The deep integration of artificial intelligence (AI) and communication technology is the typical feature of the 6G network.On the one hand, AI injects new vitality into the development of the 6G network, which can effectively use the data generated by the historical operation of the network.It enables the network to be self-maintained and selfoptimized, and accelerates the process of network intelligence.On the other hand, the rich scenarios and IoT devices of the 6G network provide a large number of application fields and massive data for AI.These can enable the better deployment of AI, fully demonstrate the performance advantages of AI, and provide high-quality services for users.However, in practice, it is difficult to give full play to the performance advantages of AI due to the difficulty of sample collection, high cost of the collection, and lack of universality which caused by the complexity of the environment.Therefore, academia and industry introduce generative adversarial network (GAN) into the design of wireless networks.The powerful feature learning and feature expression ability of GAN can generate a large number of generated samples, which realizes the expansion of the wireless database.The introduction of GAN can effectively improve the generalization ability of AI models for wireless networks.Owing to the excellent performance of GAN, the generative model represented by GAN has attracted increased attention in the field of wireless networks, and rapidly became the new research hotspot of 6G networks.Firstly, the principle of GAN and its different versions of improved derived models were summarized.Then, the framework, advantages and disadvantages of each model were analyzed.Secondly, the research and application status of these models in wireless networks were reviewed.Finally, the research trends of GAN were proposed for the 6G network requirements, which provided some valuable exploration for future research.

The intelligent reflecting surface (IRS) is considered a core technology of next-generation mobile communication.It has significant advantages in enhancing network coverage, spectrum efficiency, energy efficiency and deployment cost.The aerial intelligent reflecting surface (AIRS), which combines the high mobility of the air platform and the highquality link characteristics provided by the intelligent reflecting surface, can effectively assist the transmission from the base station to the users in complex communication scenarios and enhance the network coverage.A joint optimization problem of the base station beamforming, AIRS deployment location and phase shift design for the AIRS was studied for AIRS assisted multi-user massive multiple input multiple output (MIMO) systems.Under the condition of the statistical channel state information (CSI) being known, an optimization scheme of system ergodic sum rate based on block coordinate descent (BCD) was proposed.Firstly, an optimization model was established by jointly optimizing the transmit beamforming at base station, the placement and the phase shift for AIRS.Secondly, the nonconvex optimization problem was decoupled into three subproblems that were easy to deal with by BCD algorithm.Finally, lagrange multiplier method, relaxation variable method and RMSProp gradient descent algorithm were used to solve the subproblems respectively.The simulation results show that the proposed optimization scheme can effectively improve the ergodic sum rate of the system with good convergence properties.

The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial vehicle (UAV) and cellular communication network.To improve spectrum efficiency of the system, UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Additionally, a joint access control, mode selection, power control and resource allocation optimization problem was formulated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV users and ground cellular users.Specifically, the phase 1 method in the convex optimization was adopted for access control and feasibility check, and then the convex and concave procedure (CCCP) iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value, the original optimization problem can be simplified into a weighted maximization problem.Finally, the Kuhn-Munkres (KM) algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.

Significant attention has been paid to indoor localization using smartphones in both research and industry.However, the accuracy and robustness of localization remain challenging issues, particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning (PDR) devices in contemporary smartphones, an advanced indoor localization fusion method, anchored in the twin delayed deep deterministic policy gradient (TD3) framework, was proposed.In this approach, a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process, and a comprehensive continuous action space was introduced for the agent.To evaluate the performance of the proposed method, experiments were conducted and this approach was compared with three state-of-the-art deep Q network (DQN) based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy.

Accurate identification of influential nodes in complex networks is crucial for network management and network security.The local centrality method is concise and easy to use, but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology, which firstly applied the H-index and information entropy to assess the nodes, then combined the structural holes of the nodes with the structural characteristics of the nodes, and took into account the attribute of “bridging” while focusing on the node’s own quality and the amount of information about the neighboring nodes.The algorithm was validated by simulating the propagation process using the SIR model, and the Kendall correlation coefficient, complementary cumulative distribution function and propagation influence were applied to validate the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.

