Unmanned aerial vehicles (UAVs) have been widely deployed as aerial relays in wireless networks. Moreover, a UAV swarm can form a virtual array and improve the transmission distance and energy efficiency of relay signals through collaborative beamforming (CB). However, due to the different propagation channels between the source node and each relay UAV, there are time delays and phase offsets among the amplify-and-forward (AF) processed signals of each UAV, resulting in CB performance deterioration. Hence, a time-phase-robust collaborative beamforming algorithm (TPR-CB) for UAV swarm-enabled AF relay systems is proposed in this paper. Firstly, a TPR-CB architecture based on the tapped delay line (TDL) is designed. Subsequently, the iterative calculation formula for the TDL weight vector is deduced by minimizing mean square error (MSE) through stochastic gradient descent (SGD). The MSE expression and feasible step size range of SGD are provided. Finally, the expressions for the average far-field beampattern and coherent gain are derived to characterize the performance of TPR-CB. The simulation results show that compared with the existing CB method, TPR-CB can improve the coherent gain and beampattern in the presence of time delay and phase offset.

The space-air-ground integrated networks (SAGNs) are promising components of the next generation communication system, and unmanned aerial vehicle (UAV) swarms are the primary infrastructures of the SAGNs aerial layer, used for relaying information. Furthermore, UAV swarms form virtual antenna arrays (VAAs) and implement collaborative beamforming (CB), which can improve the deep-space communication distance and expand the ground coverage. However, in practical engineering, due to the doppler effect and local oscillator random frequency drift, there is a frequency offset between UAVs, even after frequency synchronization, resulting in CB performance degradation. Therefore, in the presence of frequency offset, this paper investigates and presents the derivation of the average far-field beampattern, 3-dB beamwidth, and complementary cumulative distribution function (CCDF) for UAV swarm executing CB in SAGNs. The results show that the average far-field beam-pattern, peak power, 3-dB beamwidth, and CCDF, deteriorate as frequency offset intensifies. Furthermore, as the UAV swarm scale increases, the peak power attenuation is aggravated, and the decrease in CCDF is steeper. With the increase in the UAV swarm distribution range, the 3-dB beamwidth expansion is alleviated.

With the proliferation of low-cost mobile edge devices, security and reliability have become crucial for mobile edge computing networks, especially for high-stakes applications. To facilitate it, Radio Frequency Fingerprint Identification (RFFI) has emerged as a promising physical layer security paradigm, offering a non-cryptographic and lightweight solution. However, most existing Deep Learning (DL) based RFFI methods operate under a closed-set assumption, limiting their ability to recognize devices not seen during training and posing a risk of misclassification. Addressing the open-set RFFI problem is critical for real-world deployments, where the system must handle both known and unknown devices, ensuring robust security in dynamic environments. In this paper, we propose OpenRFI, a novel test-time fine-tuning-based RFFI framework, consisting of two sequential stages: pre-training and test-time fine-tuning. During the pre-training stage, we design a data augmentation module, a feature extraction module, and an efficient hybrid loss function to minimize intra-class feature distances and tighten decision boundaries, enhancing the model's ability to distinguish between different classes. In the test-time fine-tuning stage, we introduce a fine-tuning dataset construction module and a full-parameter fine-tuning module to dynamically adapt to the test environment and capture information from unknown samples, further improving open-set recognition. We theoretically establish the performance boundary of the fine-tuning dataset construction method, providing insights into its robustness and scalability. Extensive numerical results based on an open source dataset demonstrate the effectiveness of the proposed OpenRFI framework in comparison with existing baselines.

Asset price jumps are very common in financial markets, and they are essential to accurately predict volatility.This article focuses on 50 randomly selected stocks from the Chinese stock market, utilising high-frequency data to construct two jump models, the heterogeneous autoregressive quarticity jump model (HARQ-J) and the full heterogeneous autoregressive quarticity jump model (HARQ-F-J), which take into account jump variables based on existing models (HARQ and HARQ-F).To further enhance the accuracy of our volatility forecasts, the study combines the newly constructed models with the machine learning (ML) to form a hybrid model.Finally, the empirical research shows that the new hybrid model performs better than existing traditional prediction methods.In particular, the long-and short-term memory (LSTM) function is significantly better than other machine learning functions.Among all the LSTM models tested by the model confidence set (MCS), the HARQ-F-J-LSTM model has the highest prediction accuracy, followed by the HARQ-J-LSTM model.

