Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming
arXiv:2607.08454v1 Announce Type: cross 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performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active.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 G
The proliferation of distributed 5G networks inherently surfaces challenges related to backhaul latency, making real-time coordination difficult.
Reliable and efficient 5G performance is critical for numerous advanced applications, and this research directly addresses a key technical bottleneck.
The ability to predict and compensate for backhaul delays allows for more effective coordinated beamforming, improving 5G network performance.
- · Telecommunication network operators
- · 5G equipment manufacturers
- · AI/ML in networking sector
- · Legacy uncoordinated network architectures
Improved performance and reliability of 5G networks, particularly in dense urban environments.
Reduced operational costs for network providers due to more efficient spectrum usage and fewer coordination failures.
Acceleration of industrial IoT and autonomous systems relying on ultra-low latency and highly reliable 5G connectivity.
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Read at arXiv cs.AI