Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

Researchers propose Prompt Decision Transformer (PromptDT), an AI framework that improves wireless network resource management through multi-task learning, achieving up to 49% QoE improvements over conventional methods while generalizing to unseen network configurations without retraining.

This research addresses a fundamental challenge in next-generation wireless networks: dynamically allocating radio resources across heterogeneous environments without constant manual reconfiguration. Traditional rule-based approaches struggle with the complexity of coordinated multipoint transmission, where selecting optimal serving cells across multiple base stations remains computationally intractable. The PromptDT framework represents a meaningful advancement by reformulating this combinatorial optimization problem as sequence modeling, leveraging offline trajectory data and task-specific prompts to enable rapid adaptation across different network topologies.

The shift from conventional deep reinforcement learning to transformer-based prompt learning directly addresses documented limitations in DRL deployment. Standard PPO methods require expensive retraining when network parameters change—a critical constraint for operators managing thousands of heterogeneous sites. PromptDT's few-shot adaptation capability eliminates this bottleneck, enabling networks to scale intelligently without full model retraining.

For telecommunications infrastructure operators and equipment manufacturers, this development carries significant implications. The 49% QoE improvement translates directly to competitive advantages in dense urban deployments where interference mitigation determines service quality. The scalability across varying base station counts and user equipment configurations suggests practical applicability to real-world networks with diverse hardware. The framework's positive performance scaling with model capacity indicates that increasing computational investment yields measurable returns.

Looking forward, the viability of this approach depends on deployment validation at network scale. Real-world testing across different propagation environments, traffic patterns, and hardware configurations will determine whether lab-validated improvements persist operationally. Integration with existing RAN control architectures and standardization pathways remain open questions for industry adoption.

• →PromptDT achieves 49% quality-of-experience improvement over baseline methods in multi-cell wireless resource allocation.
• →Framework enables few-shot adaptation to new network configurations without retraining, reducing operational overhead.
• →Transforms multi-cell selection from combinatorial optimization into learnable sequence modeling problem.
• →Performance scales positively with model capacity, suggesting investment in computational resources yields measurable network benefits.
• →Addresses critical limitations of conventional deep reinforcement learning in dynamic telecommunications environments.