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Fairness Scheduling in User-Centric Cell-Free Massive MIMO Wireless Networks


Core Concepts
The author argues for dynamic fairness scheduling in user-centric massive MIMO networks to optimize system performance and achieve high spectral efficiency.
Abstract
The content discusses the importance of fairness scheduling in user-centric cell-free massive MIMO wireless networks. It addresses the challenges of handling a large number of users with dynamic scheduling to ensure high throughput rates. The proposed approach focuses on maximizing network utility functions while considering information outage probability. By introducing a conflict graph and adaptive rate scheduling, the system can efficiently handle tens of thousands of users. The paper highlights the significance of per-user throughput rates over ergodic rates and emphasizes the need for fair scheduling to maintain system efficiency.
Stats
Most relevant scenarios involve a large number of UEs compared to antennas. The number of active users must be carefully chosen for optimal network performance. Achieving high sum SE requires dynamic scheduling over time-frequency resources. Proposed dynamic scheduling is scalable and robust to uncertainties.
Quotes
"The fairness scheduling problem is canonically formulated as the maximization of a suitable concave componentwise non-decreasing network utility function." "The proposed dynamic scheduling is the first to address such system dimensions with tens of thousand users in a scalable way."

Deeper Inquiries

How does dynamic fairness scheduling impact overall network stability?

Dynamic fairness scheduling plays a crucial role in maintaining network stability by ensuring that all users have fair access to network resources over time. By dynamically adjusting the active user set and their transmission rates based on channel conditions and system load, the scheduler can prevent congestion, optimize resource utilization, and avoid bottlenecks. This proactive approach helps distribute traffic evenly, reducing the likelihood of sudden spikes or drops in performance that could destabilize the network.

Could fixed pilot assignment improve system efficiency despite overhead concerns?

Fixed pilot assignment has the potential to enhance system efficiency by reducing signaling overhead associated with dynamic reassignment while still achieving comparable performance. By allocating pilots to users once for all instead of at each scheduling decision, the system can operate more efficiently with lower control signaling requirements. While there may be some trade-offs in adaptability compared to dynamic assignments, a well-designed fixed assignment strategy can mitigate these concerns without compromising overall system efficiency.

How does self-averaging affect interference management in large-scale systems?

Self-averaging plays a critical role in interference management within large-scale systems by helping reduce mutual interference effects among active users. In such systems with numerous randomly distributed UEs and RUs, self-averaging ensures that cumulative interference from other users becomes weakly dependent on specific individual selections of active users. This phenomenon allows for more effective interference mitigation strategies as it smooths out variations caused by random user distributions and spatial configurations. As a result, self-averaging contributes to improved overall system performance and enhanced spectral efficiency in large-scale wireless networks.
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