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

Zhe Wang, Jiayi Zhang, Hongyang Du, Dusit Niyato, Shuguang Cui, Bo Ai, Mérouane Debbah, Khaled B. Letaief, H. Vincent Poor

arXiv:2307.07340v2cs.ITeess.SP

TL;DR

XL-MIMO promises large spatial degrees of freedom for 6G but brings new near-field, electromagnetic, hardware, and computational challenges. This survey synthesizes four hardware architectures, channel models, electromagnetic boundaries, signal-processing methods, applications, and future directions. It highlights low-complexity and deep-learning-empowered processing as routes toward practical implementation.

  • Problem

    XL-MIMO’s extremely large antenna arrays introduce near-field electromagnetic characteristics and high signal-processing complexity that conventional mMIMO methods do not fully address.

  • Method

    The paper comprehensively surveys four XL-MIMO architectures, their relationships, channel models, electromagnetic properties, signal-processing schemes, applications, and future research directions.

  • Results

    The survey presents XL-MIMO hardware designs, channel-modeling fundamentals, electromagnetic distance boundaries, and signal-processing approaches, emphasizing low-complexity and deep-learning-empowered schemes.

  • Takeaways & Limitations

    XL-MIMO research can draw on the survey’s comparative architecture analysis, channel-modeling guidelines, signal-processing review, application coverage, and proposed future directions.

Abstract

from arXiv · show

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

I. INTRODUCTION

XL-MIMO extends mMIMO with far more antennas and larger apertures, creating opportunities for higher spectral efficiency, spatial resolution, and degrees of freedom while introducing near-field, electromagnetic, hardware, and complexity challenges. The survey organizes these designs, models, signal-processing schemes, applications, and research directions.

  • XL-MIMO uses much larger antenna arrays and apertures than mMIMO to support higher spectral efficiency, spatial resolution, and spatial degrees of freedom.
  • Its key challenges arise from the extremely large antenna count and near-field characteristics, which increase processing complexity and require electromagnetic effects beyond conventional mMIMO.
  • XL-MIMO encompasses ULA, UPA with patch or point antennas, and CAP-based designs, whose characteristics and relationships guide implementation choices.
  • The survey reviews XL-MIMO channel modeling, including LoS, NLoS, and hybrid propagation, together with electromagnetic characteristics, distance boundaries, and EM regions.
  • It surveys channel estimation, beamforming, and machine-learning-enabled processing, emphasizing low-complexity methods to support practical XL-MIMO implementation.
  • The paper also discusses applications including physical-layer security, integrated sensing and communications, and the Internet of Things, alongside future directions such as AI-aided resource allocation and green communication.

C. Organization of the Survey

The survey proceeds from XL-MIMO hardware designs to channel modeling, then to electromagnetic characteristics, distance boundaries, and propagation models. It next reviews low-complexity signal processing, including channel estimation, beamforming, and deep-learning-enabled design.

  • Hardware designs: Section II introduces ULA-based, UPA-based, and CAP-based XL-MIMO hardware designs.
  • Channel modeling: Section III reviews electromagnetic characteristics, distance boundaries, EM regions, and LoS, NLoS, and hybrid channel models.
  • Signal processing: Section IV focuses on low-complexity signal processing for XL-MIMO, covering channel estimation, beamforming, and deep-learning-empowered processing.

II. OVERVIEW OF XL-MIMO HARDWARE DESIGNS

The survey introduces four XL-MIMO hardware designs and compares their architectures, antenna choices, implementation features, and relationships. Discrete ULA and UPA designs are reviewed alongside continuous-aperture implementations, with practical complexity and design trade-offs highlighted.

  • Hardware designs: Four general designs are introduced: ULA-based XL-MIMO, UPA-based XL-MIMO with patch or point antennas, and CAP-based XL-MIMO.The ULA and both UPA variants use discrete apertures, whereas CAP-based XL-MIMO uses a continuous array aperture.
  • Comparative overview: The survey compares the four designs through hardware diagrams and characteristic tables, emphasizing their implementation features and interrelationships.The ULA, UPA, and CAP architectures are treated as related XL-MIMO realization schemes.
  • ULA-based XL-MIMO: ULA-based XL-MIMO extends conventional array sizes to 512, 1024, 2048, or thousands of antennas, increasing hardware and processing complexity.Most studies assume half-wavelength spacing; sub-arrays can reduce processing complexity and support distributed processing and spatial non-stationarity.
  • UPA-based XL-MIMO: UPA-based XL-MIMO uses rectangular or square planes with M = MV MH antennas, vertical and horizontal spacings ΔV and ΔH, and side lengths LV and LH.Its elements may be practical finite-size patches or analytically convenient sizeless point antennas.
  • UPA-based XL-MIMO: For fixed-size UPA-based XL-MIMO, reducing antenna spacing may not improve performance while significantly increasing design complexity.Antenna spacing therefore remains an important vertical and horizontal design parameter.

