As the multiple-input–multiple-output (MIMO) technologies are increasingly emerging in the arena of modern wireless systems, their cost-effective and efficient over-the-air (OTA) performance verification under arbitrary fading channels, especially, for high-volume beam-reconfigurable product verifications, play a crucial role toward their technology adoption. The 2-D multi-probe anechoic chamber (MPAC) MIMO OTA measurement method has been well-developed, but requires both precise infrastructure and heavy calibration. Moreover, the extension to 3-D channel model appears impractical for the MPAC method with its significantly increased probe numbers in 3-D scenario. To address these challenges, we propose a novel single-probe anechoic chamber (SPAC) measurement method that transforms spatial synthesization of MPAC into time synthesization whereby the only source of measurement error can be restrained in time synchronization. The wireless community can be widely benefited with this proposed low-cost and efficient methodology, especially in the research and product prototyping stage. In this article, a proof-of-concept measurement system has been designed and implemented, which validates the efficiency of the proposed solution.

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder–decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.

In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

Over-The-Air (OTA) measurement is considered the preferred method for measuring the antenna system and end-to-end performance of Multiple-Input-Multiple-Output (MIMO) devices under test. Spatial correlation has been widely utilized as a key metric for evaluating the accuracy of MIMO OTA measurements. However, there is no guarantee that the standard signal streams convoluted with specified impulse responses will be ideally independent of each other in the implementation of the MIMO OTA testing system. Thus, it is envisaged that the spatial correlation in practical MIMO OTA testing systems may not be exactly equivalent to the expected value of the ideal theoretical model. In this paper, we propose a new evaluation framework for evaluating the spatial correlation performance of MIMO OTA testing system. This evaluation framework provides a novel observation method for spatial correlation, which reflects the non-ideal configuration of the MIMO OTA testing system and can be utilized to predict or cross-validate spatial correlation errors that deviate from the theoretical model. The experimental and simulation results have been verified against the theoretical model, demonstrating good consistency between the theoretical model and the proposed evaluation framework. Furthermore, several test scenarios have been verified with the different varying factors.

With the development and application of Fifth-Generation (5G) mobile communication technologies, cutting-edge techniques including Multiple-Input Multiple-Output (MIMO) and beamforming have come to the forefront. However, the performance gain of these techniques is in the trade of deep understanding of regional multipath channel. To obtain the Channel Impulse Response (CIR) of a given area will typically require pointwise scanning in both channel measurements or Ray Traceing (RT) based modelling method. In this paper, we propose the Ray Inversion (RI) method. The RI method aims to obtain the Region Channel Response (RCR) with only a limited number of observed CIR and avoid the involvement of surrounding environment information. The method classifies of observed propagation paths first and then inverts the ray transmission process to identify the locations of the secondary wave sources and obtain the local regional response. The method significantly enhances the efficiency of obtaining RCR. An iterative fission method has been further designed to guarantee both the efficiency and accuracy. The utilization of this technology can assist the RT method in achieving rapid acquisition of RCR. The RI method is based on geometric inversion, making it applicable to different frequencies. Compared to the traditional point-by-point scanning RT method, the computation speed in an open scene is increased by 15 times, and in a closed scene by 4 times when the receiving antenna spacing of RCR is 1m. The RI method provides an innovative perspective for the research and application of next-generation wireless communication.

With the continuous expansion of low-Earth-orbit (LEO) satellite networks, the services within these networks have exhibited diverse and differentiated demand characteristics. Due to the limited onboard resources, efficient network resource allocation is required to ensure high-quality network performance. However, the dynamic topology and differentiated resource requirements for diversified services pose great challenges when existing resource awareness or prediction methods are applied to satellite networks, resulting in poor awareness latency and the inaccurate prediction of resource status. To solve these problems, a network resource allocation method based on awareness–prediction joint compensation is proposed. The method utilizes the node awareness latency as a prediction step and employs a long short-term memory model for resource status prediction. A dynamic compensation model is also proposed to compensate for the prediction results, which is achieved by adjusting compensation weights according to the awareness latencies and prediction accuracies. Furthermore, an efficient, accelerated alternating-direction method of multipliers (ADMM) resource allocation algorithm is proposed with the aim of maximizing the satisfaction of service resources requirements. The simulation results indicate that the relative error between the compensation data and onboard resource status does not exceed 5%, and the resource allocation method can improve the service resource coverage by 15.8%, thus improving the evaluation and allocation capabilities of network resources.