Waveform-to-Waveform End-to-End Learning Framework in a Seamless Fiber-Terahertz Integrated Communication System

Seamless fiber-terahertz integrated communication has emerged as a promising technology in the special field of 6G, including mobile fronthaul and wireless bridges. Electronic terahertz communication systems have the advantages of high-level integration, small form factors, and potentially low costs, but their drawbacks are their small bandwidths and high harmonic interference levels. In this paper, an end-to-end learning-based waveform-to-waveform automatic equalization framework (W2WAEF) is proposed to overcome the above shortcomings in a seamless fiber-terahertz integrated communication system. An attention-based three-tributary heterogeneous neural network (ATTH) is designed to simulate the channel model of a seamless fiber-wireless communication system, taking fiber optics, optical-to-electrical conversion, and electrogenerated terahertz waves into account. With the proposed method, a data rate of 80.78 Gbps is experimentally demonstrated for discrete multitone modulation (DMT) signals over 5 km of fiber and 1 m of 209-GHz terahertz signals under a 20% soft decision-forward error correction (SD-FEC) threshold of 2.4 × 10
⁻²
. Compared with an approach without preprocessing, a receiver sensitivity gain exceeding 1.3 dB is successfully achieved at a data rate of 60 Gbps. The proposed method is a promising scheme for meeting the high speed and low-cost demands of future seamless fiber-terahertz integrated communication systems.