Low-Complexity MIMO Detection in 5G IoT: The Box Decoding Approach

Abstract The selection of an algorithm for MIMO detection often involves a trade-off between detection performance and the efficiency of the implementation. Tree-based algorithms, such as Sphere Decoding (SD), offer superior performance compared to linear schemes like MMSE and ZF and non-linear schemes like successive interference cancellation (SIC). In the SD approach, the algorithm must select the most suitable branch at each tree node. Due to this conditional behavior, the number of computational steps is data-dependent and is only determined at run-time. Moreover, due to the sequential nature of the tree traversal, it is not well suited for parallel implementation on a modern vector DSP. Approaches like K-Best exist to address this challenge, but they remain computationally intense, especially for higher-order QAM modulations. We study the use case of the 5G-based IoT standard NR-Redcap, which requires a low-complexity baseband kernel implementation for a cost-effective modem design. We propose a novel MIMO detection scheme called Box Decoding, whose computational complexity does not depend on the QAM modulation order and offers a significant reduction in complexity compared to K-Best for our chosen use case. Furthermore, the algorithm doesn’t necessitate the expensive sorting of candidate symbols, making it particularly appealing for implementation on contemporary vector Digital Signal Processors (DSPs). Testing on 16QAM and 64QAM modulation systems for the Redcap channel indicates negligible MIMO detection performance degradation compared to K-Best, with significantly lower complexity. For our use case, the Box Decoding algorithm proves to be a cost-effective implementation scheme for MIMO detection in 5G IoT modems, offering a 2dB SNR gain over MMSE-based SIC for an extra 23% of clock cycles consumed for this task on a 512-bit vector DSP.