Unmanned aerial vehicle (UAV) assisted wireless network has been recognized as an effective technology to facilitate the formation of a super flexible low-altitude platform for relieving the strain on traditional ground cellular systems. However, the on-demand deployment of the UAV-assisted wireless networks (OWN) becomes an essential yet challenging issue, as the constraints of UAVs’ location, resource provisioning, and demand distribution should be jointly considered. In this work, we investigate the OWN problem by proposing an autonomous learning framework (ALF) consisting of three sequential stages: demand prediction, proactive deployment, and resource allocation fine-tuning, which can be capable of autonomous network planning without reliance on manual operations in an extremely dynamic environment. In the demand prediction stage, we first design a dual transformer network (DTN) to capture the temporal and spatial dependencies of wireless traffic. We further reduce the computational complexity of DTN from quadratic time complexity to log-linear time complexity. In the proactive deployment stage, we jointly optimize the UAVs’ location and resource provisioning by proposing a modified general benders decomposition algorithm with a$\Gamma $-optimal convergence, where a learning-based discerning module is designed to accelerate the algorithm. In the resource allocation fine-tuning stage, we propose a simulated annealing-based algorithm to minimize the transmission rate degradation of users to reduce the bias caused by traffic demand prediction. Extensive numerical results based on an open source dataset demonstrate the effectiveness of the proposed methods in comparison with existing baselines.

In recent years, cooperative jamming (CJ) is introduced as a promising method to improve the security performance in the presence of eavesdroppers. By masking the confidential signal with the help of the cooperative jammer, the signal-to-noise ratio of the eavesdropper can be selectively reduced without prior information, whereas the authorized receiver is unaffected since the CJ can be suppressed by some means. Unfortunately, with the impacts of the clock jitter caused by non-ideal crystals, the received CJ may suffer sampling clock offset, carrier frequency offset and phase noise, which deteriorates the performance of the CJ cancellation at the authorized receiver. In this paper, the impacts of clock jitter on CJ cancellation are investigated. First, the deterioration of CJ caused by sampling clock offset is modeled as inter-symbol interference (ISI) in the time domain. Then, the impacts of the sampling clock offset, carrier frequency offset and phase noise on the spectral purity of CJ are modeled as inter-frequency interference (IFI). Furthermore, the expression of the power of residual CJ (PRCJ) is derived to analyze the impacts of clock jitter on CJ cancellation. Finally, numerical results prove the theoretical analysis, indicating that the PRCJ increases as the carrier frequency offset and the 3 dB coherence bandwidth of phase noise increases, and is more sensitive to phase noise.

Unmanned aerial vehicle (UAV) assisted wireless network is emerging as a promising technology to address the extremely high and dynamic traffic demands in future communication systems. In this paper, we investigate the on-demand deployment of UAV assisted wireless networks (OWN) problem. We propose an efficient autonomous learning framework (ALF), for learning a proactive and optimal on-demand deployment policy to complement terrestrial networks. In ALF, the OWN problem is solved in two co-related stages: the demand prediction stage and the proactive deployment stage. We first design a dual transformer network (DTN) to forecast the wireless traffic in the demand prediction stage. To decrease the complexity of DTN, we employ a patch embedding method and a modified self-attention scheme to improve the efficiency. With the predicted traffic demands, we jointly optimize the UAVs' location and wireless resource allocation by formulating it as a non-convex mixed integer nonlinear programming (MINLP) problem in the proactive deployment stage. To provide an efficient guaranteed solution to the MINLP problem, a multi-cut general benders decomposition algorithm is proposed to decompose the optimization problem into two subproblems. We theoretically prove that the proposed algorithm can achieve a T-optimal convergence. Extensive simulation results show the proposed solution outperforms existing baselines.

