Examinando por Autor "Interdonato, Giovanni"

Mostrando 1 - 4 de 4

Subgroup-Centric Multicast Cell-Free Massive MIMO
(Institute of Electrical and Electronics Engineers, 2024-10-29) de la Fuente, Alejandro; Femenias, Guillem; Riera-Palou, Felip; Interdonato, Giovanni
Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging technology for beyond fifth-generation (5G) systems aimed at enhancing the energy and spectral efficiencies of future mobile networks while providing nearly uniform quality of service to all users. Moreover, multicasting has garnered increasing attention in recent years, as physical-layer multicasting proves to be an efficient approach for serving multiple users simultaneously, all with identical service demands while sharing radio resources. A multicast service is typically delivered using either unicast or a single multicast transmission. In contrast, this work introduces a subgroup-centric multicast CF-mMIMO framework that splits the users into several multicast subgroups. The subgroup creation is based on the similarities in the spatial channel characteristics of the multicast users. This framework benefits from efficiently sharing the pilot sequence used for channel estimation and the precoding filters used for data transmission. The proposed framework relies on two scalable precoding strategies, namely, the centralized improved partial MMSE (IP-MMSE) and the distributed conjugate beamforming (CB). Numerical results demonstrate that the centralized IP-MMSE precoding strategy outperforms the CB precoding scheme in terms of sum SE when multicast users are uniformly distributed across the service area. In contrast, in cases where users are spatially clustered, multicast subgrouping significantly enhances the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Interestingly, in the latter scenario, distributed CB precoding outperforms IP-MMSE, particularly in terms of per-user SE, making it the best solution for delivering multicast content. Heterogeneous scenarios that combine uniform and clustered distributions of users validate multicast subgrouping as the most effective solution for improving both the sum and per-user SE of a multicast CF-mMIMO service.
User Subgrouping and Power Control for Multicast Massive MIMO Over Spatially Correlated Channels
(IEEE, 2022-07-28) de la Fuente, Alejandro; Interdonato, Giovanni; Araniti, Giuseppe
Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the favorable propagation , hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices’ similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.
User subgrouping in multicast massive MIMO over spatially correlated rayleigh fading channels
(Institute of Electrical and Electronics Engineers, 2021-06-14) de la Fuente, Alejandro; Interdonato, Giovanni; Araniti, Giuseppe
Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming services. Multicast in the physical layer is an efficient way of serving multiple users, simultaneously demanding the same service and sharing radio resources. This work proposes a subgrouping strategy of multicast users based on their spatial channel characteristics to improve the channel estimation and precoding processes. We employ max-min fairness (MMF) power allocation strategy to maximize the minimum spectral efficiency (SE) of the multicast service. Additionally, we explore the combination of spatial multiplexing with orthogonal (time/frequency) multiple access. By varying the number of antennas at the base station (BS) and users’ spatial distribution, we also provide the optimal subgroup configuration that maximizes the spectral efficiency per subgroup. Finally, we show that serving the multicast users into two orthogonal time/frequency intervals offers better performance than only relying on spatial multiplexing.
User Subgrouping in Scalable Cell-Free Massive MIMO Multicasting Systems
(VDE, 2024-09-09) de la Fuente, Alejandro; Femenias, Guillem; Riera-Palou, Felip; Interdonato, Giovanni
Cell-free massive multiple-input multiple-output (CF-mMIMO) is a breakthrough technology for beyond-5G systems, designed to significantly boost the energy and spectral efficiencies of future mobile networks while ensuring a consistent quality of service for all users. Additionally, multicasting has gained considerable attention recently because physical-layer multicasting offers an efficient method for simultaneously serving multiple users with identical service demands by sharing radio resources. Typically, multicast services are delivered either via unicast transmissions or a single multicast transmission. This work, however, introduces a novel subgroup-centric multicast CF-mMIMO framework that divides users into several multicast subgroups based on the similarities in their spatial channel characteristics. This approach allows for efficient sharing of the pilot sequences used for channel estimation and the precoding filters used for data transmission. The proposed framework employs two scalable precoding strategies: centralized improved partial MMSE (IP-MMSE) and distributed conjugate beamforming (CB). Numerical results show that for scenarios where users are uniformly distributed across the service area, unicast transmissions using centralized IP-MMSE precoding are optimal. However, in cases where users are spatially clustered, multicast subgrouping significantly improves the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Notably, in clustered scenarios, distributed CB precoding outperforms IP-MMSE in terms of per-user SE, making it the best solution for delivering multicast content.