The explosive growth and increasing complexity of modern 5G/6G networks, driven by Internet-of-Things (IoT), industrial automation, and real‑time multimedia streaming, demand forecasting methods that address non‑stationarity, abrupt shifts, and incomplete observations. Non‑stationarity involves changing statistical properties, abrupt shifts stem from events or outages, and data gaps can impair model accuracy. Traditional statistical models and deep sequence learners partially handle these challenges but often leave systematic residuals, which are structured errors from unmodeled scenarios and overlook high‑level contextual cues such as external events or semantic patterns. To overcome these limitations, we propose a hybrid forecasting framework combining a convolutional neural network–long short-term memory (CNN–LSTM) trend predictor for capturing local fluctuations and long‑range dependencies, an Extreme Gradient Boosting (XGBoost) residual corrector to refine forecast errors, and a Low-Rank Adaptation (LoRA)‑fine‑tuned large language model (LLM) that generates semantic labels of trend direction, anomaly type, and volatility regime to enrich residual learning. Experimental evaluation on real‑world cellular traffic data shows up to 15% reduction in root-mean-square error (RMSE) and 10% reduction in mean absolute percentage error (MAPE) compared to state‑of‑the‑art hybrid baselines, with substantially improved resilience to noise, missing data, and abrupt traffic surges. Our contributions include a parameter‑efficient prompt‑based LoRA fine‑tuning pipeline for adapting LLMs to time‑series forecasting, a context‑aware residual learning architecture fusing numerical and linguistic features, and comprehensive empirical validation demonstrating superior accuracy and robustness in dynamic network environments.

This paper focuses on secure communications in UAV-assisted wireless sensing systems in the presence of a mobile UAV eavesdropper (MUE), without prior information about the traffic demands of ground users (GUs) in the sensing environment. To maximize secrecy performance, we propose a digital-twin enhanced proximal policy optimization (DTEPPO) algorithm that optimizes the GUs' sensing scheduling and the UAVs' trajectory planning and network formation. Unlike traditional reinforcement learning methods that require complete environment-interacted information, we build a digital twin (DT) model using Gaussian process regression (GPR) based on the historical sensing information collected by the UAVs. Moreover, DT model can be dynamically updated by exploiting the PPO algorithm to lean the UAVs' interactions with the environment, continuously enhancing its accuracy and providing a reliable virtual learning environment for fast network adaptation. Numerical simulations demonstrate that the proposed DTEPPO algorithm achieves rapid convergence and improved secure throughput with reduced communication overhead compared to conventional approaches. Moreover, the proposed framework enables simultaneous learning and modeling (SLAM) in an unknown environment, providing a general framework to solve complex network control problems in wireless and mobile systems with high costs of environmental interactions.

LoRa-based backscatter communication technology is promising in enabling ubiquitous connectivity for the Internet of Things (IoT) over large distances with extremely low power consumption. In this paper, we design and implement CPLoRa, a high-throughput parallel LoRa backscatter communication system compatible with commodity LoRa receivers. The core idea of CPLoRa is to enable multiple backscatter tags to communicate with remote LoRa receivers simultaneously by generating standard LoRa packets from a common single-tone RF emitter, which can be extracted from ambient LoRa transmitters or generated from dedicated mobile devices. CPLoRa employs a modified low-power direct digital synthesizer (DDS) scheme for precise frequency synthesis, ensuring compatibility with commodity LoRa receivers and enhancing data rates for long-range backscatter transmissions. Each tag is assigned a unique frequency offset in the synthesizer, allowing parallel transmissions and creating orthogonal, independent LoRa channels. Moreover, we design a harmonic-canceling switch network at the RF front end to reduce mutual interference among different tags. Finally, we implement the CPLoRa tag prototype using low-cost circuit components and rigorously tested in outdoor and indoor environments, demonstrating that CPLoRa supports long-range transmissions of up to 1000 meters while achieving a throughput of 9.6 kbps with 10 parallel tags compatible with commodity LoRa receivers.

With the rapid advancement of wireless communication technologies, the scarcity of available spectrum resources has become increasingly pronounced. Dynamic Spectrum Access (DSA) emerges as a promising solution to address this challenge. Traditional DSA methods based on Q-learning emphasize autonomous learning by individual nodes, whereas more recent approaches incorporating Federated Learning (FL) introduce collaborative learning among nodes but remain reliant on a central server. In this paper, we propose a novel DSA scheme based on Swarm Learning (SL), which enables a fully decentralized, distributed machine learning paradigm by establishing a blockchain-based peer-to-peer network. This approach capitalizes on the strengths of SL, facilitating cooperative learning among multiple nodes to enhance DSA performance. By allowing IoT terminals to share model parameters within a blockchain framework, the proposed scheme mitigates the vulnerabilities associated with centralized servers. Simulation results demonstrate that the SL-based DSA scheme not only surpasses the access efficiency of FL-based methods but also obviates the necessity of a central aggregation server. Furthermore, the fully decentralized architecture enhances the auditability of system data, thereby bolstering user privacy protection.

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.

