Photonic-assisted W-band flexible integrated sensing and communication system for fiber-wireless network based on CE-LFM-OFDM.

We have experimentally demonstrated a constant envelope linear frequency modulated orthogonal frequency division multiplexing (CE-LFM-OFDM) signal by employing an orthogonal frequency division multiplexing (OFDM) signal to phase modulate the linear frequency modulation (LFM) carrier. The experimental verification was conducted in the photonic-based integrated sensing and communication (ISAC) system working at 94.5 GHz. In our system, a 10-km optical fiber and a 1-m free space transmission are incorporated, achieving seamless fiber-wireless networks. As a result, we achieved data rates ranging from 8 to 15.4 Gbit/s and range resolution ranging from 1.5 to 7.5 cm, respectively.

Net-432-Gb/s/λ PS-PAM transmission based on advanced DSP and single DAC for 400†G/λ IM/DD links.

The escalating surge in datacenter traffic creates a pressing demand for augmenting the capacity of cost-effective intensity modulation and direct detection (IM/DD) systems. In this Letter, we report the demonstration of the single-lane 128-GBaud probabilistically shaped (PS)-PAM-20 IM/DD transmission using only a single digital-to-analog converter (DAC) for a net 400 G/λ system. Based on the advanced digital signal processing (DSP), we achieve net bitrates of up to 437 Gb/s for optical back-to-back and 432 Gb/s after the 0.5-km SSMF transmission in the C-band with 128-Gbaud PS-PAM-20 signals. This work is the latest demonstration on ultra-high-order PS-PAM signals achieving net bitrates exceeding 400 Gb/s despite symbol rate limitations. Notably, to the best of our knowledge, the realized net information rate ([net bitrate]/[symbol rate]) of 3.37 marks a new achievement within the domain of 400 G/λ IM/DD systems, with promising implications for enhancing bandwidth efficiency in the upcoming 1.6-Tb Ethernet scenario.

Deep-learning-based multi-user framework for end-to-end fiber-MMW communications.

Fiber-wireless integration has been widely studied as a key technology to support radio access networks in sixth-generation wireless communication, empowered by artificial intelligence. In this study, we propose and demonstrate a deep-learning-based end-to-end (E2E) multi-user communication framework for a fiber-mmWave (MMW) integrated system, where artificial neural networks (ANN) are trained and optimized as transmitters, ANN-based channel models (ACM), and receivers. By connecting the computation graphs of multiple transmitters and receivers, we jointly optimize the transmission of multiple users in the E2E framework to support multi-user access in one fiber-MMW channel. To ensure that the framework matches the fiber-MMW channel, we employ a two-step transfer learning technique to train the ACM. In a 46.2 Gbit/s 10-km fiber-MMW transmission experiment, compared with the single-carrier QAM, the E2E framework achieves over 3.5 dB receiver sensitivity gain in the single-user case and 1.5 dB gain in the three-user case under the 7% hard-decision forward error correction threshold.