Repetitive motor rehabilitation is often experienced as monotonous, especially in home settings with limited guidance. This project reinvents rehabilitation as an expressive and embodied activity in which movement serves as a vehicle for artistic sound exploration. We present ReSound, an interactive, low-cost system that creates dynamic musical output by fusing real-time sound production with physical movement. The system creates a movement–sound interaction where gestures continuously modify rhythm, timbre, and intensity using camera-based motion tracking (MediaPipe) and real-time sound synthesis (Max/MSP). Based on well-established principles of auditory-motor rehabilitation, the design incorporates goal-oriented engagement, continuous motion sonification, and rhythmic signals into a cohesive expressive framework. Through motion analysis and visualization, an AI-assisted approach facilitates post-session reflection. The approach turns repetitive workouts into interesting, emotive, and artistically significant experiences by incorporating creativity into therapy.

To support the intelligent evolution of Internet of Things (IoT) systems toward enhanced comprehensiveness and sophistication, this paper proposes a distributed training and inference oriented toward continual learning. By integrating federated continual learning with inference offloading, the system addresses key challenges in IoT scenarios, including large-scale data processing, catastrophic forgetting during incremental updates, and resource constraints. A joint optimization framework of training-inference is established to analyze model accuracy, latency, and energy consumption. The optimization objective and strategy are formulated as a Markov Decision Process (MDP) with a hybrid action space and customized reward mechanism. The Soft Actor-Critic (SAC) algorithm enables action grouping and the transformation between discrete and continuous actions, achieving unified optimization of training and inference. Simulation results show that compared to existing approaches, the proposed method improves node selection, resource allocation, and offloading strategy by jointly considering communication and computation costs.

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex environments of communication, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a dueling double deep Q network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms comparison schemes significantly.

The growing demand for edge services calls for an efficient microservice placement strategy to ensure optimal deployment in dynamic edge-cloud computing environments. In this work, we propose a heterogeneous workload-aware microservice placement framework that optimizes deployment by maximizing edge throughput while minimizing costs. To account for the diverse deployment requirements of microservices under heterogeneous workloads, we explicitly address the placement and resource constraints for both light and heavy workloads—an aspect rarely addressed in existing studies. The formulated problem is inherently non-continuous, non-convex, and involves fractional operations, posing significant computational challenges. To overcome this, we develop a Sparsity-promoting Fractional Programming (SFP) algorithm that relaxes and reformulates the problem into a sparse-promoting linear program using l0-norm approximation. Extensive evaluations demonstrate the effectiveness of our framework in improving edge throughput, reducing deployment costs, and efficiently managing workload heterogeneity.

With the advancement of urbanization, communication-based train control (CBTC) systems for urban rail transit and train-to-train (T2T) communication have garnered significant attention. T2T communication establishes mobile ad hoc networks (MANETs), similar to those in vehicular ad hoc networks (VANETs). Building upon this foundation, we propose an adaptive cooperative (ADCO) MAC protocol for T2T communication. The scheme introduces clustering and cooperative transmission mechanisms, which enhance the efficiency and reliability of safety packet transmission. Additionally, the protocol assigns different priorities to packets engaging in contention on the control channel (CCH) and enables trains to access service channels (SCHs) without contention through pre-reserved time slots. To analyze the transmission probabilities and success rates of packets with varying priorities, a Markov-based model is utilized, ultimately determining the optimal ratio of the CCH interval (CCHI) to the SCH interval (SCHI) for maximizing channel utilization. Theoretical analysis and simulation results demonstrate that the proposed MAC protocol ensures reliable transmission of safety packets while simultaneously optimizing the throughput on SCHs.

This study investigates the cognitive impact of dynamic icons in mobile interfaces by integrating electroencephalography (EEG) and eye-tracking technologies. Traditional research on mobile app interface design has relied mainly on questionnaire and eye-tracking methods for behavioral analysis. This research adds a new dimension by examining the neural mechanisms associated with dynamic icons. We employed EEG to analyze channel-wise power spectrum density (PSD), focusing on the alpha and theta frequency bands related to attention and working memory. Concurrently, eye-tracking data were analyzed through Areas of Interest (AOIs) and fixation metrics to assess visual attention patterns. The results indicate that dynamic icons significantly enhance neural activity, with a 15% increase in alpha band power and a 20% increase in theta band power compared to static icons. Additionally, eye-tracking data show a 30% increase in total fixation duration on AOIs containing dynamic icons, particularly in the left-top quarter of the mobile interface. This effect was observed without changes in the first fixation duration, suggesting that dynamic icons have a stronger impact on sustained attention rather than on initial capture. These findings highlight that dynamic icons not only attract and maintain visual attention more effectively but also enhance cognitive processing efficiency. This study provides valuable insights for optimizing mobile app interface design, emphasizing the benefits of incorporating dynamic elements to improve user engagement and interface effectiveness.

