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A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications

Yiping Zuo, Jiajia Guo, Ning Gao, Yongxu Zhu, Shi Jin, Xiao Li

arXiv:2305.08604v2cs.ITeess.SP

TL;DR

6G wireless networks face difficult resource-management, scalability, privacy, and security demands. This paper surveys blockchain and AI for 6G, covering their fusion, secure services, IoT applications, and research challenges. It concludes that the combined area is promising but remains in its infancy and lacks unified 6G standards and solutions for key tensions.

  • Problem

    6G must address resource-constrained devices, complex wireless-resource management, heterogeneous architectures, explosive computing and storage needs, and privacy and security threats.

  • Method

    The paper provides a comprehensive survey of blockchain and AI for 6G wireless communications, including their fusion, services, IoT applications, and open challenges.

  • Results

    The survey reviews blockchain-AI applications in spectrum management, computation allocation, content caching, security and privacy, and IoT domains including healthcare, transportation, smart grids, and UAVs.

  • Takeaways & Limitations

    Blockchain and AI are presented as promising technologies for improving 6G services and applications, although their joint deployment remains an emerging research area.

  • Takeaways & Limitations

    6G has no unified definition or standard, and blockchain-AI integration remains in its infancy with unresolved scalability, data-management, and determinism-versus-randomness challenges.

Abstract

from arXiv · show

The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resource-constrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks.

I. INTRODUCTION

6G networks face escalating demands for intelligent resource management, security, privacy, and scalability. This survey examines how blockchain and AI can jointly address these challenges across wireless services and IoT applications.

  • I. INTRODUCTION: 6G must manage explosive data traffic, massive device connections, and complex resource-control demands while maintaining stringent performance and security requirements.The surveyed challenges include resource management, latency, privacy, sustainability, scalability, and network security.
  • I. INTRODUCTION: Blockchain offers distributed architecture, consensus, and smart contracts for trusted and secure 6G infrastructure.Its integration with 6G is presented as support for security, privacy, sustainability, and scalability requirements.
  • I. INTRODUCTION: Blockchain and AI complement each other because blockchain strengthens AI data trust, while AI improves blockchain intelligence, optimization, and evolution.The paper frames both technologies as data-centered and mutually reinforcing.
  • I. INTRODUCTION: The survey reviews blockchain and AI integration for secure 6G services, including spectrum management, computation allocation, content caching, and security and privacy.These topics are treated as major service areas in the survey’s research coverage.
  • I. INTRODUCTION: It also covers joint blockchain-AI applications in smart healthcare, transportation, smart grids, and UAVs, alongside open research challenges.The contribution is positioned as a comprehensive analysis and outlook of current progress.

C. Outline of the Survey

The survey proceeds from foundational concepts of blockchain and AI to their integration in wireless communications, 6G services, IoT applications, and unresolved challenges.

  • C. Outline of the Survey: Section II introduces blockchain and AI concepts, characteristics, categories, and representative wireless-communication applications.The outline separates the background into blockchain, AI, and their mutual integration.
  • C. Outline of the Survey: The survey then examines blockchain-assisted AI, AI-assisted blockchain, and the benefits of combining both technologies for wireless communications.This fusion is presented as the bridge from foundational background to 6G applications.
  • C. Outline of the Survey: Later sections address blockchain- and AI-assisted 6G secure services and IoT smart applications.The outline identifies wireless services and IoT applications as major application domains.
  • C. Outline of the Survey: The survey concludes by discussing 6G operating frequencies, visions, requirements, open issues, and future research challenges.The paper’s organization reserves a later section for perspectives and unresolved problems.
  • C. Outline of the Survey: Blockchain background includes distributed-ledger structure, consensus algorithms, and transaction execution, with Fig. 1 outlining the paper’s organization.The listed consensus mechanisms include PoW, PoS, DPoS, and PBFT; no consensus protocol is presented as universally optimal.

2) Characteristics of Blockchain:

Blockchain characteristics support trusted wireless communication without relying on a central intermediary. Its core properties are decentralization, non-tampering, traceability, and anonymity.

