The rapid growth of mobile data traffic is straining cellular networks. A natural approach to alleviate cellular networks congestion is to use, in addition to the cellular interface, secondary interfaces such as WiFi, Dynamic spectrum and mmWave to aid cellular networks in handling mobile traffic. The fundamental question now becomes: How should traffic be distributed over different interfaces, taking into account different application QoS requirements and the diverse nature of radio interfaces. To this end, we propose the Discounted Rate Utility Maximization (DRUM) framework with interface costs as a means to quantify application preferences in terms of throughput, delay, and cost. The flow rate allocation problem can be formulated as a convex optimization problem. However, solving this problem requires non-causal knowledge of the time-varying capacities of all radio interfaces. To this end, we propose an online predictive algorithm that exploits the predictability of wireless connectivity for a small look-ahead window w. We show that, under some mild conditions, the proposed algorithm achieves a constant competitive ratio independent of the time horizon T. Furthermore, the competitive ratio approaches 1 as the prediction window increases. We also propose another predictive algorithm based on the "Receding Horizon Control" principle from control theory that performs very well in practice. Numerical simulations serve to validate our formulation, by showing that under the DRUM framework: the more delay-tolerant the flow, the less it uses the cellular network, preferring to transmit in high rate bursts over the secondary interfaces. Conversely, delay-sensitive flows consistently transmit irrespective of different interfaces' availability. Simulations also show that the proposed online predictive algorithms have a near-optimal performance compared to the offline prescient solution under all considered scenarios.
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Online Learning to Transport via the Minimal Selection Principle
Motivated by robust dynamic resource allocation in operations research, we study the Online Learning to Transport (OLT) problem where the decision variable is a probability measure, an infinite-dimensional object. We draw connections between online learning, optimal transport, and partial differential equations through an insight called the minimal selection principle, originally studied in the Wasserstein gradient flow setting by Ambrosio et al. (2005). This allows us to extend the standard online learning framework to the infinite-dimensional setting seamlessly. Based on our framework, we derive a novel method called the minimal selection or exploration (MSoE) algorithm to solve OLT problems using mean-field approximation and discretization techniques. In the displacement convex setting, the main theoretical message underpinning our approach is that minimizing transport cost over time (via the minimal selection principle) ensures optimal cumulative regret upper bounds. On the algorithmic side, our MSoE algorithm applies beyond the displacement convex setting, making the mathematical theory of optimal transport practically relevant to non-convex settings common in dynamic resource allocation.
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- Award ID(s):
- 2042473
- NSF-PAR ID:
- 10417128
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- Volume:
- 178
- Issue:
- COLT 2022
- ISSN:
- 2640-3498
- Page Range / eLocation ID:
- 4085--4109
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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