Uplink resource allocation optimization for user-centric cell-free MIMO networks
Zehua Li, Raviraj Adve
TL;DR
This work tackles uplink resource allocation in a user-centric cell-free MIMO network, addressing the challenge of achieving high data rates while maintaining scalability. It develops FP-based resource allocation algorithms for centralized, distributed, and semi-distributed operation modes, and introduces a decentralized pseudo-SINR metric to avoid real-time inter-AP information exchange. The approach combines fractional programming with compressive sensing to iteratively update beamformers, auxiliary variables, and scheduling, yielding a spectrum of trade-offs between performance and overhead. Results show that fully decentralized schemes incur only modest rate penalties (often under 9%) while eliminating AP-AP information exchange, and the semi-distributed mode provides a favorable balance between throughput and scalability for dense deployments.
Abstract
We examine the problem of optimizing resource allocation in the uplink for a user-centric, cell-free, multi-input multi-output network. We start by modeling and developing resource allocation algorithms for two standard network operation modes. The centralized mode provides high data rates but suffers multiple issues, including scalability. On the other hand, the distributed mode has the opposite problem: relatively low rates, but is scalable. To address these challenges, we combine the strength of the two standard modes, creating a new semi-distributed operation mode. To avoid the need for information exchange between access points, we introduce a new quality of service metric to decentralize the resource allocation algorithms. Our results show that we can eliminate the need for information exchange with a relatively small penalty on data rates.