With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input-soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths. Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.
Keywords: adaptive algorithms; continuous-phase modulation; short bursts; soft input–soft output.