49 research outputs found
Learning Joint Detection, Equalization and Decoding for Short-Packet Communications
We propose and practically demonstrate a joint detection and decoding scheme
for short-packet wireless communications in scenarios that require to first
detect the presence of a message before actually decoding it. For this, we
extend the recently proposed serial Turbo-autoencoder neural network (NN)
architecture and train it to find short messages that can be, all "at once",
detected, synchronized, equalized and decoded when sent over an unsynchronized
channel with memory. The conceptional advantage of the proposed system stems
from a holistic message structure with superimposed pilots for joint detection
and decoding without the need of relying on a dedicated preamble. This results
not only in higher spectral efficiency, but also translates into the
possibility of shorter messages compared to using a dedicated preamble. We
compare the detection error rate (DER), bit error rate (BER) and block error
rate (BLER) performance of the proposed system with a hand-crafted
state-of-the-art conventional baseline and our simulations show a significant
advantage of the proposed autoencoder-based system over the conventional
baseline in every scenario up to messages conveying k = 96 information bits.
Finally, we practically evaluate and confirm the improved performance of the
proposed system over-the-air (OTA) using a software-defined radio (SDR)-based
measurement testbed.Comment: Submitted to IEEE TCO
Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements
When operating massive multiple-input multiple-output (MIMO) systems with
uplink (UL) and downlink (DL) channels at different frequencies (frequency
division duplex (FDD) operation), acquisition of channel state information
(CSI) for downlink precoding is a major challenge. Since, barring transceiver
impairments, both UL and DL CSI are determined by the physical environment
surrounding transmitter and receiver, it stands to reason that, for a static
environment, a mapping from UL CSI to DL CSI may exist. First, we propose to
use various neural network (NN)-based approaches that learn this mapping and
provide baselines using classical signal processing. Second, we introduce a
scheme to evaluate the performance and quality of generalization of all
approaches, distinguishing between known and previously unseen physical
locations. Third, we evaluate all approaches on a real-world indoor dataset
collected with a 32-antenna channel sounder