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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.

To improve the spectrum efficiency and enhance the information security of wireless network, a sum secrecy rate maximization-based robust resource allocation algorithm was proposed for a downlink multicarrier NOMA security communication system.Firstly, the artificial noise (AN) precoding vector was designed to be orthogonal to the legitimate channel, so that the legitimate transmission would not be affected by the AN.Then, considering the constraints of rate requirement of each user and the maximum transmit power of base station, a joint AN precoding, downlink data precoding and subcarrier allocation optimization problem was formulated under eavesdropping channel uncertainties.To solve that non-convex problem, it was decomposed into two joint optimization subproblems: precoding optimization and subcarrier allocation.The former was solved based on S-procedure and block coordinate descent (BCD) algorithm, the latter was solved by dynamic matching theory.Simulation results demonstrate that the proposed algorithm has good robustness and can effectively improve the sum secrecy rate.

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.

To solve the problem of large power consumption caused by a large number of phase shifter (PS) in millimeter wave multi-antenna systems, a new type of dynamic connection structure was designed.With the goal of maximizing spectrum efficiency, two hybrid precoding schemes, successive interference cancellation (SIC) and successive interference cancellation-alternating direction multiplier (SIC-ADMM), were proposed.In SIC scheme, the sum-rate problem was decomposed into multiple sub-rate problems of different radio frequency links, the analog part was solved by iteration, and the digital part was solved by a low-dimensional equivalent matrix.Based on SIC scheme, an improved SIC-ADMM scheme was further proposed by studying the multi-variable sub-rate problem to achieve rapid convergence and reduce computational complexity effectively.The simulation results show that the proposed schemes have good spectrum and energy efficiencies by comparison with the existing schemes, and are more suitable for large-scale antenna systems.In addition, when a certain percentage of PS is turned off, the proposed scheme can greatly improve energy efficiency by sacrificing a small amount of spectrum efficiency.

In a multi-user multiple-input multiple-output (MU-MIMO) communication system, multiple RF (radio frequency) chains are generally connected corresponding to the antenna elements. However, the system complexity, hardware cost, and energy consumption are challenging while using many RF chains. In addition, an information-centric Internet of Things (IoT) will be significant to guarantee fast service demand for radio access in the fifth generation (5G) mmWave wireless systems. In this paper, a new beam training method based on a single RF chain is proposed to overcome these challenges by using the downlink–uplink and downlink–downlink beam training techniques to establish the elite users’ subset group which can be trained in a single training time slot. Additionally, the users’ subset mechanism is implemented by selecting an arbitrary function to guarantee the convergence. Moreover, the proposed techniques are compared with the conventional full search method regarding searching time, cost, and complexity. Different user subset functions are modeled, evaluated, and compared with the existing ones with respect to effective channel gain and average system capacity. The simulation results show that the proposed method can achieve a significant system performance, and the single RF chain is serviceable for a large number of users when connected with a maximum of 64 antenna elements at the BS. The proposed method can also be implemented in a massive MU-MIMO system by using multiple RF chains in which each RF chain is connected to a group of 64 antenna elements. This work is applicable in 5G mmWave communications, which require robust infrastructure to meet the future IoT service demand in 5G hot scenarios.

The performance of the wireless mesh information-centric Internet of Thing (IC-IoT) networks can be greatly enhanced by adopting multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). However, the network interference and channel assignment in IC-IoT networks become more complicated while using both MRMC and POCs. In this paper, a logical link-based partially overlapping channels interference model is analyzed to mitigate the inter-channel interference, and a channel selection scheme is formulated as a potential game. Moreover, a variable probability learning algorithm is proposed by selecting a channel in the strategy space based on its probability. The channel usage probability can be changed by its link utility. The channel with a larger link utility is then with a bigger probability in the strategy space. The simulation results show that our proposed algorithm can achieve high system throughput with fast network convergence.

A novel multi-mode frequency reconfigurable MIMO and an ultrawideband (UWB) MIMO antenna design for cognitive radio applications is proposed in this paper. The antenna structure contains two modified triangular-shaped printed monopole antenna elements and a combination of varactor and PIN diodes. The proposed antenna has three operating modes: UWB mode for spectrum sensing, a frequency reconfigurable MIMO mode for communication over different frequencies, and UWB antenna operating in MIMO configuration. The UWB mode and reconfigurable communication mode are obtained by utilizing different switching ON/OFF states of PIN and varactor diodes. In the UWB mode, the antenna can cover the spectrum from 1 to 4.5 GHz, while it achieves the frequency reconfigurability over a wide range from 0.9 to 2.6 GHz using varactor diode tuning in the reconfigurable communication mode. The proposed antenna has a compact and planar structure with overall dimensions of 120 × 60 × 1.5 mm3. The proposed design also exhibits good MIMO performance; the minimum isolation measured is 12.5 dB, while the envelope correlation coefficient (ECC) of less than 0.19 is achieved for the whole operating band. To validate the proposed concept, the prototype of the antenna system is fabricated and measured. The simulation results are in good agreement with the measurement results.

