Interference Prediction in Wireless Networks

Updated 28 December 2025

Interference prediction approaches are ensembles of theoretical and algorithmic techniques that estimate future interference based on physical models, historical data, and side information.
Statistical models, including Markov chains, kernel density estimation, and extreme value theory, capture stochastic interference behaviors to meet stringent reliability and latency demands.
Machine learning methods such as Gaussian Process Regression and deep neural forecasting enhance prediction accuracy in dynamic wireless, edge, and industrial networks.

Interference prediction encompasses the set of theoretical and algorithmic approaches that estimate the future behavior of interference in wireless, edge-computing, and communication environments. Accurate interference prediction enables dynamic resource allocation, robust link adaptation, and reliable scheduling in systems with stringent reliability and latency demands, such as 5G/6G URLLC, industrial networks, and edge clouds. Since interference is fundamentally stochastic, time-varying, and connected to spatial, temporal, and protocol-induced correlations, both statistical and machine learning methods have been developed to address the problem across diverse system models, operating regimes, and performance requirements.

1. Fundamental Principles and Problem Formulation

Interference prediction approaches are driven by the need to proactively mitigate or manage interference, as opposed to purely reactive or average-based resource allocation. The prediction problem can be formalized as estimating the conditional distribution, moments, or quantiles of future interference values $I(t+\Delta)$ , given physical models, historical observations, or side information. The target metric may be the mean interference, specific quantiles (for outage-aware systems), or full predictive distributions with uncertainty quantification.

The challenge is exacerbated in URLLC and HRLLC contexts, where block error rates (BLER) down to $10^{-7}$ and latencies $\leq 1$ ms necessitate accounting for rare but extreme interference excursions. The predictive models must therefore capture heavy-tailed statistics, non-stationary regimes, and potentially high-dimensional feedback (e.g., CQI vectors, spatial channel state).

2. Statistical and Analytical Interference Prediction

2.1 Markov and Mobility Models

Discrete-state Markov chains (DTMCs) model aggregate interference as a stochastic process with finite state space, representing quantized interference levels. The transition matrix is estimated from observed time series, providing recursive prediction and enabling resource allocation under risk constraints (Mahmood et al., 2020). In mobile ad hoc networks, general-order linear mobility models are combined with the Compound Gaussian Point Process Functional (CGPPF), yielding exact expressions for the predicted mean and MGF of aggregate interference under general path loss and mobility laws (Cong et al., 2015).

2.2 Distribution Fitting and Stochastic Geometry

System-level analytical interference models employ fitted parametric distributions. For beamforming MIMO cellular networks, heavy-tailed mixture models—Inverse Gaussian, Inverse Weibull, and their mixtures—are proposed, with parameters fitted via moment matching or EM, and parameter surfaces further fitted as polynomials of pathloss exponent and log-normal shadowing (Elkotby et al., 2017). Stochastic-geometry-based approaches, such as the circular model, reduce arbitrary point patterns to a small set of virtual interferers, preserving exact aggregate-interference statistics via finite-sum Gamma representations (Taranetz et al., 2015).

2.3 Nonparametric Kernel Density Estimation (KDE) and Quantile Methods

Nonparametric conditional density estimation approaches (e.g., KDE with optimal or subset-specific bandwidths) are used to predict the interference distribution in time-series or history-conditioned scenarios (Brighente et al., 2021). Maximum-quantile (MQ) strategies select outage-quantile interference predictions from the estimated conditional CDF to ensure reliability constraints in link adaptation (Brighente et al., 2020). Subset-based estimators further reduce bias and computational cost in periodic or semi-deterministic traffic regimes.

3. Machine Learning and Hybrid Statistical–ML Approaches

3.1 Gaussian and Student-t Process Regression

Gaussian Process Regression (GPR) models interference as a sample path of a GP, enabling closed-form posterior predictive mean and variance updates as new data arrives. Automatic kernel hyperparameter learning via marginal likelihood provides adaptation to interference coherence time. The GPR-based predictor achieves near-genie performance in HRLLC settings with minimal data, providing calibrated uncertainty for resource allocation (Shah et al., 23 Jan 2025). Student-t process regression extends GPR to heavy-tailed regimes and confers robustness to outliers, with sparse (inducing point) approximations reducing complexity and enabling integration with vector state-space models and modified unscented Kalman filters for CQI-driven prediction (Gautam et al., 10 Jul 2025).

3.2 Deep Neural Forecasting

Nonlinear Autoregressive Neural Networks (NARNN) with tapped delay inputs, feedforward hidden layers (e.g., log-sigmoid activations), and one-step-ahead outputs have demonstrated sub-8% MAPE for Rayleigh-fading interference time-series, outperforming DTMC baselines and reducing resource usage for the same reliability (Padilla et al., 2021). Transformer-based architectures, e.g., inverted quantile patch transformers (iQPTransformer), process patch-wise time windows of interference vectors with attention and LSTM blocks, using quantile projection and conformal calibration to quantify uncertainty and ensure BLER targets in tail regimes (Gautam et al., 4 Jul 2025).

