Deep reinforcement learning has demonstrated outstanding performance in autonomous driving (AD). However, independent single vehicle training struggles to cope with complex traffic environments, while collaborative training across multiple vehicles causes security risk in data sharing. To address these challenges, this paper proposes a dual-layer blockchain-enabled federated reinforcement learning algorithm (DBFRL) for personalized AD. The proposed DBFRL algorithm constructs a FRL architecture based on a dual-layer blockchain to ensure data security during training. Practical driving behaviors data from the HighD dataset are used to classify driving styles into three categories: timid, normal and aggressive. Correspondingly, the personalized multi-objective reward functions are designed to reflect individual driving preferences. Then, the improved TD3 algorithm with different experience replay buffers and prioritized experience replay mechanism are using in the local model training. Furthermore, the reputation values of connected autonomous vehicles are introduced to ensure high-quality global model aggregation. The effectiveness of the DBFRL algorithm is validated on the CARLA simulator. Simulation results confirm that it significantly improves training performance and preserves data safety simultaneously.

Spectrum cartography (SC) based on tensor completion has been extensively studied in recent years, with most algorithms operating in two-dimensional (2D) regions and based on balanced spectrum tensors only. To meet the growing demand for three-dimensional (3D) SC, expanding the 2D tensor completion algorithms to 3D versions is either straightforwardly applicable with an intrinsic accuracy deficiency or technically challenging to process the 3D-structured spatial data meticulously. Additionally, these algorithms suffer significant performance degradation when encountering unbalanced spectrum tensors. To address these issues, we propose multi-band 3D SC algorithms based on unbalanced spectrum tensors sporadically collected in 3D space across multiple bands. The unbalanced spectrum tensors are first transformed into balanced tensors using the 3D-based ket augmentation algorithm (3DKA), overcoming the unbalanced tensor’s incapability to leverage its low rank for estimating the entire tensor. Subsequently, tensor train matricization is employed to obtain more balanced matrices and improve tensor completion accuracy. Finally, tensor completion is accomplished using either the 3DKA-aided parallel matrix factorization (3DKA-PMF) or the Frobenius norm-based singular value decomposition-free (3DKA-SVDF) algorithm. Simulations show that the 3DKA-PMF algorithm achieves a minimum improvement of 20.06% over the conventional PMF algorithm. Notably, the 3DKA-SVDF algorithm exhibits slightly inferior performance but significantly shorter runtime—at most 58.3% of that of the 3DKA-PMF algorithm.

With the evolution of 6G technologies, spectrum scarcity has emerged as a critical challenge. Semantic communication, as a content and task-oriented paradigm, offers a promising solution to alleviate spectrum limitations. We propose a novel resource allocation scheme based on a signal-to-interference-plus-noise ratio (SINR)-aware feature optimization mechanism (SFOM) for dynamic semantic image transmission (DSIT). To better characterize semantic transmission efficiency, we define a new performance metric, image semantic spectrum efficiency (ISSE), which employs semantic features as information conveyors rather than traditional bit-level representations. To maximize ISSE under image quality constraints, we formulate a joint optimization problem involving multiple discrete variables, including compression ratio (CR), channel assignment, and power allocation. To address this problem, we develop an SFOM-driven proximal policy optimization (SFOM-PPO) algorithm that evaluates the significance of semantic feature channels and models the nonlinear relationships among the peak signal-to-noise ratio (PSNR), CR, and SINR, enabling adaptive semantic feature selection and optimal resource allocation under dynamic communication conditions. Experimental results demonstrate that the proposed SFOM-PPO significantly outperforms baselines in ISSE performance, achieving an effective balance between image transmission quality and resource efficiency. This work provides initial insights into semantic spectral-efficient resource management and suggests a potential framework for future wireless semantic image transmission systems.

Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.

This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.

This paper presents a reinforcement learning (RL)-driven strategy to improve the transient stability of power systems via tuning parameters of multiple virtual synchronous generators (VSGs). We proposed a scalable method to support RL training convergence probability and speed, even when a large number of contingencies are considered. The proposed scalable RL framework first decomposes the large number of contingencies into multiple groups and then conducts parallel training for each group, decreasing the state space and complexity of each training. Additionally, we propose a contingency grouping algorithm to streamline the RL action space and facilitate the training. The proposed method is validated across various standard test systems.

The control parameters of the grid-forming (GFM) inverter-based resources (IBRs) directly impact power system dynamics. The primary control requires the GFM inverter to balance generation and load. It is also preferred that a GFM inverter maintain the terminal frequency after a power system fault. In this paper, we investigate the trade-off between the primary control objective and the transient response. To quantify the transient performance of the GFM inverter, we introduce a new real-time transient stability index (TSI). This index plays a crucial role in our investigation, as it allows us to compare the performance trade-offs of different sets of GFM control parameters.