B. Uniform Planar Array (UPA)–Based XL-MIMO

CAP-based XL-MIMO embeds extremely dense antennas in compact regions to approximate a continuous aperture. Existing studies consider one-dimensional line segments and two-dimensional planes across diverse sizes, architectures, and carrier frequencies.

  • CAP architecture: CAP-based XL-MIMO uses meta-materials to embed extremely dense antennas in compact spaces, approximating a continuous aperture.An ideal CAP comprises infinitely many infinitesimal antennas forming a spatially continuous electromagnetic volume; related names include holographic MIMO and LISs.
  • Aperture architecture: CAP apertures are studied as one-dimensional line segments or two-dimensional planes, commonly rectangular or square but also allowing arbitrary shapes.The reviewed literature includes both 1D continuous lines and 2D continuous planes.
  • Aperture size: CAP aperture size is determined by line length for 1D designs and area or side length for 2D designs, rather than antenna count and spacing.Examples include 5 m line segments and circular planes with a 10 m radius.
  • System architecture: CAP systems may place line segments or planes at both transmitter and receiver, using centralized or distributed architectures.Distributed CAP architectures can employ multiple randomly deployed CAP planes and therefore multiple CAP-equipped base stations.
  • Carrier frequencies: CAP-based XL-MIMO studies span carrier frequencies from 2 GHz to 300 GHz, including Sub-6GHz, mmWave, and THz operation.Reported examples include 2.4 GHz, 2 GHz, 28 GHz, 30 GHz, and 300 GHz.

D. Design Interconversion and Analysis Comparison

The four XL-MIMO hardware designs are mathematically interconvertible, enabling systematic comparisons based on aperture, antenna configuration, and analysis framework. Existing EDoF results show discrete ULA performance converging to 1D CAP performance, while 2D comparisons remain unresolved.

  • Design Interconversion: UPA-based XL-MIMO with patch antennas is a general design, while ULA, point-antenna UPA, and CAP designs are special cases.Point antennas arise as patch size approaches zero; ULA uses one row or column, and 1D CAP uses infinitesimal spacing.
  • Analysis Comparison: Existing comparison frameworks derive ULA EDoF from a channel matrix and 1D CAP EDoF through a source auto-correlation kernel and asymptotic analysis.These methods provide guidelines for comparing different XL-MIMO hardware designs.
  • Analysis Comparison: EDoF for discrete ULA converged to 1D CAP EDoF as antenna count increased under equivalent array-aperture conditions.The comparison analytically computed EDoF and then derived capacity for both designs.
  • Open Issues: Performance behavior and comparison-metric effectiveness require further study, especially for UPA-based versus 2D CAP-based XL-MIMO.The 2D CAP case requires a new source auto-correlation kernel, extending the 1D framework.
  • Analysis Comparison: Performance comparisons should equalize array aperture, antenna number, or other geometric settings to preserve fairness.ULA and 1D CAP comparisons use the same array length, while discrete-array comparisons can use the same antenna count or aperture.
  • Comparison Criteria: The survey organizes antenna number, spacing, aperture size, and analysis framework as design-comparison dimensions for future analysis and optimization.XL-MIMO may use thousands or tens of thousands of antennas, with aperture determined by array geometry and spacing.

III. CHANNEL MODELING

XL-MIMO’s very large apertures create near-field conditions in which spherical waves, spatial non-stationarity, polarization, and mutual coupling must be modeled. Distance boundaries, especially Rayleigh distance, organize these electromagnetic regimes and guide channel analysis.

