The belief-selective propagation (BsP) algorithm has recently emerged as a promising approach for massive MIMO detection. However, when applied in MIMO turbo receivers, known for their superior performance compared to separated detection and decoding (SDD) receivers, the BsP-based receiver suffers from significant performance degradation and high processing latency. To overcome these limitations, this paper proposes a fixed-constellation BsP (FCBsP) detector tailored for MIMO turbo receivers. By buildingfixed configuration setsand utilizing theapproximate multi-user interferencefor message updates, the proposed FCBsP detector achieves a better trade-off between error performance and computational complexity compared to the BsP. Furthermore, two unexplored features:information compensation and decoding-first mechanismare proposed to fine-tune the exchanged information and lower the processing latency of the FCBsP-based turbo receiver. Numerical results demonstrate that the proposed FCBsP-based turbo receiver earns about 0.7 and 1.8 dB performance gains over the BsP-based turbo receiver at BLER=10−3in an LDPC-coded 32 × 12 64-QAM MIMO system under Rayleigh and practical channels, respectively.

Thanks to its near-optimal performance, breadth-first search detection (BFSD) finds widespread application in small-scale MIMO systems. However, existing BFSD methods struggle to effectively configure the width (number of candidate nodes) for each layer, resulting in prohibitive complexity in large-scale MIMO systems. To address this, we propose two width optimization schemes for BFSD. We introduce a layer-by-layer optimization framework to reduce the design space of width configurations, and a Monte Carlo-assisted method to link width configurations to detection performance. Using this linking scheme in the reduced design space, we formulate the first width optimization scheme given specific performance constraints. Then, we present another scheme that employs a theoretical linking method as an alternative to the Monte Carlo approach. Although slightly less effective, the second scheme has negligible complexity for width optimization, making it well-suited for communication scenarios with time-varying characteristics. In 128×128 MIMO systems, numerical results demonstrate that the optimized BFSD using our first and second schemes can reduce complexity by up to 82% and 65%, respectively, while achieving superior detection performance compared to state-of-the-art BFSD.

When faced with challenging antenna configurations or high-order modulations in realistic propagation environments, the Belief Propagation (BP) MIMO detector outperforms its linear counterparts. To mitigate the error floor issue and lower the complexity, a revised BP detector, named the Belief-selective Propagation (BsP) detector, has recently emerged by selectively utilizing trusted incoming messages for updates. Despite those promising potentials, the straightforward hardware implementation of the BsP detector still suffers from high complexity and necessitates further optimization. To bridge the gap between the BsP algorithm and implementation, this paper introduces anapproximatebut implementation-friendly BsP detector called aBsP, based on which the very first BsP hardware is proposed. Two unexplored features:approximate initializationandsimplified message updatessave the complexity (more than$84$%) with acceptable performance penalization. Multi-level optimization techniques involving group-layered message updating, approximate arithmetic circuits, and hybrid-precise quantization are developed to boost the hardware efficiency A$128\times 8$$256$-QAM aBsP MIMO detector ASIC in$40$nm CMOS occupies an area of$0.68$mm$^2$and reaches a throughput of$790.52$Mbps. Benchmarking with the recent arts, this work achieves$1.08\times$area efficiency and$3.34\times$gate efficiency.

Compared to the linear MIMO detectors, the Belief Propagation (BP) detector has shown greater capabilities in achieving near-optimal performance and better nature to iteratively cooperate with channel decoders. Aiming at real applications, recent works mainly fall into the category of reducing the complexity by simplified calculations, at the expense of performance sacrifice. However, the complexity is still unsatisfactory with exponentially increasing complexity or required exponentiation operations. Furthermore, the state-of-the-art (SOA) BP detectors persistently encounter error floor in high signal-to-noise ratio (SNR) region, which becomes even worse with calculation approximation. This work aims at a revised BP detector, named Belief-selective Propagation (BsP) detector by selectively utilizing the trusted incoming messages with sufficiently large a priori probabilities for updates. Two proposed strategies: symbol-based truncation (ST) and edge-based simplification (ES) squeeze the complexity (orders lower than the BP detector), while greatly relieving the error floor issue over a wide range of antenna and modulation combinations. For the 256-QAM$128 \times 64$uplink massive multiuser MIMO (MU-MIMO) system, the$\mathcal {B}(1,1)$BsP detector achieves more than 1dB performance gain (@$\text {BER}=10^{-4}$) with lower complexity than the state-of-the-art (SOA) BP detector. Trade-off between performance and complexity towards different application requirements can be conveniently obtained by tuning the parameters of the ST and ES strategies.