Effective decoding of wireless signals requires various parameter acquisition techniques including user activity detection, synchronization, channel estimation, and channel equalization.
In traditional systems, these unknown, underlying parameters of the communication channel are individually estimated. This work proposes a novel joint estimation process applying deep learning.
The proposed method shows superior performance to traditional methods and is further able to find the multiplicity of collisions, handle synchronization and channel estimation in the case of colliding non-orthogonal transmissions, and is able to discover superior preamble sequences using an auto-encoder structure.
The proposed method is intended for decoding transmissions at the base-station in a massive connectivity scenario with many low-complexity devices operating concurrently.
Excellent performance is demonstrated in estimating Time-of-Arrival (ToA), Carrier-Frequency Offset (CFO), channel gain and collision multiplicity from a received mixture of transmissions using the random access preamble structure structure of the NB-IoT standard.
The proposed estimation scheme, employing a convolutional neural network (CNN), achieves a ToA Root-Mean-Square Error (RMSE) of 2.88 us and a CFO RMSE of 3.44 Hz at 10 dB Signal-to-Noise Ratio (SNR), whereas a conventional estimator using two cascaded stages have RMSEs of 16.20 us and 7.98 Hz, respectively.