Timing Recovery for Non-Orthogonal Multiple Access with Asynchronous Clocks
Qingxin Lu, Haide Wang, Wenxuan Mo, Ji Zhou, Weiping Liu, and, Changyuan Yu

TL;DR
This paper introduces a novel timing recovery algorithm for NOMA-based passive optical networks, effectively compensating asynchronous clock errors and phase noise to improve signal integrity and system performance.
Contribution
It proposes a combined timing and phase noise compensation method using an absolute timing error detector and pilot-based carrier phase recovery for NOMA-PON.
Findings
Achieved 20% FEC limit after compensation
Validated algorithm feasibility with 25G NOMA-PON experiment
Demonstrated effective separation of timing error and phase noise
Abstract
A passive optical network (PON) based on non-orthogonal multiple access (NOMA) meets low latency and high capacity. In the NOMA-PON, the asynchronous clocks between the strong and weak optical network units (ONUs) cause the timing error and phase noise on the signal of the weak ONU. The theoretical derivation shows that the timing error and phase noise can be independently compensated. In this Letter, we propose a timing recovery (TR) algorithm based on an absolute timing error detector (Abs TED) and a pilot-based carrier phase recovery (CPR) to eliminate the timing error and phase noise separately. An experiment for 25G NOMA-PON is set up to verify the feasibility of the proposed algorithms. The weak ONU can achieve the 20% soft-decision forward error correction limit after compensating for timing error and phase noise. In conclusion, the proposed TR and the pilot-based CPR show great…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNetwork Time Synchronization Technologies · Advancements in PLL and VCO Technologies · EEG and Brain-Computer Interfaces
MethodsSparse Evolutionary Training