3.3 Unsupervised, Kalman, and State-Space Filtering

Discrete-time nonlinear (extended) Kalman filter (EKF) frameworks combined with state-space models parameterized by CQI observations alone enable real-time, unsupervised AP-side prediction in 6G sub-networks, matching supervised LSTM baselines and making practical use of standard CQI feedback without explicit channel measurements (Gautam et al., 6 Dec 2024). vDSSM architectures at SN controllers synergize vectorized state-space latent dynamics with robust SPTPR-based measurement fusion and UKF filtering, compensating for protocol feedback delays and maintaining stringent HRLLC constraints (Gautam et al., 10 Jul 2025).

4. Extreme Value Theory and Tail Probability Methods

Prediction of rare, extreme interference excursions crucial for ultra-high reliability is addressed using Extreme Value Theory (EVT). EVT quantifies the upper tail of the interference distribution with peaks-over-threshold modeling and the Generalized Pareto Distribution; kernel density estimation is used for the bulk distribution, and mixture models combine the two (Salehi et al., 20 Jan 2025, Gautam et al., 4 Jul 2025). Quantile selection is governed by the desired coverage probability, directly linking outage control to tail parameter estimates. Risk-aware resource allocation leverages predicted $(1-\eta)$ -quantiles to provision radio resources, and simulation confirms that EVT-hybrid methods yield $100\times$ lower outage probability and up to 15% lower resource usage compared to DTMC-based schemes.

5. Multidimensional, Distributed, and Edge System Prediction

5.1 Graph-based and Distributed Learning

In CSMA multi-hop networks, structural graph neural networks, specifically decoupled GCNs (D-GCN), explicitly separate self- and neighbor-interference components, leveraging attention to weight per-neighbor effects and yielding interpretable, scalable throughput prediction well below the exponential complexity of Markov-chain solvers (Tarzjani et al., 15 Oct 2025).

5.2 Matrix Completion in Edge Computing

In edge cloud multi-tenancy, interference-aware matrix completion methods extend collaborative filtering with learned low-rank interference factorization, modeling the asymmetric, platform-specific slowdowns induced by co-running workloads. Conformal quantile regression with split-conformal calibration yields provably tight uncertainty bounds, achieving 5.2% MAPE—twice as accurate as black-box neural or microbenchmark baselines—and robustly predicting workload runtime under arbitrary interference with limited historical data (Huang et al., 9 Mar 2025).

6. Integration with Resource Allocation and Practical Guidelines

Interference prediction approaches are operationally integrated into resource management (RRM), link adaptation (LA), and scheduling frameworks by estimating the required blocklength, rate, or channel uses to meet target reliability (BLER) under predicted interference scenarios. Finite-blocklength (normal approximation) coding theory is used to compute allocations as a deterministic function of the predicted interference quantile or confidence-level, with the resource/TRP-outage trade-off adjustable by quantile tuning, scaling factors, or conformal calibration (Padilla et al., 2021, Salehi et al., 20 Jan 2025, Gautam et al., 4 Jul 2025, Brighente et al., 2021).

Summary tables and key performance metrics indicate that model complexity, sample efficiency, and suitability for low-latency hardware are critical considerations (see Table 1 below for representative approaches):

Approach	Statistical Tool(s)	Typical Performance / Regime
DTMC / Markov	Discrete-state process	Modest resource overhead, finite tail bias
KDE / Quantile	Nonparametric density, MQ	$>$ 30% SE gains vs. OLLA, BLER $\le10^{-5}$
Gaussian Process, Student-t PR	Bayesian regression, sparsity	Genie-level BLER, minimal data required
Neural (NARNN, Transformer)	DNN forecasting, attention	$<$ 8% MAPE, 10--15% resource savings
EVT+KDE (mixture)	Peaks-over-thresh, GPD	Outage to $10^{-7}$ , 15% fewer resources
D-GCN, Matrix Completion	Graph NN / MF w/ calibration	3.3--5.2% NMAE/MAPE in multi-hop/edge cloud

7. Limitations, Extensions, and Selection Guidelines

Interference prediction methods must be matched to system constraints:

Model complexity vs. accuracy: DTMC and moving-average predictors suffice for modest reliability; advanced nonparametric, ML, or EVT-based predictors are required for BLER $<10^{-5}$ and detection of rare extremes.
Sample efficiency and coverage: Nonparametric and GPR-based methods are data-efficient; EVT and conformal calibration provide explicit coverage guarantees.
Scalability and real-time operation: Sparse process regression, local covariance estimators, and convex optimization with dual-based solvers enable sub-ms prediction in large, dense networks (Gautam et al., 10 Jul 2025, Huang et al., 9 Mar 2025, Li et al., 1 Feb 2024).
Physical model incorporation: Fitted analytically for MIMO/beamforming, user-centric, or spatially non-homogeneous deployments (Elkotby et al., 2017, Taranetz et al., 2015).
Measurement type: CQI-only approaches obviate the need for explicit channel or interference feedback, reducing signaling and supporting scalable AP-side prediction (Gautam et al., 6 Dec 2024).

Selection depends on specific reliability, latency, system topology, feedback types, and computational resources. Empirical performance (e.g., spectral efficiency, outage, margin) should be validated for the deployment regime under realistic traffic and mobility conditions.

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