Notice bibliographique
Résumé
Prof. Chenhao Qi of Southeast University, China, and Columbia University in the US, talks about the signifi-cance of the paper ‘Uplink channel estimation for massive MIMO systems exploring joint channel sparsity’, page 1770. Prof. Chenhao Qi My research area lies in multi-antenna wireless communications and sparse signal processing. Typically, base stations (BSs) are equipped with several antennas and each mobile user is served by a single antenna, which makes up a multi-user multi-input multi-output (MU-MIMO) system. The signal processing for both the BS and the users in such a MU-MIMO system is crucially important. We find that by exploring the spar-sity of wireless signals, the efficiency of signal processing can be improved and the complexity can be reuced. We started our work on sparse signal processing for multi-antenna wireless communications in 2008, when compressed sensing (CS) technology was proposed and drew great attention in the signal processing community. The popularisation of wireless mobile devices raises demand for high data rate of wireless communications. Nowadays, we use mobile phones and networks that support 4G, such as the time-division duplex (TDD) long term evolution (LTE) or frequency-division duplex (FDD) LTE. However, the data requirement is still unsatisfied due to the rapid development of audio and video services. So what will be the features of 5G? Undoubtedly, the MU-MIMO technology will still be the basis. The METIS, which is the EU flagship 5G project, shows that massive MIMO will be a key technology, where the BS will be equipped with orders of magnitude more antennas that can be even more than the number of served users. Our work will be applied to massive MIMO and therefore the 5G systems. It is shown in the existing literature that as the number of BS antennas grows to infinity, the additive noise and Rayleigh fading effect will be negligible, leading to very high spectral efficiency and energy efficiency. In such a massive MIMO system working in TDD mode, however, the bottleneck of the performance is the inter-cell interference (ICI) caused by pilot contamination. To mitigate the pilot contamination, one potential choice is to reduce the number of pilots used for the uplink channel estimation. Our research shows that by exploring the joint sparsity of the uplink channel, the pilot overhead can be substantially reduced. We propose a block sparse model where the block coherence is analysed. We also present an algorithm for the model so that a solution can be obtained quickly. With the proposed block sparse model, we can jointly estimate different uplink channels at the BS. Compared to the current method, where the BS makes individual channel estimations for each uplink channel, the joint spare channel estimation can significantly reduce the pilot overhead, supposing that the latter achieves the same channel estimation performance as the former. We will be working on the sparse signal processing to explore the inherent sparsity of wireless systems, aiming to reduce the complexity as well as to save the temporal and frequency resource. Particularly, we will keep our focus on the design of efficient channel estimation methods to acquire channel state information (CSI) for both TDD and FDD systems. We will study the sparse channel estimation and the pilot optimisation. One of the challenges is how to efficiently acquire the CSI of wireless channels so that the optimal or near-optimal beamforming can be achieved. Currently, there are two different modes that massive MIMO systems can work in, including TDD mode and FDD mode. In TDD mode, the downlink CSI can be obtained by uplink channel estimation based on the channel reciprocity. The challenge essentially comes from the ICI caused by the pilot contamination, which is discussed in our paper. In FDD mode, the downlink CSI is first obtained by the users and then fed back to the BS. Therefore the challenge in this mode is the computational complexity of channel estimation and the pilot overhead that grows linearly with the number of channels to be estimated. In massive MIMO systems, the number of wireless links and channels is very large, leading to the proliferation of pilot overhead and thus the reduced resource for data. Also, considering that the mobile users usually use power constrained devices, reducing the complexity of channel estimation for so many wireless links will be a challenging issue.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».