A Survey on Channel Measurements and Models for Underground MIMO Communication Systems

The high reliability of the communication system is critical in metro and mining applications for personal safety,channel optimization,and improving operational performance.This paper surveys the progress of wireless communication systems in underground environments such as tunnels and mines from 1920 to 2022,including the evolution of primitive technology,advancements in channel modelling,and realization of various wireless propagation channels.In addition,the existing and advanced channel modeling strategies,which include the evolution of different technologies and their applications;mathematical,analytical,and experimental techniques for radio propagation;and significance of the radiation characteristics,antenna placement,and physical environment of multiple-input multiple-output(MIMO)communication systems,are analyzed.The given study introduces leaky coaxial cable(LCX)and distributed antenna system(DAS)designs for improving narrowband and wideband channel capacity.The paper concludes by figuring out open research areas for the future technologies.

Generalized 3D Scattering Channel Model with MIMO Antenna Systems

In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the azimuth and elevation planes in an environment where uniformly distributed scatterers are assumed to be present in hemispheroids around the base station(BS) and mobile station(MS). Using this channel model, we first derive the closed-form expression for the joint and marginal probability density functions of the angle-of-arrival and time-of-arrival measured at the BS and the MS corresponding to the azimuth and elevation angles. Next, we derive an expression for the Doppler spectral distribution caused by motion of the MSs. Furthermore, we analyze the performance of multiple-input multiple-output antenna systems numerically. The results show that the proposed 3D scattering channel model performs better than previously proposed two-dimensional(2D) models for indoor and outdoor environments. We compare the results with previous scattering channel models and measurement results to validate the generalizability of our model.

Statistical Modeling of the High Altitude Platform Dual-Polarized MIMO Propagation Channel

In order to investigate the benefit of multiple-input multiple-output(MIMO) technique applying to the high altitude platform(HAP), a 2×2 MIMO statistical model, which can accurately describe the channel between HAP and high-speed train, is presented. And dual polarization diversity is particularly considered. Based on first-order three-state Markov chain, the single-input single-output(SISO) channel, a subset of the MIMO channel is first established. The ray tracing approach applied to the digital relief model(DRM) which covers the railway between Xi'an and Zhengzhou is used to obtain the state probability vector and matrix of the state transition probability. The proposed model considers both Doppler shift and temporal correlation, and the polarization correlation and spatial correlation statistical properties of large-scale fading and smallscale fading are analyzed. Moreover, useful numerical results on the MIMO HAP channel outage capacity are provided based on which, significant capacity gains with respect to the conventional SISO case are illustrated. Such statistical channel model can be applied to the future wireless communication system between HAP and high-speed train.

Inter-carrier Interference Analysis for MIMO-OFDM Systems in High-Speed Train Environment

The orthogonality between the subcarriers of multipleinput multiple-output orthogonal frequency division multiplexing( MIMO-OFDM) systems is destroyed due to the Doppler frequency offset,particularly in the high-speed train( HST) environment,which leads to severe inter-carrier interference( ICI). Therefore,it is necessary to analyze the mechanism and influence factor of ICI in HST environment. In this paper, by using a non-stationary geometry-based stochastic model( GBSM) for MIMO HST channels, ICI is analyzed through investigating the channel coefficients and the carrier-to-interference power ratio( CIR). It is a fact that most of signal energy spreads on itself and its several neighborhood subcarriers. By investigating the amplitude of subcarriers, CIR is used to evaluate the ICI power level. The simulation results show that the biggest impact factor for the CIR is the multipath number L and the minimum impact factor K; when the train speed υR> 400 km / h,the normalized Doppler frequency offset ε > 0. 35,the CIR tends to zero,and the communication quality will be very poor at this condition. Finally,bit error rate( BER) is investigated by simulating a specific channel environment.

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems

Asymmetric massive multiple-input multiple-output(MIMO)systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G).However,in the asymmetric massive MIMO system,reciprocity between the uplink(UL)and downlink(DL)wireless channels is not valid.As a result,pilots are required to be sent by both the base station(BS)and user equipment(UE)to predict doubledirectional channels,which consumes more transmission and computational resources.In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems.It can predict multiple DL channel parameters including path loss(PL),multipath number,delay spread(DS),and angular spread.Both the UL channel parameters and environment features are chosen to predict the DL parameters.Also,we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations(SHAP)value and the minimum description length(MDL)criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features.In addition,the instance transfer method is introduced to support the prediction model in new propagation conditions,where it is difficult to collect enough training data in a short time.Simulation results show that the proposed method is more accurate than the back propagation neural network(BPNN)and the 3GPP TR 38.901 channel model.Additionally,the proposed instancetransfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.

Auto-tuning of FOPI Controllers for TITO Processes with Experimental Validation

In this paper, the auto-tuning of a fractional order proportional and integral(FOPI) controller is proposed and experimentally validated for two-input two-output(TITO) processes. The proposed method first identifies an unknown TITO plant into fractional order TITO model with time delay. Furthermore, decoupling the TITO process into two fractional order single-input single-output(SISO) transfer function models makes it easier for designing the decentralized FOPI controllers. The proposed control method is a generalization of both integer order and fractional order TITO systems depending on the value of the order of the model. One advantage of this method is the non-requirement of a-priori information of gain and phase crossover frequencies of the system while tuning the controllers. The proposed algorithm is validated both by simulation of a class of TITO process models as well as by experimental analysis of a coupled tank system(CTS).