In this paper, we propose a three-dimensional (3D) geometry-based stochastic model (GBSM) for double-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) air-to-ground (A2G) communication systems. We develop the GBSM for dual-RIS channels, where RIS arrays are strategically mounted on unmanned aerial vehicles (UAVs) to reflect signals from the UAV transmitter towards the ground receiver via cascaded RIS links. The flexible trajectories and on-demand deployment of UAVs effectively mitigate the degradation caused by obstructive elements like buildings and trees. Furthermore, we incorporate a beam-domain channel model (BDCM) into the geometric framework to systematically analyze its propagation framework. This approach reduces computational complexity and enables systematic analysis of the system’s propagation mechanisms. The model captures dynamic behaviors in realistic scenarios by integrating real-time kinematic parameters, including velocities and accelerations of the UAV transmitter, ground receiver, and RIS-mounted UAVs. Key propagation characteristics, such as cross-correlation functions (CCFs), autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacity, are analyzed by comparing the proposed beam-domain approach with conventional geometric methods. Simulation results demonstrate that the statistical properties obtained from the beam-domain channel model closely match those derived from the geometry-based stochastic model, validating the accuracy of the proposed approach. Moreover, the beam-domain method significantly reduces computational complexity over traditional geometric techniques, offering valuable insights for designing efficient distributed RIS-assisted A2G communication systems.

Large-scale multiple-input-multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. For near-field scenarios, the spherical wavefront model is commonly utilized to depict the propagation characteristics of large-scale MIMO communication channels. However, employing this modeling method necessitates the computation of angle and distance parameters for each antenna element, resulting in challenges regarding computational complexity. To solve this problem, we introduce a subarray decomposition scheme with the purpose of dividing the whole large-scale antenna array into several smaller subarrays. This scheme is implemented in the near-field channel modeling for large-scale MIMO communications between the base station (BS) and mobile receiver (MR). Essential channel propagation statistics, such as spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities, are derived and discussed. A comprehensive analysis is conducted to investigate the influences of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics. The proposed channel model criterions, such as the modeling precision and computational complexity, are also theoretically compared. Numerical results demonstrate the effectiveness of the presented communication model in obtaining a good tradeoff between modeling precision and computational complexity.

Satellite edge computing can provide ubiquitous and reliable connectivity to remote or disaster area networks that are difficult to serve. However, due to the explosive growth of Internet-of-Things (IoT) data traffic, satellite edge services alone make it difficult to meet the latency and energy demands of abundant IoT devices. Edge learning, combined with edge computing and machine learning, is expected to be the key to solving this problem. This paper constructs a Satellite-assisted IoT network model consisting of terminals, satellites, and a cloud center. Terminals can offload tasks according to actual needs for load balancing. An edge learning approach using cloud edge collaboration is proposed. Then, for concurrent random tasks with different service demands, a minimization problem for the weighted sum of system latency and energy consumption is formulated. Finally, a Model-assisted Two-tier Reinforcement Learning (MTRL) optimization algorithm for task offloading decisions and resource allocation is proposed to solve this problem. Simulation results show that the proposed algorithm has a good performance in convergence. The system performance is better than existing algorithms, which can lead to a 27.5% reduction in the system cost. Furthermore, the proposed algorithm can lead to a smaller increase in system cost as the number of IoT devices increases.

In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) empowered by reconfigurable intelligent surface (RIS) is presented for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication systems. Owing to the channel non-stationarity, spherical wavefront, and antenna configurations in RIS-empowered V2V channel, the geometry-based channel models suffer from high computational complexity, thereby leading to high hardware burden. To address this issue, a novel beam domain channel model (BDCM) is generated from the proposed geometry-based channel model through a beamforming operation based on discrete Fourier transform (DFT). To describe the non-stationarities of the V2V channels empowered by RIS, the channel model presented in this paper introduces real-time velocities and accelerations to capture the motion features of the communication terminals. The propagation characteristics including spatial cross-correlation functions (CCFs), temporal autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacities of the proposed communication system are derived and discussed. Some comparisons between the propagation characteristics of the proposed GBSM and those based on BDCM with respect to the different physical parameters of RIS and different environmental variables are investigated. Furthermore, numerical results indicate that the proposed channel model works well by changing the velocity parameters in different motion states.

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

Deep learning (DL) based autoencoder is a potential architecture to implement end-to-end communication systems. In this paper, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal-to-noise ratio (SNR) in DL-based communication systems and show that training at high SNR can produce a good training convergence performance for the autoencoder.

Universal filtered multi-carrier (UFMC) is a potential waveform technology, which can efficiently combat different carrier frequency offsets (CFOs) of multiple users. First, adaptive modulation and power allocation are applied for each subcarrier to meet a preset bit error rate (BER) requirement by ignoring CFOs. Then, we prove that different CFOs will cause different interference variances to adjacent users, which results in performance degradation in UFMC systems. To reduce the interference caused by CFOs and improve achievable rate, a novel adaptive filter configuration algorithm is proposed to adaptively design the parameters of the finite impulse response filters. Specifically, the proposed algorithm is available for the UFMC systems, where the user equipments are allocated with different bandwidths. Finally, simulation results show that the proposed adaptive filter configuration algorithm can dramatically eliminate the interference caused by different CFOs, and achieve better BER performance and a higher achievable rate than the conventional scheme.