To improve the robustness and reduce the energy consumption of non-orthogonal multiple access (NOMA) networks, based on hardware impairments (HWI) of transceiver and non-perfect channel state information (CSI), an intelligent reflecting surface (IRS) assisted transmission power minimization algorithm for NOMA networks was proposed.The joint optimization problem of active beam assignment at the base station and passive beam assignment at the IRS was modeled based on HWI and non-perfect CSI.The system considered the user quality of service (QoS) constraint, the serial interference cancellation constraint and the reflection phase shift constraint of the IRS.To solve this nonconvex optimization problem, the QoS constraints were firstly transformed using linear approximation and S-Procedure methods.Then the optimization problem was decomposed into two subproblems.The active beam assignment subproblem was solved using the successive convex approximation (SCA) method.The passive beam assignment subproblem was solved using the penalized convex-concave process algorithm.Finally, the final solution was obtained by iterating the subproblems alternately using alternating optimization.The simulation results show that the proposed algorithm reduces 17.05% compared to the or thogonal multiple access robust algorithm in terms of transmitted power.In terms of system robustness, the proposed algorithm improves by 20.69% and 31.14% compared to the HWI robust algorithm and the CSI robust algorithm, respectively.

In a vehicular status update network based on broadcast communication, a D/Geo/1/1 queuing mechanism with minimum timestamp priority was adopted to transmit data packets.The queuing theory was used to analyze the age-ofinformation (AoI) outage probability of the receivers and the average transmit power of the transmitter.Based on the monotonicity of the AoI outage probability with respect to packet sampling rate and transmit power, an iterative algorithm for jointly optimizing sampling rate and transmit power was proposed to minimize the maximum AoI outage probability of all receivers under power constraint, which fairly improved information freshness of all receivers under sampling and power control.Simulation results verify the accuracy of theoretical analysis and the efficiency of the algorithm.

In the context of the Internet of vehicles, the placement and deployment number of edge servers directly affect the efficiency of edge computing.Due to the high cost of deploying a large edge server on a macro base station and a base station, it can be complemented by deploying a small edge server on a micro base station, and the cost reduction needs to be optimized by optimizing the placement of large edge servers.In order to minimize the deployment cost and service delay of the edge server, and maximize the operator’s revenue and server load balance, the edge server placement problem combined with the vehicle networking user application service was modeled as a multi-objective optimization problem and a placement scheme based on improved NSGA-Ⅱ algorithm was proposed.The experimental results show that the proposed scheme can reduce the deployment cost of edge servers by about 44%, the latency by about 14.2%, and improve the revenue of operators by 24.2%, which has good application value.

In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.

With the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges, including issues related to mutual interference between users and poor model robustness.Therefore, a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network (Wiblack-Net) to extract unique features for each individual user.Firstly, the common features among multiple users were extracted using the backbone network.Then, a binary classifier was assigned to each user to determine the presence of the target user within a given group, thereby achieving identity recognition for multiple users based on concurrent behavior.In addition, experiments comparing Wiblack with several independent binary classification models and a single multiclassification model were conducted to analyze operational efficiency.System performance experimental results demonstrate that when simultaneously identifying the identities of three users, Wibalck achieves an average accuracy of 92.97%, an average precision of 93.71%, an average recall of 93.24%, and an average F1 score of 92.43%.