  • 2) Characteristics of Blockchain:: Blockchain decentralizes data interaction through a peer-to-peer architecture in which nodes jointly maintain the network without a traditional central server.Every node participates in data recording, storage, and updating.
  • 2) Characteristics of Blockchain:: Non-tampering links blocks through previous-block hashes, creating an immutable chronological ledger that makes malicious modification difficult.Transaction data are permanently stored after being packaged on the chain.
  • 2) Characteristics of Blockchain:: Traceability makes blockchain transactions publicly queryable and adds a time dimension through timestamped chained blocks.Transaction records remain connected through cryptographic links between adjacent blocks.
  • 2) Characteristics of Blockchain:: Anonymity protects participating nodes by allowing transactions through published addresses without requiring mutual identity disclosure.The paper associates this property with privacy protection among nodes that do not need to trust one another.

3) Categories of Blockchain:

Blockchains are categorized by application access and trust construction, while their properties support secure wireless communication services and IoT applications.

  • 3) Categories of Blockchain:: Blockchains are classified as public, private, or consortium systems according to their application scenarios.Public blockchains are open and decentralized, private blockchains restrict participation to authorized nodes, and consortium blockchains are jointly maintained by multiple organizations.
  • 3) Categories of Blockchain:: Permissionless blockchains are open to anyone, whereas permissioned blockchains restrict participation to designated members with differentiated rights.
  • 3) Categories of Blockchain:: Blockchain’s decentralization, traceability, distribution, and tamper resistance motivate its use across wireless communication systems.Blockchain records interactions through multi-party consensus, supports historical resource queries, and helps deter fabricated resource usage.
  • 3) Categories of Blockchain:: Blockchain-enabled 6G secure services include spectrum sharing, computing and storage management, and interference management.Reported approaches use verification and access control, smart contracts, credit-based transactions, distributed algorithms, and full-node deployment.
  • 3) Categories of Blockchain:: Blockchain also supports IoT applications in healthcare, transportation, smart grids, and UAV systems.Examples include patient-centric medical-record management, secure grid authentication and key agreement, and private-blockchain access control for smart-meter data.

B. An Overview of AI

AI is difficult to define rigorously, and its practical development has increasingly centered on machine learning methods that extract patterns from data.

  • B. An Overview of AI: AI lacks a unanimously accepted scientific definition and remains a debated academic concept.The paper presents AI as concerning knowledge representation, acquisition, and use, while noting that definitions remain unsettled.
  • B. An Overview of AI: Machine learning addresses tasks that are easy for humans but difficult to describe formally by learning patterns from raw data.ML reduces the need to specify all required knowledge manually by improving systems through experience and data.

2) Characteristics of AI:

AI is characterized by data-driven learning, uncertainty, environmental perception, and scalability, with machine-learning and deep-learning architectures providing common implementation approaches.

  • 2) Characteristics of AI:: AI increasingly uses large datasets and computation to learn knowledge rather than relying primarily on manually engineered rules.Data-driven ML treats the target function as an unknown black box and trains a model using substantial input data.
  • 2) Characteristics of AI:: AI systems face uncertainty because the field’s conceptual framework is not yet complete.The paper distinguishes AI from established mathematical and physical theories while noting connections to cognitive and behavioral psychology.
  • 2) Characteristics of AI:: AI uses sensing to perceive external environments and can adapt parameters or optimization models as environments, data, or tasks change.
  • 2) Characteristics of AI:: AI scalability reflects expanding hardware, software, model size, and deep-neural-network architectures for increasingly complex applications.
  • 2) Characteristics of AI:: Machine learning is divided into supervised, unsupervised, and reinforcement learning, while deep learning includes CNN, RNN, and GAN architectures.Supervised learning uses labeled data, unsupervised learning mines unlabeled data, and reinforcement learning learns through rewards from environmental interaction.

4) AI for Wireless Communications:

AI is applied to both physical-layer and upper-layer wireless-communication problems, using learned models for signal processing, resource management, and network control.