Edge computing network (ECN), which could process learning tasks at the edge, is considered as a potential solution to release the burden of the cloud. Meanwhile, to protect user privacy, federated learning (FL) is used in the ECN to establish models by multi-party collaborative learning on numbers of edge nodes (ENs). However, due to the frequent data interaction between the cloud server and distributed ENs, the reliability of data transmission and the privacy protection capability of the network cannot be guaranteed. In this paper, a distributed ECN is considered, to improve the learning efficiency in the multi-party FL while ensuring the reliability of ENs, a consortium blockchain enabled asynchronous federated learning (AFLChain) algorithm is proposed, which could dynamically allocate the learning tasks to ENs according to their computing capabilities. Moreover, an entropy weight-based reputation mechanism is introduced for the EN evaluation to further improve the performance of the AFLChain. Finally, the simulation results demonstrate the effectiveness of the proposed algorithms.

Unmanned aerial vehicles (UAVs) could be dispatched to areas of interest and act as intermediate relays to transmit information from areas of interest to the ground data center due to their flexible mobility. The UAV-assisted communication network consists of the aerial subnetwork and the ground subnetwork. The aerial subnetwork is forming by UAVs, which aids the ground subnetwork through air-to-air (A2A) and air-to-ground (A2G) communications link. To collect information with the minimum energy consumption of UAVs, in this paper, we jointly optimize the trajectory, the number and locations of UAVs while considering the coverage of the area. The optimization problem is a mixed integer non-convex problem, we decompose it into two sub-problems and solved separately. Firstly, the UAVs deployment (UD) algorithm is used to determine the number and locations of UAVs with the consideration of the coverage. Secondly, the improved shuffled frog-leaping algorithm (ISFLA) based on the Dubins path is proposed to optimize the trajectory of UAVs with obstacle avoidance. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance compared with algorithms in the literatures.

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.

In fog network, device-to-device (D2D) sharing is an important way to obtain data. However, due to an untrusted environment, it is difficult for a device to assess the reliability of the received data. What’s more, devices may be reluctant to share data because of selfish, resulting in data supply and demand imbalances. In this regard, a data sharing scheme assisted by blockchain and matching algorithm is proposed. In order to ensure the authenticity of the data, the Bayesian inference model is employed to predict quality of the data, and a multi-factor data evaluation method is presented to make accurate judgments. Furthermore, different utility functions for data requesters and providers are defined, and a two-way matching game is introduced to balance of data supply and demand. To reduce the blockchain consensus delay and ensure the activeness of fog nodes, a practical byzantine fault tolerates (PBFT) consensus mechanism based on the frequency of interaction is investigated. The simulation results verify the effectiveness of the algorithm. The proposed data sharing scheme promotes the interaction of information in the fog computing network.

Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs. Besides, there are two types of IDs, namely, requested-IDs (R-IDs), which has task offloading requirement, and free-IDs (F-IDs), which could offload tasks for R-IDs with idle computation resources. Two offloading links are considered: 1) the device-to-device (D2D) link and 2) the ground-to-air (G2A) link, which are responsible for both the uplink and downlink offloading procedure. To minimize the total network overhead, we jointly optimize the UAV trajectory, transmission power, and computation offload radios, while satisfying Quality-of-Service (QoS) requirements of R-IDs. The optimization problem is nonconvex, and the UAV-assisted task offloading optimization algorithm is proposed to obtain the local optimal solutions, which decomposes the original problem into two parallel subproblems and solved alternately. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of the network overhead compared with algorithms in the literature.

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.

Blockchain technology is used to improve the security of users data in the network. However, the traditional blockchain structure is not suitable for the fog network due to the low throughput and scalability limitations. To solve the above issues, in this paper, we propose a sharding blockchain to improve the security of the fog network, while increasing the throughput. Sharding blockchain will divide the network into several shards, and each of them could process the transactions parallelly. In addition, the normalized entropy of the fog network could be calculated by the main opinion and secondary opinion in the consensus result. Then, the main-chain layer could calculate the probability of malicious fog nodes (FN) in the network and the maximum number of shards. Finally, the S-type fog nodes assignment algorithm (S-NA) is proposed to optimize the association between shards and FNs, which combines the greedy algorithm and the max-min fair algorithm. Simulation results verify the efficiency of the proposed S-NA algorithm.

