Summary
The reconfigurable intelligent surfaces (RIS), cell-free (CF) massive multiple-input multiple-output (mMIMO), and vehicle-to-everything (V2X) are key technologies for next-generation wireless communication, i.e., beyond 5G and 6G. The CF mMIMO involves deploying many APs in a large geographical area to jointly serve all users. V2X allows vehicle user equipment (VUEs) to communicate with base stations, pedestrians, and other vehicles. The high mobility of VUEs leads to frequent handovers and channel ageing. This makes the CF mMIMO ideal for V2X since handovers are eliminated. To reduce the impact of channel ageing, more APs must be deployed which increases the power consumption. The RIS comprises many passive reflecting elements (REs), which can independently induce phase and amplitude change, with marginal power needs. Thus, the RIS can improve the spectral/energy efficiency (SE/EE). However, the passive RIS suffers from double-fading attenuation which may be detrimental for VUEs.
This proposal seeks to analyze the SE/EE of the active RIS (aRIS)-aided CF mMIMO enabled for V2X. Each AP serves VUEs with the aid of multiple aRISs. We will derive closed-form solutions for the uplink/downlink SE by assuming imperfect channel state information, spatially correlated Rician channels, and channel ageing. For the aRIS, while increasing the reflected signal amplitude improves the desired signal, it also enhances the noise. Also, the aRIS requires extra power to amplify the signal which grows the network power consumption. The tradeoff between the SE and EE will be analyzed to identify the best operating regions. Increasing the APs (or VUEs) transmit power may increase the SE but saturates at a point due to interference. Therefore, we seek to jointly optimize the power, aRIS amplitude and phase with the objectives of (1) maximizing the sum SE (2) maximizing the EE. Finally, we propose a federated learning algorithm for CSI forecasting to mitigate channel ageing effects.
This proposal seeks to analyze the SE/EE of the active RIS (aRIS)-aided CF mMIMO enabled for V2X. Each AP serves VUEs with the aid of multiple aRISs. We will derive closed-form solutions for the uplink/downlink SE by assuming imperfect channel state information, spatially correlated Rician channels, and channel ageing. For the aRIS, while increasing the reflected signal amplitude improves the desired signal, it also enhances the noise. Also, the aRIS requires extra power to amplify the signal which grows the network power consumption. The tradeoff between the SE and EE will be analyzed to identify the best operating regions. Increasing the APs (or VUEs) transmit power may increase the SE but saturates at a point due to interference. Therefore, we seek to jointly optimize the power, aRIS amplitude and phase with the objectives of (1) maximizing the sum SE (2) maximizing the EE. Finally, we propose a federated learning algorithm for CSI forecasting to mitigate channel ageing effects.
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| Web resources: | https://cordis.europa.eu/project/id/101153315 |
| Start date: | 01-01-2025 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | - 211 754,00 Euro |
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Original description
The reconfigurable intelligent surfaces (RIS), cell-free (CF) massive multiple-input multiple-output (mMIMO), and vehicle-to-everything (V2X) are key technologies for next-generation wireless communication, i.e., beyond 5G and 6G. The CF mMIMO involves deploying many APs in a large geographical area to jointly serve all users. V2X allows vehicle user equipment (VUEs) to communicate with base stations, pedestrians, and other vehicles. The high mobility of VUEs leads to frequent handovers and channel ageing. This makes the CF mMIMO ideal for V2X since handovers are eliminated. To reduce the impact of channel ageing, more APs must be deployed which increases the power consumption. The RIS comprises many passive reflecting elements (REs), which can independently induce phase and amplitude change, with marginal power needs. Thus, the RIS can improve the spectral/energy efficiency (SE/EE). However, the passive RIS suffers from double-fading attenuation which may be detrimental for VUEs.This proposal seeks to analyze the SE/EE of the active RIS (aRIS)-aided CF mMIMO enabled for V2X. Each AP serves VUEs with the aid of multiple aRISs. We will derive closed-form solutions for the uplink/downlink SE by assuming imperfect channel state information, spatially correlated Rician channels, and channel ageing. For the aRIS, while increasing the reflected signal amplitude improves the desired signal, it also enhances the noise. Also, the aRIS requires extra power to amplify the signal which grows the network power consumption. The tradeoff between the SE and EE will be analyzed to identify the best operating regions. Increasing the APs (or VUEs) transmit power may increase the SE but saturates at a point due to interference. Therefore, we seek to jointly optimize the power, aRIS amplitude and phase with the objectives of (1) maximizing the sum SE (2) maximizing the EE. Finally, we propose a federated learning algorithm for CSI forecasting to mitigate channel ageing effects.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
06-11-2024
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