arXiv subjects

Emil Björnson

Publications and source records attributed to Emil Björnson.

At least 73 records · Page 4Linked to original sources

arXiv2025

Multi-Antenna Users in Cell-Free Massive MIMO: Stream Allocation and Necessity of Downlink Pilots

We consider a cell-free massive multiple-input multiple-output (MIMO) system with multiple antennas on the users and access points (APs). In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well for single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent when having multi-antenna users. The reason is that the effective channel matrix does not harden. We propose a pilot-based downlink estimation scheme, derive a new SE expression, and show numerically that it yields substantially higher performance when having correlated Rayleigh fading channels. In cases with multi-antenna users, the APs can either transmit the same or different data streams. The latter reduces the fronthaul signaling but comes with a SE loss. We propose precoding and combining schemes for these cases and consider whether channel knowledge is shared between the APs. Finally, we show numerically how the number of users, APs, and the number of antennas on users and APs affect the SE.

cs.IT↗

arXiv2025

Near-Field Beamfocusing, Localization, and Channel Estimation with Modular Linear Arrays

This paper investigates how near-field beamfocusing can be achieved using a modular linear array (MLA), composed of multiple widely spaced uniform linear arrays (ULAs). The MLA architecture extends the aperture length of a standard ULA without adding additional antennas, thereby enabling near-field beamfocusing without increasing processing complexity. Unlike conventional far-field beamforming, near-field beamfocusing enables simultaneous data transmission to multiple users at different distances in the same angular interval, offering significant multiplexing gains. We present a detailed mathematical analysis of the beamwidth and beamdepth achievable with the MLA and show that by appropriately selecting the number of antennas in each constituent ULA, ideal near-field beamfocusing can be realized. In addition, we propose a computationally efficient localization method that fuses estimates from each ULA, enabling efficient parametric channel estimation. Simulation results confirm the accuracy of the analytical expressions and that MLAs achieve near-field beamfocusing with a limited number of antennas, making them a promising solution for next-generation wireless systems.

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arXiv2025

Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications

This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.

cs.IT↗

arXiv2025

Hardware Distortion Aware Precoding for ISAC Systems

The impact of hardware impairments on the spectral efficiency of communication systems is well studied, but their effect on sensing performance remains unexplored. In this paper, we analyze the influence of hardware impairments on integrated sensing and communication (ISAC) systems in cluttered environments. We derive the sensing signal-to-clutter-plus-noise ratio (SCNR) and show that hardware distortions significantly degrade sensing performance by enhancing clutter-induced noise, which masks target echoes. The isotropic nature of transmit distortion due to multiple stream transmission further complicates clutter suppression. To address this, we propose a distortion- and clutter-aware precoding strategy that minimizes the deviation from the communication-optimized precoder while improving sensing robustness. We also propose an alternative power allocation-based approach that reduces computational complexity. Numerical results confirm the effectiveness of the proposed approaches in overcoming hardware- and clutter-induced limitations, demonstrating significant performance gains over distortion-unaware designs.

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arXiv2025

Flexible Continuous Aperture Arrays

A novel electromagnetic (EM) structure termed flexible continuous aperture array (FCAPA) is proposed, which incorporates inherent surface flexibility into typical continuous aperture array (CAPA) systems, thereby enhancing the degrees-of-freedom (DoF) of multiple-input multiple-output (MIMO) systems equipped with this technology. By formulating and solving a downlink multi-user beamforming optimization problem to maximize the weighted sum rate (WSR) of the multiple users with FCAPA, it is shown that the proposed structure outperforms typical CAPA systems by a wide margin, with performance increasing with increasing morphability.

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arXiv2025

Flexible MIMO for Future Wireless Communications: Which Flexibilities are Possible?

