- The paper presents a novel scalable framework for cell-free massive MIMO, achieving high spectral efficiency with a structured massive access approach.
- It introduces an initial access algorithm, a partial LSFD method, and innovative pilot assignment schemes to mitigate contamination and reduce complexity.
- The work also develops a scalable fractional power control policy that balances fairness and performance, as evidenced by significant SE gains in simulations.
Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
This paper addresses the challenges of implementing scalable cell-free massive multiple-input multiple-output (MIMO) systems, focusing on structured massive access. Cell-free massive MIMO is a promising candidate for beyond fifth-generation (B5G) networks, which demand higher data rates, vast connectivity, and uniform quality of service (QoS). The solution proposed in this work comprises an initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and a fractional power control policy, each forming constituent parts of a comprehensive new framework aiming at optimized spectral efficiency (SE).
The paper highlights unsolved practical issues of cell-free massive MIMO systems with emphasis on scalable implementation. The proposed initial access algorithm uses a competitive mechanism ensuring each user equipment (UE) is served by the most appropriate access points (APs), subject to a constraint on the number of APs an AP can serve, especially when UE density is high. This aspect is significant given the macro-diversity gains cell-free MIMO holds over traditional cellular architectures, mainly due to coordinated AP operation in a user-centric network model.
Introducing the P-LSFD strategy enables achieving excellent performance akin to the optimal LSFD but with reduced complexity, enhancing the system scalability. The P-LSFD's computational efficiency makes it viable for networks with a large user base. Closed-form SE expressions derived for maximum ratio (MR) combining substantiate the SE benefits achievable with this framework. Simulation results demonstrate P-LSFD's capacity to deliver high spectral efficiency using local partial minimum mean-square error (LP-MMSE) and MR combining, close to the optimal scenario.
The paper further explores pilot assignment critical to managing pilot contamination in scenarios where pilots are fewer than UEs. Two novel schemes are outlined: the interference-based K-means (IB-KM) and User-Group pilot assignment schemes. These approaches aim to alleviate mutual interference amongst the pilot-sharing users, especially in dense UE deployments. The User-Group scheme, being more effective than the IB-KM, minimizes inter-user interference by structuring user groups based on the overlap of their AP service sets, promoting fairness and average SE.
An essential addition is the scalable fractional power control policy designed to balance user fairness and average SE. This is particularly relevant for maintaining network performance amidst varying user densities and path loss conditions. The power control policy exploits the fractional path loss compensation allowing for dynamic trade-offs, providing fair user service while optimizing spectral resources.
Evaluation of the proposed framework through extensive simulation demonstrates the significant 95\%-likely SE gains over benchmark schemes. The proposed User-Group and IB-KM schemes show marked improvements over geography-based K-means (GB-KM) and random pilot assignments by addressing the pilot contamination challenge effectively. The performance advantage is consistent across varied user densities and network configurations.
The implications of this research are manifold—both theoretically and practically. Theoretically, it underscores the utility of structured access techniques in enhancing cell-free massive MIMO efficiency for future networks. Practically, the results validate the feasibility of deploying such frameworks in real-world settings, suggesting potential pathways for improving network capabilities in handling massive connectivity with maintained service quality. Insights into power control and pilot assignment are potentially extensible to other distributed antenna systems in dense environments.
Future research could focus on incorporating these frameworks with considerations for energy efficiency, hardware imperfections, and limits of fronthaul capacity, reinforcing the potential scalability of cell-free massive MIMO systems. Furthermore, adapting the current framework to support multi-antenna UEs, other fading models, and comprehensive downlink studies constitute areas ripe for exploration. In sum, the solutions herein offer a credible blueprint for the structured integration of cell-free massive MIMO within the larger B5G ecosystem, presenting a robust case for its adoption and further enhancements.