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The radio frequency spectrum serves as a fundamental resource for wireless communication, encompassing various frequency bands allocated for diverse services and applications. Dynamic spectrum management (DSM) is essential to optimise the utilisation of this limited and valuable natural resource, with the aim of improving performance and adapting to changing wireless communication demands. Traditional static spectrum allocation methods have shown inefficiencies, leading to spectrum scarcity and under-utilisation. To address these challenges, the integration of blockchain and Cognitive Radio (CR) technologies has emerged as a promising approach. Blockchain, with its decentralised and secure attributes, can improve transparency and trust in spectrum allocation processes, while CR enables intelligent spectrum sensing and allocation to maximise utilisation. However, this promising approach comes with its own critical challenges, especially when dealing with the 6th Generation (6G) mobile communication. These challenges are related to the fact that the blockchain ecosystem needs to be interoperable and scalable enough to be compatible with the 6G high-demand and substantial resources. Specifically, integrating blockchain with CR requires efficient interoperability techniques where blockchain can easily and effectively interact with the CR platforms as well as radio spectrum environments. Furthermore, the spectrum management system over 6G networks needs to be designed in a way where massive 6G resources can be accommodated and managed without having any service performance degradation. This paper introduces a novel radio spectrum management model in 6G networks, named as BSM-6G, which integrates blockchain technology with CR where interoperability is preserved and scalability is maximised. Specifically, the proposed BSM-6G model merges blockchain’s transparent record keeping with CR’s intelligent spectrum management capabilities. To overcome the interoperability issue, BSM-6G provides an interoperable blockchain Oracle approach which facilitates the real-time interaction among the blockchain platform, the CR, and any data sources off-chain. This paper details all the technical and procedural challenges when implementing the proposed interoperability Oracle approach. To address the scalability challenge, BSM-6G utilizes the Proof-of-History (PoH) consensus protocol to align with the requirements of DSM in advanced networks like Beyond 5th Generation (B5G) and 6G. Evaluation results indicate that BSM-6G offers viable and less complex blockchain Oracle integration architecture measured by the technical implementation of BSM-6G, as well as low interoperability cost measured by transaction response time and transaction fee cost. Compared to state-of-the-art spectrum-based blockchain systems, BSM-6G shows a high scalable DSM-based blockchain in 6G networks measured by transactions per second (TPS).

In this paper, we study a wireless powered cognitive internet of things (IoT) network, where cognitive radio (CR) and non-orthogonal multiple access (NOMA) technologies are exploited to improve spectral efficiency, and radio frequency based energy harvesting (RF-EH) technology is integrated to achieve the sustainable IoT network. To ensure the freshness of information delivery, we investigate the age of information (AoI) as a performance metric, and formulate a long-term average AoI minimization problem under energy sustainability constraint, in which the working mode and transmit power of the secondary devices (SDs) are jointly optimized. Then, we reformulate it as a decentralized Markov decision process (Dec-MDP) with continuous action space. Accordingly, a deep reinforcement learning (DRL) framework is exploited, and a multi-agent twin delayed deep deterministic policy gradient algorithm with dual action selection mechanism (MATD3-DAS) is proposed, which adopts the centralized training and decentralized execution (CTDE) framework and exploits both actor and critic networks to select actions for improving exploration ability. Simulation results show that the proposed algorithm can significantly reduce the long-term average AoI, where the decrements approach 9.58% and 52.34% compared with the MATD3 algorithm and TD3-DAS algorithm with centralized training and centralized execution (CTCE).

In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.

This paper presents a new low-area and low-power Field Programmable Gate Array (FPGA) implementation of a Radio Frequency (RF) modulation classifier based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, known as DBCLASS. The proposed architecture demonstrates a novel approach for the efficient hardware realisation of the DBSCAN algorithm by utilising parallelism, a bespoke sorting algorithm, and eliminating memory access. The design achieves 100% classification accuracy with lab-captured RF data above 8 dB signal-to-noise ratio(SNR) whilst exhibiting an improvement of latency in comparison to the next quickest design by a factor of 7.5, a reduction in terms of total FPGA resources used in comparison to the next smallest complete system by a factor of 3.65, and a reduction in power consumption over the next most efficient by a factor of 4.75. The proposed design is well suited for resource-constrained applications, such as mobile cognitive radios and spectrum monitoring systems.