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A New Paradigm of 6G Networks: Proactive Channel Cognition and Reconfiguration

Wenyan Ma, Zixiang Ren, Weitong Zhai, Ge Yan, Lipeng Zhu, Zhenyu Xiao, Rui Zhang

arXiv:2608.29725v1eess.SP

TL;DR

6G requires wireless networks to move beyond reactive adaptation of difficult-to-control channels. This paper systematically surveys CKM-based channel cognition and MA- and IRS-enabled channel reconfiguration, then outlines closed-loop research directions. It frames cognition and reconfiguration as coupled components for making channels more understandable, predictable, and controllable.

  • Problem

    Conventional passive adaptation cannot directly improve unfavorable channel conditions, while existing studies largely focus on separate technology branches rather than their closed-loop integration.

  • Method

    The paper provides a systematic overview covering CKM definitions, construction, and applications; MA- and IRS-enabled reconfiguration; and future cognition-reconfiguration architectures.

  • Results

    The review establishes a unified cognition-reconfiguration framework and synthesizes how CKMs, MAs, and IRSs support proactive channel management.

  • Takeaways & Limitations

    Wireless channels can be treated as network resources that are learned, predicted, and actively reconfigured through coupled cognition and reconfiguration.

  • Takeaways & Limitations

    Accurate, real-time, and scalable CKM construction in dynamic environments remains an important topic for future study.

Abstract

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The sixth-generation (6G) wireless networks are expected to enable the deep integration of communication, sensing, computing, control, and intelligence in highly dynamic environments. This evolution drives a fundamental transition from conventional passive channel adaptation to proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured. This paper presents a comprehensive overview of this emerging paradigm. We first review channel cognition through the channel knowledge map (CKM) as a systematic framework for learning and exploiting channel characteristics across space, time, and frequency domain. The definitions, construction methods, and applications in wireless networks of CKMs are comprehensively reviewed. Building upon channel cognition, we then review channel reconfiguration technologies from two complementary perspectives: transceiver-side reconfiguration enabled by movable antennas (MAs) and environment-side reconfiguration enabled by intelligent reflecting surfaces (IRSs). For both MA- and IRS-enabled wireless systems, we review their architectures, performance advantages, and key design challenges. Finally, we discuss several promising research directions to inspire further innovations in this burgeoning field.

1 Introduction

6G motivates a shift from passive adaptation of fixed channels toward proactive channel cognition and reconfiguration. The paper unifies these directions and reviews CKMs, movable antennas, IRSs, and future closed-loop architectures.

  • Motivation: Fixed infrastructure cannot promptly accommodate dynamic environments, mobile user clusters, or spatially nonuniform traffic, while 6G increases channel-acquisition and optimization overhead.These conditions can produce coverage blockages, overloaded regions, and underutilized resources.
  • Paradigm shift: Passive adaptation can exploit signal-processing DoFs but cannot directly create favorable channel conditions when blockage, deep fading, or insufficient scattering limits channel gain or rank.The paper therefore treats the channel as a network resource that can be understood, predicted, and proactively improved.
  • Paradigm shift: Channel cognition provides knowledge and predictions, while channel reconfiguration uses them to adjust transceivers, propagation environments, or deployment.Together, they form coupled components of proactive channel management.
  • Contributions: The paper establishes a unified cognition-reconfiguration framework and reviews CKM fundamentals, construction methods, and representative applications.It positions this integration as a response to existing work being concentrated in separate technology categories.
  • Contributions: Transceiver-side reconfiguration reviews MAs and antenna position, orientation, and structural DoFs, including modeling, channel acquisition, movement optimization, and scheduling challenges.Environment-side reconfiguration reviews IRSs for coverage enhancement and interference suppression, including deployment, acquisition, and joint optimization challenges.
  • Future directions: Future directions include full-scenario cognition, collaborative reconfiguration, and embodied AI architectures based on a cognition-reconfiguration-feedback loop.The paper also specifies separate sections for CKMs, MAs, IRSs, future directions, and conclusions.

2 Channel Cognition through CKM

Channel cognition uses CKMs to represent and query location-dependent channel knowledge. The section defines CKMs, classifies their spatial and knowledge outputs, and connects them to prediction and proactive reconfiguration.

