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.

This work proposes deploying stacked intelligent metasurface (SIM) on individual Internet of Things (IoT) devices to enhance the uplink transmission capability under a finite blocklength (FBL) regime. Aiming to maximize the achievable sum rate, a joint transmit power allocation, SIM phase shifts, and receiving beamforming design optimization problem is formulated. By decomposing the original problem into three sub-problems, reducing the intractable quadratic fraction of signal-to-interference-plus-noise ratio (SINR), the nonconvex channel dispersion function, and the constant modulus constraints to linear forms, we propose an iterative algorithm to obtain the solutions. Numerical results demonstrate that in a multi-user uplink FBL network, the incorporation of SIM yields approximately a 40% enhancement in the sum rate. The optimization of phase shifts leads to an improvement of nearly 70% in the sum rate compared to a random phase setting scheme, highlighting the crucial role of proper phase shift configuration in realizing the significant performance gains offered by SIM. The performance of the proposed algorithm is validated to be close to the slack upper bound. Furthermore, with the same total number of metasurface elements, the multi-layer SIM performs better than the traditional single-layer RIS, which reveals the advantages of multi-layer structure.

This paper investigates physical layer secure communication in a wideband wireless system, where a base station (BS) equipped with an extremely large scale antenna array (ELAA) transmits confidential information to a legitimate receiver under the threat of a potential eavesdropper. Due to the high carrier frequency and large antenna aperture, both the receiver and eavesdropper lie in the near-field region of the BS. In order to mitigate the beam split effect and reduce the hardware cost, a true-time delayer-based hybrid beamfocusing architecture is designed. Then, a nonconvex sum secrecy capacity maximization problem (SSCM) is formulated for securing wideband communications. Based on alternating optimization, the SSCM is decomposed into three subproblems solved iteratively for designing the digital beamfocusing vectors, time delay matrices, and phase shift matrices on each subcarrier, respectively. Simulation results show that the proposed scheme yields significantly high secrecy capacity compared to benchmarks, which validates the effectiveness of our scheme in enhancing secure wideband communications and mitigating the beam split effects.

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.

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.

Label distribution learning (LDL) is a newly arisen machine learning method that has been increasingly studied in recent years. In theory, LDL can be seen as a generalization of multi-label learning. Previous studies have shown that LDL is an effective approach to solve the label ambiguity problem. However, the dramatic increase in the number of possible label sets brings a challenge in performance to LDL. In this paper, we propose a novel label distribution learning algorithm to address the above issue. The key idea is to exploit correlations between different labels. We encode the label correlation into a distance to measure the similarity of any two labels. Moreover, we construct a distance-mapping function from the label set to the parameter matrix. Experimental results on eight real label distributed data sets demonstrate that the proposed algorithm performs remarkably better than both the state-of-the-art LDL methods and multi-label learning methods.

Multiuser multiple input multiple output (MU‐MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam‐vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal‐to‐leakage plus noise ratio (SLNR), we analyze the ergodic sum‐rate capacity using statistical Eigen‐mode (SE) and zero‐forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum‐rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum‐rate capacity.