-xapp: A large language model empowered radio resource management xapp for 5g o-ran,’’ 2025

    work page 2025

  9. [9]

    quadri-a/wiser,

    WiSER, “quadri-a/wiser,” GitHub. Accessed: Apr. 14, 2026. [Online]. A vailable: https://github.com/quadri-a/wiser

    work page 2026

  10. [10]

    Bejarano, S

    O. Bejarano, S. Quadri, O. Gurewitz, and E. W. Knightly, ‘‘Scaling multi-user mimo wlans: The case for concurrent up- link control messages,’’ in 2015 12th Annual IEEE Interna- tional Conference on Sensing, Communication, and Networking (SECON). IEEE, 2015, pp. 238–246

    work page 2015

  11. [11]

    Quadri, Q

    A. Quadri, Q. Li, and H. Li, ‘‘User grouping and resource allocation for uplink of mu-mimo-ofdma-enabled wlan using multi-agent reinforcement learning,’’ IEEE Access, vol. 13, pp. 191 990–192 005, 2025

    work page 2025

  12. [12]

    Graph API overview

    “Graph API overview. ” . Accessed: Apr. 13, 2026. [Online]. A vailable: https://docs.langchain.com/oss/python/langgraph/graph- api

    work page 2026

  13. [13]

    Ollama library

    “Ollama library. ” Accessed: Apr. 13, 2026. [Online]. A vailable: https://ollama.com/library

    work page 2026

  14. [14]

    ” Ac- cessed Aug

    ”Using MATLAB with Python. ” Ac- cessed Aug. 1, 2025. [Online]. A vailable: https://www.mathworks.com/products/matlab/matlab-and- python.html

    work page 2025

  15. [15]

    ” Accessed Aug

    ”Filter signal through 802.11ax multipath fading channel. ” Accessed Aug. 1, 2025. [Online]. A vailable: https://www.mathworks.com/help/wlan/ref/wlantgaxchannel- system-object.html

    work page 2025