Recently, mobile operators have been shifting to an intelligent autonomous network paradigm, where the mobile networks are automated in a plug-and-play manner to reduce the manual intervention. Under this circumstance, serious inter-cell interference becomes inevitable which may severely deteriorate system throughput performance and users’ quality of service (QoS), especially for dense residential small base station (SBS) deployment. This paper proposes an intelligent inter-cell interference coordination (ICIC) scheme for autonomous heterogeneous networks (HetNets), where the SBSs agilely schedule sub-channels to individual users at each Transmit Time Interval (TTI) with aim of mitigating interferences and maximizing long-term throughput by sensing the environment. Since the reward function is inexplicit and only few samples can be used for prior-training, we formulate the ICIC problem as a distributed inverse reinforcement learning (IRL) problem following the POMDP games. We propose a non-prior knowledge based self-imitating learning (SIL) algorithm which incorporates Wasserstein Generative Adversarial Networks (WGANs) and Double Deep Q Network (Double DQN) algorithms for performing behavior imitation and few-shot learning in solving the IRL problem from both thepolicyandvalue. Numerical results reveal that SIL is able to implement TTI level’s decision-making to solve the ICIC problem, and the overall network throughput of SIL can be improved by up to 19.8% when compared with other known benchmark algorithms.

In this paper, we investigate the resource management for radio access network slicing from user access control and wireless bandwidth allocation perspectives. First, to guarantee users' QoS, we propose two admission control (AC) policies to select admissible users from the perspective of optimizing the QoS and the number of serving users respectively. Then, to optimize the bandwidth utilization for the selected admissible users, we investigate the slice association and bandwidth allocation (SABA) problem and propose network centric and UE centric SABA policies respectively. Numerical results show that in typical scenarios, our proposed AC and SABA policies can significantly outperform traditional policies in terms of wireless bandwidth utilization and number of admissible users.

4G systems employ discontinuous reception (DRX) mechanism to conserve energy by intermittently suspending network connections. Moving to 5G, a wide range of applications with diverse characteristics need to be supported. Especially, machine-type communication (MTC) has been identified as one of the three generic 5G services. Compared with that of human-type communication (HTC), the traffic patterns of MTC could be very bursty and even nonstationary. Thus, using the legacy DRX mechanism will cause longer access delay and/or higher power consumption. In this paper, we propose a new online learning-based DRX mechanism, called AC-DRX, with aim to improve device energy efficiency for MTC services by adapting to varying traffic pattern. In AC-DRX, the time is slotted into intervals and actor-critic (AC) algorithm is used for adjusting DRX cycles by learning the traffic statistics at the beginning of every time interval. To accelerate the learning process, we propose a symmetric sampling method in the AC algorithm. Numerical results show that our proposed AC-DRX mechanism significantly outperforms the legacy DRX and extended DRX mechanisms in terms of both delay and energy efficiency. The performance is fairly close to the upper bound where perfect traffic knowledge is assumed known.

The integration of multiple Radio Access Technologies (RATs) of licensed or unlicensed bands is considered as a cost-efficient way to greatly increase network capacity of mobile networks. In this paper, we propose a Smart Aggregated RAT Access (SARA) strategy with aim to maximize network throughput while meeting diverse traffic Quality of Service (QoS) requirements. We consider a scenario where users with different QoS requirements access to the Heterogeneous Network (HetNet) with coexisting Cellular-WiFi. In order to maximize network resource utilization in such a complex and dynamic environment, we exploit multi-agent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual users based on sensing dynamic channel states and traffic characteristics. We first use Nash Q-learning to provide a set of feasible RAT access strategies, and then employ Monte-Carlo (MCTS) based Q-learning to perform resource allocation which tries to maximize system throughput while meeting traffic QoS requirements. Numerical results reveal that the network access capacity can be maximized while meeting traffic QoS requirements with limited number of searches by using our proposed SARA. Compared with traditional WiFi offloading schemes, SARA can significantly improve system resource utilization and capacity while guaranteeing QoS requirements of UEs.