  • Electromagnetic Characteristics: XL-MIMO’s increased antenna count and aperture make receivers likely to lie in the near field, where plane-wave channel models become less applicable.Vectorial spherical-wave characteristics should instead be considered for XL-MIMO channel modeling.
  • Spatial Non-Stationarity: XL-MIMO arrays exhibit spatial non-stationarity because different array regions observe propagation paths with different powers or different visible paths.A terminal’s visible region is called its visibility region, and terminal power is concentrated mainly within that region.
  • Mutual Coupling: Closely packed antennas make mutual coupling significant; the resulting channel deterioration can degrade SINR and processing-algorithm convergence.The voltage at one element depends on both its incident field and voltages on other elements.
  • Electromagnetic Characteristics: Four representative XL-MIMO electromagnetic characteristics are spherical waves, spatial non-stationarity, EM polarization, and mutual coupling.These effects are often omitted in conventional mMIMO but are vital for accurate XL-MIMO modeling and analysis.
  • Distance Boundaries: The Rayleigh distance divides near- and far-field regions according to phase discrepancy caused by wave curvature.Near-field waves are spherical, whereas far-field waves can use a first-order phase approximation and planar-wave characteristics.
  • Distance Boundaries: 96% of maximum antenna array gain was achieved at the Björnson distance for N = 10^4 and A = (λ/4)^2, while Rayleigh distance was about 35 times larger.The reported normalized antenna array gain was almost 1 when propagation distance reached the Rayleigh distance.
  • Distance Boundaries: Multiple distance boundaries offer distinct perspectives for choosing electromagnetic models and analyzing XL-MIMO systems.The survey presents these boundaries as tools for design and analysis rather than a single universal criterion.

C. LoS Propagation Channel Modeling

XL-MIMO commonly involves near-field, predominantly line-of-sight propagation because its extremely large aperture shortens transmission range. The survey reviews Green’s-function and complex-valued channel-response approaches for modeling these links.

  • Motivation: XL-MIMO’s large aperture shortens transmission range, making line-of-sight links common and predominant in electromagnetic transmission.Near-field receiver locations further motivate dedicated LoS channel models.
  • Modeling Schemes: The two major LoS modeling schemes are Green’s-function-based channel modeling and complex-valued channel-response representation.Both approaches provide channel models for XL-MIMO analysis.
  • Green’s-Function Modeling: Green’s-function modeling numerically solves Maxwell’s equations to describe the electric field between arbitrary transmitting and receiving points.The associated formulation relates current distribution density at transmit points to electric fields at receive points.
  • Modeling Schemes: Table IX organizes Green’s-function-based LoS channel modeling for single-base-station multi-user and single-user scenarios.SB-MU and SB-SU denote these two deployment settings.

1) Green’s Function Based Channel Modeling:

Green’s-function and complex-valued channel-response models provide complementary ways to represent XL-MIMO propagation, especially its near-field electromagnetic behavior. Modeling choices trade physical fidelity against analytical tractability.

  • Green’s-function modeling: The dyadic Green’s function links transmit current density to received electric fields and reduces to a far-field approximation when distance greatly exceeds wavelength.It can be interpreted as the system impulse response.
  • Green’s-function modeling: Dyadic Green’s-function channels capture near-field evanescent waves and provide about one-fold EDoF improvement over scalar Green’s-function channels.Scalar models remain easier to analyze, so the choice depends on the research objective.
  • Complex-valued channel-response modeling: Complex-valued channel-response modeling separates propagation into amplitude and phase components whose behavior changes across electromagnetic regions.Amplitude models can vary in precision, while phase modeling uses propagation characteristics specific to the region.
  • Complex-valued channel-response modeling: In the NUSW region, both amplitude and phase vary across the aperture, requiring exact propagation distances between transmitting and receiving points.This region is defined by d < d_UPD.
  • Complex-valued channel-response modeling: In the USW region, amplitude is treated as uniform across the aperture while phase retains exact spherical-wave variation; beyond the Rayleigh distance, planar-wave modeling neglects both variations.The regions are d_UPD < d < d_ra for USW and d > d_ra for UPW.
  • Amplitude modeling: Generalized gain modeling captures distance variation, effective element areas, and polarization-mismatch losses across the array aperture.Free-space-pathloss and reference-power models provide simpler amplitude representations based on propagation distance or a reference gain.

D. NLoS Propagation Channel Modeling

XL-MIMO NLoS channel modeling must represent scattering, near-field behavior, and spatial non-stationarity. Fourier plane-wave methods offer a general electromagnetic formulation that can be discretized for tractable analysis.