In mobile computing scenarios, federation learning allows users to jointly train global models in a decentralized manner without exposing private data. However, due to the heterogeneity of the network and devices, the traditional global model often fails to fit the user data distribution, which is inconsistent with the primary condition of federation learning, resulting in accuracy decreasing of global models. Besides, the security of federated learning is decreasing with the increase of malicious attacks. To address the aforementioned issues, in this paper, we explore the cosine similarity of model gradients and design a clustered mechanism to improve learning efficiency. Furthermore, we combine the clustered federated learning with the blockchain-supported fog computing networks, which could verify local models uploaded by users and generate the traceable global models to improve the learning efficiency. Finally, we conduct experiments on several frameworks with the real-world dataset FEMNIST, and the experimental results demonstrate the efficiency and robustness of the blockchain-enabled clustered federated learning framework.

In the vehicular ad hoc networks (VANETs), vehicles share content with other vehicles and roadside units (RSU) to improve traffic efficiency. However, the vehicles and RSUs are not always credible. If they have malicious behavior, sharing false information put lives in danger. To address these security challenges, we propose a content sharing management method based on blockchain in VANETs. Specifically, we propose a hybrid trust model to evaluate the credibility of content based on the vehicle entity and interactive data. We deploy practical Byzantine fault tolerates (PBFT) consensus protocol based on the interaction frequency between RSUs and vehicles. The higher the interaction frequency, the more likely the RSU is to gain the right to package the block. In this way, RSUs and vehicles actively participate in the network, and achieve the content sharing honestly and effectively. We conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms.

With the emergence of a large number of computational resource-intensive applications and various content delivery services, there is an explosion of data growth in the Internet of Vehicles (IoV). To improve the transmission performance of the IoV, caching content on the edge of the network is considered as a potential solution to reduce the content transmission delay. In this article, we investigate the content caching decisions optimization method in the IoV to minimize the content fetching delay for vehicles, which is based on the vehicle-to-vehicle (V2V) collaboration. A delay-aware content caching (DCC) algorithm in the IoV is proposed, which consists of vehicle associations, content caching, and precaching decisions optimization. First, a delay-aware vehicle associations (DVAs) algorithm is proposed to optimize the vehicle associations. Consequently, based on the vehicle associations results, the content caching decisions are optimized in two network scenarios according to the existence of the handover vehicles. Finally, a practical scenario of Shanghai with time-varying traffic flow is used for simulations and the effectiveness of the proposed DCC algorithm is verified.

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.

Recently, a fog computing network that widely deploys fog nodes (FNs) at the edge of the network has been able to provide better communication performance and powerful computation support to the resource-limited Internet-of-Things (IoT) devices. In this article, we analyze the energy-efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical energy consumption and the current energy consumption. The FN that reports nonzero computation capability is considered as the candidate FN and included in the candidate set. Moreover, a candidate FN-based EE resource allocation (CF-EE) algorithm is proposed to maximize network EE, which is converted into the Lyapunov optimization for each time slot. The optimal resource allocation can be obtained by minimizing the upper bound of the Lyapunov drift function and the penalty term to guarantee the loading balance and network stability. Finally, the optimization problem is decomposed into two suboptimization problems: 1) transmission resource allocation optimization and 2) power allocation optimization, and solved separately. The simulation results demonstrate that the proposed CF-EE algorithm can achieve a considerable performance improvement compared with algorithms in the literature.

Nowadays, the shortage of the licensed spectrum and the rapid development of mobile communication technologies have brought new challenges on the spectrum utilization. LTE in the unlicensed band, namely LTE-U, under the carrier aggregation technology has been widely concerned. In the multiple LTE-U base station scenario, the main problem is to design a proper access mechanism in the unlicensed band to avoid mutual interference. Meanwhile, LTE-U can not continuously transmit in the unlicensed band, which would affect the transmission performance of delay-sensitive users. In this paper, we proposes a delay-based priority resource scheduling scheme for multiple LTE-Us that could ensure the fairness resource allocation among LTE-U, the transmission performance of WiFi, as well as the transmission quality of delay-sensitive LTE-U users. The optimization problem is solved by two steps: delay-based priority resource scheduling and price-based resource allocation scheme. Simulation results demonstrate the effectiveness of the proposed algorithm.

