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LLM4WM: Adapting LLM for Wireless Multi-Tasking

Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, Liuqing Yang

arXiv:2501.12983v2eess.SP

TL;DR

Wireless channel-associated tasks can benefit from shared representations, but existing methods face data, generalization, and multi-task scaling challenges. LLM4WM addresses this with MoE-LoRA, task-specific preprocessing, and adapters that align wireless features with a pre-trained LLM. Experiments report strong performance and generalization across multiple wireless tasks, including few-shot and transfer settings.

  • Problem

    Existing AI methods face high data requirements, retraining burdens, and limited robustness in complex dynamic wireless scenarios, while multi-task methods struggle with imbalance and limited task capacity.

  • Method

    LLM4WM fine-tunes a pre-trained LLM for wireless multi-tasking with MoE-LoRA, task-specific preprocessing and output layers, and adapters aligning wireless and semantic feature spaces.

  • Results

    LLM4WM reports excellent performance across channel estimation, channel prediction, localization enhancement, and beam management, with robust generalization across wireless tasks.

  • Takeaways & Limitations

    The framework consistently outperforms other methods across most tasks in few-shot and cross-scenario transfer evaluations.

Abstract

from arXiv · show

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed--a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM's general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM's semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities.

I. INTRODUCTION

Wireless communication tasks depend on extracting channel characteristics, but existing AI and multi-task methods face data, generalization, imbalance, capacity, and scaling challenges. LLM4WM addresses these issues with pre-trained LLMs, MoE-LoRA, task-specific processing, and adapters for joint wireless multi-task modeling.

  • Motivation: Existing AI methods require substantial high-quality data and retraining, while complex dynamic scenarios expose limitations in model scale and generalization.These constraints can increase communication burden as environments change.
  • Motivation: Jointly learning channel-associated tasks can extract shared channel representations and provide training benefits, but existing approaches face data imbalance, seesaw effects, and limited task diversity.Many methods combine only two closely related tasks because of limited model capacity.
  • Framework: LLM4WM freezes most pre-trained LLM parameters and applies MoE-LoRA so experts share common knowledge while gating preserves task-specific features.The framework uses expert independence and gating to balance shared and differentiated representations.
  • Framework: Task-specific preprocessing, output headers, and multi-task adapters align wireless feature spaces with the LLM’s semantic feature space.Adapters are designed to bridge cross-domain representations and improve adaptability.
  • Scope and outcome: The framework targets channel estimation, channel prediction, localization enhancement, and beam management, with reported robust performance and generalization across wireless tasks.The paper presents LLM4WM as a large-model approach for concurrent channel-associated multitasks.

B. Signal Model

The signal model describes downlink MISO-OFDM transmission, beam selection, and sub-6G uplink processing for the considered wireless links. It connects channel estimation accuracy with beamforming quality and spectral efficiency.

  • mmWave transmission: The mmWave downlink uses MISO-OFDM with K activated subcarriers, and channel state information is obtained through estimation or prediction.The received signal is formulated using the channel at time t and subcarrier fk.
  • Beam selection: Beam training traverses the codebook and selects the beam vector with the highest spectral efficiency.The optimal beam is chosen by comparing achievable SE across candidate beam vectors.
  • Sub-6G transmission: Sub-6G downlink transmission also uses MISO-OFDM, while its uplink channel at pilot positions is estimated with the Least Squares method.The sub-6G link uses digital precoding after uplink estimation.
  • Channel accuracy: The effectiveness of sub-6G matched-filter precoding depends on the accuracy of the estimated channel, because inaccurate estimates produce mismatched beam vectors.The resulting mismatch reduces received SNR and impairs spectral efficiency.

III. TASK DESCRIPTION

The paper organizes channel-associated tasks into channel reconstruction, beam management, and radio environment mining, with channel reconstruction covering estimation and prediction across time, frequency, and antenna domains.

