With the advancement of wireless communication technologies, available spectrum resources are becoming increasingly scarce. Dynamic Spectrum Access (DSA) is one of the effective approaches to address the challenge. Traditional Q-learning DSA relies on node self-learning, while recent Federated Learning (FL) DSA introduces node collaboration but still depends on a central server. This paper proposes a DSA scheme based on Swarm Deep Reinforcement Learning (SDRL), achieving a fully decentralized distributed machine learning through the construction of a blockchain-based peer-to-peer network. This scheme leverages the advantages of swarm learning (SL), utilizing collaborative learning among multiple nodes to enhance DSA performance. IoT terminals share model parameters, utilizing the benefits of blockchain networks to mitigate the risks associated with centralized servers. Simulation results demonstrate that the SDRL scheme not only improves DSA access efficiency compared to FL-based schemes but also eliminates the need for a central aggregation server. The fully decentralization architecture enhances the auditablity of the data in the system which further preserves each user’s privacy.

Low Earth orbit (LEO) satellite systems play an important role in next generation communication networks due to their ability to provide extensive global coverage with guaranteed communications in remote areas and isolated areas where base stations cannot be cost-efficiently deployed. With the pervasive adoption of LEO satellite systems, especially in the LEO Internet-of-Things (IoT) scenarios, their spectrum resource management requirements have become more complex as a result of massive service requests and high bandwidth demand from terrestrial terminals. For instance, when leasing the spectrum to terrestrial users and controlling the uplink transmit power, satellites collect user data for machine learning purposes, which usually are sensitive information such as location, budget and quality of service (QoS) requirement. To facilitate model training in LEO IoT while preserving the privacy of data, blockchain-driven federated learning (FL) is widely used by leveraging on a fully decentralized architecture. In this paper, we propose a hybrid spectrum pricing and power control framework for LEO IoT by combining blockchain technology and FL. We first design a local deep reinforcement learning algorithm for LEO satellite systems to learn a revenue-maximizing pricing scheme. Then the agents collaborate to form an FL system. We also propose a reputation-based blockchain which is used in the global model aggregation phase of FL to optimize the power control. Based on the reputation mechanism, a node is selected for each global training round to perform model aggregation and block generation, which can further enhance the decentralization of the network and guarantee the trust. Simulation tests are conducted to evaluate the performances of the proposed scheme. Our results show the efficiency of finding the maximum revenue scheme for LEO satellite systems while preserving the privacy of each agent.

The evolution of Internet of Things (IoT) devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e., IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving, and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection, and heterogeneous coexistence. In this article, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitive-radio-based IoT networks.

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both the number of users and the available bandwidth. More efficient spectrum management schemes require deeper investigation in order to meet the ever-increasing demand for broadband spectrum. In this paper, we propose a band allocation method for multibeam satellite systems by introducing a market-driven pricing mechanism. Instead of adopting static and fixed band selling, we consider a satellite network operator that utilizes the mode of price bargaining to trade the unused band with terrestrial network operators. By applying market-based mechanism to support satellite spectrum allocation, higher spectrum efficiency can be attained in order for satellite systems to meet the increasing demands for satellite bandwidth at an affordable cost. Besides, for the one-to-many bargaining case without terrestrial operator involved in, a differential spectrum pricing solution is devised to address heterogeneous users' spectrum preferences. In a typical price bargaining model, market participants (i.e., terrestrial network operators) are assumed to know exactly their needs dynamically, which is hard to achieve in near real-time; thus, our approach approximates it with a sub-optimal estimation on the network operators' benefit threshold. To be specific, we obtain the optimal pricing at every round of bargaining by predicting the overall benefits of terrestrial network operators and reaching the Nash equilibrium. Essential discussions and proofs for the pricing rationality are provided. Numerical results are given to evaluate the impact of the pricing scheme on the profits of satellite systems.

With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spectrum pricing into a Hotelling game model according to spectrum quality diversity. Particularly, we first build a pricing system model where unused spectrum from primary systems with different qualities forms a spectrum pool and can be divided into a number of uniform channels. A secondary user purchases a channel for usage according to its selection preference which is closely related to the channel quality and spectrum evaluation. The secondary user not only needs to consider the channel's quality and price, but also the interference cost on primary system. Detailed analysis on the policy preference of both primary system and secondary buyer are provided. By forming a game problem of spectrum pricing between primary and secondary users, we apply the Hotelling game model to handle the interaction between the participants. Specifically, by fixing Nash equilibrium of the game, an iterative algorithm for spectrum pricing is proposed based on the distribution characteristics of secondary user's preference. Essential analysis for the existence and uniqueness of the Nash equilibrium along with algorithm's convergence conditions are provided. Numerical results are also supplemented to show the effectiveness of the proposed algorithm in ensuring spectrum owner's profit.

