arXiv subjects

Emil Björnson

Publications and source records attributed to Emil Björnson.

At least 91 records · Page 5Linked to original sources

arXiv2025

Impact of Phase Noise and Power Amplifier Non-Linearities on Downlink Cell-Free Massive MIMO-OFDM Systems

Cell-free massive MIMO (multiple-input multiple-output) is a key enabler for the sixth generation (6G) of mobile networks, offering significant spectral and energy efficiency gains through user-centric operation of distributed access points (APs). However, its reliance on low-cost APs introduces inevitable hardware impairments, whose combined impact on wideband downlink systems remains unexplored when analyzed using behavioral models. This paper presents a comprehensive analysis of the downlink spectral efficiency (SE) in cell-free massive MIMO-OFDM systems under practical hardware impairments, including phase noise and third-order power amplifier nonlinearities. Both centralized and distributed precoding strategies are examined. By leveraging the Bussgang decomposition, we derive an SE expression and quantify the relative impact of impairments through simulations. Our results reveal that phase noise causes more severe degradation than power amplifier distortions, especially in distributed operation, highlighting the need for future distortion-aware precoding designs.

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arXiv2025

On-Demand HAPS-Assisted Communication System for Public Safety in Emergency and Disaster Response

Natural disasters often disrupt communication networks and severely hamper emergency response and disaster management. Existing solutions, such as portable communication units and cloud-based network architectures, have improved disaster resilience but fall short if both the Radio Access Network (RAN) and backhaul infrastructure become inoperable. To address these challenges, we propose a demand-driven communication system supported by High Altitude Platform Stations (HAPS) to restore communication in an affected area and enable effective disaster relief. The proposed emergency response network is a promising solution as it provides a rapidly deployable, resilient communications infrastructure. The proposed HAPS-based communication can play a crucial role not only in ensuring connectivity for mobile users but also in restoring backhaul connections when terrestrial networks fail. As a bridge between the disaster management center and the affected areas, it can facilitate the exchange of information in real time, collect data from the affected regions, and relay crucial updates to emergency responders. Enhancing situational awareness, coordination between relief agencies, and ensuring efficient resource allocation can significantly strengthen disaster response capabilities. In this paper, simulations show that HAPS with hybrid optical/THz links boosts backhaul capacity and resilience, even in harsh conditions. HAPS-enabled RAN in S- and Ka-bands ensures reliable communication for first responders and disaster-affected populations. This paper also explores the integration of HAPS into emergency communication frameworks and standards, as it has the potential to improve network resilience and support effective disaster management.

cs.NI↗

arXiv2025

Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios

The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in the near-field. However, it suffers from high computational complexity, which becomes especially prohibitive in near-field scenarios due to the need for exhaustive 3D grid searches. This paper presents a machine learning-based approach for 3D localization of near-field sources in mixed line-of-sight (LoS)/non-LoS scenarios. A convolutional neural network (CNN) learns the mapping between the eigenvectors of the received signal's covariance matrix at the anchor node and the sources' 3D locations. The detailed description of the proposed CNN model is provided. The effectiveness and time efficiency of the proposed CNN-based localization approach is corroborated via numerical simulations.

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arXiv2025

DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar

Sensing emerges as a critical challenge in 6G networks, which require simultaneous communication and target sensing capabilities. State-of-the-art super-resolution techniques for the direction of arrival (DoA) estimation encounter significant performance limitations when the number of targets exceeds antenna array dimensions. This paper introduces a novel sensing parameter estimation algorithm for orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar systems. The proposed approach implements a strategic two-stage methodology: first, discriminating targets through delay and Doppler domain filtering to reduce the number of effective targets for super-resolution DoA estimation, and second, introducing a fusion technique to mitigate sidelobe interferences. The algorithm enables robust DoA estimation, particularly in high-density target environments with limited-size antenna arrays. Numerical simulations validate the superior performance of the proposed method compared to conventional DoA estimation approaches.

