Wireless networks boosted by smarter user pairing and scheduling
Efficient User Association and Wireless Scheduling with Shorter Time-Scale Rate Adaptation
Networking and Internet Architecture
Summary
Wireless networks need to decide which users get access and how fast their connection should be. The authors created a new method that smartly pairs users and schedules their access while quickly adjusting connection speeds. Their approach balances fairness and speed, making sure no user is left behind over time. They also made a simpler version to reduce the heavy computation usually needed. Tests with real data show their methods work well.
What this means in practice
- •For network schedulers: Assign users to wireless channels and schedule transmissions to maximize throughput and fairness over multiple timescales using the proposed algorithms.
- •For cellular network operators: Implement adaptive rate and access control schemes to improve user experience by balancing speed and fairness across diverse user demands.
Authors
Xiaoyi Wu, Huacheng Zeng, Bin Li
Abstract
Rate adaptation is a crucial mechanism in IEEE 802.11 networks and next-generation cellular systems. Since the time scale for rate adaptation is typically much shorter than that for user association and scheduling, we investigate a joint design of wireless user association and scheduling and rate adaptation across different time scales to maximize cumulative network throughput while ensuring desired fairness among users. We develop a MaxWeight-type user association and scheduling algorithm that integrates virtual queues -- tracking each user's scheduling debt to maintain fairness -- and Upper Confidence Bound (UCB) estimates in its weight measure. Each selected user then employs the UCB algorithm for rate adaptation on a short time scale. Our theoretical findings reveal that the proposed algorithm achieves cumulative regret that grows with the square root of the time horizon up to a logarithmic factor and results in zero cumulative fairness violation after a certain number of time frames. Furthermore, since the MaxWeight-type algorithm involves evaluating all the feasible schedules that can be exponential to the number of users due to the interference constraints, leading to high computational complexity, we introduce a low-complexity alternative utilizing the so-called pick-and-compare (PC) approach. We demonstrate the effectiveness of both algorithms through simulations based on real-world data traces.