Modern Mixed-Criticality Systems (MCSs) rely on hardware heterogeneity to satisfy ever-increasing computational demands. However, most of the heterogeneous co-processors are designed to achieve high throughput, with their micro-architectures executing the workloads in a streaming manner. This streaming execution is often non-preemptive or limited-preemptive, preventing tasks’ prioritisation based on their importance and resulting in frequent occurrences of algorithmic priority and/or criticality inversions. Such problems present a significant barrier to guaranteeing the systems’ real-time predictability, especially when co-processors dominate the execution of the workloads (e.g., DNNs and transformers).In contrast to existing works that typically enable coarse-grained context switch by splitting the workloads/algorithms, we demonstrate a method that provides fine-grained context switch on a widely used open-source DNN accelerator by enabling instruction-level preemption without any workloads/algorithms modifications. As a systematic solution, we build a real system, i.e., Make Each Switch Count (MESC), from the SoC and ISA to the OS kernel. A theoretical model and analysis are also provided for timing guarantees. Experimental results reveal that, compared to conventional MCSs using non-preemptive DNN accelerators, MESC achieved a 250 x and 300 x speedup in resolving algorithmic priority and criticality inversions, with less than 5% overhead. To our knowledge, this is the first work investigating algorithmic priority and criticality inversions for MCSs at the instruction level.

Federated learning, leveraging distributed data from multiple nodes to train a common model, allows for the use of more data to improve the model while also protecting the privacy of original data. However, challenges still exist in ensuring privacy and security within the interactions. To address these issues, this paper proposes a federated learning approach that incorporates blockchain, homomorphic encryption, and reputation. Using homomorphic encryption, edge nodes possessing local data can complete the training of ciphertext models, with their contributions to the aggregation being evaluated by a reputation mechanism. Both models and reputations are documented and verified on the blockchain through consensus process, which then determines the rewards based on the incentive mechanism. This approach not only incentivizes participation in training, but also ensures the privacy of data and models through encryption. Additionally, it addresses security risks associated with both data and network attacks, ultimately leading to a highly accurate trained model. To enhance the efficiency of learning and the performance of the model, a joint adaptive aggregation and resource optimization algorithm is introduced. Finally, simulations and analyses demonstrate that the proposed scheme enhances learning accuracy while maintaining privacy and security.

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

Systems safety is becoming increasingly challenging due to the presence of ever-more complex applications. Safety analysis is an important aspect of Safety-Critical Systems Engineering (SCSE) to discover problems in system design that can potentially lead to hazards with risks that may lead to accidents. Performing safety analysis requires significant manual effort — its automation has become the research focus in the critical system domain due to the increasing complexity of systems and the emergence of open adaptive systems. In this paper, we propose a novel methodology in which automated safety analysis drives the design of safety-critical systems. We delve into the specifics of our approach and the supporting tools. Additionally, we discuss the method to integrate our approach into the current practice of SCSE. The experimental results reveal that the proposed approach with its supporting tool promotes the efficiency of safety analysis significantly, whilst maintaining high degrees of correctness, coverage and scalability.

The emergence of communication-based train control (CBTC) system within urban rail transport has improved the efficiency of safe train operations. At the same time, the CBTC system enhances the reliability of the train system and lowers latency. Nevertheless, there are still certain critical issues that need to be considered in the CBTC: 1) limited coverage and high maintenance costs of wayside equipment; 2) multiple ground devices configuration and complex system architecture; and 3) insufficient computing capacity of the train leads to heavy latency and energy consumption. The multi-hop ad hoc network coexisting with train-to-train communication and train-to-wayside communication is applied to simplify the networking architecture, together with the employment of mobile edge computing (MEC) servers to provide massive computing and communication resources for trains. Therefore, in this paper, a new multi-hop ad hoc network and MEC-assisted CBTC framework are developed for computing offloading and resource allocation. Offloading decisions, offloading ratio, computing and communication resource allocation are integrated to minimize latency and energy consumption. Furthermore, the proposed problem is a mixed-integer non-convex problem that is transformed into a solvable convex problem, and the consensus alternating direction method of multipliers-based (ADMM) algorithm is employed to solve the problem. The simulation results show that our proposed method has remarkable advantages over other schemes.

Driven by numerous emerging mobile devices and various quality of service requirements, mobile edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource, 2) simple or non-intelligent resource management, 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval and transmission power, it aims to minimize the consumption overheads of system energy and service latency. Then the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.

