Examinando por Autor "de la Fuente, Alejandro"

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Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems
(IEEE, 2022-04-05) Melgar, Antonio; de la Fuente, Alejandro; Carro-Calvo, Leopoldo; Barquero-Pérez, Óscar; Morgado, Eduardo
Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.
Massive MIMO Channel Estimation With Convolutional Neural Network Structures
(Institute of Electrical and Electronics Engineers, 2024-07-29) Carro-Calvo, Leopoldo; de la Fuente, Alejandro; Melgar, Antonio; Morgado, Eduardo
Massive multiple-input-multiple-output (mMIMO) enables a significant increase in capacity in fifth-generation (5G) communications systems, both in beamforming and spatial multiplexing scenarios, demanding highly accurate channel estimates. We present two models based on convolutional neural networks (CNNs) for 5G mMIMO channel estimation that differ in complexity and flexibility. The results achieved with both models are competitive compared to traditional methods, such as least squares (LS) which presents a poor estimate in the low signal-to-noise ratio (SNR) region, or minimum mean square error (MMSE) which requires prior statistical knowledge of the channel and noise estimation. Furthermore, the proposed CNN models outperform estimation structures based on conventional deep neural networks (DNNs). Our approach achieves results close to the MMSE estimates, improving them in the low SNR regime, and enabling them to a wide range of channel conditions, i.e., variability in time, frequency, and SNR, not requiring any prior channel statistics information. Furthermore, we present a deep analysis of the computational and cost complexity, demonstrating the suitability of the proposed models for real hardware structure implementation
Multiuser scheduling
(Wiley, 2020-05-16) de la Fuente, Alejandro
Scheduling and resource allocation (SRA) strategies play a crucial role in the emerging 5G systems based on massive MIMO (MaMIMO) transmissions. The set of resources to allocate is increased, adding to the time, frequency, modulation and coding resources, the spatial dimension introduced by MaMIMO. These spatial resources are provided by precoding schemes based on the channel state information knowledge at the transmitter side (CSIT). The joint optimization of all the input variables together with the QoS requirements established by the upper layers results in a complex cross-layer optimization problem. Different metric criteria for SRA algorithms are studied in the literature. MaMIMO SRA is based on CSIT knowledge to select a feasible set of users to allocate the resources of a scheduling interval. This article provides a detailed view of the required mechanisms to understand the SRA process in the emerging 5G systems. To conclude this article, some challenging solutions regarding the utilization of heterogeneous scenarios, MaMIMO transmissions, millimeter wave (mmWave) frequencies, and artificial intelligence are presented.
New Technologies and Trends for Next Generation Mobile Broadcasting Services
(IEEE, 2016-10-19) de la Fuente, Alejandro; Pérez Leal, Raquel; García Armada, Ana
It is expected that by the year 2020, video services will account for more than 70 percent of mobile traffic. It is worth noting that broadcasting is a mechanism that efficiently delivers the same content to many users, not only focusing on venue casting, but also distributing many other media such as software updates and breaking news. Although broadcasting services are available in LTE and LTE-A networks, new improvements are needed in some areas to handle the demands expected in the near future. In this article we review the actual situation and some of the techniques that will make the broadcast service more dynamic and scalable, meeting the demands of its evolution toward the next generation. Resource allocation techniques for broadcast/multicast services, integration with new waveforms in 5th generation mobile communications (5G), initiatives for spectrum sharing and aggregation, or the deployment of small cells placed together with the existing macro cells, are some enhancements that are examined in detail, providing directions for further development. With this evolution, 5G broadcasting will be a driver to achieve the spectral efficiency needed for the 1000 times traffic growth that is expected in upcoming years, leading to new applications in 5G networks that are specifically focused on mobile video services.
Radio Resource Allocation for Multicast Services Based on Multiple Video Layers
(IEEE, 2017-12-27) de la Fuente, Alejandro; Escudero-Garzás, José Joaquín; García-Armada, Ana
Mobile broadcast/multicast video services have become highly demanded in mobile networks and require effective and low-complexity radio resource management (RRM) strategies. This paper proposes an RRM strategy based on multicast subgrouping and the scalable video coding (SVC) technique for multicast video delivery that focuses on reducing the search space of solutions and optimizes the aggregated data rate (ADR). The results in terms of ADR, spectral efficiency, and fairness among multicast users, along with the low complexity of the algorithm, show that this new scheme is adequate for real systems.
Subband CQI Feedback-based Multicast Resource Allocation in MIMO-OFDMA Networks
(IEEE, 2018-01-17) de la Fuente, Alejandro; Femenias, Guillem; Riera-Palou, Felip; García Armada, Ana
Multicast transmission is one of the key enablers toward a more spectral- and energy-efficient distribution of multimedia content in current and envisaged cellular networks. In order to ensure that all users in a multicast group are able to correctly decode the received data, most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS) whose characteristics are determined by the propagation conditions experienced by the worst user in the group. Obviously, this robustness comes at the prize of a low spectral efficiency. Moreover, in the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs) providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. However, most subband-CQI multicast schemes proposed so far rely on overpessimistic assumptions that preclude the achievement of high data rates. In this paper, novel subband CQI-based multicast strategies are proposed that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed quality of service metrics. To this end, a constrained optimization problem is posed that seeks to maximize the data rate of the whole multicast group while ensuring that the average block error rate for all users remains below a threshold and that a minimum data rate is guaranteed for all users in the group.
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.