  • 4) AI for Wireless Communications:: Physical-layer applications include channel estimation, signal detection, CSI feedback and reconstruction, channel decoding, and end-to-end wireless communication.Deep-learning methods are used for channel estimation and for mapping received signals to original signal bits.
  • 4) AI for Wireless Communications:: Deep learning can improve decoding by combining neural networks with iterative algorithms, although some methods remain limited to particular code structures or lengths.A DL polar-code decoder reduced training times and network complexity under comparable performance, while other approaches were unsuitable for random or long codes.
  • 4) AI for Wireless Communications:: Upper-layer AI applications address user access, power allocation, spectrum control, handover, channel allocation, fault self-healing, and base-station switching.The surveyed approaches include supervised learning, deep reinforcement learning, and combinations of access control with resource allocation.
  • 4) AI for Wireless Communications:: Deep reinforcement learning learns decisions from dynamic environments and can support self-optimizing network management.

C. Integration of Blockchain and AI

The paper presents blockchain and AI as complementary technologies, examining blockchain-assisted AI through data management, decentralized intelligence, security and privacy, and efficiency and scalability. Blockchain supplies trusted, distributed data and coordination mechanisms for AI, while the surveyed works address collaborative learning, data protection, explainability, and participation incentives.

  • Blockchain for AI: Blockchain-assisted AI is organized around data management, decentralized intelligence, security and privacy, and efficiency and scalability.The section frames these as the main dimensions through which blockchain supports AI.
  • Data Management: Blockchain’s distributed database lets network participants collect, share, and store AI data, improving data access and supporting data monetization.The distributed structure addresses fragmented, uneven-quality, and poorly maintained AI datasets.
  • Decentralized Intelligence: Blockchain enables decentralized intelligence by supporting data or model sharing and aggregation across distributed IoT and edge devices.The surveyed work includes blockchain-based federated learning that delegates model storage and aggregation to network nodes without a central server.
  • Security and Privacy: Blockchain mechanisms such as anonymity, immutability, access control, and signature authentication protect AI transaction security and privacy.The section links data integrity to the validity of AI decisions and overall system performance.
  • Efficiency and Scalability: Smart contracts, interaction records, consensus, and incentive mechanisms can make AI systems more trustworthy, explainable, efficient, scalable, and participatory.The cited framework uses smart contracts to manage interactions and provide consensus for trusted oracles.

2) AI for Blockchain:

The paper surveys how AI improves blockchain scalability, energy consumption, security and privacy, and intelligent decision-making. Examples include learning-based parameter optimization, learning-oriented consensus, vulnerability detection, and prediction of blockchain behaviors.

  • AI for Blockchain: AI for blockchain targets four areas: scalability, energy consumption, security and privacy, and intelligent decision-making.The section introduces these areas as the main directions for AI-driven blockchain optimization.
  • Scalability: DRL and deep Q-learning dynamically optimize consensus choices, block parameters, and sharding to improve blockchain throughput while meeting security or decentralization constraints.SkyChain further adjusts resharding intervals, shard numbers, and block size to balance performance and security in dynamic environments.
  • Energy Consumption: Proof-of-Learning and Proof-of-Deep-Learning use machine-learning tasks or models within consensus to reduce the resource burden of conventional mining.These protocols rank or verify learned models through participating mining nodes to achieve distributed consensus.
  • Security and Privacy: Machine learning can detect blockchain threats by identifying suspicious smart contracts and filtering incompatible or malicious data before consensus.One surveyed approach uses XGBoost to detect potential Ponzi schemes in smart contracts, while another adds supervised detection before consensus.
  • Intelligent Decision: Machine-learning models predict blockchain behaviors including Bitcoin prices, Ethereum transaction execution times, and blockchain forks.These predictions support intelligent decisions about blockchain applications and operations.

3) Motivations of the Integration of Blockchain and AI for Wireless Communications:

The paper motivates blockchain–AI integration for 6G by combining blockchain’s secure, decentralized resource sharing with AI’s ability to address uncertain and complex problems. It surveys mutual benefits, summarizes prior solutions, and frames the integration across 6G services and applications.

