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Ala Eddine Nouali

Publications and source records attributed to Ala Eddine Nouali.

2 recordsLinked to original sources

Impact of Sociality Regimes on Quality of Service and Energy Efficiency in Cell-Free MIMO Networks

The cell-free architecture represents a significant advancement in network design, where each User Equipment (UE) is served by a group of distributed Access Points (APs), aimed at delivering uniformly high data rates to UEs across all locations. To ensure network scalability, user-centric clustering (UCC) has emerged as a practical approach, wherein only a selected subset of preferred APs jointly serves each UE. Forming the optimal cluster of APs for each UE is a challenging task, particularly when limited fronthaul and processing capabilities of both APs and UEs are considered. This challenge is exacerbated by the need for dynamic adjustments to enhance energy efficiency while meeting quality of service (QoS) requirements, which introduces conflicting interests between these entities. In this paper, we investigate the sociality regime of UEs and their clusters of APs, characterizing their intra-team cooperation as either selfish, egalitarian, or altruistic. These sociality regimes are crucial in achieving a balance between QoS and energy efficiency per UE. We address this problem by modeling it as a many-to-many social matching game with externalities, where connections can be established based on the sociality regime of both teams. To solve this, we introduce two novel algorithms based on Deferred Acceptance (DA) and Early Acceptance (EA) matching games. Numerical results show the significant impact of the sociality regime adopted by UEs and their clusters of APs on UE's QoS satisfaction and energy efficiency, with the egalitarian regime adopted by both entities proving the best performance trade-off.

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Early Acceptance Matching Game for User-Centric Clustering in Scalable Cell-free MIMO Networks

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.

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