  • CKM role: CKM-enabled cognition provides prior channel knowledge for unvisited locations and can reduce measurement overhead in dense, mobile, wideband, and large-array systems.It acquires, organizes, infers, and exploits channel knowledge across space, time, and frequency.
  • CKM fundamentals: A CKM maps transmitter and/or receiver location vectors to application-dependent channel knowledge through environmental information, location-tagged measurements, or both.Its outputs may include channel gains, LoS states, beam indices, path parameters, or complex-valued CSI.
  • CKM classification: B2X CKMs query user locations with a fixed BS, whereas X2X CKMs characterize channels between arbitrary transmitter-receiver pairs whose endpoints may both move.Typical input dimensions are D = 2 for terrestrial users and D = 3 for three-dimensional users such as UAVs.
  • CKM classification: CKMs range from coarse location-specific knowledge such as LoS conditions, gains, path directions, and beam indices to fine-grained multipath parameters and complete CSI.Representative categories include channel gain and channel path maps.

2.2 CKM Construction Methods

CKM construction methods derive location-dependent channel knowledge from physical environment information, channel measurements, or hybrid combinations of both. The field is moving toward integrated approaches, but accurate, real-time, scalable construction in dynamic environments remains unresolved.

  • Method taxonomy: CKM construction methods are classified as environment-information-driven, data-driven, or hybrid environment- and data-driven approaches.The classification is based on each method’s primary information sources.
  • Environment-information-driven methods: Environment-information-driven methods use physical propagation knowledge, such as maps, layouts, material properties, deployment parameters, and ray tracing.Accurate environment models can yield physically interpretable CKMs with fewer field measurements, but large-scale computation and dynamic updates remain difficult.
  • Data-driven methods: Data-driven methods infer unmeasured channel knowledge from sparse location-tagged measurements using Kriging, kernel regression, or matrix completion.Kriging and kernel regression use spatial-correlation or kernel assumptions, whereas matrix completion typically imposes a low-rank structure.
  • Hybrid methods: Hybrid methods jointly use environmental layouts, transmitter locations, channel samples, and propagation priors to reconstruct CKMs.Examples include image-based RadioUNet and RadioTransformer, scatterer-centric models, virtual scatterer models, and physics-aware NeRF2.
  • Open challenge: CKM construction has progressed from single-source approaches toward frameworks integrating environmental knowledge, measured data, and propagation priors.The remaining challenge is achieving accurate, real-time, and scalable construction in dynamic environments.

2.3 CKM-Enabled Channel Cognition Applications

A constructed CKM acts as a reusable spatial knowledge base that converts location-tagged observations into channel and environmental information for planning, acquisition, prediction, localization, and proactive reconfiguration.

  • CKM applications: CKMs support coverage evaluation, deployment planning, channel acquisition, beam selection, trajectory prediction, and environmental-change detection.They provide channel and environmental priors for subsequent proactive channel reconfiguration.
  • Coverage and deployment: CKM-based coverage evaluation identifies coverage holes and assesses how BSs, APs, relays, and IRSs affect propagation before deployment or transmission.The evaluation uses location-specific channel gains, LoS conditions, and interference information.
  • Channel acquisition: CKM priors reduce unknown channel parameters and pilot overhead when online measurements remain necessary.Channel matrix maps and channel path maps provide coarse estimates directly from transceiver locations in large-scale antenna systems.
  • Beam training: Channel angle maps and beam index maps recommend dominant directions or a small candidate-beam set, reducing beam-training overhead while maintaining reliable alignment.The approach confines beam training to beams selected from transceiver locations.
  • Prediction and proactive control: Combining CKMs with predicted user trajectories enables advance decisions about coverage holes, blockage, interference, scheduling, handover, power control, and node activation.A UAV can anticipate future channel conditions rather than reacting only after channel quality degrades.
  • Localization and sensing: Inverse CKM mappings support user, obstacle, and scatterer localization, while deviations from stored maps can reveal dynamic environmental changes.LoS maps aid anchor selection in NLoS environments, and channel features can act as localization fingerprints.
  • Feedback and reconfiguration: Post-reconfiguration measurements update the CKM, forming a cognition-reconfiguration-feedback loop involving antenna and IRS actions.The loop connects CKM-based cognition with MA- and IRS-based channel reconfiguration.

