With the rapid advancement of artificial intelligence (AI), deep learning (DL) has been extensively applied in channel modeling to capture complex nonlinear relationships and uncover underlying signal propagation mechanisms. However, current DL-based channel modeling methods typically rely on extensive training data, which limits their performance and adaptability in dynamic and changing environments. To address this imperative, this paper proposes a DL-enhanced channel parameter prediction model based on adaptive meta mask region-based convolutional neural networks (AMM-RCNN). First, an optimized Mask RCNN network is designed to extract the key environmental information from satellite images. The extracted scatterer feature maps and propagation statistics are used as the multimodal input of network, so as to capture the correlation between propagation environment and channel characteristics. Second, the meta-learning algorithm is applied to improve the prediction accuracy of the network with sparse training samples. The method utilizes measurement data from multiple routes as meta-learning tasks, thereby enhancing the generalization ability of the model in new scenarios. Finally, the proposed model is trained and validated with using real-world channel measurement data collected from a university campus. Simulation results demonstrate that the proposed model can accurately predict channel parameters across diverse routes in campus scenarios.

With the proliferation of sensitive information transmitted in Internet of Things (IoT), physical layer security (PLS) has emerged as a key technique to ensure the data confidentiality in complex cellular IoT environments. However, the massive IoT devices (IoTDs) organized in multiple cells pose challenges to secure IoT such as severe inter-cell interference and limited spectrum resources. In this article, we focus on the secure communication and interference management in multi-cell multi-carrier multiple-input single-output non-orthogonal multiple access (MISO-NOMA) IoT networks with imperfect channel state information (CSI). To maximize the secrecy sum rate (SSR), a resource optimization problem is formulated by jointly designing subchannel assignment, secure beamforming and artificial noise (AN) injection, while guaranteeing the quality-of-service (QoS) requirements, power budget, and subchannel assignment constraints. To solve the non-convex problem, an interference-aware secure resource allocation scheme is proposed, which decomposes the problem into two joint optimization subproblems. For the first subproblem regarding subchannel assignment, we develop an improved matching algorithm based on adaptive interference hypergraph (AIHG). The second involves secure beamforming and AN injection, which is designed by using accurate surrogate functions and alternating optimization (AO) algorithm. Simulation results validate the framework’s robustness and convergence, demonstrating a significant improvement in secrecy performance over benchmarks.

Accompanied by the construction of new power system with renewable energy, like the offshore wind power and desert photovoltaic power, terrestrial ground 5G is no longer able to fulfill the communication requirement of industrial power IoT (IPIoT) with tremendous equipment in remote areas. Under this circumstances, we put forward the NOMA-enabled space–air–ground integrated IPIoT network (SAGIN-IPIoT) model, with a satellite to achieve wide coverage, and multiple UAVs to strengthen hot spot communication. NOMA is leveraged to improve system throughput by allowing the common frequency resource shared among multiple users. An energy-efficient (EE) maximization problem is formulated to jointly optimize subchannel and terminal power. However, since the objective involves both continuous and binary variables, it is a mixed integer nonlinear programming (MINLP) issue. Thus, we decompose it into two subproblems, which are respectively solved by matching game and Lagrange dual method with low complexity. According to the theoretical analysis and simulations, we can conclude that the method has better performance than the benchmark method.

Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult to meet the growing demand for seamless network connection. Meanwhile, current network resource allocation methods mainly research on how to improve system performance only from the perspective of resource utilization, but rarely take users’ specific needs for network content into consideration. This brings severe challenges to efficient network service and flexible resource allocation. Therefore, we construct the content service-oriented resource allocation model for space–air–ground integrated sixth generation networks (SAGIN 6G), and formulate the three-sided matching issue among the space–air–ground integrated network equipment (SAGINE), content sources, and users. In this model, users request to establish connection with SAGIN which forwards users’ request to content service provider (CSP). CSP manages the creation of content data, and finally returns the requested content to users through SAGIN. For the content service-oriented resource allocation in SAGIN, finding the optimal stable three-sided matching with the largest cardinality is an NP-complete problem. Therefore, to efficiently solve the above issue, we design some reasonable restrictions and convert it to a restricted three-sided matching problem with size and cycle preferences. We further develop the content-oriented resource allocation algorithm (COR2A) and the user-oriented resource allocation algorithm (UOR2A) in a distributed manner. Extensive simulations verify our approach outperforms traditional benchmark resource allocation schemes in terms of system throughput, CSP revenue, and user experience.

Abstract In order to overcome the disadvantages of traditional channel simulation approaches and achieve more accurate millimetre‐wave (mmWave) channel simulation under the condition of limited measured data, a novel Long Short‐term Memory Networks (LSTM) based hybrid mmWave channel simulation approach is proposed in this work. The proposed hybrid approach takes advantages of LSTM‐based time‐varying model of path loss and large‐scale channel parameters, statistical model of intra‐cluster multipath parameters and ray tracing, which are able to accurately simulate path loss, large‐scale channel parameters, multipath parameters and channel impulse responses, respectively. Based on the mmWave channel data measured in the waiting hall of Qingdaobei Railway Station, it is verified that the simulation results of our proposed hybrid approach are in good agreement with the measured data and better than that of existing channel simulation approaches. The hybrid channel simulation approach proposed in this work has important application value for channel modelling and simulation in the case of small amount of data.

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.

Abstract In this work, an artificial neural network (ANN) based time‐varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN‐based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high‐voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

Abstract In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically.