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arXiv2025

A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation

In massive MIMO systems, fully digital precoding offers high performance but has significant implementation complexity and energy consumption, particularly at millimeter frequencies and beyond. Hybrid analog-digital architectures provide a practical alternative by reducing the number of radio frequency (RF) chains while retaining performance in spatially sparse multipath scenarios. However, most hybrid precoder designs assume ideal, infinite-resolution analog phase shifters, which are impractical in real-world scenarios. 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. To minimize the sum rate degradation caused by quantized analog and digital precoders, we propose novel designs inspired by the sphere decoding (SD) algorithm. We demonstrate numerically that our proposed designs outperform traditional methods, ensuring minimal sum rate loss in hybrid precoding systems with low-resolution phase shifters and limited fronthaul capacity.

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arXiv2025

Control Signaling for Reconfigurable Intelligent Surfaces: How Many Bits are Needed?

Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel changes. In this paper, we examine how the feedback load, measured in bits, affects the performance of an RIS-aided system. Specifically, we investigate the trade-offs between codebook-based and element-wise feedback schemes, and how these influence the signal-to-noise ratio (SNR). We propose a novel quantization codebook tailored for line-of-sight (LoS) that guarantees a minimal SNR loss using a number of feedback bits that scale logarithmically with the number of RIS elements. We demonstrate the codebook's usefulness over Rician fading channels and how to extend it to handle a non-zero static path. Numerical simulations and analytical analysis are performed to quantify the performance degradation that results from a reduced feedback load, shedding light on how efficiently RIS configurations can be fed back in practical systems.

cs.IT↗

arXiv2025

Enabling 6G Performance in the Upper Mid-Band by Transitioning From Massive to Gigantic MIMO

The initial 6G networks will likely operate in the upper mid-band (7-24 GHz), which has decent propagation conditions but underwhelming new spectrum availability. In this paper, we explore whether we can anyway reach the ambitious 6G performance goals by evolving the multiple-input multiple-output (MIMO) technology from massive in 5G to gigantic in 6G. We describe how many antennas are needed to reach the envisioned 6G peak user rates, how many can realistically be deployed in practical radio equipment, and what the practical spatial degrees-of-freedom might become. We further suggest a new deployment strategy that enables the utilization of radiative near-field effects in these bands for precise beamfocusing, localization, and sensing from a single base station site. Finally, we identify open research and standardization challenges that must be overcome to efficiently use gigantic MIMO dimensions in 6G from hardware, cost, and algorithmic perspectives.

cs.IT↗

arXiv2025

Optimizing Movable Antennas in Wideband Multi-User MIMO With Hardware Impairments

Movable antennas represent an emerging field in telecommunication research and a potential approach to achieving higher data rates in multiple-input multiple-output (MIMO) communications when the total number of antennas is limited. Most solutions and analyses to date have been limited to \emph{narrowband} setups. This work complements the prior studies by quantifying the benefit of using movable antennas in \emph{wideband} MIMO communication systems. First, we derive a novel uplink wideband system model that also accounts for distortion from transceiver hardware impairments. We then formulate and solve an optimization task to maximize the average sum rate by adjusting the antenna positions using particle swarm optimization. Finally, the performance with movable antennas is compared with fixed uniform arrays and the derived theoretical upper bound. The numerical study concludes that the data rate improvement from movable antennas over other arrays heavily depends on the level of hardware impairments, the richness of the multi-path environments, and the number of subcarriers. The present study provides vital insights into the most suitable use cases for movable antennas in future wideband systems.

cs.IT↗

arXiv2025

Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems

We propose a novel resource-efficient over-the-air(OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate the OTA computation of sufficient statistics. The proposed framework scales effectively with an increase in the number of APs. We also make a comprehensive study of the benefits of an OTA framework compared to a conventional digital fronthaul in terms of the overhead associated in transferring the sufficient statistics from the APs to the CPU. To evaluate the performance of the OTA framework, we give closed-form expressions for the mean-square error (MSE)of the estimators of sufficient statistics and the overall data estimator. Furthermore, we assess the symbol error rate (SER)and bit error rate (BER) of the user equipment (UEs) data to demonstrate the efficacy of our method, and benchmark them against the state-of-the-art wired fronthaul networks.

