Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
The intelligent information society, which is highly digitized, intelligence inspired, and globally data driven, will be deployed in the next decade. The next 6G wireless communication networks are the key to achieve this grand blueprint, which is expected to connect everything, provide full dimensional wireless coverage and integrate all functions to support full-vertical applications. Recent research reveals that intelligent reflecting surface(IRS)with wireless environment control capability is a promising technology for 6G networks. Specifically, IRS can intelligently control the wavefront, e. g. , the phase, amplitude, frequency, and even polarization by massive tunable elements, thus achieving fine-grained 3-D passive beamforming. In this paper, we first give a blueprint of the next 6G networks including the vision, typical scenarios, and key performance indicators(KPIs). Then, we provide an overview of IRS including the new signal model, hardware architecture, and competitive advantages in 6G networks. Besides, we discuss the potential application of IRS in the connectivity of 6G networks in detail, including intelligent and controllable wireless environment, ubiquitous connectivity, deep connectivity, and holographic connectivity. At last, we summarize the challenges of IRS application and deployment in 6G networks. As a timely review of IRS, our summary will be of interest to both researchers and practitioners engaging in IRS for 6G networks.
We provide an overview of the recent progresses on the system architecture design and performance prediction for microwave signal detection under weak signal intensity regime, up to quantumized level. The technique roadmap includes two perspectives, the opto-electro-mechanical (OEM) and superconducting devices. For the former one, we first overview the concept of OEM, and then introduce the signal detection based on capacitive-opto-electro-mechanical systems and piezoopto-electro-mechanical systems. For the latter one, we first overview the concept and architecture of Josephson junction, and then introduce the signal detection based on superconducting Hanbury Brown-Twiss (HBT) experiments and Λ energy-level splitting system. Besides, we review the microwave detection based on Rydberg atom system. We believe that this overview can provide a guidance for future transmission limit, signal processing, detection device fabrication and real experiments.
Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput, lower latency, and wider coverage. However, spectrum scarcity places obstacles in the sustainable development. To accommodate the expanding network within a limited spectrum, spectrum sharing is deemed as a promising candidate. Particularly, cognitive radio (CR) has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically. However, the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability. In this paper, we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence (AI). Specifically, SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and interoperability. AI is further used to make predictive environment perception and to configure the network for optimal resource allocation. Briefly, the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.
Reliable identity management and authentication are significant for network security. In recent years, as traditional centralized identity management systems suffer from security and scalability problems, decentralized identity management has received considerable attention in academia and industry. However, with the increasing sharing interaction among each domain, management and authentication of decentralized identity has raised higher requirements for cross-domain trust and faced implementation challenges galore. To solve these problems, we propose BIdM, a decentralized crossdomain identity management system based on blockchain. We design a decentralized identifier (DID) for naming identities based on the consortium blockchain technique. Since the identity subject fully controls the life cycle and ownership of the proposed DID, it can be signed and issued without a central authentication node’s intervention. Simultaneously, every node in the system can participate in identity authentication and trust establishment, thereby solving the centralized mechanism’s single point of failure problem. To further improve authentication efficiency and protect users’privacy, BIdM introduces a one-way accumulator as an identity data structure, which guarantees the validity of entity identity. We theoretically analyze the feasibility and performance of BIdM and conduct evaluations on a prototype implementation. The experimental results demonstrate that BIdM achieves excellent optimization on cross-domain authentication compared with existing identity management systems.
In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users’communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network are basically consistent with the ground-truth of the betting site community which has been announced to the public. Meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
Automatic modulation classification (AMC) aims to identify the modulation format of the received signals corrupted by the noise, which plays a major role in radio monitoring. In this paper, we propose a novel cascaded convolutional neural network (CasCNN)-based hierarchical digital modulation classification scheme, where M-ary phase shift keying (PSK) and M-ary quadrature amplitude modulation (QAM) modulation formats are considered to be classified. In CasCNN, two-block convolutional neural networks are cascaded. The first block network is utilized to classify the different classes of modulation formats, namely PSK and QAM. The second block is designed to identify the indexes of the modulations in the same PSK or QAM class. Moreover, it is noted that the gird constellation diagram extracted from the received signal is utilized as the inputs to the CasCNN. Extensive simulations demonstrate that CasCNN yields performance gain and performs stronger robustness to frequency offset compared with other recent methods. Specifically, CasCNN achieves 90% classification accuracy at 4 dB signal-to-noise ratio when the symbol length is set as 256.
To complement terrestrial connections, the space-air-ground integrated network (SAGIN) has been proposed to provide wide-area connections with improved quality of experience(QoE). Spectrum management is an important issue in SAGIN due to the explosive proliferation of wireless devices and services. While the progress on enabling dynamic spectrum access shows promise in advancing increased spectrum sharing, the issue of reliable spectrum sensing under low signal-to-noise ratio (SNR)remains one of the key challenges faced by the spectrum management. As artificial intelligence can provide wireless networks intelligence through learning and data mining, deep learning-based spectrum sensing is proposed in order to improve the spectrum sensing performance, where a deep neural network-based detection framework is built to extract features in a data-driven way based on the covariance matrix of the received signal. To eliminate the impact of noise uncertainty, a blind threshold setting scheme is proposed without using the system prior information. Numerical analyses on simulated and real-world signals show that the detection performance of the proposed scheme is improved under a low SNR regime.
As a representative architecture of contentcentric paradigms for the future Internet, named data networking(NDN)enables consumers to retrieve content duplicates from either the original server or intermediate routers. Each node of NDN is equipped with cache that buffers but not validates the data, making it vulnerable to various attacks. Cache pollution, one of the specific attacks in NDN, fraudulently alters the cached contents by excessively requesting worthless information, squeezing the space of real popular contents and thus degrading the experience of normal users. In order to address the issue, this paper proposes a defense scheme based on deep reinforcement learning (DRL) against cache pollution attack, in which whether a data packet is to be cached is decided by a trained intelligent agent, that is adaptive to dynamic network states and following long term rewards, the accumulative data-requesting delays. Finally, the DRL-based scheme is evaluated and compared to two other existing schemes. Experimental results show that the proposed defense mechanism outperforms the others significantly, and is proved to be effective against cache pollution attacks.
