PHY72024-06-05T10:27:34+00:00

PHY7 – Cell-free communication

Wednesday, 5 June 2024, 16:00-17:30, room Galapagos

Session Chair: Ramoni O Adeogun (Aalborg Univ., DK), Nurul Huda Mahmood (Univ. Oulu, FI)

Joint Sequential Fronthaul Quantization and Hardware Complexity Reduction in Uplink Cell-Free Massive MIMO Networks
Vida Ranjbar, Robbert Beerten, Marc Moonen and Sofie Pollin (KU Leuven, Belgium)
Fronthaul quantization causes a significant distortion in cell-free massive MIMO networks. Due to the limited capacity of fronthaul links, information exchange among access points (APs) must be quantized significantly. Furthermore, the complexity of the multiplication operation in the base-band processing unit increases with the number of bits of the operands. Thus, quantizing the APs’ signal vector reduces the complexity of signal estimation in the base-band processing unit. Most recent works consider the direct quantization of the received signal vectors at each AP without any pre-processing. However, the signal vectors received at different APs are correlated mutually (inter-AP correlation) and also have correlated dimensions (intra-AP correlation). Hence, cooperative quantization of APs fronthaul can help to efficiently use the quantization bits at each AP and further reduce the distortion imposed on the quantized vector at the APs. This paper considers a daisy chain fronthaul and three different processing sequences at each AP. We show that 1) de-correlating the received signal vector at each AP from the corresponding vectors of the previous APs (inter-AP de-correlation) and 2) de-correlating the dimensions of the received signal vector at each AP (intra-AP de-correlation) before quantization helps to use the quantization bits at each AP more efficiently than directly quantizing the received signal vector without any pre-processing and consequently, improves the bit error rate (BER) and normalized mean square error (NMSE) of users signal estimation.

Distributed User-Centric Cell-Free Massive MIMO with Architectural Constraints
Pere Garau Burguera, Hanan Al-Tous and Olav Tirkkonen (Aalto University, Finland)
We consider a network-level coordination architecture operating on a disaggregated Radio Access Network (RAN). We introduce the concept of Remote Radio Head (RRH) multi-partitioning, which allows users to be served by clusters of RRHs in a user-centric manner. The network consists of a number of Centralized Units (CUs) connected to several Distributed Units (DUs), each controlling a priori a cluster of multiple-input multiple-output (MIMO) RRHs. Aiming at providing uniform coverage and user performance over the network area, RRHs at cluster boundaries are connected to two or more more DUs, leading to a multi-partitioning architecture in which RRHs may belong to more than one cluster. Each DU, in turn, manages multiple overlapping clusters of RRHs. The total system bandwidth is divided into orthogonal resource parts, each of them is assigned to a different partition. We consider a coordination framework to allocate resources, where each DU, in a distributed way, assigns radio resources to the different RRH clusters that it manages. Each DU manages the scheduling of its users to their corresponding serving clusters. Simulation results show that the 5th percentile downlink user spectral efficiency improves by 45% when five RRH partitionings are used, compared to only one.

Energy Reduction in Cell-Free Massive MIMO Through Fine-Grained Resource Management
Özlem Tuğfe Demir (TOBB University of Economics and Technology, Turkey); Lianet Mendez-Monsanto Suarez (Universidad Carlos III de Madrid, Spain); Nicola Bastianello (KTH Royal Institute of Technology, Italy); Emma Fitzgerald (Lund University, Sweden & Warsaw University of Technology, Poland); Gilles Callebaut (KU Leuven, Belgium)
The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.

Expectation Propagation for Distributed Semi-Blind Channel Estimation in Cell-Free Networks
Zilu Zhao and Dirk Slock (EURECOM, France)
This paper examines the role of Cell-Free (CF) Massive MIMO (MaMIMO) in advancing wireless communication networks, particularly for beyond 5G and 6G networks. Building on the foundational work by Ngo et al., CF MaMIMO, with its distributed architecture, addresses the demands for high data rates, uniform quality of service (QoS), and power efficiency. A central challenge in CF networks is pilot contamination, arising from the absence of traditional cellular boundaries and an excess of user terminals (UTs) relative to pilot sequences. We introduce an Expectation Propagation (EP)-based method for Semi-Blind bilinear estimation in CF MaMIMO networks, providing a low-complexity solution by utilizing the Central Limit Theorem. This method enhances scalability and efficiency compared to existing approaches. Additionally, we propose a shift from distributed to decentralized EP, allowing for local information sharing among Access Points (APs) about user signals.

Location-Based Load Balancing for Energy-Efficient Cell-Free Networks
Robbert Beerten, Vida Ranjbar, Andrea P. Guevara, Hazem Sallouha and Sofie Pollin (KU Leuven, Belgium)
Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity. However, these CF mMIMO networks are designed for peak traffic loads, and when this is not the case, they are significantly over-dimensioned and not at all energy efficient. Adaptive Access Point (AP) ON/OFF Switching (ASO) strategies have been developed to save energy when the network is not at peak traffic loads by putting unnecessary APs to sleep. Unfortunately, the existing strategies rely on measuring channel state information between every user and every access point, resulting in significant measurement energy consumption overheads. Furthermore, the current state-of-art approach has a computational complexity that scales exponentially with the number of APs. In this work, we present a novel convex feasibility testing method that allows checking per-user quality-of-service requirements without necessarily considering all possible access point activations. We then propose an iterative algorithm for activating access points until all users’ requirements are fulfilled. We show that our method has comparable performance to the optimal solution whilst avoiding solving costly mixed-integer problems and measuring channel state information on only a limited subset of APs.

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