| Full text | |
| Author(s): |
Boas, Evandro C. Vilas C.
;
S. e Silva, Jefferson D. S.
;
de Figueiredo, Felipe A. P.
;
Mendes, Luciano L. L.
;
de Souza, Rausley A. A.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING; v. 2022, n. 1, p. 63-pg., 2022-12-02. |
| Abstract | |
Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings. (AU) | |
| FAPESP's process: | 21/06946-0 - Reconfigurable intelligent surface aided communications for 6G and beyond |
| Grantee: | Rausley Adriano Amaral de Souza |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 20/05127-2 - SAMURAI: smart 5G core and multiran integration |
| Grantee: | Aldebaro Barreto da Rocha Klautau Junior |
| Support Opportunities: | Research Projects - Thematic Grants |