Low‐voltage broadband power line communication (PLC) system transmits its information by the power line on the existed infrastructure. However, PLC attenuation and its harsh environment make it difficult to establish a reliable communication between the source and destination nodes, and relay‐aided (RA) PLC technology can be used to solve this issue to increase the transmission rate and coverage. In this study, an indoor three‐hop RA PLC system is under investigation for the first time. Based on a simple PLC attenuation model, the optimal relay node positions and transmitted power distribution of each node are studied to get the best system performance in theory. Then, a criterion of selection optimal relay node positions in a practical power‐line network is derived by solving an optimisation problem. Finally, for a specific power‐line network, the criterion for the selection of optimal relay node positions at the existed sockets between the source and destination nodes is verified. The theoretical work in this study could be useful in the design of broadband multi‐hop RA PLC systems and their engineering applications.

In this article, a simultaneous wireless information and power transfer (SWIPT) system with full-duplex (FD) Internet of Things (IoT) nodes is considered and investigated. We induce the adaptive interference decoding (AID) strategy as well as the switch and inverter structure with antenna selection (SIAS)-based hybrid precoding scheme to the SWIPT system. A joint optimization of hybrid precoder, decoding rule, and power splitting (PS) ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it is a nonconvex problem, we propose a suboptimal solution with three stages. In the first stage, a search algorithm which can reduce the complexity of exhaustive search is proposed to decide the sending mode of each node. In the second stage, we utilize the semidefinite relaxation (SDR) approach to find the optimal digital precoder and PS ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise (IAN) systems and the digital precoding schemes. Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system. Moreover, the self-interference causes little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by the antenna separation technique.

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.

Based on outdoor microcellular measurements at 26 and 32 GHz, the path loss models are modified by a proposed dynamic rain model to see the difference of the path loss models between clear and rainy air with respect to rainfall intensities and transceiver distances. Moreover, a dynamic rain cell model for a multi‐user system is developed to investigate the total rain attenuation. The results show that the parameters for floating‐intercept (FI) model have a bigger change with different rainfall intensities than close‐in (CI) model. When considering the dynamic rain model with a different radius and moving speeds, the larger rain cell radius, the larger path loss exponents in CI and FI models, the smaller path loss intercept in FI model and system capacity will be achieved. For a fixed rain cell radius, the larger moving speed of the rain cell, the shorter effective time on the path loss and system capacity will be achieved. In addition, the rain cell has a bigger effect on the path loss exponent, intercept and system capacity at higher frequency band with respect to its size, moving speed, and rainfall intensity.

In this study, the performances of commonly used precoding schemes are evaluated based on channel measurements carried out at 28 GHz in a railway station for a massive antenna system configuration. And a hybrid precoding algorithm for a fifth generation (5G) multi‐user antenna system is proposed and its performance is evaluated based on real measured channels. Specifically, An upper bound for achievable sum‐rate of phased zero forcing (PZF) algorithm is derived and a precoding technique by reduction of phase shifters (PSs) named reduced‐PZF (RPZF) is proposed, which can obviously reduce power consumption in hybrid precoding systems. In addition, the authors give the closed‐form expression for achievable sum‐rate when RPZF is used. There are no requirements in the authors’ proposed scheme with complicated matrix decomposition and optimisation techniques. The simulation results show that it is possible to reduce 30 and 50% of the PSs by using the proposed hybrid precoder with better system performance in the line‐of‐sight (LoS) and non‐LoS scenarios, respectively. In addition, the digital PSs with 3 bits low resolution can achieve the similar performance when using analogue ones.

In this paper, based on 28 GHz indoor MIMO channel measurements, a cluster model based on Euclidean distance is analyzed. Moreover, according to QuaDRiGa (Quasi-Deterministic Radio Channel Generator) simulation platform, large-scale parameters like delay spread, ASA (Azimuth Spread of Arrival), Ricean K-factor and time evolution characteristics are simulated and validated. Results show that the cluster model describes the multipath environment physically. The QuaDRiGa simulation results fit quite well with measurement data. This verifies the applicability of the QuaDRiGa simulation model in millimeter wave bands. Received power and delay spread modeled as a function of distance in both LOS and NLOS cases, and the QuaDRiGa has better space-time continuity. The provided results are useful for design of 5G millimeter wave communication systems.

In this paper, the link budget of the multiband UWB channel is studied based on empirical analysis of path loss and shadow fading by dividing the frequency range into multiple sub- bands with 500 MHz bandwidth. The work is based on ultra- wide band (UWB) propagation measurements performed in two indoor environments of a hall and a corridor in the frequency range of 3-10 GHz. It is found that in the multiband channels, the path loss exponent is independent on frequency, and the mean path loss can be modelled as a function of frequency. Shadowing (in dB) fits well with normal distribution, and the standard deviation (STD) of shadowing shows a tendency of increasing with frequency. Furthermore, the shadow fading margin with 90% link success probability is found to bee fairly low (less than 4 dB) the multiband channels. The presented results can be used for design of UWB radio systems.

In this paper, based on indoor ultra-wide band (UWB) propagation channel measurements for both LOS and NLOS scenarios, the frequency dependency of propagation in the 3-10 GHz UWB range is investigated by examining the power delay profile, rms delay spread, path loss and shadowing as functions of frequency. Moreover, the bandwidth dependency of channel parameters is also investigated for gaining more understanding of the general channel properties