With the explosive growth in communication de mand for the fifth generation (5G) mobile communication system, research into access based on new radio in the unlicensed band (NR-U) has become key to improve network performance. Existing coexistence methods typically simulate NR-U as WiFi nodes and dynamically adjust their listen-before-talk (LBT) related parameters for coexistence. However, these approaches struggle to reliably manage channel occupancy, resulting in wasted channel time resources and limited coexistence perfor mance. In light of this, this paper proposes a channel occupancy notification-based fair coexistence (CON-FC) method for NR-U and WiFi, addressing the interference and fairness issues between the two types of networks. The method optimizes the channel utilization efficiency of the NR-U system through a reliable channel occupancy notification mechanism based on the Clear-to-Send (CTS) frame and Physical Layer Convergence Procedure (PLCP) header of valid WiFi communication frame, and the design of policies and algorithms for NR-U accesses, which reduces ineffective fallbacks andWiFi device conflicts. The results of the simulation test show that, compared to the traditional LBT method, CON-FC improves the fairness index by an average of 7.43% and increases overall throughput by 11.53%-71.82% in various network configurations. This method not only meets the fairness requirements of the 3rd Generation Partnership Project (3GPP) but also significantly enhances overall system perfor mance, providing a reliable solution for the efficient coexistence of NR-U and WiFi in the unlicensed spectrum.

Spectrum resources play a key supporting role in the communication system and are the basic guarantee for the normal operation of the communication system. In mobile communication and the Internet of Things scenarios, efficient coordination of spectrum resources is a necessary process for the communication process to exert its effectiveness. However, although the traditional central model-based spectrum resource allocation method has the advantages of global resource grasp and resource utilization, it may lead to a decrease in allocation efficiency due to frequent synchronization of spectrum resource demand, while the distributed spectrum resource allocation method faces the difficulties of local game and poor allocation effect. In view of this, this paper proposes a set of spectrum resource planning blockchain scheme based on a beneficial consensus mechanism, which gives full play to the planning ability of each device node while ensuring that the investment of computing power and other resources is effectively returned. This paper designs the formation of block cycle and equipment resource allocation process in the scheme, and analyzes the characteristics of the proposed scheme through four aspects: security, activity, complexity and adaptability, and finally relies on simulations to prove the effectiveness, feasibility and advantages of the proposed blockchain spectrum resource allocation scheme.

The deployment of 5G NR-U (New Radio in Unlicensed Spectrum) technology has become a key strategy for expanding network capacity. However, the traditional listen before talk (LBT) coexistence mechanism suffers from unstable channel occupancy in heterogeneous scenarios involving NR-U and WiFi. The existing performance analysis models mostly assume that WiFi networks are in a saturated state, which deviates from the unsaturated service characteristics commonly found in real networks. For this reason, this paper proposes a fair coexistence mechanism, NS-CON-FC (Non-Saturated Channel Occupancy Notification-Fair Coexistence), based on channel occupancy notification, aiming to improve the reliability of NR-U channel occupancy and achieve fair coexistence with WiFi networks. This mechanism effectively suppresses potential conflicts of WiFi devices by proactively sending composite occupancy notification frames (combining 802.11a CTS frame with 802.11n PLCP header) from NR-U to pre-declare the transmission time slots. In this paper, a system model comprising unsaturated WiFi nodes is constructed, and an optimization problem is established to maximize the total system throughput while adhering strictly to 3GPP fairness constraints. The analytical solution of the optimal time allocation strategy is obtained through mathematical derivation, which provides a low-complexity parameter configuration guide for practical deployment. Simulation results show that, compared with the traditional LBT scheme, NS-CON-FC improves the total throughput by 30.7% while maintaining the fairness constraint, thereby verifying the effectiveness of the proposed mechanism.

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.

LEO satellite network (LEO-SN) constitutes an indispensable part of the space-air-ground integrated network (SAGIN). However, the characteristics of unbalanced traffic load, intricate orbits, high mobility, frequent link handovers, diverse storage and communication capabilities of nodes gradually lead to the ineffectiveness of the central routing planning methods. In order to enhance the flexibility and agility of the planning process, this paper presents an intelligent decentralized load balancing routing algorithm using deep reinforcement learning for the LEO-SN, which takes into consideration the queuing delay, storage space, communication bandwidth and propagation delay of merely one-hop satellite node. Simulation and analysis demonstrate that the proposed load balancing routing algorithm could converge quickly for both training and test sets, and possesses better performance than the comparison methods in terms of both the packet loss rate and transmission latency.

Localization constitutes the basic requirement of many applications in wireless sensor networks. Directional transmission has become very common with the development of electronic and antenna technologies. This paper aims to estimate both the position and transmission orientation of the directional target on the basis of combined received-signal-strength (RSS) and time-difference-of-arrival (TDOA) measurements in the non-line-of-sight (NLOS) environments. The localization problem is first formulized by the maximum likelihood estimation. Considering the problem is nonconvex, this paper then converts it into three relatively simple and intuitive sub-problems. Through converting them into generalized trust region subproblem (GTRS) and using bisection as well as multi-resolution search methods, the estimates is finally obtained. Simulation results show that the proposed method can estimate the orientation and position of the target effectively, its localization accuracy also outperforms the comparison methods significantly.

LEO satellite network (LEO-SN) plays an important role in the sixth generation mobile networks (6G) and the vision of space-air-ground integrated network (SAGIN). Unfortunately, the traditional routing decision methods for LEO-SN based on the central mode are facing more and more difficult challenges due to the complex orbits, large quantity, diverse loads, different storage spaces and communication capabilities of the satellite nodes. This paper proposes an intelligent decentralized routing algorithm for satellite nodes in dynamic LEO-SN. The nodes can select the return link adaptively according to the spatial position, mutual distance, queuing delay and available bandwidth of the surrounding satellite nodes on the basis of the model obtained through training of the Deep Q Networks (DQN). Simulation and analysis show that the proposed intelligent routing method possesses well convergence characteristics and generalization ability, and has better latency performance compared with a variety of routing methods.