To address the issue of severe network congestion in the scenario of massive machine-type communications (mMTC), where machine-type communication devices (MTCD) employing traditional random access schemes often encountered challenges leading to a large number of MTCD failing to access the network successfully, a novel approach called preamble retransmission-based dynamic access class barring (PRT-ACB) was proposed.By utilizing the number of MTCD preamble retransmissions, the MTCD attempting to initiate access in each random access opportunity (RAO) were categorized into different sets of high and low priority.In conjunction with an estimation model for the number of payloads in each RAO, high and low-priority limiting factors and available preamble pools were dynamically adjusted based on the access load in each RAO.This allowed more MTCD to successfully access the network before reaching the maximum number of preamble transfers.Simulation results have demonstrated that the proposed scheme effectively enhances the MTCD successful access rate and reduces the time delay required for MTCD to access the network.The proposed scheme can serve as a solution to alleviate the congestion caused by a massive influx of communication devices attempting simultaneous network access.

Secure cognitive radio (SCR) system based on reconfigurable intelligent surface (RIS) assumes that secondary user (SU) and primary user (PU) locate on the same side of the base station (BS), which can only cover part of the communication area and limits the deployment flexibility and effectiveness of the RIS.In order to solve the above problem, a new SCR system based on simultaneously transmitting and reflecting surface (STARS) was proposed.In the system, STARS could achieve full coverage of communication area, improve the received signal strength of the SU with the transmission beamforming vector and reduce the interference of the SU to the PU with the reflection beamforming vector, which provided new optimization degree of freedom (DoF) for the design of the SCR system.Considering secrecy rate constraint of the SU, interference power constraint (IPC) of the PU and transmission/reflection parameters constraint of the STARS, BS active beamforming vector and STARS transmission/reflection beamforming vectors were jointly optimized to minimize the BS’s transmit power from the perspective of reducing the total power consumption of the system.The minimization problem is a variable coupling non-convex problem, which is difficult to tackle directly.An alternating optimization (AO) algorithm based on difference-of-convex relaxation (DCR) method and sequential rank-one constraint relaxation (SROCR) method was proposed to jointly design the BS active beamforming vector and STARS transmission/reflection beamforming vectors.Simulation results show that the proposed algorithm has good convergence performance and effectively reduces the interference of SU on PU.Compared with the traditional RIS, random phase, maximum-ratio transmission (MRT) and equal energy splitting (Equal ES) schemes, the BS transmitting power is reduced by 8.3%, 15.4%, 5.9% and 5.3%, respectively.

A miniaturized ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna was designed.The antenna was composed of two square spiral resonators shorted inside and outside.By adjusting the edge length of the outer ring spiral resonator and the spacing between the inner and outer ring spiral resonators, the resonant frequency of the proposed tag antenna could be effectively tuned.The simulation results show that the proposed tag antenna has good impedance matching and the frequency band range covers the whole UHF band (840～960 MHz).It has a dimension of 7.60 mm×7.60 mm×0.05 mm, which is 96% smaller than the traditional spiral resonator (SR) antenna.The measure result shows that the proposed tag antenna achieves the maximum reading distance at 910 MHz in 4 W effective isotropic radiated power.The proposed UHF RFID tag antenna has some advantages of small size, wide frequency band, low cost and easy batch fabrication, and is suitable for applications with small size requirements.

Considering the practical communication environments, the outage performance and the throughput were investigated for multiple reconfigurable intelligent surfaces (RIS) assisted non-orthogonal multiple access (NOMA) system with imperfect serial interference cancellation (SIC).The channel coefficients of the RIS-assisted NOMA system including the direct and the reflecting paths were approximated by Gamma distribution.An approximate closed-form expressions of outage probability were derived and validated through Monte Carlo simulations.The study investigated the relationship between the outage performance and system parameters, including the impact of imperfect SIC, the number of RIS and the number of reflecting units.The simulation results demonstrate that, compared with the single RIS-assisted NOMA and the traditional NOMA system, the multi-RIS-assisted system achieves a 14.2 dBm gain in signal-to-noise ratio (SNR) for a given outage probability.Thus, employing multiple RISs can improve the outage performance and the system throughput effectively, thereby compensating for the performance degradation caused by imperfect SIC.