Improved symbol-based belief propagation detection for large-scale MIMO

Abstract

In this paper, BP detection based on belief propagation in real domain for large-scale MIMO systems is proposed. Numerical results have shown that, with quadrature phase shift keying (QPSK) modulation, this approach can show 1 dB performance improvement at the BER of 10 −2 , compared to conventional single-edge based BP (SE-BP) in complex domain. Based on the proposed BP detection, its symbol-based variation is also investigated for applications in large-scale MIMO systems with a high-order modulation. This symbol-based method successfully reduces computational complexity by avoiding large dimensional matrix inversion and decomposition. Since the proposed method can also shrink the constellation size, its complexity can be further reduced. Numerical simulation results and complexity comparison have demonstrated that the proposed symbol-based BP detection can show advantages in both performance and complexity compared to existing ones. Therefore, it is suitable for large-scale MIMO system applications, especially for those with high-order modulations.Index Terms-Large-scale MIMO, belief propagation (BP), symbol-based, message passing, iterative detection.

Improving Massive MIMO Message Passing Detectors With Deep Neural Network

“…Meanwhile, damping can also be utilized in the MS algorithm to form a damped OMN/NMS. Combining Eq.s (7) and (12) we get…”

“…Many massive MIMO detection methods were presented, e.g., [3][4][5][6], among which the message passing approach, belief propagation (BP), has been paid intensive attentions and broadly researched in recent years. BP detectors provides a superior performance in comparison to the aforementioned detection algorithms due to its lower-complexity, strong robustness and also the so-called large-dimension behavior, i.e., the detection performance is closer to optimal as the MIMO dimension increases [7,8]. However, it has some drawbacks when dealing with practical problems: 1) Loopy factor graph: The factor graphs defined by typical MIMO channels are fully-connected, hence heavily loopy.…”

“…and the s k that maximize γ i (s k ) is chosen as the final decision of the received signal. More details of BP are given in [7].…”

“…It was observed in aforementioned works like [7,8] that the damping could improve the convergence of the BP algorithms. However, the optimal damping factor is difficult to find.…”

“…Let ∆ denote the set of the parameters to be optimized, our DNN detector can be described as following, The iterative algorithm Damped BP [7] Max-Sum BP [14] Training parameters ∆ δ δ, λ, ω…”