Immutability, decentralization, and linear promoted scalability make sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportions of cross-shard transactions (CST). On the other hand, assemblage characteristics of collaborative computing in IoT have not been received attention. Therefore, in this paper, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: k-means clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Network (DNN) which requires significant computing power. Therefore, reducing the computational overhead and improving the efficiency are urgent problems to be solved. In this paper, we first propose a CAV cooperative learning architecture based on blockchain to improve the positioning accuracy of vehicles. Then, we introduce an error precision sharing model between CAVs. The proposed framework enables CAVs to train vehicle positioning accuracy models locally and exchange them via a blockchain network. Such a distributed training architecture further reduces the computing power required. Extensive simulation results show that the proposed scheme can also significantly improve the accuracy of the trajectory error compared to existing approaches.

Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps:K-means-clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

Vehicular ad hoc networks (VANETs) have become an important branch of future 6G smart wireless communications. As an emerging key technology, multi-access edge computing (MEC) provides low-latency, high-speed, and high-capacity network services for the VANETs. In this paper, we propose a novel framework for blockchain-based, hierarchical multi-access edge computing for the future VANET ecosystem (BMEC-FV). In the underlying VANET environment, we propose a trust model to ensure the security of the communication link between vehicles. Multiple MEC servers calculate the trust between vehicles through computing offloading. Meanwhile, the blockchain system plays an important role to manage the entire BMEC-FV architecture. We aim to optimize the throughput and the quality of services (QoS) for MEC users in the lower layer of the system architecture. In this framework, the main challenge is how to effectively reach consensus among blockchain nodes while ensuring the performance of MEC systems and blockchains. The blocksize of blockchain nodes, the number of consensus nodes, reliable features of each vehicle, and the number of producing blocks for each block producer are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Since it is difficult for this to be solved by traditional methods, we propose a novel deep compressed neural network scheme. Simulation results illustrate the superiority of the BMEC-FV ecosystem.

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep$Q$-learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ($ETX$) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.

In this paper, the optimal navigation control design for biomedical untethered microrobot is comprehensively investigated in discrete-time domain with stochastic network-induced delays. First, the error dynamics of the microrobot tracking location and velocity are analyzed based on the 3D-based microrobot navigation modeling. Then, the optimal navigation optimization problem is formulated to regulate the microrobot to achieve the target reference trajectory, and a two-step control algorithm is proposed by using a backward recursion method. In particular, for each sampling interval, the optimal control gain is iteratively derived off-line and the control strategy can be calculated on-line in a real-time fashion.

As a promising technique, mobile edge computing (MEC) has attracted significant attention from both academia and industry. However, the offloading decision for computing tasks in MEC is usually complicated and intractable. In this paper, we propose a novel framework for offloading decision in MEC based on Deep Reinforcement Learning (DRL). We consider a typical network architecture with one MEC server and one mobile user, in which the tasks of the device arrive as a flow in time. We model the offloading decision process of the task flow as a Markov Decision Process (MDP). The optimization object is minimizing the weighted sum of offloading latency and power consumption, which is decomposed into the reward of each time slot. The elements of DRL such as policy, reward and value are defined according to the proposed optimization problem. Simulation results reveal that the proposed method could significantly reduce the energy consumption and latency compared to the existing schemes.

Considering the distributed controllers, this paper studies the optimal control law for the wireless sensor and actuator network (WSAN) with stochastic network-induced delays. First, the structure of the WSAN including multiple controllers is presented, and the network stochastic properties such as the network delay and plant noise are analyzed. Then, the optimization problem to minimize the total cost in order to keep the system stability is formulated, and the optimal state estimation control strategy is derived using the Kalman filter approach and the non-cooperative game. Finally, the proposed algorithm is validated by the simulation experiments of a stable control system and the load frequency control system.

In this paper, we propose a joint dynamic pilot and data power allocation scheme for time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, so as to both adaptively mitigate pilot contamination and balance the mutual interference. Due to the unknown of instant channel state information before pilots, we exploit the Gauss-Markov process of temporally-correlated channels and use the Kalman filter to not only filter out the pilot contamination but also provide the priori estimation values. Subsequently, the deterministic approximation of the rate is derived as a function of the priori channel estimation and the priori estimate errors, and accordingly the rate-profile maximization to achieve max-min fairness is formulated. To deal with this optimization coupled across the pilot power and data power as well as the users, we give an iterative alternating rate-suboptimal algorithm composed of two sub-problems, both of which are further solved by introducing the successive convex approximation (SCA) methods and slack variables. Numerical results confirm the improved rate provided by the proposed scheme.

