Search arXiv⌕ Search

arXiv · 2611.06566

Box-Shift: Fixed-Complexity Counter-Hypothesis Generation for Soft-Output MIMO Box Decoding

Abstract

Box Decoding is a sort-free tree-search multiple-input multiple-output (MIMO) detector with a fixed per-layer cost independent of the quadrature amplitude modulation (QAM) order. Its regular datapath is well suited to hardware implementation, but the detector has so far been limited to hard-output operation in uncoded systems. Its fixed four-point box spans two adjacent Gray-coded QAM levels per axis, leaving most bits without counter-hypotheses for higher-order QAM. This missing counter-hypothesis (MCH) problem prevents the direct computation of finite max-log log-likelihood ratios (LLRs). We propose the \emph{Box-Shift} algorithm, which shifts the existing box to the nearest node carrying the complementary value of each Gray-coded bit at each detection layer. Unlike Bit-Flip, Box-Shift analytically identifies the partially optimal counter-hypothesis using one inverter per bit and updates the path metric through an index-domain increment. The proposed method preserves the sort-free, QAM-order-independent structure of Box Decoding. Coded $4\times4$ MIMO simulations using 5G~NR low-density parity-check (LDPC) coding show that Box Decoding with Box-Shift achieves a $0.7$--$1.5$\,dB gain in $E_b/N_0$ over the same detector with Bit-Flip at a bit error rate of $10^{-5}$, with equal soft-candidate budgets. For the soft-candidate metrics, the incremental update of Box-Shift needs $75\%$ fewer real multiplications than the explicit evaluation of Bit-Flip at a comparable number of real additions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shengchun Yang, Amit Sravan Bora, Emil Matúš, Gerhard Fettweis. 2026-11-04. Box-Shift: Fixed-Complexity Counter-Hypothesis Generation for Soft-Output MIMO Box Decoding. https://arxiv.org/abs/2611.06566

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

DDA-Net: Accurate TDD Channel Estimation via Deep Unfolding the Doppler-Delay-Angle Representation of Channel Signals

In TDD massive MIMO systems, channel estimation under sparse frequency-hopping pilots is challenging: each snapshot captures only one narrow pilot block that hops across frequency, with tens of milliseconds between adjacent snapshots. Finite-window leakage and off-grid effects weaken the ideal Doppler-delay-angle (DDA) sparsity, limiting both classical sparse recovery and purely data-driven approaches lacking an explicit structured transform-domain model. We propose DDA-Net, a model-driven 3D deep unfolding network for joint multi-snapshot channel state reconstruction. DDA-Net integrates an ADMM-based formulation with a closed-form data-consistency update that avoids tensor inversion, a lightweight Doppler-domain learned prior, and delay oversampling to mitigate basis mismatch. It consistently outperforms strong baselines across three channel settings from 3GPP TR 38.901: UMa-NLOS, UMi-NLOS, and CDL-B. Ablations confirm that window-level 3D processing and explicit Doppler modeling yield target-dependent improvements. With minimal target-domain fine-tuning, the UMa-pretrained model surpasses both its zero-shot version and counterparts trained from scratch with the same number of target-domain samples. The Doppler-domain design proves consistently superior to its time-domain equivalent, with a wider margin after fine-tuning. These results demonstrate that combining exact physical data consistency with a learned DDA-domain prior is an effective and sample-efficient approach to channel state acquisition under sparse frequency-hopping pilots.

eess.SP↗

Optimal symmetric low-rank BD-RIS configuration maximizing the determinant of a MIMO link

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) significantly improve wireless performance by allowing tunable interconnections among elements, but their design in multiple-input multiple-output (MIMO) systems has so far relied on complex iterative algorithms or suboptimal approximations. This work introduces a simple yet powerful approach: instead of directly maximizing the achievable rate, we maximize the absolute value of the determinant of the equivalent MIMO channel. We derive a closed-form symmetric unitary scattering matrix whose rank is exactly twice the channel's degrees of freedom ($2r$). Remarkably, this low-rank solution achieves the same determinant value as the optimal unitary BD-RIS. Using log-majorization theory, we prove that the rate loss relative to the optimal unitary BD-RIS vanishes at high signal-to-noise ratio (SNR) or when the number of BD-RIS elements becomes large. Moreover, the proposed solution can be perfectly implemented using a $q$-stem BD-RIS architecture with only $q=2r-1$ stems, requiring a minimum number of reconfigurable circuits. The resulting Max-Det solution is orders of magnitude faster to compute than existing iterative methods while achieving near-optimal rates in practical scenarios. This makes high-performance BD-RIS deployment feasible even with large surfaces and limited computational resources.

eess.SP↗

Constrained Tensor Decomposition-Based Target Sensing for Sparse Non-Uniform Array-Enabled AFDM ISAC Systems

Sparse non-uniform array-enabled affine frequency division multiplexing (AFDM) is a promising candidate for integrated sensing and communication (ISAC), while its performance critically depends on accurate target parameter estimation. In this paper, we propose a constrained tensor decomposition-based sensing framework for delay, Doppler, and angle estimation. Specifically, a manifold-constrained alternating least squares algorithm is developed by exploiting the sparse array geometry structure, enabling robust factor matrix extraction and direct angle estimation. In addition, we analyze the uniqueness condition of the tensor decomposition. From the decomposed factor matrices, delays and Doppler shifts are coarsely estimated by pulse compression, and refined by an iterative one dimensional golden-section search.Moreover, the Cramér-Rao bound (CRB) is derived to characterize the fundamental performance limit of the considered estimation problem. Finally, simulation results validate the effectiveness of the proposed estimation algorithm.

eess.SP↗