Low Complexity MIMO Detection Using Complement and LSB Set Approximation

Chakraborty, Sourav; Sinha, Nirmalendu Bikas; Mitra, Mandar

doi:10.1109/icscc51209.2021.9528259

Cited by 1 publication

(2 citation statements)

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“…The remaining (N T − i − 1) nondiagonal elements of (2i − 1)th and 2ith rows in R matrix are computed over (N T − i − 1) iterations (lines 20-27). The remaining (N T − i − 1) elements of sqnorm are updated by subtracting the effects of previous layers (line 23). An alternative approach to compute real-valued SQRD is available in literature, 39 which can achieve the same complexity reduction.…”

Section: Proposed Low Complexity Preprocessingmentioning

confidence: 99%

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Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

Chakraborty

Sinha

Mitra

2022

Trans Emerging Tel Tech

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In this article, we have proposed a dynamic branch pruning aided low switching fixed complexity sphere decoding (LSFSD) algorithm that can reduce the number of node visits in conventional fixed‐complexity sphere decoder (FSD) or imbalanced fixed‐complexity sphere decoder (IFSD) detector dynamically based on the channel condition. Further, we propose a low complexity QR‐based FSD ordering scheme in the real domain that requires half of the number of real arithmetic operations in the conventional case. As a case study, we have simulated the proposed algorithm for 4prefix×4$$ 4\times 4 $$ and 6prefix×6$$ 6\times 6 $$ MIMO system with 64‐QAM modulation. The simulation results show that the proposed algorithm can reduce approximately 50% of node visits to achieve a BER of 10prefix−3$$ 1{0}^{-3} $$. Additionally, a low complexity solution of the soft bit information generation method is also proposed. The concept is further extended to massive MIMO applications where the number of receiving antenna to transmit antenna ratio is close to one. A smaller node extension parameter is defined for the proposed detector to reduce the complexity in massive MIMO detection. Simulation results demonstrate that the proposed detection algorithm has significantly lower computational complexity than other recent state‐of‐the‐art detection algorithms for massive MIMO and large MIMO systems.

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Section: Proposed Low Complexity Preprocessingmentioning

confidence: 99%

“…Moreover, the dynamic power consumption of these algorithms is minimized by reducing the hardware resources, and precomputation. 20 Recently 1's complement-based fixed-point computation technique is proposed 22,23 to minimize hardware resources for FSD and IFSD detectors.…”

Section: Introductionmentioning

confidence: 99%