In conventional multiple-input multiple-output (MIMO), static array configurations struggle in dynamic environments, and further antenna scaling is bounded by cost, energy, and footprint. Emerging approaches, which can enable next-generation wireless communication networks with modest spectrum availability by leveraging flexibility and adaptability rather than sheer array growth, are therefore needed. In this paper, we present a taxonomy framework, referred to as flexible MIMO technology, that systematically categorizes a wide range of evolving MIMO technologies. The focus is on MIMO technologies with flexible physical configurations and integrated applications. We categorize twelve representative flexible MIMO technologies into three major classifications: flexible deployment characteristics-based, flexible geometry characteristics-based, and flexible real-time modifications-based. We then comprehensively overview their fundamental characteristics, potential, and challenges. In addition, we highlight three vital enablers for flexible MIMO technology, including efficient channel state information acquisition schemes, low-complexity beamforming design, and explainable artificial intelligence (AI)-enabled optimization, and discuss eight representative sub-techniques. Finally, two brief case studies -- pre-optimized irregular array for high-speed railway network and cell-free movable antenna -- are presented, showing how flexible MIMO can open new design possibilities and inspire future research directions for next-generation wireless networks.

cs.IT↗

arXiv2025

Impact of Antenna Arrays Misalignment on the Near Field Distance in Terahertz Communications

The extremely short wavelength of terahertz (THz) communications leads to an extended radiative near-field region, in which some canonical far-field assumptions fail. Existing near-field boundary formulations (Fraunhofer distance) for uniform linear/planar array (ULA/UPA) configurations assume ideal alignment between transceivers, overlooking practical misalignments caused by mobility or mechanical imperfections. This paper addresses this critical gap by analyzing the impact of spatial misalignment on near-field distance calculations in THz systems. We derive exact analytical expressions and simplified approximations for the near-field boundary in both ULA--ULA and UPA--UPA configurations under arbitrary misalignment offsets. Through numerical simulations, we validate our theoretical models and quantify how misalignment reshapes the near-field region. These findings provide essential guidelines for optimizing THz system deployment in realistic scenarios.

cs.NI↗

arXiv2025

Near-Field SWIPT with gMIMO in the Upper Mid-Band: Opportunities, Challenges, and the Way Forward

This paper explores the integration of simultaneous wireless information and power transfer (SWIPT) with gigantic multiple-input multiple-output (gMIMO) technology operating in the upper mid-band frequency range (7-24 GHz). The near-field propagation achieved by gMIMO introduces unique opportunities for energy-efficient, high-capacity communication systems that cater to the demands of 6G wireless networks. Exploiting spherical wave propagation, near-field SWIPT with gMIMO enables precise energy and data delivery, enhancing spectral efficiency through beam focusing and massive spatial multiplexing. This paper discusses theoretical principles, design challenges, and enabling solutions, including advanced channel estimation techniques, precoding strategies, and dynamic array configurations such as sparse and modular arrays. Through analytical insights and a case study, this paper demonstrates the feasibility of achieving optimized energy harvesting and data throughput in dense and dynamic environments. These findings contribute to advancing energy-autonomous Internet-of-Everything (IoE) deployments, smart factory networks, and other energy-autonomous applications aligned with the goals of next-generation wireless technologies.

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arXiv2025

Limited-Resolution Hybrid Analog-Digital Precoding Design Using MIMO Detection Methods

While fully-digital precoding achieves superior performance in massive MIMO systems, it comes with significant drawbacks in terms of computational complexity and power consumption, particularly when operating at millimeter-wave (mmWave) frequencies and beyond. Hybrid analog-digital architectures address this by reducing radio frequency (RF) chains while maintaining performance in sparse multipath environments. However, most hybrid precoder designs assume ideal, infinite-resolution analog phase shifters, which cannot be implemented in real systems. Another practical constraint is the limited fronthaul capacity between the baseband processor and array, implying that each entry of the digital precoder must be picked from a finite set of quantization labels. This paper proposes novel designs for the limited-resolution analog and digital precoders by exploiting two well-known MIMO symbol detection algorithms, namely sphere decoding (SD) and expectation propagation (EP). Unlike prior works that rely on heuristic or sub-optimal designs for the low-resolution hybrid precoder, the proposed transformative MIMO detection-inspired designs are able to achieve optimal and near-optimal solutions. The main objective is to minimize the Euclidean distance between the optimal fully-digital precoder and the hybrid precoder to minimize the degradation caused by the finite resolution of the analog and digital precoders. Taking an alternating optimization approach, we first apply the SD method to find the precoders in each iteration optimally. Then, we apply the lower-complexity EP method which finds a near-optimal solution at a reduced computational cost. The effectiveness of the proposed designs is validated through extensive numerical simulations, which demonstrate that both SD-based and EP-based hybrid precoding schemes significantly outperform widely-used sub-optimal approaches.