  • NLoS modeling requirements: NLoS channel models must account for scattered propagation environments, near-field characteristics, and spatial non-stationarity across the array.These requirements distinguish XL-MIMO NLoS modeling from simpler propagation representations.
  • Fourier plane-wave representation: Fourier plane-wave representation models arbitrary scattering by decomposing spherical waves into plane waves and representing source, received, and angular responses.The angular response captures scattering propagation and random channel characteristics through a 4D power spectral density.
  • Fourier plane-wave representation: Discrete Fourier plane-wave series expansion samples the finite wavenumber-support region to produce a tractable and easily analyzed channel model.The discretized plane waves are defined within lattice ellipses.
  • Extensions and limitations: Fourier plane-wave models have been extended from CAP transmitter–receiver pairs to UPA-based systems, multiple BSs and UEs, and more practical antenna effects.These extensions jointly consider factors including angular power spectra, antenna-pattern distortion, and antenna-efficiency decrease.
  • Extensions and limitations: Existing Fourier plane-wave studies focus on small-scale fading and neglect large-scale fading gain, leaving its integration as a future research direction.Full electromagnetic-characteristic capture also requires the number of antennas per plane to exceed the number of sampling lattice ellipses.

2) Array Response Vector Representation Based Modeling:

Array-response-vector modeling represents NLoS propagation as a superposition of path-dependent responses, with near-field vectors encoding both direction and distance. Hybrid propagation models combine LoS and NLoS components through complex-valued or Fourier plane-wave formulations.

  • Array response vector representation: Array-response-vector NLoS modeling expresses the channel as a superposition of response vectors or matrices associated with scattering paths.For ULA-based XL-MIMO, each matrix depends on path gain and transmitter and receiver response vectors.
  • Array response vector representation: Near-field array-response vectors depend on both AoA/AoD and distance to represent spherical waves, whereas far-field vectors depend only on AoA/AoD.This distinction enables near-field channel structure to be retained in the representation.
  • Model-selection tutorial: The channel-modeling tutorial first selects LoS or NLoS propagation, then chooses a method suited to the propagation type and aperture.Green’s-function models derive from Maxwell’s equations, while Fourier plane-wave models contain source, received, and angular responses.
  • Hybrid propagation-path modeling: Hybrid propagation-path modeling combines LoS and NLoS links using either complex-valued channel responses or Fourier plane-wave representations.The paper provides tutorials for generating the resulting hybrid channel from the two components.
  • Hybrid propagation-path modeling: The complex-valued hybrid model generates LoS responses and NLoS array-response components separately before combining them into the hybrid channel.The described procedure uses geometric characteristics, position coordinates, and electromagnetic parameters as inputs.
  • Hybrid propagation-path modeling: The Fourier plane-wave hybrid model similarly generates 2D Fourier LoS and 4D Fourier NLoS components before combining them.This formulation relies on the Fourier plane-wave representation for both propagation types.

2) Hybrid-Field Channel Modeling:

Hybrid-field modeling represents practical channels containing both near-field and far-field paths, while XL-MIMO channel estimation must capture near-field structure without excessive complexity. Polar-domain methods exploit angular and distance information to estimate such channels.

  • Hybrid-field channel modeling: Hybrid-field channels combine near-field and far-field path components because practical scatters can occupy both regions.An adjustable parameter controls the proportion of the two path types.
  • Channel estimation motivation: XL-MIMO channel estimation requires accurate CSI while accounting for spherical waves, spatial non-stationarity, electromagnetic polarization, and high computational complexity.The paper identifies high accuracy and acceptable complexity as joint design goals.
  • Polar-domain estimation: Angular-domain compressive-sensing methods developed for conventional mMIMO cannot be directly applied because XL-MIMO channels do not exhibit the same angular-domain sparsity.Near-field methods instead exploit sparsity in a polar domain.
  • Polar-domain estimation: Polar-domain representations encode both angular and distance information, enabling P-SOMP to estimate LoS and NLoS channels and P-SIGW to estimate near-field parameters.These methods are designed around near-field spherical-wave characteristics.
  • Polar-domain estimation: P-SOMP and P-SIGW outperformed angular-domain SOMP and SIGW algorithms that rely on angular-domain channel sparsity.The comparison supports using polar-domain structure for XL-MIMO near-field estimation.
  • Hybrid-field estimation: Hybrid-field OMP estimates far-field and near-field path components by exploiting hybrid-field structure and polar-domain sparsity.Simulation results showed better NMSE performance than the compared far-field approach.
  • Complexity considerations: Polar-domain transform dictionaries capture near-field sparsity but impose high storage requirements and computational complexity.A distance-parameterized angular-domain representation was proposed as an alternative direction.