Since uncrewed aerial vehicles (UAVs) possess inherent characteristics such as exceptional maneuverability and versatile deployment, they can offer integrated sensing and communication (ISAC) services to vehicles in mobile environment. This paper designs a UAV-assisted ISAC system model, wherein the UAV is employed to provide sensing and communication services to mobile vehicles during its flight. In order to evaluate the radar detection performance of the ISAC system, we introduce radar mutual information (MI) from the information theory perspective. A resource optimization problem for the system model is formulated, which seeks to maximize the system communication rate under the constraints of signal-to-noise ratio (SNR) and MI of the radar detection link by jointly optimizing ISAC task scheduling, UAV transmit power allocation and UAV flight trajectory. The simulation results indicate that the proposed scheme significantly improves both the communication rate and radar MI.

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.

Low Earth orbit (LEO) satellite systems are receiving increasing attention as they provide extensive global coverage. Secure and efficient management of limited spectrum bands and power resources are crucial for controlling operational costs and ensuring reliable communication in LEO satellite systems. However, spectrum pricing and power control optimization are challenging tasks. First, dynamic pricing is needed for leasing idle satellite spectrum to terrestrial users, as it must consider user mobility and real-time demand changes. Additionally, there is a trust concern that when utilizing the leased spectrum, terrestrial users may maliciously exceed limited transmit power to improve the quality of service (QoS). Moreover, users' privacy should be protected because the data collected by satellites often contain sensitive information such as location, budget, and QoS needs. In this paper, we propose a hybrid spectrum pricing and power control framework for LEO satellite-enabled networks to mitigate the above concerns by combining blockchain technology and Federated Learning (FL). We first design a local deep reinforcement learning algorithm for LEO satellite systems to learn a revenue-maximizing pricing strategy and power control scheme. Subsequently, these individual agents collaborate to establish an FL system without sharing their sensitive raw data. We also propose a reputation-based blockchain used in the global model aggregation phase to further enhance the traceability of the network and guarantee the trust. We conduct simulation tests to evaluate the efficacy of the proposed scheme, and our results show its capability to efficiently find the maximum revenue scheme for LEO satellite systems while preserving the privacy of each participating agent in an auditable mode.

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices; thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints; (2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets.

In this paper, we consider a reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicle (UAV) relaying communication system, where the RIS is mounted on the UAV and can move at a high speed. Compared with the conventional static RIS, better performance and more flexibility can be achieved with the assistance of the mobile UAV. We maximize the average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming and source power allocation for each time slot. The formulated non-convex optimization problem is decomposed into three subproblems: passive beamforming optimization, trajectory optimization and power allocation optimization. An alternating iterative optimization algorithm of the three subproblems is proposed to achieve the suboptimal solutions. The numerical results indicate that the RIS-UAV relaying communication system with trajectory optimization can get higher throughput.

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.

By supporting asynchronous transmission and flexible subband (SB) setting, filtered orthogonal frequency division multiplexing (f-OFDM) has been identified as one of the most promising waveforms for future wireless communications, which makes the deep investigation on the f-OFDM an urgent mission. Therefore, this paper investigates the downlink interference of asynchronous f-OFDM systems under different SB configurations. We initially classify the overall interference into two categories, termed as the inner-SB and inter-SB interferences. Subsequently, the closed-form expressions for interference and its variances are derived on the basis of interference structures and are then verified via simulations. Some important influencing factors, such as the timing offset between the user of interest and the interfering user, the width of guard band, as well as the choice of SB filter, are simulated and analyzed. Our simulations and comparisons explicitly reveal the interference characteristic of the f-OFDM systems, which will benefit the design of the f-OFDM systems in the future.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

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 cognitive radio (CR), the spectrum sensing of the primary user (PU) may consume some electrical power from the battery capacity of the secondary user (SU), yielding to decrease the transmission power of the SU. In this paper, a multislot simultaneous spectrum sensing and energy harvesting model is proposed, which uses the harvested radio frequency (RF) energy of the PU signal to supply the spectrum sensing. In the proposed model, the sensing duration is divided into multiple sensing slots consisted of one local-sensing subslot and one energy-harvesting subslot. If the presence of the PU is detected in the local-sensing subslot, the SU will harvest RF energy of the PU signal in the energy-harvesting slot, otherwise, the SU will continue spectrum sensing. The global decision is obtained through combining local sensing results from all the sensing slots by adopting "OR Rule". A joint optimization problem of sensing time and time splitter factor is proposed to maximize the throughput of the SU under the constraints of probabilities of false alarm and detection and energy harvesting. The simulation results have shown that the proposed model can improve the maximal throughput of the SU obviously compared to the traditional sensing-throughput tradeoff model.

In this paper, a cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems is proposed. Specifically, the secondary system gains primary spectrum access by allocating a fraction of its power to forward the primary signal helping the primary system achieve the target rate, and meanwhile exploits the remaining power to transmit its own signal. We analyze the achievable rates for the primary and secondary systems, and determine the optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

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.

Distributed antenna systems (DASs) are promising for future wireless systems to provide high data transmission rates. So far, most works on resource allocation schemes for DASs have not considered the Quality of Services (QoS) and fairness of mobile users simultaneously. This paper investigates and presents an optimal max-min fair resource allocation scheme for min-rate guaranteed services in downlink multiuser DASs. By exploring the properties of the optimization problem, we transform it into an equivalent convex optimization problem. Then we propose an iterative allocation algorithm to solve the problem and get the optimal solution. Numerical results show that the proposed method realizes the max-min fair resource allocation for min-rate guaranteed services, in which the values of minimum transmission rates are maximized.

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.