cs.IT↗

arXiv2025

RIS-Assisted Survivable Fronthaul Design in Cell-Free Massive MIMO System

This paper investigates the application of reconfigurable intelligent surfaces (RISs) to improve fronthaul link survivability in cell-free massive MIMO (CF mMIMO) systems. To enhance the fronthaul survivability, two complementary mechanisms are considered. Firstly, RIS is set to provide reliable line-of-sight (LOS) connectivity and enhance the mmWave backup link. Secondly, a resource-sharing scheme that leverages redundant cable capacity through neighboring master access points (APs) to guarantee availability is considered. We formulate the redundant capacity minimization problem as a RIS-assisted multi-user MIMO rate control optimization problem, developing a novel solution that combines a modified weighted minimum mean square error (WMMSE) algorithm for precoding design with Riemannian gradient descent for RIS phase shift optimization. Our numerical evaluations show that RIS reduces the required redundant capacity by 65.6% compared to the no RIS case to reach a 99% survivability. The results show that the most substantial gains of RIS occur during complete outages of the direct disconnected master AP-CPU channel. These results demonstrate RIS's potential to significantly enhance fronthaul reliability while minimizing infrastructure costs in next-generation wireless networks.

cs.IT↗

arXiv2025

Low-Complexity SDP-ADMM for Physical-Layer Multicasting in Massive MIMO Systems

There is a demand for the same data content from several user equipments (UEs) in many wireless communication applications. Physical-layer multicasting combines the beamforming capability of massive MIMO (multiple-input multiple-output) and the broadcast nature of the wireless channel to efficiently deliver the same data to a group of UEs using a single transmission. This paper tackles the max-min fair (MMF) multicast beamforming optimization, which is an NP-hard problem. We develop an efficient semidefinite program-alternating direction method of multipliers (SDP-ADMM) algorithm to find the near-global optimal rank-1 solution to the MMF multicast problem in a massive MIMO system. Numerical results show that the proposed SDP-ADMM algorithm exhibits similar spectral efficiency performance to state-of-the-art algorithms running on standard SDP solvers at a vastly reduced computational complexity. We highlight that the proposed ADMM elimination procedure can be employed as an effective low-complexity rank reduction method for other problems utilizing semidefinite relaxation.

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arXiv2025

Fair and Energy-Efficient Activation Control Mechanisms for Repeater-Assisted Massive MIMO

Massive multiple-input multiple-output (mMIMO) has been the core of 5G due to its ability to improve spectral efficiency and spatial multiplexing significantly; however, cell-edge users still experience performance degradation due to inter-cell interference and uneven signal distribution. While cell-free mMIMO (cfmMIMO) addresses this issue by providing uniform coverage through distributed antennas, it requires significantly more deployment cost due to the fronthaul and tight synchronization requirements. Alternatively, repeater-assisted massive MIMO (RA-MIMO) has recently been proposed to extend the coverage of cellular mMIMO by densely deploying low-cost single-antenna repeaters capable of amplifying and forwarding signals. In this work, we investigate amplification control for the repeaters for two different goals: (i) providing a fair performance among users, and (ii) reducing the extra energy consumption by the deployed repeaters. We propose a max-min amplification control algorithm using the convex-concave procedure for fairness and a joint sleep mode and amplification control algorithm for energy efficiency, comparing long- and short-term strategies. Numerical results show that RA-MIMO, with maximum amplification, improves signal-to-interference-plus-noise ratio (SINR) by over 20 dB compared to mMIMO and performs within 1 dB of cfmMIMO when deploying the same number of repeaters as access points in cfmMIMO. Additionally, our majority-rule-based long-term sleep mechanism reduces repeater power consumption by 70% while maintaining less than 1% spectral efficiency outage.

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arXiv2025

Spatial Frequencies and Degrees of Freedom: Their Roles in Near-Field Communications

As wireless technology begins to utilize physically larger arrays and/or higher frequencies, the transmitter and receiver will reside in each other's radiative near field. This fact gives rise to unusual propagation phenomena such as spherical wavefronts and beamfocusing, creating the impression that new spatial dimensions -- called degrees-of-freedom (DoF) -- can be exploited in the near field. However, this is a fallacy because the theoretically maximum DoF are already achievable in the far field. This paper sheds light on these issues by providing a tutorial on spatial frequencies, which are the fundamental components of wireless channels, and by explaining their role in characterizing the DoF in the near and far fields. In particular, we demonstrate how a single propagation path utilizes one spatial frequency in the far field and an interval of spatial frequencies in the near field. We explain how the array geometry determines the number of distinguishable spatial frequency bins and, thereby, the spatial DoF. We also describe how to model near-field multipath channels and their spatial correlation matrices. Finally, we discuss the research challenges and future directions in this field.