2.4 Related Channel Cognition Technologies

Channel cognition extends beyond CKMs through complementary technologies that provide spectrum, spatial, mechanism-level, temporal, system-level, and learning-based knowledge. CKMs organize location-dependent channel features while drawing on these technologies for construction and exploitation.

  • Spectrum awareness: Cognitive radio provides spectrum-domain knowledge by sensing spectrum usage, detecting interference, and adapting access strategies.Its knowledge concerns spectrum occupancy, interference conditions, and dynamic access opportunities.
  • Spatial RF characterization: Radio maps store spatially indexed RF observations for coverage analysis, localization, and resource management.Their values often reflect aggregate RF service conditions rather than a specific transmitter-receiver channel.
  • Propagation modeling: Geometry-based and statistical channel models explain propagation mechanisms and regularities through quantities such as path loss, shadowing, angular spread, and delay spread.3GPP TR 38.901 and analytical models provide tractable tools for analysis and simulation.
  • Temporal forecasting: Channel prediction forecasts future CSI or communication performance from historical observations, mobility, and environmental dynamics.This supports low-latency proactive control when future channel states must be anticipated.
  • System-level virtualization: Digital twins provide system-level cognition through virtual network representations used for monitoring, simulation, testing, and optimization.They can integrate traffic, protocols, topology, mobility, and propagation environments before physical deployment of policies.
  • AI-based channel learning: AI and foundation models learn mappings among environments, network configurations, and channel responses while supporting multimodal fusion and future-CSI prediction.Examples include ChannelGPT and LLM4CP.
  • CKM’s position: CKM occupies a complementary role by organizing location-dependent channel features into a queryable spatial representation.Other technologies can provide information and models for CKM construction.

3 Transceiver-Side Channel Reconfiguration through MA

Channel reconfiguration improves wireless systems by adjusting transceiver structures or programming propagation environments. This section focuses on movable antennas, which exploit continuous translational and rotational degrees of freedom to improve effective channel conditions.

  • Reconfiguration paradigm: Channel reconfiguration adjusts transceiver structures and/or programs signal propagation environments.Transceiver-side technologies include movable, reconfigurable, fluid, pinching, and rotatable antennas; environment-side technologies include IRS variants.
  • Movable antennas: Movable antennas improve effective channel conditions by exploiting continuous translational and rotational degrees of freedom.The section reviews MA hardware architectures, performance advantages, design issues, and related technologies.

3.1 Architecture

MA architectures provide position and orientation degrees of freedom, implemented through mechanical, fluidic, micro-electromechanical, electronic, and deployable structural methods. These approaches trade tuning range, response speed, power consumption, hardware weight, and deployment constraints.

  • Degrees of Freedom: MA spatial adaptability primarily uses position and orientation degrees of freedom.Position relocates the antenna phase center in one-, two-, or three-dimensional regions, while orientation provides pitch, yaw, and roll.
  • Degrees of Freedom: Position movement adjusts path phase responses and can proactively reconfigure line-of-sight conditions, shadowing, and path loss.
  • Degrees of Freedom: Orientation movement aligns directional main beams with target transceivers and facilitates polarization matching with incident waves.
  • Implementation Methods: Motor-based methods offer large tuning ranges and strong load-bearing capability but generally require high power and heavy hardware.
  • Implementation Methods: Liquid, pinching, inflatable, and foldable architectures reconfigure radiating structures within fluidic, waveguide, aerospace, or terrestrial deployment constraints.Inflatable structures target compactly stored aerospace and satellite arrays, while foldable structures support compact-to-deployed terrestrial base-station geometries.
  • Implementation Methods: MEMS and electronically reconfigurable methods provide fast, low-power or well-integrated tuning, but electronic approaches generally have more limited tuning ranges.MEMS response times are typically in the microsecond range; electronic mechanisms avoid mechanical latency and wear.

3.2 Performance Advantages

MA systems improve communication and sensing by repositioning or reorienting antennas to exploit favorable propagation, shape beams, adapt macro-geometry, and enlarge sensing apertures. These capabilities support flexible coverage, interference management, spatial multiplexing, and resolution improvements.