With the fast growth of demand for the ubiquitous, precise, and instant indoor location information, the Received Signal Strength (RSS) based Wi-Fi indoor localization has been greeted with an avalanche of publicity. The studies in this field so far rarely consider the diversity of Wi-Fi signals, and thereby the RSS measures involving gross error on account of the complicated indoor environment deteriorate localization accuracy. In response to this compelling problem, we propose to use the concept of Asymptotic Relative Efficiency (ARE) to design a new synergetic test of Wi-Fi signal for indoor localization. Specifically, first of all, the Jarque-Bera (JB) test is conducted to test the normality of Wi-Fi signals at each Reference Point (RP). Second, the result of JB test is fed into the synergetic Mann-Whitney U and T test to construct the set of matching RPs corresponding to the newly-collected RSS data. Finally, the location coordinate of the target is obtained by calculating the K-nearest neighbor of matching RPs. Furthermore, the experimental results demonstrate that the proposed approach is featured with higher localization accuracy compared with the existing Wi-Fi indoor localization approaches.

With the widespread adoption of Wireless Local Area Network (WLAN) in indoor environment, indoor WLAN intrusion detection has become a key technique in various fields with the advantage of accomplishing intrusion detection without any requirement of special device or collaboration from the target. However, this technique is suffered by a serious problem that the offline database construction normally leads to high manpower and time cost especially for the large-scale indoor environment. To address this problem, a new indoor WLAN intrusion detection approach with low effort is proposed in this paper. Specially, first of all, the difference between the Received Signal Strength (RSS) data in source and target domains at the same locations is reduced by intra-class transfer learning with the purpose of applying the relations between the offline RSS data and their labels to the online RSS data. Second, the RSS data in target domain are classified by using the classifier trained from the relations of RSS data and the corresponding labels in source domain. Third, the iterative transfer learning between source and target domains is conducted to obtain the labels of RSS data in target domain. Finally, the experimental results demonstrate that the proposed approach is able to achieve high detection accuracy as well as the strong robustness to the number of RSS data used for database construction.

Summary In this paper, we study the full‐duplex relay selection strategy based on a potential game in a cognitive cooperative network under the interference power constraint from secondary users to the primary receivers, the total available transmission power constraint for the secondary system, and the self‐interference constraint at each secondary relay. The relay selection problem is modeled as a non‐cooperative game where the total rate of a cognitive cooperative network has common utility. Then, we prove that the game is a potential game that has at least a pure strategy Nash equilibrium (NE), and the optimal strategy set that able to maximize cognitive cooperative system rate is also a pure strategy NE of the proposed game model. On the premise of having no information of infeasible strategy sets, we solve the feasibility conditions of the pure NE in the proposed game. Furthermore, we propose a cognitive full‐duplex relay iterative algorithm that can achieve a pure strategy NE, and the complexity and the convergence of the proposed algorithm are studied. Simulation results show that the proposed algorithm can achieve optimal or near optimal rate performance with low complexity and offers significant performance gain compared with the traditional half‐duplex mode.

The phenomenal growth of mobile data has brought new challenges on the limited spectrum resource. Deploying LTE on unlicensed bands has been introduced, namely LTE-U, which could provide a higher transmission data rate, spectrum efficiency as well as seamless mobile user experience by taking advantage of the Carrier Aggregation technology. In this paper, we first introduce the coexistence model between LTE-U and WiFi in the multi-operator scenario. To avoid long colliding time among operators due to the same backing off window size, a full duplex-based listen and talk access scheme is introduced. The proposed listen-and-talk based imperfect sensing power adaption (LAT-ISPA) scheme could avoid the interference among multi-LTE-U operators, maximize the effective throughput of LTE-U while ensuring the transmission performance of WiFi by optimizing the backing off window as well as the transmission power in different scenarios. Due to the residual self-interference by the LAT scheme, the imperfect sensing is taken into consideration. Simulation results show that the proposed algorithm could achieve a considerable performance improvement with respect to the schemes in literatures.

We consider a holistic approach for dual‐access cognitive small cell (DACS) networks, which uses the LTE air interface in both licensed and unlicensed bands. In the licensed band, we consider a sensing‐based power allocation scheme to maximize the sum data rate of DACSs by jointly optimizing the cell selection, the sensing operation, and the power allocation under the interference constraint to macrocell users. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a nonconvex integer programming. We reformulate the problem to a nonconvex power allocation game and find the relaxed equilibria, quasi‐Nash equilibrium. Furthermore, in order to guarantee the fairness of the whole system, we propose a dynamic satisfaction‐based dual‐band traffic balancing (SDTB) algorithm over licensed and unlicensed bands for DACSs which aims at maximizing the overall satisfaction of the system. We obtain the optimal transmission time in the unlicensed band to ensure the proportional fair coexistence with WiFi while guaranteeing the traffic balancing of DACSs. Simulation results demonstrate that the SDTB algorithm could achieve a considerable performance improvement relative to the schemes in literature, while providing a tradeoff between maximizing the total data rate and achieving better fairness among networks.