  • Motivations of the Integration of Blockchain and AI for Wireless Communications: Blockchain provides secure and decentralized resource sharing, while AI addresses uncertain, time-varying, and complex 6G problems.The paper presents this complementarity as the central motivation for their joint deployment.
  • Blockchain for AI: Blockchain improves AI through broader data access, decentralized collaboration, and stronger security and privacy protections.The surveyed benefits include distributed data collection, model interaction across devices, and authentication or authorization mechanisms.
  • AI for Blockchain: AI improves blockchain scalability, energy efficiency, security and privacy, and intelligent decision-making through learning-based optimization and detection.The paper specifically highlights DRL, data sharding, process analysis, and vulnerability identification.
  • Motivations of the Integration of Blockchain and AI for Wireless Communications: Together, blockchain and AI can improve AI decision-making from trusted data while mitigating blockchain scalability, energy, and security problems.The paper describes the relationship as mutual promotion rather than one-way enhancement.
  • Scope of the Survey: The survey covers foundational concepts, integration directions, wireless communication benefits, 6G secure services, and IoT smart applications.Its stated purpose is to provide a foundation for exploring integration approaches and suitable use cases.

A. Secure Services

The secure-services section examines blockchain and AI jointly for managing scarce 6G wireless resources and protecting network operations. It covers spectrum management, computation allocation, content caching, and security and privacy, with examples emphasizing dynamic sharing, offloading, and learning-based optimization.

  • Secure Services: Blockchain and AI are jointly surveyed for spectrum management, computation allocation, content caching, and security and privacy in 6G.These services address wireless-resource demands and the security and privacy requirements associated with explosive user-data growth.
  • Spectrum Management: Dynamic spectrum management is motivated by inefficient fixed allocation under spectrum scarcity, with blockchain supporting secure auctions and AI learning user behavior or decisions.The section identifies blockchain decentralization and AI-based learning as complementary capabilities for spectrum management.
  • Spectrum Management: A blockchain- and AI-supported architecture uses a low-cost, low-complexity hierarchical blockchain for dynamic resource sharing and AI for data-management optimization.The surveyed work targets 5G-beyond and 6G wireless communications.
  • Computation Allocation: Blockchain–AI methods allocate and offload computing resources through permissioned consensus, blockchain-guided edge offloading, smart contracts, Merkle hash trees, and online learning.The examples address scalability, data availability, fair task offloading, security attacks, and offloading optimization.

3) Content Caching:

Blockchain and AI are surveyed as complementary tools for addressing content-caching latency, load balancing, privacy, and security challenges in 6G and IoT networks.

  • Content caching places popular content closer to users, reducing transmission delay, improving user experience, and balancing network load.
  • AI-Chain combines deep learning with blockchain by training neural-network components at lightweight edge nodes and sharing local learning results.
  • Blockchain- and edge-computing-enabled caching frameworks improve data caching and computing capabilities while supporting secure and efficient data handling.
  • A smart-contract-guided mechanism incentivizes D2D and MEC cache sharing through expected rewards and uses partially PBFT consensus to reduce consensus latency.
  • Blockchain and AI are also applied to security and privacy, including permissioned-blockchain IoT architectures and blockchain-authorized federated learning for data-integrity verification.
  • Blockchain, federated learning, and deep learning support healthcare applications such as secure diagnosis and collaborative COVID-19 detection from CT images across hospitals.

2) Smart Transportation:

The survey covers blockchain- and AI-enabled smart transportation and related IoT applications, emphasizing secure coordination, localization, video sharing, and intelligent energy management.

  • Smart Transportation: Blockchain and AI support smart transportation by combining secure transaction records, reinforcement learning, edge computing, vehicle localization, autonomous-driving analytics, and decentralized model exchange.
  • Smart Transportation: Blockchain and reinforcement learning enable collaborative service composition for 6G vehicles while securely recording interactions among adjacent nodes.
  • Smart Transportation: Blockchain-enhanced federated learning enables decentralized autonomous-vehicle communication in which vehicles exchange and validate local machine-learning updates.
  • Smart Grid: Blockchain, AI, and IoT are presented as technologies for improving smart-grid engineering quality, efficiency, stability, and intelligent power-system operation.
  • Smart Grid: A permissioned Hyperledger Fabric scheme uses machine learning to forecast peer-to-peer energy transactions and smart contracts to schedule microgrid distribution in real time.
  • UAVs: For UAV and satellite networks, blockchain and federated learning support knowledge sharing and collaborative learning while accounting for miner number, block transfer, mobility, and fork probability.