  • Small-scale Fading Utilization: MAs relocate to favorable spatial regions, improving equivalent channel gain and SNR while reducing interference through weaker-interference positions.Position optimization can also provide average power gains across wideband frequency-selective fading.
  • Flexible Beamforming: Joint antenna-geometry and weight optimization enables customized multibeams or wide-beam coverage beyond weight-only optimization.
  • Flexible Beamforming: MA beamforming can align high-gain main lobes, place nulls toward interferers, reduce inter-user channel correlation, and enhance spatial multiplexing.
  • Flexible Beamforming: 3-D antenna orientation aligns directional arrays with dominant AoA or AoD, maximizing effective directional gain.
  • Macro-level Adaptation: Slow-moving 6DMAs or XL-MAs can align macro-geometry with changing user distributions to improve coverage near localized hotspots.Examples include clustered UAV networks and dense terrestrial users.
  • Sensing: Antenna movement enlarges the effective virtual aperture, improving sensing resolution and reducing AoA-estimation CRB and MSE.Adaptive trajectories and positions can also reduce grating lobes and ambiguity error.
  • Integrated Sensing and Communication: MA-aided ISAC can distribute antennas for sparse, high-resolution sensing while adapting positions to balance communication throughput and sensing accuracy.

3.3 Design Issues

MA deployment requires accurate channel models and CSI across the movement region, together with movement optimization that accounts for timescale and hardware costs. Prototype results support feasibility, while actuator, energy, space, and complexity constraints remain central design issues.

  • Overview: MA design issues include channel modeling, channel acquisition, movement optimization and scheduling, prototype development, and experimental validation.
  • Channel Modeling: CKM-based environmental and location-specific knowledge supports channel models that characterize continuous channel variation caused by antenna movement.
  • Channel Modeling: Physical field-response models express channels as propagation-path superpositions parameterized by antenna position, orientation, path gain, AoA, and AoD.
  • Channel Modeling: Statistical spatial-correlation models provide robustness against moderate environmental mismatch when their underlying assumptions hold.
  • Channel Acquisition: Channel cognition reduces uncertainty and narrows channel-acquisition search spaces, enabling accurate CSI estimation with reduced measurements.
  • Channel Acquisition: Model-based acquisition reconstructs complete field-response information from fewer spatial samples by exploiting angular sparsity and compressed sensing.
  • Movement Optimization and Scheduling: Movement optimization selects antenna positions and orientations using acquired CSI to improve communication metrics while considering movement costs and timescale.Statistical CSI can support slower reconfiguration and reduce physical movement frequency while retaining spatial degrees of freedom.
  • Movement Optimization and Scheduling: Actuator speed, movement energy, accessible space, and implementation complexity constrain achievable gains and require coordinated scheduling.A mechanically actuated prototype achieved 0.05 mm movement resolution, while a 6λ trajectory at 3.5 GHz produced received-power variations exceeding 40 dB.

3.4 Other Relevant Technologies

Reconfigurable antennas add flexibility by dynamically changing operating frequency, polarization, and radiation pattern through electrical or physical structural changes. These capabilities target multiband communication, interference suppression, and capacity improvement.

  • Reconfigurable Antennas: Reconfigurable antennas dynamically adjust operating frequency, polarization, and radiation pattern by changing their electrical or physical structures.The resulting flexibility is relevant to multiband communication, interference suppression, and system capacity improvement.

4 Environment-Side Channel Reconfiguration through IRS

IRSs enable environment-side channel reconfiguration by controlling reflected waves, creating additional propagation paths, improving coverage and multiplexing, and supporting sensing. Their practical deployment requires coordinated hardware, placement, channel acquisition, and optimization designs.

