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The breadth-first searching algorithms, typically represented by K-best algorithm, are widely studied for multiple-symbol differential detection in multiple-input multiple-output systems due to the advantages of fixed complexity and latency which are very attractive for hardware implementation. However, it needs a large K value to achieve near maximum likelihood performance, which results in large complexity. In this study, a dynamic K-best detection with reduced average K value is proposed. It reduces the complexity on path expanding, path updating and comparing and swapping (C&S) operations by 24.24, 25 and 43.46%, respectively, with less performance degradation. After that, two low-complexity sorting architectures, Batcher%s merge sort and K cycles sort, are presented and applied to the proposed dynamic K-best detection. The complexity analysis and simulation results show that, compared with the traditional Bubble sorting dynamic K-best detection, the K cycles sorting and the Batcher's merge sorting dynamic K-best detections can further save C%operations by 59.5 and 11.2%, respectively, while performance cost capable of being ignored. Moreover, the K cycles sorting dynamic K-best detection achieves best trade-off on throughput and required memory, and the architecture of the Batcher's merge sorting dynamic K-best detection is more beneficial to parallel processing and multiple-processor structure.