C. Analysis of Operating Frequencies, Visions, and Requirements from the 6G Perspective

The survey characterizes AI and blockchain in 6G as evolving technologies whose frequencies, visions, and requirements remain dependent on future standards and application scenarios.

  • Operating Frequencies: No established operating-frequency standards or fixed ranges currently exist for AI and blockchain in 6G, partly because 6G remains in research and standardization.
  • Operating Frequencies: Adaptive adjustment strategies can monitor network conditions, application demands, and resource availability to dynamically adjust operating frequencies and optimize network performance.
  • Operating Frequencies: Operating frequencies should be selected by application scenario, device characteristics, bandwidth, capacity, communication requirements, and resource constraints rather than fixed universally.
  • Visions: The 6G vision includes highly intelligent, connected, and adaptive networks with ultra-high rates, ultra-low latency, and stronger security mechanisms for AI and blockchain services.
  • Visions: The survey notes that 6G has no unified definition or standard, so its vision is based primarily on current research and academic discussions.
  • Requirements: The section synthesizes blockchain-and-AI services, IoT applications, operating frequencies, visions, and requirements as guidance for future 6G research and development.

IV. OPEN ISSUES, RESEARCH CHALLENGES, AND FUTURE WORK

The survey identifies scalability, energy, security, privacy, data-management, communication-overhead, and heterogeneous-service requirements as major barriers to integrating blockchain and AI into 6G.

  • Towards Blockchain: Blockchain scalability is identified as the biggest adoption hurdle as transaction volume grows, with storage overhead and unresolved limitations in proposed scaling techniques.
  • Towards Blockchain: Blockchain security and privacy remain constrained by attacks, increasingly fragile asymmetric encryption, transparent transaction records, and potential leakage of stored personal data.
  • Towards AI: AI deployment in 6G still faces unsolved challenges in implementing and managing complex intelligent communication systems.
  • Towards AI: AI training is hindered by inefficient data management, high information-exchange overhead, limited wireless access to massive training data, and difficulty aggregating heterogeneous sources.
  • Towards AI: Future AI protocols must accommodate differing application preferences, such as throughput and latency for video streaming versus security for payment software, while balancing network resources.
  • Security and Privacy: Coordinating participants without private-data leakage remains an urgent challenge because 6G AI functions rely on extensive user-data collection across many devices.

C. Towards Blockchain- and AI-assisted Wireless Communications

The survey frames blockchain–AI-assisted 6G communications as a promising but immature area requiring solutions to integration conflicts, scalability, energy, security, privacy, and data-management challenges.

  • Open issues: Large-scale deployment must address blockchain–AI conflicts, massive data processing, and collaborative optimization across multiple systems and indicators.The survey identifies scalability, energy efficiency, security, privacy, efficient data management, and cross-layer AI protocols as future priorities.
  • Integration challenges: The combination faces a fundamental mismatch between deterministic blockchain smart contracts and uncertain, random, and unpredictable AI outputs.Proposed directions include approximate smart-contract calculations and consensus protocols that produce sufficiently certain and accurate decisions.
  • Research status: Blockchain and AI integration remains at an infant stage, with few works deeply integrating both technologies into wireless communications and substantial implementation uncertainties.The potential outcomes of their fusion for 6G networks remain difficult to assess.
  • Survey scope: The survey comprehensively reviews blockchain and AI across 6G secure services and IoT smart applications, including spectrum management, computation allocation, content caching, and security and privacy.It also covers applications in smart healthcare, smart transportation, smart grids, and UAVs.
  • Conclusion: The survey concludes that blockchain and AI may uplift diverse 6G services and applications, while their effectiveness and feasibility still require large-scale wireless-network validation.This conclusion preserves the paper’s stated scope: a promising research direction whose practical outcomes remain unverified.
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