  • The task set T = {CE, CP, PF, BF, DE, PE} spans channel estimation, channel prediction, path finding, beamforming, distance estimation, and path loss estimation.
  • Channel Reconstruction: Channel reconstruction uses known channel matrices to predict or interpolate target matrices across time, frequency, and antenna domains.
  • Channel Reconstruction: The channel reconstruction inputs and outputs are formed by selecting timestamp, frequency, antenna, and pilot-index slices from the channel tensor.
  • Channel Reconstruction: Channel estimation uses comb-type pilots and interpolates missing frequency-domain channels from observed pilot positions.
  • Channel Reconstruction: Channel prediction includes time-domain and frequency-domain scenarios, both designed to reduce pilot overhead.

B. Beam Management: Sub-6G Aided Beamforming

Sub-6G aided beamforming uses a super-resolution DFT codebook and exploits cross-band spatial correlation to improve mmWave beam selection while reducing pilot overhead.

  • Beamforming predicts the optimal weight vector from a codebook W, using a super-resolution DFT codebook with Nc > Nv for higher spatial resolution.
  • Sub-6 GHz channel information assists mmWave beamforming because dominant paths are often highly correlated across bands under Line-of-Sight conditions.

C. Radio Environment Mining: Distance Estimation and Path Loss Estimation

Radio environment mining extracts distance and path-loss information from channel data, while task-specific preprocessing aligns differing channel characteristics with the model input.

  • Radio environment mining uses estimated channel information to extract the UE–BS distance and main-path path loss for communication-system configuration.
  • The distance-estimation and path-loss-estimation tasks are represented by separate task-specific output definitions.
  • Each task receives a corresponding preprocessing function because a unified preprocessing approach cannot fully exploit task-specific channel characteristics.
  • Channel reconstruction preprocessing tokenizes CSI by flattening spatial and frequency features, while angle-dependent tasks transform spatial-domain CSI into the angle domain.

B. Multi-Task Adapter Module

LLM4WM uses task-specific adapters and MoE-LoRA fine-tuning to align wireless features with the LLM while sharing and differentiating task representations efficiently.

  • Multi-Task Adapter: Multi-task adapters parallelize task-specific adapters, jointly feed their outputs into the LLM, and perform alignment operations for each task.
  • Multi-Task Adapter: The adapter’s linear alignment layer maps task features into the LLM’s token-length and hidden-dimension space before residual feature extraction.
  • MoE-LoRA Fine-tuning: MoE-LoRA is applied to LLM feed-forward linear layers while other parameters remain frozen, reducing trainable parameters and training costs.
  • MoE-LoRA Fine-tuning: MoE-LoRA uses independent low-rank matrix pairs as experts and a gating network to select and combine task-specific features.
  • MoE-LoRA Fine-tuning: Increasing the number of experts can improve representation capacity but linearly increases training and inference costs, creating an accuracy–speed trade-off.

D. Multi-Task Output Module

The multi-task output module adapts wireless-task features to the LLM’s semantic space while avoiding the costly vocabulary-sized output mapping. It uses task-specific processing to produce predictions or estimations for different channel-associated tasks.

  • The specialized output layer captures wireless-task targets while reducing resource demands associated with large vocabulary sizes.A conventional GPT-2-style output layer requires at least 50000 dimensions.
  • A multi-task adapter connected to the LLM output aligns each task’s output feature vector with the LLM’s semantic space.
  • CNNs process and dimensionally align channel estimation and prediction features because these tasks are more sensitive to local features.
  • Flattening and MLP processing align features for beamforming, distance estimation, and path loss estimation, which require global channel representations.
  • The task-specific output produces each task’s prediction or estimation result after feature alignment.

E. Training Configuration

LLM4WM uses staged training to first align task features with the pretrained LLM and then fine-tune the LLM for joint multi-task modeling. The experiments use simulated, time-varying CSI data generated under sub-6G and mmWave settings.