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arXiv2025

Sensing Management for Pilot-Free Predictive Beamforming in Cell-Free Massive MIMO Systems

This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no communication request. Upon receiving a communication request, predictive beamforming is employed based on the tracked user position, thereby reducing the need for channel estimation. Our framework incorporates an extended Kalman filter (EKF) based tracking algorithm with adaptive sensing management to perform sensing operations only when necessary to maintain high tracking accuracy. The simulation results demonstrate that our proposed sensing management approach provides uniform downlink communication rates that are higher than with existing methods by achieving overhead-free predictive beamforming.

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arXiv2025

Low-Complexity Receiver Design for Multicarrier CAPA-based Systems in Doubly-Dispersive Channels

We propose a novel low-complexity receiver design for multicarrier continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. The receiver leverages a Gaussian Belief Propagation (GaBP)-based framework that hinges only on element-wise scalar operations for the detection of the transmitted symbols. Simulation results for the orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) waveforms demonstrate significant performance improvements in terms of uncoded bit error rate (BER) compared to conventional discrete antenna array systems, while maintaining very low computational complexity.

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arXiv2025

Finite Beam Depth Analysis for Large Arrays

Most wireless communication systems operate in the far-field region of antennas and antenna arrays, where waves are planar and beams have infinite depth. When antenna arrays become electrically large, it is possible that the receiver is in the radiative near-field of the transmitter, and vice versa. Recent works have shown that near-field beamforming exhibits a finite depth, which enables a new depth-based spatial multiplexing paradigm. In this paper, we explore how the shape and size of an array determine the near-field beam behaviors. In particular, we investigate the $3$ dB beam depth (BD), defined as the range of distances where the gain is greater than half of the peak gain. We derive analytical gain and BD expressions and prove how they depend on the aperture area and length. For non-broadside transmissions, we find that the BD increases as the transmitter approaches the end-fire direction of the array. Furthermore, it is sufficient to characterize the BD for a broadside transmitter, as the beam pattern with a non-broadside transmitter can be approximated by that of a smaller/projected array with a broadside transmitter. Our analysis demonstrates that the BD can be ordered from smallest to largest as ULA, circular, and square arrays.

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arXiv2025

Cell-Free Beamforming Design for Physical Layer Multigroup Multicasting

In many wireless communication applications, it is desirable to transmit the same data to multiple user equipments (UEs). Physical layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several UEs. An advantage of multicasting is to avoid unnecessary co-channel interference between UEs requesting the same data. The difficulty is to find the suitable beamforming configuration that guarantees an acceptable minimum data rate, among the receiving UE group, to the multicast transmission. This paper addresses the max-min fair multigroup multicast optimization problem and proposes a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-optimal rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network. The proposed optimization procedure significantly improves computational complexity and spectral efficiency compared to common methods that use SDR followed by some randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The importance of the proposed procedure is that it is applicable to any SDR problem where a low-rank solution is desirable. Further, we propose a low-complexity algorithm that achieves 87 % of the optimal rank-1 solution at orders-of-magnitude lower computational time.

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arXiv2025

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures

Cell-free massive multiple-input multiple-output (MIMO) implemented in virtualized cloud radio access networks (V-CRAN) has emerged as a promising architecture to enhance spectral efficiency (SE), network flexibility, and energy efficiency (EE) in next-generation wireless systems. In this work, we develop a holistic optimization framework for the efficient deployment of cell-free massive MIMO in V-CRAN with multiple mobile network operators (MNOs). Specifically, we formulate a set of integer programming models to jointly optimize access point (AP) selection, user equipment (UE) association, cloud resource allocation, and MNO assignment while minimizing the maximum total power consumption (TPC) across MNOs. We consider two different scenarios based on whether UEs can be assigned to arbitrary MNOs or not. The numerical results demonstrate the impact of different deployment assumptions on power consumption, highlighting that flexible UE-MNO assignment significantly reduces TPC. The findings provide key insights into optimizing resource management in cell-free massive MIMO V-CRAN, paving the way for energy-efficient wireless network implementations.