We consider a sensing-based power allocation scheme in a cognitive small cell network to maximize the sum rate of each small cell by jointly optimizing both the cell selection, the sensing operation and the power allocation over channels, under the condition of interference to primary users below a certain value. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a non-convex integer programming which is NP-hard. In order to deal with the non-convexity, we reformulate the problem to a non-convex power allocation game and use the relaxed equilibria concept, namely, quasi-Nash equilibrium. A sensing-based power allocation optimization algorithm that converges to a quasi-Nash equilibrium is also discussed in this paper. Simulation results show that the proposed approach achieves substantial performance gains with respect to a deterministic approach.

Access Point (AP) optimization is one of the most important components in indoor Wireless Local Area Network (WLAN) localization technique since the AP number and locations have significant impact on the variations of Received Signal Strength (RSS) in target environment. Different from the conventional AP optimization approaches, we propose to use the concept of adaptive channel power allocation to construct a water-filling model, and then conduct AP optimization based on the weights of candidate AP locations which are calculated by the fast discrete water-filling algorithm. The experimental results demonstrate that the proposed approach is able to achieve high localization precision, and meanwhile consume low time overhead.

In cognitive radio networks, a critical issue is to exploit the spectrum holes based on spectrum sensing while avoiding interference to the primary users. However, the reliability of sensing is uncertain. In order to increase the probability to access the channel, cognitive users may report false detection results and become malicious users (MUs), which could significantly degrade the performance of spectrum sensing. In this paper, we proposed an energy efficient MUs detection algorithm which is able to perform coalition-based cooperative detection and spatial correlation with Geary'C theory to maximize the probability of MUs detection. The problem is reformulated as a coalition game with the theoretical certification of its stability. Simulation results show that our algorithm is able to achieve a significant improvement while saving the energy in the MUs detection process compared with other algorithms in the literature.

In this paper, we consider a sensing-based spectrum sharing scenario in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The resulting optimization problem for each cognitive user is non-convex, thus leading to a non-convex game, which presents a new challenge when analyzing the equilibria of this game where each cognitive user represents a player. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). A QNE is a solution of a variational inequality obtained under the first-order optimality conditions of the player's problems, while retaining the convex constraints in the variational inequality problem. In this work, we state the sufficient conditions for the existence of the QNE for the proposed game. Specifically, under the so-called linear independent constraint qualification, we prove that the achieved QNE coincides with the NE. Moreover, a distributed primal-dual interior point optimization algorithm that converges to a QNE of the proposed game is provided in the paper, which is shown from the simulations to yield a considerable performance improvement with respect to an alternating direction optimization algorithm and a deterministic game.

In this paper, we consider a sensing-based spectrum sharing scenario and present an efficient decentralized algorithm to maximize the total throughput of the cognitive radio users by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the sensing accuracy. This optimization problem can be formulated as a distributed non-cooperative power allocation game, which can be solved by using an alternating direction optimization method. The transmit power budget of the cognitive radio users and the constraint related to the rate-loss of the primary user due to the interference are considered in the scheme. Finally, we use variational inequality theory in order to find the existence and uniqueness of the Nash equilibrium for our proposed distributed non-cooperative game.

Owing to the non-zero probability of the missed detection and false alarm of active primary transmission, a certain degree of performance degradation of the primary user (PU) from cognitive radio users (CRs) is unavoidable. In this paper, we consider OFDM-based communication systems and present efficient algorithms to maximize the total rate of the CR by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the probabilities of missed detection and false alarm, namely, the sensing accuracy. The optimization problem can be formulated as a two-variable non-convex problem, which can be solved approximately by using an alternating direction optimization method. Our algorithm can operated basically in two regimes depending on our constraints that are involved, while keeping the performance degradation of the PU bounded properly. Simulation results demonstrate that the proposed solution can considerably improve system performance.

Efficient spectrum sensing ensures cognitive radio users opportunistically use the under-utilized frequency band without causing harmful interference to primary users. However, in practice, owing to the non-zero probability of the missed detection and false alarm of active primary transmission, a certain degree of performance degradation of the primary user is unavoidable. In this paper, we consider OFDM-based communication systems and present efficient algorithms to maximize the total throughput of the cognitive radio by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the probabilities of missed detection and false alarm.The optimization problem can be formulated as a two-variable non-convex problem, which can be solved approximately by using an alternating direction optimization method. A novel criterion is introduced to ensure that the performance degradation of the primary user is bounded properly. First, we analyze the case of only one cognitive radio and then we generalize to the case of a two cognitive radio non-cooperative power allocation game, showing that a Nash equilibrium can be achieved in few iterations by using an iterative alternating direction optimization method. Simulation results demonstrate that the proposed solution can considerably improve system performance.