  • Training has two stages: adapters and the output layer are trained with the LLM frozen, then MoE-LoRA fine-tunes the LLM while adapters are frozen.The output layer remains trainable in the second stage.
  • Dynamic Weight Average adjusts task weights from epoch-level losses, using cross-entropy for classification and NMSE for regression.
  • The experiments evaluate overall performance, generalization, stability, and module contributions through ablation experiments.
  • QuaDRiGa generates time-varying CSI datasets compliant with 3GPP standards for 1.9 GHz sub-6G and 28 GHz mmWave links.
  • The channel-prediction setup predicts future 4 RBs from 16 historical RBs with a 0.5 ms pilot interval.
  • The dataset contains 20000 samples, including 15000 training samples and 1600 validation samples.

2) Baselines:

The evaluation compares LLM4WM with traditional, small-model, large-model, and single-task baselines spanning channel reconstruction, beam management, localization, and radio-environment tasks. Network and training settings also specify adapter, expert, LoRA, and output-module configurations.

  • Traditional Methods (without deep learning): Traditional baselines use bilinear interpolation for channel reconstruction, a spatial-correlation codebook for beam management, and FIFS for radio-environment mining.
  • Single-task Small Model Methods: Single-task small-model baselines include MLP, LSTM, and CNN architectures for radio sensing, beam management, and channel reconstruction.
  • Multi-Task Small Model Methods: Multi-task small-model comparisons use Cross-stitch networks with shared or single-task configurations.
  • Single-task Large Model Methods: Large-model baselines include LLM4CP for channel prediction and LLM4WM(s), which applies the proposed framework to a single task.
  • Network and Training Parameters: The experimental configuration uses 8 input/output adapters, 8 MoE experts, rank 8 LoRA matrices, and either a three-layer MLP or three-layer CNN output processor.
  • Network and Training Parameters: Training begins with a 50-epoch linear warm-up from 1×10^-5 to 1×10^-3, followed by cosine-annealed learning-rate adjustment.

4) Performance Metric:

Performance is assessed with task-specific reconstruction, classification, distance, path-loss, average, and spectral-efficiency metrics, alongside expert-weight correlation analysis. LLM4WM outperforms the evaluated baselines across tasks, while large models gain more from multi-task learning than small models.

  • 4) Performance Metric:: Task-specific metrics include NMSE for channel reconstruction, Top-1 accuracy for beam management, MAE for distance estimation, and NMSE for path loss estimation.
  • 4) Performance Metric:: Spectral efficiency measures overall communication-system performance and achievable rate, using actual CSI and predicted-CSI-based precoding.The communication SNR is set as 10 dB.
  • 1) Overall Performance:: LLM4WM outperforms non-learning methods, small models, and single-task fine-tuning across various tasks.
  • 1) Overall Performance:: 0.99 dB is the large model’s average improvement from single-task to multi-task learning, versus 0.19 dB for the small model.
  • Expert Allocation Analysis: Expert-weight correlations are generally low across tasks, while tasks with similar characteristics exhibit higher correlations.The analysis uses Pearson correlation coefficients from expert-combination heatmaps.

2) Generalization Experiments:

LLM4WM is evaluated for transfer across communication scenarios and frequencies, with results showing strong performance across most tasks. Additional experiments identify suitable MoE-LoRA settings, confirm the contributions of the adapter and backbone LLM, and indicate parameter-efficient deployment potential.

  • Generalization: Using only 10% of the RMa dataset and transferring across sub-6 GHz frequencies, LLM4WM consistently outperforms other methods across most tasks.Radio environment mining is a slight exception in the LOS scenario, while channel estimation benefits from modeling multidimensional features.
  • Ablation Experiments: All ablation configurations reduce performance, with backbone-LLM removal causing a significantly larger decline than adapter changes.The results highlight contributions from both the multi-task adapter and backbone LLM, especially the backbone’s role in multi-task joint learning.
  • Efficiency Analysis: MoE-LoRA gives LLM4WM trainable parameters comparable to smaller models, indicating training and parameter efficiency for practical deployment.The comparison averages performance across tasks and evaluates training and inference cost under the same hardware configuration.
  • Framework Design: LLM4WM combines MoE-LoRA with a multi-task adapter to extract shared representations, align task features with the large model, and support adaptive expert specialization.The framework is designed for concurrent wireless channel-associated tasks and its expert-weight heatmap supports adaptive allocation of expert resources.
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