Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming

2026-07-09

AI Summary

This study addresses the performance degradation of cooperative beamforming (CBF) in distributed 5G networks caused by backhaul latency, which renders scheduling information outdated and leads to inferior performance compared to non-cooperative baselines. The work formulates this challenge as a spatiotemporal prediction task and introduces a two-stage prediction framework. It employs a Spectral Temporal Graph Neural Network (StemGNN) to capture both structural dependencies among users and their temporal dynamics, thereby forecasting future user scheduling states. These predictions replace stale scheduling information as input to a CBF-SLNR precoder. Under a single TTI delay, the proposed method achieves a scheduling prediction accuracy of 87.57%, recovers 57–73% of the total sum-rate loss, and restores up to 83% of the edge-user fairness loss, significantly outperforming baseline approaches such as LSTM.

Abstract

Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale. Even a single transmission time interval (TTI) of delay can reduce CBF-SLNR performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active. Coordination on stale information is therefore worse than no coordination at all. To address this, we propose a two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder. Evaluated on a three-cell massive MIMO downlink with 60 UEs and 64 antennas per base station under Quadriga Urban Micro (UMi) channels and a proportional fair scheduler, StemGNN achieves a mean scheduling prediction accuracy of 87.57%, outperforming LSTM, GRU, Simple RNN, and Markov chain baselines at all evaluated horizons, with gains of up to 7.71% over LSTM at longer horizons where inter-UE structural dependencies dominate over temporal autocorrelation. When integrated into coordinated beamforming, the predictions recover 57-73% of the sum rate loss caused by one TTI of backhaul delay, improving sum rate by 9.58-14.35% over the no-prediction baseline and recovering up to 83% of the Lag-1 fairness loss for cell-edge users, with fairness gains persisting at higher lag values where throughput gains diminish. These results show that treating backhaul latency as a spatio-temporal forecasting problem is an effective approach for robust inter-cell coordination in delay-constrained networks.

Problem

• backhaul delay
• coordinated beamforming
• scheduling prediction
• spatio-temporal forecasting
• stale CSI

Innovation

• Spatio-Temporal Prediction
• Coordinated Beamforming
• Backhaul Delay
• Graph Neural Network
• Massive MIMO