Sequential Processing in Cell-free Massive MIMO Uplink with Limited Memory Access Points

Cell-free massive multiple-input multiple-output (MIMO) is an emerging technology that will reshape the architecture of next-generation networks. This paper considers the sequential fronthaul, whereby the access points (APs) are connected in a daisy chain topology with multiple sequential processing stages. With this sequential processing in the uplink, each AP refines users' signal estimates received from the previous AP based on its own local received signal vector. While this processing architecture has been shown to achieve the same performance as centralized processing, the impact of the limited memory capacity at the APs on the store and forward processing architecture is yet to be analyzed. Thus, we model the received signal vector compression using rate-distortion theory to demonstrate the effect of limited memory capacity on the optimal number of APs in the daisy chain fronthaul. Without this memory constraint, more geographically distributed antennas alleviate the adverse effect of large-scale fading on the signal-to-interference-plus-noise-ratio (SINR). However, we show that in case of limited memory capacity at each AP, the memory capacity to store the received signal vectors at the final AP of this fronthaul becomes a limiting factor. In other words, we show that when deciding on the number of APs to distribute the antennas, there is an inherent trade-off between more macro-diversity and compression noise power on the stored signal vectors at the APs. Hence, the available memory capacity at the APs significantly influences the optimal number of APs in the fronthaul.

Cell-Free Massive MIMO in the O-RAN Architecture: Cluster and Handover Strategies

Recently, the O-RAN architecture started receiving significant interest from the research community. The open interfaces and especially the possibilities for network-wide control protocols via the Near-Real Time RAN Intelligent Controller provide a significant amount of opportunities to implement newly proposed algorithms from state-of-the-art research. O-RAN follows the trend towards disaggregation of network functionalities which is especially interesting to deploy Cell-Free Massive MIMO in realistic distributed networks. Many attractive solutions have been proposed for the physical layer in Cell-Free Massive MIMO networks. Unfortunately, only limited work has been performed to map these solutions to the Next Generation of Radio Access Networks, especially also considering the existing control plane interfaces and the impact on network-level resource allocation and handover. In this work, we propose a realistic and elegant method of modelling the temporal evolution of the channel in cell-free Massive MIMO. We then build clustering and handover strategies and provide numerical results for multiple deployment scenarios. To realistically evaluate handovers and dynamic clustering for cell-free in O-RAN, we consider a fixed clustering strategy, which computes the ideal cluster whenever a handover threshold is exceeded, and an opportunistic clustering strategy, where serving units are added opportunistically as the user moves. Additionally, we map an uplink detection method from the current cell-free Massive MIMO state-of-the-art to the O-RAN architecture. We study how the ageing of the channel and especially the user-centric cluster around the UE limits the performance of Cell-Free algorithms. We identify what is currently possible and propose the few needed extensions to O-RAN to fully exploit state-of-the-art cell-free processing schemes.