This paper presents a comprehensive analysis and performance enhancement of short block length channel detection incorporating training information. The current communication systems' short block length channel detection typically consists of least squares channel estimation followed by quasi-coherent detection. By investigating the receiver structure, specifically the estimator-correlator, we show that the non-coherent term, often disregarded in conventional detection metrics, results in significant losses in performance and sensitivity in typical operating regimes of 5G and 6G systems. A comparison with the fully non-coherent receiver in multi-antenna configurations reveals substantial losses in low spectral efficiency operating areas. Additionally, we demonstrate that by employing an adaptive DMRS-data power adjustment, it is possible to reduce the performance loss gap, which is amenable to a more sensitive quasi-coherent receiver. However, both of the aforementioned ML detection strategies can result in substantial computational complexity when processing long bit-length codes. We propose an approach to tackle this challenge by introducing the principle of block or segment coding using First-Order RM Codes, which is amenable to low-cost decoding through block-based fast Hadamard transforms. The Block-based FHT has demonstrated to be cost-efficient with regards to decoding time, as it evolves from quadric to quasi-linear complexity with a manageable decline in performance. Additionally, by incorporating an adaptive DMRS-data power adjustment technique, we are able to bridge/reduce the performance gap with respect to the conventional maximum likelihood receiver and attain high sensitivity, leading to a good trade-off between performance and complexity to efficiently handle small payloads.