Content caching is a promising approach to enhancing bandwidth utilization and minimizing delivery delay for new‐generation Internet applications. The design of content caching is based on the principles that popular contents are cached at appropriate network edges in order to reduce transmission delay and avoid backhaul bottleneck. In this paper, we propose a cooperative caching replacement and efficiency optimization scheme for IP‐based wireless networks. Wireless edges are designed to establish a one‐hop scope of caching information table for caching replacement in cases when there is not enough cache resource available within its own space. During the course, after receiving the caching request, every caching node should determine the weight of the required contents and provide a response according to the availability of its own caching space. Furthermore, to increase the caching efficiency from a practical perspective, we introduce the concept of quality of user experience (QoE) and try to properly allocate the cache resource of the whole networks to better satisfy user demands. Different caching allocation strategies are devised to be adopted to enhance user QoE in various circumstances. Numerical results are further provided to justify the performance improvement of our proposal from various aspects.

Cooperative transmission is an efficient method for wireless sensor networks (WSNs) to decrease power consumption of sensor nodes and combat the fast fading inherent to wireless multipath channels. During the course, a key issue is how a sensor relay handles the balance between assisting other nodes’ communication and accomplishing its own transmission tasks. To address this problem, this study investigates the potential bargaining for resource allocation in WSNs over Rayleigh fading environments by considering energy fairness in cooperative transmission. Specifically, based on the characteristics of Rayleigh fading channel, a relevant system model is built. Then, after analysing the system capacity with a given outage probability over the fading channels, a system utility function for transmit‐time allocation is proposed to address the participants’ energy concern. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analysis and discussion on the pure equilibrium and convergence conditions of the iterative algorithm are provided. Finally, numerical results are presented to evaluate the feasibility and system performance of the proposed framework.

In this paper, we address the problems of power scheme and time-division bargaining under the mode of cooperative transmission in cognitive networks. Cognitive relay communication has been regarded as an effective method to solve the problem of the coexistence for cognitive users in a primary system. It is understandable that the cognitive users who have helped primary users communicate may be permitted to use the spectrum partly or timely where the problems of identifying the interference level and time division should be taken into account. In the underlay mode, we analyze the impact of dynamic interference temperature and give the relevant presentations in detail for its influence on the secondary user. In the overlay mode, we resolve the key problem of time division, which influences the benefit allocation between participants, by using game theory, which is a helpful tool for solving various problems of resource allocation in wireless networks. Furthermore, numerical results are presented to show that the proposed approach has better and encouraging effects. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we study the power allocation for both regenerative and non-regenerative relay transmission over Rayleigh fading channels in cognitive networks. Based on the analyses of features of cognitive networks, a relevant interference model is first built over Rayleigh fading channels. Then, we propose a combined power allocation strategy in order to minimize the outage probabilities in the cooperative communications. For regenerative system, we give a closed-form expression for the power allocation by taking into account the characteristics of the fading channels. For non-regenerative system, we utilize pattern search algorithm to solve the optimization problem since the objective function is complex and uneasy to be figured out directly. Numerical results show that the system performances with optimum power allocation outperform those with uniform power allocation whereas lower outage probabilities can be obtained.

In cognitive networks, the cooperative transmission between primary user and secondary user has been regarded as an effective method to promote harmonious coexistence between themselves where how to balance user's benefits and improve the system throughput is a key issue. In this paper, we study the joint pricing and power allocation strategy for the cognitive relay transmission by using the Stackelberg model which is a classical dynamic game model. We first give presentations of a cognitive relay model over Rayleigh fading channels and then investigate the primary benefits in power saving during this cooperation. According to the profits of primary user (as a game leader) and secondary user (as a follower), corresponding utility functions which describe their overall profits in the cognitive relay transmission are derived one by one. Then, optimal strategies for interference pricing and power allocation are fixed by resolving the objective functions. The existence of the Nash equilibrium and the sufficient conditions for the positive equilibrium are further discussed. Numerical results are presented to show that the algorithm can achieve encouraging outcomes with low-complexity.