Due to the characteristics of being license-free, the ISM (Industrial, Scientific, Medical) bands are frequently utilized by wireless communication systems. However, the ubiquitous and unmanaged IEEE 802.11 systems deployed in the ISM bands may bring non-negligible interference to emergency communications. This paper considers suppressing the transmission of 802.11 systems in 2. 4GHz ISM band for protecting the public safety in emergency scenarios. To this end, two kinds of suppression frames that comply with the 802.11 protocol are analyzed, which respectively borrow the ideas of virtual carrier-sense attack and spoofing. The validity of each of the frames is then verified both qualitatively and quantitatively by the designed experiments with multiple commercial off-the-shelf (COTS) 802.11 devices. Based on which we propose an integrated suppression method that is effective for all tested devices. Further experiments and analysis show that the presented method outperforms the state-of-the-art suppression methods in terms of energy consumption and applicability.

Spectrum prediction has recently gained a lot of attention due to its extensive applications in cognitive radio networks. However, most of the related research assumed that the spectrum occupancy pattern is time-invariant, which limits the performance of proposed prediction methods for the high burst frequency bands, e.g. the ISM (Industrial, Scientific, Medical) bands. In order to improve the prediction accuracy for them, this paper first analyzes the characteristics of the collected real WiFi data in 2.4GHz ISM band, and shows the burstiness of the band from multiple aspects. Based on the analysis, we then propose a MultiLayer Perceptron based Reinforcement Learning (RLMLP) method which could adaptively select the corresponding predicting action according to the state to which any piece of data belongs. The state space of the method consists of multiple data categories that are determined by the results of feature partitioning, while the action space is composed of multiple MLPs with the same mapping structure. Finally, numerical results on the collected data show that the proposed RLMLP method is significantly better than the state-of-the-art algorithms in terms of the prediction performance.

Sparsity based methods have gained its popularity in two-dimensional (2D) direction-of-arrival (DOA) estimation in recent years. However, these methods suffer from the off-grid problem, and also need additional angle pairing process, which results in degraded performance when applied in practice. In this paper, to address these problems, a novel and effective method named grid adaptive sparse Bayesian learning (GASBL) is proposed for 2D-DOA estimation with L-shape array from the perspective of sparse Bayesian learning. Specifically, an off-grid DOA model is proposed to enable grid adaptive refinement, and the auxiliary compound electric angle (CEA) is introduced to achieve automatic angle pairing of the elevation angles and azimuth angles. Then, a hierarchical probability framework with Laplacian prior is imposed. Finally, the 2D-DOA estimation problem is solved by Bayesian inferences. Compared with the state-of-the-art approaches, numerical results highlight the proposed method with more superior performance in terms of high angle resolution and robustness against the noise.

Received signal strength (RSS) measurement based source localization is highly dependent on the propagation model. However, such propagation model is not easy to be captured in the practical applications. In this paper, we address the multiple sources localization (MSL) problem while jointly estimating parametric propagation model. Specifically, we model the localization problem as being parameterized by the unknown source locations and propagation parameters. Then, the localization problem is reformulated as a joint parametric sparsifying dictionary learning (PSDL) and sparse signal recovery (SSR) problem. Finally, the problem is solved under the framework of sparse Bayesian learning with parametric dictionary approximation. We compare the proposed method with the state-of-the-art MSL algorithms as well as Cramér-Rao lower bound (CRLB). Numerical simulations highlight the effectiveness of the proposed method.

Cognitive radio (CR) has received wide attention for enhancing the spectrum utilization. Spectrum prediction can be fully utilized in both time and frequency domains for cognitive access. In this paper, Long Short-Term Memory (LSTM) networks method is adopted for spectrum prediction. Based on prediction of the power of future time slots, the method can help to achieve spectrum utilization flexibility by calculating throughput of shared channels. A new spectrum access strategy, which integrates optimal spectrum sensing interval in time domain and channel selection based on the presented LSTM networks method in frequency domain, is proposed. In particular, for multiple channels of the shared spectrum, the optimal sensing interval of each channel is calculated, which is then adopted as the reference output length of the LSTM networks method, with predicted results, a feedback which consists of a preferred channel list will be conducted for spectrum access. With both real-life and generated data, the proposed LSTM networks method is verified, which outperforms commonly used Neural Network (NN) and Hidden Markov Model (HMM) methods. Numerical results also show that the proposed spectrum access strategy is capable of increasing throughput of the cognitive system and reducing energy consumption of spectrum sensing significantly.

Cognitive satellite terrestrial networks have received widespread attention recently for improving the spectrum utilization. In this paper, a hybrid interweave-underlay spectrum access (HIUSA) scheme based on spectrum sensing in 5GHz license-exempt spectrum is proposed, which eliminates the performance degradation brought by inaccurate parameters estimation of the interference link in a large extent, and enhances the throughput of the cognitive system substantially. While analyzing relevant details, we also focus on the resource allocation (RA) problem in this scenario, the RA problem takes the maximum throughput of the cognitive satellite communications (SatCom) as target, and subjects to the requirements of cognitive terminals. Then, the convex RA problem is solved by a simplified iteration algorithm. Extensive simulation results are given to demonstrate the performance and effectiveness of the proposed HIUSA scheme as well as the RA approach, by contrasting with the legacy system as well as the common method.