Aug 2026· Problems of Information Transmission· Vol 62, pp. 20 - 37· 0 citations· 18 references
Computer Science
TL;DR
This work proposes two topology design methods that account for channel and interference statistics: SER-DU (Seriation-Based DU Partitioning), which relies on spectral seriation, and FA-DU (Free Assignment-Based DU Partitioning), which performs unconstrained optimization of antenna elements into fixed-size clusters.
The simulation results reveal that the proposed HC-DA-UA framework always outperforms the existing counterparts in terms of sum-rate, whilst substantially decreasing the computational complexity and signalling overhead, thus it is an efficient and scalable solution for next-generation hardware-impaired distributed wireless communication networks.
P. Poojitha, D. Karunakar Reddy· International Journal for Re...· 0 citations
Distributed multiple-input multiple-output (MIMO) is a promising architecture for future wireless systems because cooperation among geographically separated base stations (BSs) improves coverage, spectral efficiency, and link reliability. However, the large effective aperture formed by distributed BSs makes near-field effects non-negligible and complicates accurate channel state information acquisition. Existing near-field estimators often suffer from modeling errors caused by approximate angle–range decoupling or from the high storage and computational costs of dense two-dimensional sparse representations. This article proposes an off-grid variational Bayesian channel-estimation framework for the considered distributed near-field MIMO geometry, which comprises equally spaced, collinear BS reference points and aligned uniform linear arrays (ULAs) with common inter-element spacing. We establish a geometry-coupled model based on the exact geometric spherical-wave phase response and map the local direction–range parameters observed by different BSs into a common reference coordinate system, yielding a two-dimensional jointly sparse representation. An independent-vector variational Bayesian inference algorithm then decomposes the high-dimensional multiuser recovery problem into user-specific posterior subproblems. It operates directly on the received pilot matrices, avoiding pilot–matrix inversion and the resulting distortion of noise statistics. A two-dimensional skewed off-grid update is further embedded in an expectation-maximization procedure to jointly refine angle and range offsets, mitigating basis mismatch while permitting a coarser initial dictionary. Simulation results support the effectiveness of the proposed method in the evaluated scenarios.
This paper proposes antenna selection-based beamforming optimization algorithms for full-duplex (FD) massive multiple-input multiple-output (mMIMO) large-scale arrays. The approach jointly selects the optimal transmit and receive antennas to optimize the propagation channels, thereby mitigating both self-interference (SI) and multi-user interference while preserving the desired signal strength. Illustrative results based on measured SI channels between crosspolarized 8 $\times$ 8 Tx and 8 $\times$ 8 Rx arrays demonstrate substantial improvements in both SI suppression and achievable total rate. On average, the proposed method achieves a 129.3% FD sumrate improvement over the Power-Greedy Selection scheme and a 65.5% improvement over the Random Selection scheme. Specifically, compared with the Power-Greedy and Random Selection schemes, the proposed method improves the uplink rate by average factors of 11.3 and 29.2, respectively, with maximum improvements of up to 13.9 and 35.3 times. An average 9.1 dB enhancement in SI suppression is observed. A comprehensive analysis of antenna selection effectiveness and downlink-uplink rate tradeoff in FD communications is provided.
In this paper, we investigate the downlink performance of multi-cell RSMA-enabled ISAC networks in which base stations (BSs), communication users, and sensing targets are spatially distributed according to independent Poisson point processes (PPPs). Each BS simultaneously serves multiple users using RSMA while exploiting the common stream as a dual-functional communication and sensing waveform. The users are equipped with FAS that selects the best antenna port to maximize the received signal quality. Closed-form analytical expressions are derived for the ergodic sum-rates by combining stochastic geometry, order statistics, and Laplace-transform-based interference analysis. Furthermore, a tractable approximation for the average radar SINR is developed by characterizing the statistical properties of the common precoder. Leveraging the derived analytical expressions, a low-complexity analytical resource allocation framework is proposed to jointly optimize the RSMA power allocation, the communication-sensing beam tradeoff, and the number of scheduled users while sat- isfying the sensing quality-of-service constraint. Compared with conventional iterative optimization approaches, the proposed analytical design significantly reduces computational complexity while achieving nearly identical communication performance. Simulation results verify the accuracy of the developed analytical expressions and demonstrate substantial improvements in both RSMA sum-rate and sensing performance over conventional transmission schemes.
Abdelhamid Salem, Hana Shamata, Salma M. Elkawafi et al.· 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
Massive Multiple-Input Multiple-Output (MIMO) is a key enabling technology for fifth-generation (5G) and beyond wireless communication systems because of its ability to greatly enhance both spectral efficiency (SE) and energy efficiency (EE). However, maximizing these two performance metrics simultaneously remains a challenging multi-objective optimization problem due to the conflicting effects of the transmit power, antenna deployment, and circuit power consumption. This paper investigates the EE–SE trade-off in a downlink Massive MIMO system with Minimum Mean Square Error (MMSE) channel estimation (CE) and different linear combining and precoding techniques. A power optimization framework based on transmit power allocation and antenna configuration is analyzed to identify operating points that maximize EE while maintaining high SE. Performance analysis of the number of base station (BS) antennas in MIMO systems, user equipment density, transmit power, and inter-cell interference on system performance is evaluated through numerical simulations. The results demonstrate that appropriately selecting the number of transmit antennas and optimizing the transmit power significantly improve the EE–SE trade-off. Furthermore, although increasing the number of antennas enhances SE, EE exhibits a non-monotonic behavior because of the additional circuit power required by the radio-frequency hardware. The findings confirm that MMSE-based CE provides higher spectral efficiency than the MR, ZF, RZF, and S-MMSE schemes, albeit at increased computational complexity, offering useful design guidelines for energy-efficient Massive MIMO networks.
Unknown authors· Journal of Low Power Electro...· 0 citations
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