From Confounding to Counterfactuals: A Cross-Layer Causal Model for RAN Scheduling
Authors: Artin Elhamirad, Christo Kurisummoottil Thomas, Ali Tajer
Organizations: Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA · Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
Data-driven control in radio access networks (RANs) selects resource allocations using associations between control actions and key performance indicators (KPIs) learned from network telemetry. Because the shared radio environment influences both the scheduler's decisions and the resulting performance, a bias known as confounding, such associations overstate the effect of an allocation and misdirect resources toward user equipment (UEs) that cannot benefit. Moreover, individual UEs respond heterogeneously to the same decision, so even a corrected average effect does not identify which UEs gain from additional resources. While causal inference has recently been applied to RANs, existing approaches do not model the cross-layer mechanisms that connect an allocation to its outcome. In this paper, a hierarchical structural causal model (H-SCM) spanning the PHY, MAC, and network layers is proposed that separates cell-load confounding from UE-specific response heterogeneity. In particular, a counterfactual scheduling problem is formulated in which physical resource block (PRB) allocations are selected using per-UE counterfactual goodput rather than observational conditional averages. This problem is solved via a pipeline combining constrained causal discovery, structural-equation fitting, and abduction-action-prediction. Simulations demonstrate that naive observational estimation overstates the full-PRB response by 73.3%, whereas adjustment for cell-load confounding reduces the relative error to 9.8%. Held-out interventions are predicted with a coefficient of determination of 0.988. Counterfactual scheduling achieves a mean relative loss in total cell goodput below 0.5% against the ground-truth optimum, compared with 2.0-2.9% for a neural-network baseline.
Figures & tables
Fig. 1: Causal DAG spanning the PHY, MAC, and network layers. The DAG represents the simulated setting; operational RAN datasets may require additional nodes and dependencies.
Parameter
Value
OFDM grid
76 subcarriers, 15 kHz spacing, 14 symbols
Coding and MCS set
LDPC with 512 information bits; 12 MCSs
BLER target / HARQ limit
0.1 / 3 retransmissions
PRB and load parameters
NPRB=100 , αL=0.58 , βL=18 dB
Queue loading rate λ / utilization cap
5.5 Mbps / ρmax=0.95
Queue-loss parameters
qth=5 , qscale=10
TABLE I: Key Simulation Parameters
Fig. 2: Confounding analysis. (a) Goodput versus PRB allocation. (b) Full-PRB goodput estimates compared with interventional ground truth. (c) Goodput distributions. (d) Mean goodput by cell-load range.
Fig. 3: Counterfactual goodput gains: (a)–(c) sorted UE gains, where n is the number of UEs selected by each query, and (d)–(f) mean gains by inferred residual range. Dashed lines show the mean gain. Bar labels give UE counts.
Fig. 4: Scheduling performance across four environments: (a) loss relative to the ground-truth optimum and (b) realized cell goodput. Error bars show approximate 95% confidence intervals over 50 realizations.
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that would have been observed under alternative control actions. A key challenge is that logged telemetry may omit variables used by the controller, resulting in hidden confounding and invalidating the statistical guarantees of counterfactual analysis. In principle, this issue can be addressed using randomized telemetry, collected by assigning control actions independently of the network state. However, because such randomization may disrupt normal operation, randomized telemetry is typically scarce, causing counterfactual analysis based solely on it to produce uninformative prediction sets. To address these challenges, we propose Confounding-Valid Counterfactual Conformal Inference (CV-CCI), which combines abundant, potentially confounded observational telemetry with limited randomized data through the General Synthetic-Powered Inference (GESPI) principle. CV-CCI leverages observational data to improve efficiency while using randomized data to retain finite-sample coverage guarantees under arbitrary hidden confounding. Experiments on two representative radio access network (RAN) control tasks show that CV-CCI remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art confounding-valid baselines.
Learning-based models for radio resource management (RRM) are typically built for a single function and deployment, so each new setting repeats the development pipeline. RRM decisions, however, share a common structure: each is assembled from interdependent fields, defined by the standard, whose values are selected in view of the network state. We propose RRM-GPT, an autoregressive framework for RRM foundation models that generate these decisions as a language model generates text. An encoder maps heterogeneous network observations into a common token representation, and a decoder emits the decision one field at a time, each conditioned on the network state and the fields already committed. Pretraining on unannotated network logs teaches the model what makes a decision valid and how controllers choose among valid decisions; post-training then adapts it to deployment-specific operator objectives through imitation or reinforcement learning. The framework targets two forms of reuse: a function-specific model reused across deployments, and a model shared across RRM functions that generates their interdependent decisions as one sequence. In a 5G New Radio (NR) case study, we demonstrate that a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling. The model captures dependencies among grant fields and transfers learned behavior to an unseen scenario without adaptation.
Ahmed Aboulfotouh, Akram Bin Sediq, Koosha Pourtahmasi Roshandeh +4
Department of Electrical and Software Engineering, University of Calgary, Canada · Ericsson Canada Inc., Canada
Hybrid federated semantic communication enables user equipment (UEs) to contribute model updates or semantic features under a shared training framework. However, heterogeneous role costs and the interaction between model aggregation and feature reconstruction (FR) remain key challenges in practical radio access network (RAN) deployment. To address these issues, we propose ARC-FedSC, a RAN-aware framework that coordinates UE role assignment and server-side controlled FR under a fixed communication budget. Within ARC-FedSC, RAN Aware Role-Bandit learns the historical contribution of UEs in different roles together with current uplink conditions, while Temporal Consistency Alignment (TCA) regulates FR according to recent model aggregation directions. Experiments on CIFAR-10 with time-varying RAN channels show that ARC-FedSC outperforms baselines across all five tested role configurations in the later training stage, with the best configurations achieving about 24.17 dB average PSNR over the last 20 rounds. Component level results further show that Role-Bandit maintains an average rolling UE coverage of 85.3%, compared with about 65.7% for baseline, while TCA reduces the probability of harmful FR events from 0.245 to 0.093, corresponding to a 62.0% reduction. Ablation and sensitivity studies further support the effectiveness and robustness of the proposed design.
Yuxuan Chen, Jiao Bie, Hang Qi +1
Guangdong Prov./Zhuhai Key Lab of IRADS, Beijing Normal-Hong Kong Baptist University, Guangdong, P. R. China