In smart grid (SG), internet of things (IoT) has drawn much attention around the years due to increased volume of traffic. Various types of electrical devices in SG demand a differentiated quality of service (QoS). However, the spectrum resource for SG wireless communication is quite limited. The network performance and spectrum utilization in a SG-IoT network can be greatly enhanced by adopting wireless mesh networks (WMNs) with multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). In this paper, a game-based efficient POCs assignment algorithm is proposed to satisfy the diverse QoS and improve the spectrum utilization efficiency. The utility function in the proposed game model is taken both of the differentiated QoS and interference into consideration to optimize the network capacity. Then Nash Equilibrium (NE) of the game model is analyzed, and a variable learning step algorithm (VLSCA) is designed to improve its convergence speed. In the simulations, seven SG application services divided into four priority levels are considered to compare with the other two typical channel assignment algorithms. The results show that our algorithm has better performance to ensure a higher priority level traffic with lower end-to-end delays and packet loss ratios.

Millimeter wave (mm-Wave) communications are emerging to meet the increasing demand for high transmission data rate in high user density areas. Meanwhile, the mm-Wave base station (BS) needs to employ a large number of antenna elements to increase the gain as well as serve a huge number of users. However, a vast number of antenna elements causes dimensionality problem in channel correlation matrix (rotation matrix). Therefore, we propose a novel codebook construction design based on CUR-decomposition technique to reduce the dimensionality problem. In this paper, the original correlation matrix is decomposed to the product of three low dimension matrices (C, U, and R). The new rotated codebook is then constructed by the new rotation matrix. Moreover, we evaluate the new decomposition matrix with the original matrix in terms of compression ratio and mismatch error. We also provide the achievable sum rate capacities for singular value decomposition, zero forcing, and a matched filter techniques to compare with the proposed method. Furthermore, the system capacity enhancement related to the number of antenna elements and the required feedback bits are analyzed. Simulation results show that the proposed method achieves much better system performance since the dimensionality problem is solved. The proposed method can be applied in the fifth generation massive antenna multi-user system with over a hundred antenna elements.

In order to plan and allocate the spectrum resources ofLTE-Rscientifically,it is necessary to make reasonable forecast of total spectrum demand for LTE-R.After deep analysis of ITU-R M.1768 method,a spectrum demand forecasting method based on different delay constraints forLTE-Rsystem was provided.This method defined nine kinds of service categories,nine kinds of service environments,and derived the expression ofLTE-Rsystem traffic.Under different delay constraints condition of different services,the M/G/1 queuing model was used to analyze the spectrum requirements ofLTE-Rsystem preliminarily.The results show that the uplink and downlink spectrum demand of China’sLTE-Rsystem is asymmetric and the possible range is 14~15MHz uplink and 5~6MHz downlink,respectively.If new video traffic in the future is not considered,the current spectrum resources distributed to GSM-R system spectrum can meet the bandwidth requirements ofLTE-Rsystem.In addition,the influence of engineering frequency efficiency ofLTE-Rnetwork and high-bandwidth video traffic onLTE-Rspectrum total bandwidth demand was analyzed.This study provides technical support for the frequency planning of China’s next generation railway mobile communication system.

Wireless mesh networks (WMNs) can provide flexible wireless connections in a smart city, internet of things (IoT), and device-to-device (D2D) communications. The performance of WMNs can be greatly enhanced by adopting a multi-radio technique, which enables a node to communicate with more nodes simultaneously. However, multi-radio WMNs face two main challenges, namely, energy consumption and physical layer secrecy. In this paper, both simultaneous wireless information and power transfer (SWIPT) and cooperative jamming technologies were adopted to overcome these two problems. We designed the SWIPT and cooperative jamming scheme, minimizing the total transmission power by properly selecting beamforming vectors of the WMN nodes and jammer to satisfy the individual signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constrains. Especially, we considered the channel estimate error caused by the imperfect channel state information. The SINR of eavesdropper (Eve) was suppressed to protect the secrecy of WMN nodes. Due to the fractional form, the problem was proved to be non-convex. We developed a tractable algorithm by transforming it into a convex one, utilizing semi-definite programming (SDP) relaxation and S-procedure methods. The simulation results validated the effectiveness of the proposed algorithm compared with the non-robust design.

Wireless mesh networks (WMNs) can provide flexible wireless connections in smart city, Internet of Things (IoT), and device-to-device (D2D) communications. The performance of WMNs can be greatly enhanced by adopting the multi-radio multi-channel (MR-MC) technique, which enables a node to communicate with more nodes simultaneously. However, increasing the number of data flows will result in network congestion and longer end-to-end delays. In this paper, a distributed rate-control and delay-aware (DRDA) scheduling algorithm is proposed based on a multidimensional conflict graph. To satisfy the arrival rate and delay constraints of a flow, two virtual queues are constructed. All the actual and virtual queues are stabilized by the Lyapunov drift optimization method. The scheduling policy of each flow is optimized only based on the local information. The simulation results show that our proposed algorithm can maintain the stability of all the queues and strictly satisfy the arrival rate and delay constraint of each flow in the network as well.