Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 13 references
Abstract
Efficient uplink processing in distributed massive multiple-input multiple-output (D-mMIMO) systems requires effective local combining to significantly mitigate inter-user interference. Recent zero-forcing (ZF) based combining schemes, such as partial full-pilot ZF (PFZF) and protected weak PFZF (PWPFZF), rely on heuristic threshold-based user grouping that may lead to inefficient utilization of spatial degrees of freedom across access points. To address this limitation, we propose an adaptive pilot-aware local combining scheme, generalized PFZF (G-PFZF), that dynamically allocates spatial degrees of freedom based on local channel conditions and replaces heuristic grouping with a decentralized pilot-level optimization framework. Numerical results demonstrate that the proposed G-PFZF scheme achieves significantly higher sum spectral efficiency compared to PFZF and PWPFZF.
A unified comparative analysis of three widely used spatially correlated centralized scattering channel models in a multi-cell massive MIMO system suggests that M-MMSE is preferable in strongly correlated environments, while lower-complexity schemes such as RZF provide a favorable trade-off in scenarios with milder spatial correlation.
Waleed A. Ali, M. M. Zayed· Wireless networks· 0 citations
In dense deployments, massive multi-user multiple-input multiple-output (MU-MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset of users per scheduling interval, restricting multiuser diversity. We therefore propose Digital Twin User pre-Screening (DiTUS), a digital-twin (DT)-aided framework that identifies promising users before instantaneous CSI acquisition. DiTUS forms spatial covariances from DT-inferred departure angles and path powers. Optional Gaussian-process (GP) calibration mitigates path-power bias, while the dominant rank-r eigenspace of the aggregate covariance yields common reference beams. It prescreens the pool using DiTUS-P, a low-complexity projection-energy rule, or DiTUS-L, a greedy log-determinant rule that promotes spatial compatibility. A two-level protocol collects scalar beam reports from shortlisted users and requests r-dimensional effective-channel vectors only from the scheduled set. The framework also supports proportional-fair scheduling. At 15 dB under DT imperfections, simulations with 128 candidates, a 64-user effective-CSI acquisition budget, and a 64-user shortlist show that DiTUS-L achieves 35.06 +/- 0.49 bps/Hz versus 30.28 +/- 0.60 bps/Hz for semi-orthogonal user selection (SUS) with full-dimensional CSI from 64 users, demonstrating that DT-based prescreening preserves substantial multiuser-diversity gains by identifying strong, spatially compatible users before acquiring effective-channel vectors.
Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park et al.· 0 citations
Cell-free Massive MIMO systems promise unprecedented spectral efficiency by coherently serving users with a large number of distributed Access Points (APs). A key practical challenge, however, is the high capacity required for the fronthaul links connecting these APs to a central processing unit. To reduce cost and power, these links must employ low-resolution quantization, which introduces distortion that can severely degrade system performance. This paper tackles this problem by proposing a novel, scalable receiver scheme: the Quantization-Aware Partial MMSE (QA-P-MMSE) receiver. Unlike conventional methods that either ignore quantization effects or require non-scalable centralized processing, our proposed receiver explicitly incorporates the statistics of the quantization noise into its design. We demonstrate through simulations that the QA-P-MMSE receiver significantly outperforms other scalable schemes, such as Maximum-Ratio (MR) and Partial-MMSE (P-MMSE), in terms of both average spectral efficiency and user fairness. Crucially, it approaches the performance of an ideal, non-scalable MMSE receiver with unquantized fronthaul, proving its efficacy as a practical and high-performance solution for next-generation cellfree networks. Furthermore, energy efficiency analysis reveals that the proposed scheme maximizes bits-per-joule performance at 4-bit resolution, aligning with green 6G targets.
Lokesh Sadrani, R. Rajput· International Journal of Ele...· 0 citations
This paper proposes a novel null-space expansion (NSE) scheme based on phase-only control to exploit the degrees of freedom of massive antenna elements even in analog beamformers. Downlink multi-user Multiple-Input Multiple-Output (MIMO) spatial multiplexing is performed through precoding in the baseband digital signal processing unit. In higher frequency bands, beamforming using array antennas is essential to compensate for distance attenuation. To balance implementation costs and MIMO spatial multiplexing, a hybrid architecture combining analog and digital processing has become mainstream. Meanwhile, in massive MIMO, which offers a large spatial degree of freedom, NSE has been proposed as an effective solution to mitigate the degradation of spatial multiplexing performance caused by propagation channel variations due to user mobility. However, its application has so far been limited to fully digital configurations. We newly apply NSE to the phase-only adaptive nulling (POAN) framework and clarify its effectiveness through computer simulations. Furthermore, since commercially available phase shifters are digitally controlled, we evaluate the impact of phase quantization error to confirm the practical feasibility of the proposed scheme.
Unknown authors· Journal on Wireless Communic...· 0 citations
In this paper, we propose an fluid antenna (FA)-enhanced interference exploitation symbol-level precoding (SLP) for downlink multi-user multiple-input single-output (MU-MISO) systems, focusing on transmitter-side fluid antenna systems (Tx-FAS) that allow flexible spatial correlation effects while preserving user portability. First, we derive an explicit expression for the achievable rate of SLP under finite-alphabet inputs, and further establish a closed-form upper bound that quantifies the theoretical rate improvement achieved by spatial reconfigurability of Tx-FAS. Subsequently, a joint optimization problem involving SLP design and Tx-FAS port selection is formulated to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) under strict transmit power constraints. Due to the highly non-convex nature of the problem, we devise a genetic algorithm (GA) to efficiently obtain a near-optimal solution. Moreover, based on the optimal channel structure of SLP, we further develop a low-complexity algorithm with a spatial filtering mechanism to obtain a sub-optimal solution to the considered problem. Simulation results confirm that the proposed FAS-enhanced SLP scheme delivers notable performance improvements over both conventional precoding techniques and the FAS-only transmission approach.
Guorui Wei, Ang Li, Xiao-Yan Hu et al.· IEEE Transactions on Communi...· 0 citations
Distributed multiple-input multiple-output (D-MIMO) is envisioned as a key deployment architecture for future wireless systems, offering improved coverage and robustness through spatial separation, and favorable geometry for localization and sensing. Its greatest potential for localization lies in joint coherent processing across distributed antenna panels. However, stringent frequency-synchronization and phase-calibration requirements, together with multimodal likelihood functions, hinder the estimation process. Consequently, most existing algorithms process the panels noncoherently, potentially sacrificing localization accuracy. We present a unified family of Bayesian state-space filters that are based on concentrated Type-I and marginal Type-II likelihoods for wideband near-field D-MIMO systems and operate directly on noisy channel observations. The Type-I filters explicitly realize (i) noncoherent, (ii) coherent, and (iii) carrier-phase-based processing. For Type-II filtering, we show that a zero-mean model is inherently noncoherent under distributed processing, whereas observation stacking restores coherence. A nonzero-mean model can automatically adapt to the coherence available in the data, a property that we term ``soft coherence''. We derive posterior Cram\'er-Rao lower bounds (PCRLBs) for all three coherence levels and show that each level is fundamentally tied to the number of phase parameters used for positioning or treated as nuisance parameters. Numerical results show that the coherence-specific filters closely approach their respective PCRLBs and that coherent processing can substantially outperform noncoherent processing. We derive particle-based belief propagation methods, which parallelize over particles and distributed panels, scale linearly with the observed data, and achieve runtimes of tens of milliseconds per time step in a GPU-accelerated implementation.
Benjamin J. B. Deutschmann, L. D'Angelo, Erik Leitinger et al.· 0 citations
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