The convergence of dynamic spectrum access (DSA) and blockchain has been regarded as the new paradigm of spectrum management. Because of the inherent properties of blockchain, such as decentralization and tamper-resistance, the deployment of blockchain in future networks has advantages to address problems exposed in traditional centralized spectrum management systems, such as high security risk and low allocation efficiency. In this article, we first compare blockchain-based spectrum management with the traditional centralized approach and then present a reference architecture for blockchain-based spectrum management. In particular, we propose an interference-based consensus mechanism, which can be employed to improve transaction efficiency and reduce system overhead while promoting spectrum sharing. The proposed consensus mechanism is based on the comparison of aggregated interference experienced by each node, such that the node that suffers the most aggregated interference will obtain the accounting right as a compensation. Furthermore, to avoid harmful interference caused by spectrum traders, an interference-based transaction validation mechanism is designed to validate the spectrum transactions stored in the blocks. Different from existing transaction validation mechanisms in which every transaction needs to be validated by all nodes, a “transaction validation area” is determined for each spectrum transaction, and only the nodes located in the validation area need to validate the transaction. The simulation results show that the system fairness and nodes’ signal-to-interference-and-noise power ratio (SINR) can be improved by adopting the proposed mechanisms while reducing the system overhead.

Recently, there is an increasing yet challenging demand on broadband mobile communications for high-speed trains. In this article, cognitive Doppler spread compensation algorithms are proposed for high-speed rail broadband mobile communications, which make use of the dedicated radio environment map (REM) for railway to compensate the time-varying Doppler spread. The dedicated REM for high-speed rail can be viewed as a spatial-temporal database consisting of the radio channel parameters along a given railway. The performance of the proposed Doppler spread compensation algorithms are evaluated with a typical OFDM-based broadband mobile system. Simulation results show that the link-level performance of high-speed rail broadband mobile communications can be improved significantly due to the REM-enabled radio channel condition awareness and the cognitive Doppler spread compensation algorithms. The REM-based cognitive radio approach presents a new paradigm for systems design of high-speed rail broadband mobile communications.

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference to incumbent users caused by spectrum trading between CBRS devices (CBSDs), and the scalability issues in blockchain-based spectrum trading are rarely discussed. To address the problems above, this paper develops the blockchain-based spectrum trading mechanisms for CBRS. Particularly, we propose a queuing mechanism for intra-coexistence group (CxG) trading, in which spectrum trading is leveraged to reduce the aggregated interference to incumbents. A new parameter termed “network feature" is proposed to prioritize spectrum transactions in different queues, which helps to achieve the trade-off between the interference to incumbents and resource requirements in spectrum transactions. Furthermore, we propose a multi-blockchain architecture and a corresponding cross-chain mechanism to improve the speed of inter-CxG spectrum trading. Simulation results show that the aggregated interference to incumbents can be reduced by adopting the proposed method. Meanwhile, when adopting the proposed cross-chain spectrum trading mechanism, the network throughput can be improved by up to 24% compared with the traditional CBRS spectrum management framework.

In this letter, a layered multi-chain spectrum blockchain architecture is proposed for Space-Air-Ground Integrated Network (SAGIN) to significantly reduce system overhead and aggregated interference. Considering the dynamic characteristics of SAGIN, nodes can make hand off between different blockchains based on the interference conflict graph to maintain aggregated interference below the threshold. To further improve spectrum utilization while avoiding harmful interference caused by spectrum trading in SAGIN, a spectrum trading mechanism is proposed, which jointly considers spectrum pricing, deployment location, and transmit power optimization. Simulation results verify the effectiveness of the proposed spectrum blockchain architecture and trading mechanism.

To improve the accuracy and generalization of channel modeling in complex scenarios, an explainable neural network (XNN)-enabled self-learning channel modeling approach is proposed. With the help of model visualization and feature importance analysis, it is shown that the XNN channel model can reveal the intrinsic relationship between the channel characteristics and system parameters. The output and input of the channel model can be represented by mathematical expressions, making the channel model more transparent and credible. The self-learning optimization training (SLOT) algorithm enables fine-tuning and self-optimization of the channel model to ensure scenario adaptation. Specifically, when predicting the path loss, the simulation results show that the root mean square error (RMSE) is consistently less than the predefined error threshold in various test scenarios at different buildings.

To accommodate the rapid change of radio propagation environment for mobile communication scenarios, millimeter-wave beamforming requires instantaneous channel state information (CSI) to update its operational parameters in real time, resulting in heavy system overhead. As the number of antennas increases, the system overhead associated with beam management will increase dramatically. To address this overarching problem, a neural network-aided millimeter-wave beamforming algorithm is proposed in this paper. A new parameter, referred to as “beam adjustment interval”, is proposed to evaluate the beamforming performance. It is defined as the maximum time duration in which the signal-to-interference-plus-noise ratio (SINR) of the user equipment can be maintained above the predefined threshold. Besides, a predictive method of beam adjustment to maximize the beam adjustment interval is developed, which considers the SINR not only at the current location but also future possible locations. Simulation results show that the proposed algorithm can significantly increase beam adjustment interval and reduce the total number of beam adjustments for the moving user equipment, thus reducing the system overhead 41.4% on average over 10 randomly generated test traces.