  • IRS Architecture: IRSs use programmable metasurfaces and smart controllers to adjust element phase shifts, strengthening desired signals or suppressing interference.Passive elements such as PIN diodes or varactors are controlled through bias voltages, while the controller uses CSI or optimized configurations.
  • IRS Architecture: Passive IRS elements provide low-power channel control without the thermal noise introduced by active relays.Unlike active relays, IRSs avoid power-consuming RF chains and active amplification.
  • Performance Advantages: IRS deployment can bypass blockages by creating virtual LoS paths, while additional reflection paths can increase channel rank and spatial multiplexing.These benefits are particularly relevant to mmWave and THz links or environments with limited scattering.
  • Performance Advantages: Programmable reflection enables interference cancellation, hotspot signal enhancement, and controllable sensing paths for NLoS target perception.The reported functions include improving SINR, local SNR, cell-edge performance, throughput, and estimation of target location, velocity, and direction.
  • Design Issues: IRS deployment design jointly considers candidate sites, surface configuration, deployment cost, and coverage requirements.In dense blockages, distributed multi-IRS deployment can establish cascaded LoS paths and improve coverage.
  • Design Issues: Passive IRSs cannot directly estimate channels, motivating semi-passive sensing elements or received-power-based acquisition methods.Semi-passive architectures improve estimation accuracy but increase hardware complexity and power consumption; RSRP-based methods reduce training overhead and preserve protocol compatibility.
  • Design Issues: CSI-free blind beamforming optimizes reflection coefficients from received signal power and has been demonstrated with a 256-element IRS in a real-world 5G network.The reported deployment used four discrete phase-shift states for 2.6-GHz downlink transmission without BS-side coordination.
  • Other Relevant Technologies: New architectures address conventional IRS limitations through simultaneous transmission and reflection, beyond-diagonal coupling, flexible elements, and movable surfaces.These designs add full-space coverage, greater matrix flexibility, or spatial degrees of freedom through position and orientation control.

5 Future Perspectives

Future 6G research aims to unify full-spectrum, cross-domain channel cognition with collaborative reconfiguration and intelligent closed-loop control. These directions seek to coordinate transceiver and environment-side resources while updating knowledge from feedback.

  • 5.1 Full-Spectrum Channel Cognition in 3-D Space-Air-Ground-Sea Integrated Networks: Unified CKMs should fuse terrestrial, aerial, maritime, underwater, and satellite measurements across diverse frequency bands and 3-D domains.They must retain segment-specific propagation characteristics while supporting integrated network management.
  • 5.1 Full-Spectrum Channel Cognition in 3-D Space-Air-Ground-Sea Integrated Networks: Cross-band CKM construction can exploit frequency correlations and spatial sparsity while accounting for band-specific scattering, blockage, and material responses.Sensing, communication, and environmental information can assist construction in sparsely observed offshore and underwater regions.
  • 5.2 Collaborative Channel Reconfiguration via Diverse Technologies: Future networks will actively shape wireless propagation by jointly coordinating IRSs, MAs, and other transceiver- and environment-side reconfiguration technologies.This shift uses channel information from cognition to optimize heterogeneous spatial and electromagnetic degrees of freedom.
  • 5.2 Collaborative Channel Reconfiguration via Diverse Technologies: Unified low-overhead optimization should combine channel cognition, sensing information, and wireless environment knowledge to configure transceiver- and environment-side components.The intended benefits include improved channel capacity, coverage, and sensing accuracy with acceptable hardware complexity and implementation cost.
  • 5.3 Embodied AI Networks for Closed-Loop Channel Cognition and Reconfiguration: Closed-loop intelligent networks can use post-reconfiguration measurements and performance feedback to update knowledge, refine predictions, and optimize subsequent decisions.The proposed loop links channel cognition, reconfiguration, feedback evaluation, and self-learning.

6 Conclusions

The paper reviews a paradigm shift toward proactive channel cognition and reconfiguration in 6G. It covers CKMs, movable antennas, intelligent reflecting surfaces, and their architectures, advantages, challenges, and future directions.

  • 6 Conclusions: The paper reviews CKMs as frameworks for learning, representing, predicting, and utilizing channel knowledge across spatial, temporal, and frequency domains.It covers CKM definitions, construction methods, and representative applications.
  • 6 Conclusions: Movable antennas enable transceiver-side channel reconfiguration through adaptive antenna positioning and rotation.Their architectures, performance advantages, and design issues are reviewed.
  • 6 Conclusions: Intelligent reflecting surfaces enable environment-side channel reconfiguration by intelligently reconfiguring electromagnetic wave propagation.Their architectures, performance advantages, and design issues are reviewed.
  • 6 Conclusions: Channel cognition and reconfiguration remain in their early stages of development despite significant recent progress.The paper discusses promising research directions for further innovation.
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