2) Parameter Based Channel Estimation Schemes:

Parameter-based channel estimation schemes estimate angle and distance parameters to capture spherical-wave characteristics, while XL-MIMO motivates methods balancing estimation accuracy against sharply increased complexity.

  • Parameter-based schemes first estimate angle and distance, then use them to reconstruct the XL-MIMO channel.
  • Near-field channel estimation exploits pathloss-based models, tiled apertures, subarray structure, or scatterer visibility regions.
  • Wideband beam training controls near-field beam split to focus different frequencies at desired locations while estimating distance, angle, and channel response.
  • Grant-free access produces doubly sparse, user-specific channels, motivating bilinear inference for joint activity and channel estimation.
  • XL-MIMO channel estimation must trade accuracy against complexity because conventional MMSE estimation is rarely adopted at very large antenna dimensions.
  • Reduced-subspace LS exploits spatial-correlation eigenstructure to provide a lower-complexity alternative combining MMSE and LS ideas.
  • Bayesian turbo-OAMP performs low-complexity inference for structured sparsity in the antenna-delay domain and non-stationary channels.
  • Machine-learning estimators report high NMSE performance with low complexity, although further examination is needed before practical deployment.

B. Beamforming Schemes Design

XL-MIMO beamforming must move from far-field steering toward near-field focusing while controlling the computational burden of very large matrix operations. The surveyed methods include linear, iterative, message-passing, and learning-enhanced designs.

  • Beamforming Schemes Design: Near-field spherical-wave propagation changes XL-MIMO beamforming from far-field steering to beam focusing.
  • Beamforming Schemes Design: Linear beamforming remains attractive because increasing antennas can deliver near-optimal performance, but far-field beam-split methods perform poorly in the near field.
  • Beamforming Schemes Design: MMSE combining achieved optimal performance in the studied scenarios, whereas ZF, MR, and conventional matrix operations motivate lower-complexity alternatives.
  • Beamforming Schemes Design: Randomized Kaczmarz methods simplify linear processing, with SwoR-RK reducing computational complexity by about 51.3% versus traditional RZF.
  • Beamforming Schemes Design: A decentralized BP-VMP receiver decodes in parallel across local processing units and nearly matches a centralized benchmark.
  • Beamforming Schemes Design: AMP-GNN unfolds AMP with a graph neural network to cancel multi-user interference while retaining low-complexity processing.
  • Beamforming Schemes Design: Polynomial-expansion EP detectors achieved much lower complexity than original EP detectors while approaching their performance.

3) Optimization Design for Beamforming Schemes:

Optimization-based beamforming designs exploit near-field structure, configurable apertures, and hybrid architectures to optimize spectral efficiency, sum rate, capacity, or energy efficiency.

  • Optimization Design for Beamforming Schemes: Near-field resource allocation jointly optimizes beamforming precoding and power control using numerical, reinforcement-learning, or AI-generated optimization tools.
  • Optimization Design for Beamforming Schemes: Holographic and digital beamforming are jointly designed for UPA-based XL-MIMO satellite links to maximize sum rate.
  • Optimization Design for Beamforming Schemes: Distance-aware precoding jointly designs digital, analog, and selection components while formulating spectral-efficiency maximization.
  • Optimization Design for Beamforming Schemes: Alternating optimization jointly configures dynamic metasurface antennas and digital precoding for multi-user near-field beam focusing.
  • Optimization Design for Beamforming Schemes: Pattern-division multiplexing optimizes electric-current-density patterns on continuous apertures for sum-capacity maximization.
  • Optimization Design for Beamforming Schemes: Energy-efficiency maximization is identified as important for sustainable XL-MIMO deployment, with transmit precoding and tuning optimized using channel-state information.
  • Optimization Design for Beamforming Schemes: The surveyed optimization designs motivate further work across different scenarios, objectives, and methods for exploiting XL-MIMO potential.

C. Machine Learning Empowered Signal Processing

Machine-learning methods are surveyed as responses to XL-MIMO’s near-field modeling and high-dimensional processing challenges. Applications span channel estimation, distributed learning, resource allocation, and physical-layer security.

