A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

IEEE Communications Surveys & Tutorials, Journal Year: 2024, Volume and Issue: 26(3), P. 1560 - 1605

Published: Jan. 1, 2024

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents comprehensive survey research on XL-MIMO systems. In particular, we introduce four hardware architectures: uniform linear array (ULA)-based XL-MIMO, planar (UPA)-based utilizing either patch antennas or point antennas, continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze discuss their characteristics interrelationships. Following this, several electromagnetic general distance boundaries in Given distinct properties near-field communications, present range channel models to demonstrate benefits further summarize signal processing schemes It is worth noting that low-complexity deep learning empowered reviewed highlighted promote practical implementation Furthermore, explore interplay between other emergent 6G technologies. Finally, outline compelling directions future communication

Language: Английский

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EMI Analysis and Nonlinearity Characterization of 5G FR1 Amplifiers for UAV–RIS Networks

Drones, Journal Year: 2025, Volume and Issue: 9(5), P. 328 - 328

Published: April 24, 2025

This work introduces a broad framework for the enhanced nonlinearity assessment of PAs 5G FR1 in UAV–RIS networks with integration. study employs LabVIEW-based double-frequency IMD3 testing methodology fast automatic test bed to quantify and its EMI effect on system. The analysis includes evaluation TxRx link using SNR model. In order enhance efficiency system, 16 QAM modulator is incorporated into system modulate demodulate signal. Additionally, Deep Q agent based DRL model that enhances controllability amplifiers under dynamic environment typical networks. offers enhancing amplifier design reduction supports sound engineering future UAV–RIS-based communication outcomes prove signal integrity characterization are significantly, providing important information beyond wireless systems.

Language: Английский

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The Role of Artificial Intelligence in 6G Networks, Architecture, Protocol, Transmission, and Applications

Advances in wireless technologies and telecommunication book series, Journal Year: 2025, Volume and Issue: unknown, P. 115 - 154

Published: Feb. 7, 2025

The 6G wireless communication technology is the successor to 5G cellular-based technique. It aimed at increased bandwidth and higher dimensions of data transmission with measurement less than one microsecond latency communication. Artificial Intelligence (AI) pivotal in establishing computational intelligence infrastructure, storing, transmission, decision-making. With frequencies 6G, sampling rates will surge beyond those achievable (Fifth Generation). Access points gain capacity serve multiple clients through orthogonal frequency-division access concurrently. expected spectrum using up terahertz (THz). guides Quality Service (QoS) This chapter covers a high-level overview role AI architecture, protocols, technologies, applications.

Language: Английский

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Performance Analysis of RIS‐Assisted Relay Systems

IET Communications, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Reconfigurable intelligent surface (RIS) is an emerging technology that can enhance service coverage and spectral efficiency in sixth‐generation wireless networks. In contrast, relays are a traditional technique for achieving extension. To explore potential benefits, we investigate three probable RIS‐relay cascaded scenarios over Nakagami‐ fading channels, where the full‐duplex relay utilized ensuring efficiency. evaluate performance across different scenarios, first derive their closed‐form expressions outage probability. Next, determine respective diversity order using asymptotic approximations high signal‐to‐noise‐ratio regime. Finally, present system throughput delay‐limited transmission. Simulation results validate our analysis demonstrate significant differences among various configurations, with multi‐RIS‐assisted scenario best performance. Furthermore, considered RIS‐decode‐and‐forward schemes outperform corresponding RIS‐amplify‐and‐forward low‐SNR

Language: Английский

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Sum Rate Maximization for NOMA-Aided Cell-Free System

Published: Jan. 1, 2025

Language: Английский

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Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Electronics, Journal Year: 2024, Volume and Issue: 13(13), P. 2489 - 2489

Published: June 25, 2024

Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives traffic demands. Consequently, new multi-antenna technologies massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form basis for design two potential future mMIMO network topologies: cell-free (CF) its successor, radio stripe (RS) system. Key aspects CF are addressed, practical sequential implementation based on RSs. exploration encompasses intricate details channel estimation (CE) phase, as well uplink (UL) downlink (DL) transmission reception phases. We then focus analyzing optimization underpin resource allocation (RA) algorithms, specifically those applied UL power access point selection (APS) schemes both RS networks. comprehensive understanding serves robust foundation addressing challenges inherent achieving conflicting B5G 6G major key performance indicators (KPIs), such enhancements spectral efficiency (SE), (PE), computational complexity or load balance (LB).

Language: Английский

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Joint Design of Pilot Power and Phase Shifts in RIS-Aided Cell-Free Massive MIMO URLLC Systems

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(22), P. 37399 - 37402

Published: Aug. 5, 2024

Language: Английский

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Integrating UAVs and RISs in Future Wireless Networks: A Review and Tutorial on IoTs and Vehicular Communications

Future Internet, Journal Year: 2024, Volume and Issue: 16(12), P. 433 - 433

Published: Nov. 21, 2024

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review the critical role future potential integrating unmanned aerial vehicles (UAVs) reconfigurable intelligent surfaces (RISs) to enhance Internet Vehicles (IoV) within beyond-fifth-generation (B5G) sixth-generation (6G) We explore combination quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks their applications urban environments. covers essential areas such as channel modeling position-aware beamforming dynamic networks, including UAVs IoVs. Moreover, we investigate UAV navigation control, emphasizing development obstacle-free trajectory designs dense while meeting kinodynamic motion constraints. emerging RIS-equipped (RISeUAVs) is highlighted, along supporting IoVs mobile edge computing. Optimization techniques, convex programming methods machine learning, are explored tackle complex challenges, an emphasis studying computational complexity feasibility for real-time operations. Additionally, this highlights integrated localization strategies autonomous ground vehicle tutorial-style overview insights into technical challenges innovative solutions next-generation cities, focus vehicular communications. Finally, research directions outlined.

Language: Английский

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Multi-Layer Multi-User MIMO with Cylindrical Arrays under 3GPP 3D Channel Model for B5G/6G Networks

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 145753 - 145767

Published: Jan. 1, 2024

Language: Английский

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Multi-Agent Reinforcement Learning-Based Joint Precoding and Phase Shift Optimization for RIS-Aided Cell-Free Massive MIMO Systems

IEEE Transactions on Vehicular Technology, Journal Year: 2024, Volume and Issue: 73(9), P. 14015 - 14020

Published: April 24, 2024

Cell-free (CF) massive multiple-input multipleoutput (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due penetration loss. To overcome this issue, we introduce the reconfigurable intelligent surface (RIS) into CF mMIMO system as low-cost and power-efficient solution. In paper, focus on optimizing joint precoding design of RISaided maximize sum SE. This involves matrix at APs reflection coefficients RIS. tackle problem, propose fully multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy logic (FL). Unlike conventional approaches rely alternating optimization techniques, our FL-based MARL only requires local channel state information, which reduces need backhaul capacity. Simulation results demonstrate proposed FL-MARL effectively computational complexity while similar methods.

Language: Английский

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