Recently, with the rapid development of information and communication technologies, the infrastructures, resources, end devices, and applications in communications and networking systems are becoming much more complex and heterogeneous. In addition, the large volume of data and massive end devices may bring serious security, privacy, services provisioning, and network management challenges. In order to achieve decentralized, secure, intelligent, and efficient network operation and management, the joint consideration of blockchain and machine learning (ML) may bring significant benefits and have attracted great interests from both academia and industry. On one hand, blockchain can significantly facilitate training data and ML model sharing, decentralized intelligence, security, privacy, and trusted decision-making of ML. On the other hand, ML will have significant impacts on the development of blockchain in communications and networking systems, including energy and resource efficiency, scalability, security, privacy, and intelligent smart contracts. However, some essential open issues and challenges that remain to be addressed before the widespread deployment of the integration of blockchain and ML, including resource management, data processing, scalable operation, and security issues. In this paper, we present a survey on the existing works for blockchain and ML technologies. We identify several important aspects of integrating blockchain and ML, including overview, benefits, and applications. Then we discuss some open issues, challenges, and broader perspectives that need to be addressed to jointly consider blockchain and ML for communications and networking systems.

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In this paper, we focus on intellicise wireless networks from semantic communication (SemCom). We present a comprehensive framework of intellicise wireless networks, including components such as brain for intellicise wireless networks (BIWN), intellicise signal processing, intellicise information transmission, intellicise network organization, and intellicise service bearing. We also investigate the enabling technologies and driving factors of intellicise wireless networks. Subsequently, we introduce the applications of intellicise wireless networks and envision new services. Finally, we outline the challenges of implementing intellicise wireless networks from a broad perspective and discuss possible solutions.

Fog computing, complementary to cloud computing, has recently emerged as a promising solution that extends the computing infrastructure from the cloud center to the network edge. By offloading computational applications to the network edge, fog computing could support delay-sensitive applications and reliable access to nearby users. However, with the growing number of demands from various applications, fog computing may be overwhelmed and it may result in significant performance degradation. Thus, to process applications efficiently, we propose an integrated fog and cloud computing (FCC) approach, where users can offload a series of applications to nearby fog nodes (FNs) or cloud center cooperatively. Nevertheless, due to the constrained computing, storage, and radio resources, how to perform resource allocation to achieve an optimal and stable performance is an important problem. To address this issue, we focus on multiple resource allocation problem in a general system, which consists of multi-user, multi-FN, and a cloud center. In addition, to reduce offloading transmission latency and release the constraint of limited radio resource, non-orthogonal multiple access, which enables multiple users to transmit data to the same FN for offloading tasks on the same spectrum resource, is introduced into the proposed FCC approach. To this end, we formulate joint offloading decision, user scheduling, and resource allocation problem as an optimization problem that aims at minimizing the total system cost of energy as well as the delay of users. Furthermore, we decouple the original problem and transform it into a convex problem. Finally, we develop alternating direction method of multipliers based algorithms to solve the optimization problem in a distributed and efficient way. Simulation results show that the proposed approach achieves better performance compared with existing schemes.

The blockchain-based video systems are designed to build a decentralized and flexible video ecosystem by enabling a direct interaction among users, video providers (VPs), and service providers. In blockchain-based video systems, the heterogeneous qualities and formats of the video streams usually require massive computational resources to transcode them into different versions and formats to meet distinct requirements of users. However, current blockchains cannot handle massive and heterogeneous video streaming due to limited computing capacity and long transaction times. To deal with this issue, in this paper, leveraging mobile edge computing (MEC) technology, we propose a blockchain-based MEC architecture, where small base stations (SBSs) allocate their computation as well as communication resources for providing video streaming in a distributed and secure manner. Moreover, to improve the operation efficiency, we use a series of smart contracts to enable a self-organized video transcoding and delivery service without a centralized controller. Then, users, SBSs, and VP could adjust their strategies according to the transactional information on blockchain. Moreover, we formulate the video transcoding and delivery problem as a three-stage Stackelberg game. We analyze the sub-game equilibrium in each stage and the interplays of the three-stage game. Last, we propose an iterative algorithm to solve the problem. Simulation results show that the proposed approach could obtain the good performance in terms of average time to finality (TTF), average access delay, and network cost.

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this article, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the tradeoff between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.

With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, we identify the core challenges of wireless networks and argue why GDMs are uniquely suited to address them. We then introduce the mathematical principles of GDMs and representative models. Furthermore, we organize our comprehensive review through a structured taxonomy that categorizes GDM-based schemes into the sensing, transmission, and Applications, complemented by a security plane. For each representative scheme, we analyze its innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.