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.

This paper investigates a multi-user multiple-input single-output (MISO) downlink communication system, which is assisted by a dynamic-controlled Reconfigurable Intelligent Surface (RIS). Firstly, we formulate a problem to optimize the active - and passive beamforming matrices with the goal of maximizing the sum rate (SR) of all users under the constraints of Quality of Service (QoS) requirements. A state selection matrix is creatively introduced to control the number of RIS elements used for reflection. And quantized phases are considered in order to be more practical, which is often neglected in existing works. Then, an algorithm based on block coordinate descent (BCD) is proposed to obtain the stationary solution. The algorithm can guarantee the optimal SR regardless of any proportion of closed RIS elements. Finally, we perform simulation verification and analysis the results of the proposed scheme and six baseline schemes. Simulation results show that the proposed scheme is much better than the existing schemes in terms of SR, and realizes good energy efficiency (EE) performance simultaneously. Moreover, the proposed scheme can achieve more significant EE gain by sacrificing less SR.

Billions of mobile terminals will be deployed in various Internet of Things (IoT), in which millimeter-wave (mmWave) technology will be gradually applied. Accurate modeling and simulation of wireless channel is the base for efficient design and performance evaluation. This becomes more important for industrial scenarios, which might be highly dynamic and potentially different from well-investigated cellular deployment scenarios. In this work, a novel semi-deterministic mmWave dynamic channel modeling approach based on optimal neural network (ONN) principle is proposed. The ONNs are radial basis function (RBF) neural networks (NNs) trained with optimal variance parameters and are applied to predict large-scale channel parameters (LSCPs) [e.g., path loss (PL), delay spread (DS), and angle spread (AS)]. Based on the ONNs’ predicted large-scale parameters and simplified propagation environment including the layout of transmitter (TX), receiver (RX), and major scatterers, the proposed channel modeling approach can generate accurate dynamic channel parameters. The proposed approach is validated by the channel data measured at a high-voltage substation. Large-scale parameters, multipath component (MPC) distributions, and power delay profiles (PDPs) are validated. The proposed approach is demonstrated to be an accurate, fast, and robust channel modeling method, which can be used for both link-level and system-level channel simulation for future design and optimization of industrial IoT.

In this paper, 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 ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it’s a non-convex problem, we propose a sub-optimal 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 semidefinite relaxation (SDR) approach to find the optimal digital precoder and power splitting ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. Simulation results show that our proposed sub-optimal 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 cause little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by antenna separation technique.

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.

The emerging reconfigurable intelligent surface (RIS) is a prospective technique to modulate the wireless channel and improve performance, in which large amounts of passive elements manipulate independently, inevitably resulting in a high-dimensional optimization problem that is intractable to solve. With the aim to strike a balance between optimality and complexity for RIS assisted multi-user systems, in this paper, we formulate the achievable sum rate maximization problem under a novel RIS segmentation structure, where the distributions and sizes of each segmentation can be adaptively adjusted. Since the formulated optimization problem considering the quality of service (QoS) requirements for the users is non-convex, we suggest a computationally-efficient approach to derive an optimal solution by exploiting fractional programming, successive convex approximation (SCA), greedy algorithm, and alternating optimization. Finally, numerical simulations reveal that the proposed optimization design enables RIS to configure by grouping elements into some sub-surfaces without significant performance degradation while with much lower computational complexity than conventional element-wise optimization RIS. Moreover, our proposed adjustable segmentation outperforms the fixed one employing the determined positions and equal number of reflecting elements in each sub-surface. Additionally, the results demonstrate that the optimization of segmentation is much more significant than the phase shift optimization, showing the superiority and practical significance of the sub-surface segmentation strategy.