Massive MIMO is a promising technology to improve both the spectrum efficiency and the energy efficiency. The key problem that impacts the throughput of a massive MIMO system is the pilot contamination due to the nonorthogonality of the pilot sequences in different cells. Conventional channel estimation schemes cannot mitigate this problem effectively, and the computational complexity is increasingly becoming larger in views of the large number of antennas employed in a massive MIMO system. Furthermore, the channel estimation is always carried out with some ideal assumptions such as the complete knowledge of large-scale fading. In this paper, a new channel estimation scheme is proposed by utilizing interference cancellation and joint processing. Highly interfering users in neighboring cells are identified based on the estimation of large-scale fading and then included in the joint channel processing; this achieves a compromise between the effectiveness and efficiency of the channel estimation at a reasonable computational cost, and leads to an improvement in the overall system performance. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum–energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

As an emerging architecture for the future 6G Internet of vehicles (IoV), the air-ground integration network has become a paradigm to achieve reliable interconnection everywhere. Unmanned aerial vehicles (UAVs), as low-altitude aerial platforms, can cooperate with ground infrastructures with the advantages of high flexibility and low cost. However, wireless resource allocation for vehicle-to-UAV (V2U) communications has encountered various challenges, such as air-ground spectrum sharing, dynamic topology, fast-changing channels and time-sensitive services. In this paper, we propose a multi-agent federated learning and dueling double deep Q network (D3QN) based resource allocation, namely Fed-D3QN, to jointly optimize channel selection and power control to meet low latency and reliability requirements of IoV services. Simulation results demonstrate that the proposed Fed-D3QN algorithm has good stability in the highly dynamic air-ground integration network. Additionally, it reduces the total delay of vehicle-to-infrastructure (V2I) links and improves the transmission success rate of V2U links.

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.

Sector antenna azimuth plays an important role in the operation and maintenance of mobile networks. It is adjusted frequently so as to guarantee the high quality of coverage and low interference among neighboring cells. As one of the key elements of base station almanac (BSA), the azimuth of all the base stations need to be precisely managed by the operator. However, currently it is generally acquired by on-site measurement and updated manually, which is neither timely nor cost-effective. In addition, it is not open for third parties who need the information for network analysis. In view of this, by transforming the problem of azimuth estimation into one of searching for the optimal cell boundaries under the constraint of site location, this paper proposes an azimuth estimation method based on an improved multi-class soft-margin support vector machine (SVM). The explicit expression of the objective function of the problem is deduced through Lagrange duality. A circular one-versus-one strategy is utilized in dealing with the multi-class classification problem. In addition, a so-called average confusion index is designed to evaluate the degree of boundary separability of a base station so as to prejudge whether it is worthwhile to estimate its azimuths. Experiments are undertaken to validate the proposed algorithm by utilizing the spatial-temporal wireless signal dataset crowdsensed from massive user terminals in the 4G network. The experiments show that the proposed algorithm gives a significantly higher accuracy compared to that of two existing methods, namely the Gauss and radial rasterization methods. The impacts of data volume, penalty term and the boundary separability on the azimuth estimation is also discussed. The new algorithm is a promising alternative to the conventional manual solution with a much higher efficiency and lower cost, thus improving the intelligence of mobile network operation.

In view of the facts that traditional traffic models are less effective in describing the new mobile internet applications and that the mobile user service behavior among regions with different levels of economic development are always different, a new modeling methodology of user service behavior specifically for mobile internet applications is proposed in this paper. Based on the crowdsourcing measurement data of the service behavior collected through massive end users in live mobile networks of China, the properties of three typical mobile internet services in three typical geographical regions are analyzed and compared with each other, by utilizing the proposed modeling methodology. And the relationships between some key properties of service behavior are analyzed with the maximal information coefficient method and the Pearson correlation coefficient method. The key parameters, differences, and internal relations among these services in typical geological regions of China are then identified. It provides a solid foundation for wireless product design, network planning, and optimization of mobile Internet services-oriented networks specifically in China, and is also a valuable reference for other countries of the similar situation.