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arXiv2025

Near-Field Channel Estimation with ELAA Modular Arrays Under Hardware Impairments

Extremely large-scale antenna arrays (ELAAs) enable high spatial resolution and multiplexing, especially for user equipments (UEs) in the radiative near-field. To reduce hardware cost, modular ELAA architectures with distributed baseband units (BBUs) are gaining traction. This paper addresses near-field line-of-sight (LOS) channel estimation under low noise amplifier (LNA)-induced hardware impairments in such modular systems. We propose computationally efficient estimators that exploit the array geometry and constant-modulus structure of near-field LOS channels, including a novel two-dimensional (2D) discrete Fourier transform (DFT) masking technique that improves estimation accuracy and significantly reduces fronthaul signaling. Numerical results show that the proposed methods significantly outperform the conventional least squares (LS) method.

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arXiv2025

Doubly-Dispersive Continuous MIMO Systems: Channel Modeling and Beamforming Design

We address the modeling and optimal beamforming (BF) design for multiple-input multiple-output (MIMO) continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. First, a comprehensive DD continuous MIMO (DDC MIMO) channel model that incorporates CAPAs at both the transmitter (TX) and receiver (RX) is derived, which is used to obtain explicit input-output (I/O) relations for various waveforms well suited to integrated sensing and communications (ISAC) and robust to DD channels, namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Then, functional optimization problems are formulated for the design of TX and RX BF matrices that maximize received power, in which novel low-complexity, closed-form solutions are obtained via the calculus of variations (CoV) method, yielding expressions closely related to the classical matched filter commonly used in conventional MIMO systems. Simulation results confirm that the proposed TX/RX BF designs with CAPAs provide significant performance and computational complexity gains over conventional MIMO systems in DD channels.

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arXiv2025

Channel-Coherence-Adaptive Two-Stage Fully Digital Combining for mmWave MIMO Systems

This paper considers a millimeter-wave wideband point-to-point MIMO system with fully digital transceivers at the base station and the user equipment (UE), focusing on mobile UE scenarios. A main challenge when building a digital UE combining is the large volume of baseband samples to handle. To mitigate computational and hardware complexity, we propose a novel two-stage digital combining scheme at the UE. The first stage reduces the $N_{\text{r}}$ received signals to $N_{\text{c}}$ streams before baseband processing, leveraging channel geometry for dimension reduction and updating at the beam coherence time, which is longer than the channel coherence time of the small-scale fading. By contrast, the second-stage combining is updated per fading realization. We develop a pilot-based channel estimation framework for this hardware setup based on maximum likelihoodestimation in both uplink and downlink. Digital precoding and combining designs are proposed, and a spectral efficiency expression that incorporates imperfect channel knowledge is derived. The numerical results demonstrate that the proposed approach outperforms hybrid beamforming, showcasing the attractiveness of using two-stage fully digital transceivers in future systems.

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arXiv2025

Comprehensive Analysis of Behavioral Hardware Impairments in Cell-Free Massive MIMO-OFDM Uplink: Centralized Operation

Cell-free massive MIMO is a key 6G technology, offering superior spectral and energy efficiency. However, its dense deployment of low-cost access points (APs) makes hardware impairments unavoidable. While narrowband impairments are well-studied, their impact in wideband systems remains unexplored. This paper provides the first comprehensive analysis of hardware impairments, such as nonlinear distortion in low-noise amplifiers, phase noise, in-phase-quadrature imbalance, and low-resolution analog-to-digital converters, on uplink spectral efficiency in cell-free massive MIMO. Using an OFDM waveform and centralized processing, APs share channel state information for joint uplink combining. Leveraging Bussgang decomposition, we derive a distortion-aware combining vector that optimizes spectral efficiency by modeling distortion as independent colored noise.

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