Vehicular ad hoc networks (VANETs) have become a promising technology in smart transportation systems with rising interest of expedient, safe, and high- efficient transportation. Dynamicity and infrastructure-less of VANETs make it vulnerable to malicious nodes and result in performance degradation. In this paper, we propose a software- defined trust based deep reinforcement learning framework (TDRL-RP), deploying a deep Q-learning algorithm into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the highest routing path trust value of a VANET environment by convolution neural network, where the trust model is designed to evaluate neighbors' behaviour of forwarding packets. Simulation results are presented to show the effectiveness of the proposed TDRL-RP framework.

The asymmetry traffic between downlink (DL) and uplink (UL) in massive machine‐type communication (mMTC) systems is so prominent that it makes the traditionally fixed frame protocols insufficient to handle. Meanwhile, the dynamic time‐division duplexing (D‐TDD) is a promising and attractive technology since its number of time slots for the DL and UL can be asymmetric and adjusted dynamically. In the cellular and mMTC co‐existing network, to balance the discrepancy of the UL/DL ratio and alleviate the interference as well, in this study, the authors design a D‐TDD‐based transmission frame structure to first fulfil the basic transmission requirements of the human‐type communication (HTC) users with a low power almost blank subframe. Herein, the stochastic geometry methods are adopted to calculate spectral efficiencies of the HTC user equipment. Then, focusing on the worst queue state in mMTC, they devise the slot allocation problem with the min–max objective of the UL/DL queues and utilise the sub‐gradient descent (SGD) method for a solution. Simulation results show that the proposed traffic aware sub‐frame configuration is more appropriate for the dynamical asymmetry environment. Meanwhile, the adopted dynamic step size SGD algorithm can achieve a trade‐off between the worst‐case queue and the network throughput.

Recently, unmanned aerial vehicle (UAV) cooperation networks have attached much attention due to their successful applications in complex military and civilian missions. In this paper, we propose a navigation data-assisted optimal opportunistic spectrum access scheme for wireless communications in heterogeneous UAV networks, to achieve maximized data rate by flexibly scheduling the spectrum subbands. The system architecture is introduced, and thanks to the navigation data that is always available locally at the entities in the network, prediction of wireless link quality and routing information can be obtained to assist subband allocations. Furthermore, the spectrum allocation process is formulated as an optimization problem. Simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing one.

This paper is concerned with the challenging problem of joint channel estimation and data detection for high mobility multiple-input multiple-output orthogonal frequency division multiplexing systems. We propose a new iterative channel estimation and detection scheme, which reduces the unexpected effects of both detection errors and channel estimation errors. Detection errors in channel estimation are analyzed and transformed as part of the noise. To filter this equivalent noise by Kalman estimator, we derive the covariance of both the channels and data errors in detection. Besides, we propose a new detection algorithm with an optimized weight to minimize the detection error caused by channel estimation errors. To obtain this optimized weight, the error covariance of the estimated channels is derived from the error estimate covariance matrix in Kalman estimator. Simulation results are presented to demonstrate the significant performance improvement in joint channel estimation and data detection with the proposed iterative scheme.

In Cognitive Radio (CR) networks, there could be a spectrum market which operates in real time with primary users (PU) as manager, where the secondary users (SU) pay the PUs for spectrum resource usage. Multi-carrier systems such as OFDM are best candidates for applying in CR networks because of the spectrum shaping and high adaptive capabilities. In this paper, an optimal resource allocation scheme aims at maximizing PU's reward in OFDM-based CR networks is proposed. Both the interference limit and BER requirements of SUs are considered by the PU to allocate its spectrum resources. The scheme is modeled as restless bandits problem, which can dramatically simplify the computation and implementation. Furthermore, extensive simulation results show that our proposed scheme can improve the reward of PU significantly compared to the existing random scheme and greedy scheme.

Spectrum pooling, which allows the secondary networks to utilize the available spectrum bands from different licensed networks, is one of the most promising technologies for dynamic spectrum sharing in cognitive radio systems. Most previous work on spectrum pooling concentrates on the system architecture and the design of flexible access algorithms and schemes. In this paper, a distributed scheme for optimal internetwork spectrum sharing among multiple cognitive radio systems is proposed. Besides, the spectrum access price and spectrum efficiency are considered as the design criteria in the proposed scheme. The spectrum sharing problem is formulated as a restless bandits system, which dramatically reduces the computational complexity by simply allocating the new available band to the secondary network with the lowest index. Furthermore, extensive simulation results illustrate the significant performance improvement of the proposed scheme improves compared to the existing scheme.