  • Machine Learning Empowered Signal Processing: Machine-learning methods are proposed because spherical-wave propagation makes traditional signal-processing methods less accurate for XL-MIMO near-field analysis.
  • Machine Learning Empowered Signal Processing: Distributed learning addresses XL-MIMO’s antenna-dependent data-processing burden by moving computation toward network edges.
  • Machine Learning Empowered Signal Processing: MARL treats base stations, users, and antennas as agents for resource allocation, with antenna selection achieving a 26% EE improvement.
  • Machine Learning Empowered Signal Processing: Fuzzy logic combined with MARL reduces computational complexity in centralized-training and centralized- or decentralized-execution architectures.
  • Machine Learning Empowered Signal Processing: A decoupled architecture prioritizing high-loss experiences achieved faster convergence and comparable performance with conventional methods.
  • Machine Learning Empowered Signal Processing: Spherical-wavefront propagation supports physical-layer security against eavesdroppers in similar angular orientations.
  • Machine Learning Empowered Signal Processing: Leakage subspace precoding increased spectral secrecy efficiency by more than 40 percent over traditional zero-forcing under varied eavesdropper collusion strategies.
  • Machine Learning Empowered Signal Processing: Security analyses report higher secrecy rates for spherical-wavefront than plane-wave models across colluding and non-colluding eavesdropper settings.

B. UAV Communications

XL-MIMO is reviewed across UAV, ISAC, IoT, and edge-computing scenarios, where its large apertures support coverage, spatial resolution, capacity, QoS, and energy-efficiency objectives. The section emphasizes models and optimization methods that account for near-field, non-stationary, and high-density conditions.

  • UAV Communications: UAV–XL-MIMO integration can improve communication performance, energy efficiency, and coverage, while UAV channel models address dynamic propagation environments.Reviewed models include geometric 3D non-stationary mmWave wideband channels with stationary and moving clusters.
  • UAV Communications: RIS-assisted UAV optimization jointly considers trajectory and RIS phase decisions to reduce energy consumption while maintaining users’ QoS, with extensions proposed for XL-MIMO.The framework can incorporate immense antenna arrays and XL-MIMO features to further reduce UAV and BS energy consumption.
  • Integrated Sensing and Communications (ISAC): XL-MIMO supports ISAC by combining communication with radar and near-field radio sensing, using models that capture spherical wavefronts and spatial non-stationarities.Its large spatial extent enhances communication performance and provides finer spatial resolution.
  • IoT Communications: High-density IoT deployments require QoS-aware scheduling and distance-aware precoding to manage massive connections across heterogeneous devices.A DAP framework exploits near-field effects by optimizing activated RF chains and precoding matrices according to increased DoFs.
  • Edge Computing: XL-MIMO combined with edge computing provides reliable access links, spatial diversity, and resource coordination for ultra-low-latency services.Related work formulates joint communication and computing resource allocation under delay constraints and uses distributed cooperative learning.

F. Massive Connectivity

The massive-connectivity discussion examines XL-MIMO access, visibility regions, AI-assisted resource allocation, sustainability, security, semantic communications, and experimental validation. It identifies non-stationary near-field channels, system complexity, energy use, and implementation as central challenges for future deployments.

  • Massive Connectivity: XL-MIMO enables massive connectivity through large apertures but introduces near-field access channels, high costs, and spatially varying visibility regions.Users may perceive different array parts, requiring connectivity mechanisms linked to physical location and visible regions.
  • Massive Connectivity: NOVR-XL improves the average sum rate from 160 to 520 bits/channel use when the average number of contending UEs per pilot is 2.The scheme combines NOMA with XL-MIMO and allows overlapping users to share the same XL subarray.
  • A. AI-Aided Resource Allocation Scheme: Future AI methods can adapt channel estimation, beamforming, scheduling, and resource allocation to changing environments and dense XL-MIMO user distributions.AI-based beamforming can exploit near-field effects to improve SINR and SE, while generative diffusion models are proposed for resource allocation.
  • Future Research Directions: Sustainable XL-MIMO requires energy-efficient processing, power allocation, antenna selection, and distributed computation strategies.Distributed processing can offload complex computations to cloud-based BBUs.
  • Future Research Directions: Semantic communications can reduce wireless data-transmission burden by extracting task-related information and using semantic importance in resource allocation and beamforming.Under constrained resources, beamforming can prioritize more critical semantic information.
  • Future Research Directions: Future work must address XL-MIMO security, privacy, and practical validation through secure processing, authentication, encryption, testbeds, and prototypes.The paper also identifies jamming and eavesdropping as threats requiring attention.
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