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arXiv2025

Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading channels. We provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination.

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arXiv2025

Nonlinear Distortion Radiated from Large Arrays and Active Reconfigurable Intelligent Surfaces

Extremely large aperture arrays (ELAAs) and reconfigurable intelligent surfaces (RISs) are candidate enablers to realize connectivity goals for the sixth-generation (6G) wireless networks. For instance, ELAAs can provide orders-of-magnitude higher area throughput compared to what massive multiple-input multiple-output (MIMO) can deliver through spatial multiplexing, while RISs can improve the propagation conditions over wireless channels but a passively reflecting RIS must be large to be effective. Active RIS with amplifiers can deal with this issue. In this paper, we analyze the distortion generated by nonlinear amplifiers in both ELAAs and active RIS. We derive analytical expressions for the angular directions and depth of nonlinear distortion in both near-field and far-field channels. These insights are then used in a distortion-aware scheduling scheme that predicts the beamforming directions of the distortion and strategically allocates users in frequency to minimize its impact. Numerical results validate our theoretical analysis and compare distortion-aware and distortion-unaware scheduling methods, highlighting the benefits of accounting for nonlinearities. We conclude that nonlinearities can both create in-band and out-of-band distortion that is beamformed in entirely new directions and distances from the transmitter.

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arXiv2025

UAV-Based Cell-Free Massive MIMO: Joint Placement and Power Optimization under Fronthaul Capacity Limitations

We consider a cell-free massive multiple-input multiple-output (mMIMO) network, where unmanned aerial vehicles (UAVs) equipped with multiple antennas serve as distributed UAV-access points (UAV-APs). These UAV-APs provide seamless coverage by jointly serving user equipments (UEs) with out predefined cell boundaries. However, high-capacity wireless networks face significant challenges due to fronthaul limitations in UAV-assisted architectures. This letter proposes a novel UAV-based cell-free mMIMO framework that leverages distributed UAV-APs to serve UEs while addressing the capacity constraints of wireless fronthaul links. We evaluate functional split Options 7.2 and 8 for the fronthaul links, aiming to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among the UEs and minimize the power consumption by optimizing the transmit powers of UAV-APs and selectively activating them. Our analysis compares sub-6 GHz and millimeter wave (mmWave) bands for the fronthaul, showing that mmWave achieves superior SINR with lower power consumption, particularly under Option 8. Additionally, we determine the minimum fronthaul bandwidth required to activate a single UAV-AP under different split options.

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arXiv2025

Pre-Optimized Irregular Arrays versus Moveable Antennas in Multi-User MIMO Systems

Massive multiple-input multiple-output (MIMO) systems exploit the spatial diversity achieved with an array of many antennas to perform spatial multiplexing of many users. Similar performance can be achieved using fewer antennas if movable antenna (MA) elements are used instead. MA-enabled arrays can dynamically change the antenna locations, mechanically or electrically, to achieve maximum spatial diversity for the current propagation conditions. However, optimizing the antenna locations for each channel realization is computationally excessive, requires channel knowledge for all conceivable locations, and requires rapid antenna movements, thus making real-time implementation cumbersome. To overcome these challenges, we propose a pre-optimized irregular array (PIA) concept, where the antenna locations at the base station are optimized a priori for a given coverage area. The objective is to maximize the average sum rate and we take a particle swarm optimization approach to solve it. Simulation results show that PIA achieves performance comparable to MA-enabled arrays while outperforming traditional uniform arrays. Hence, PIA offers a fixed yet efficient array deployment approach without the complexities associated with MA-enabled arrays.

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arXiv2025

Predictive Beamforming with Distributed MIMO

In vehicle-to-everything (V2X) applications, roadside units (RSUs) can be tasked with both sensing and communication functions to enable sensing-assisted communications. Recent studies have demonstrated that distance, angle, and velocity information obtained through sensing can be leveraged to reduce the overhead associated with communication beam tracking. In this work, we extend this concept to scenarios involving multiple distributed RSUs and distributed MIMO (multiple-input multiple-output) systems. We derive the state evolution model, formulate the extended Kalman-filter equations, and implement predictive beamforming for distributed MIMO. Simulation results indicate that, when compared with a co-located massive MIMO antenna array, distributed antennas lead to more uniform and robust sensing performance, coverage, and data rates, while the vehicular user is in motion.

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