A 3-D Hybrid Dynamic Channel Model for Indoor THz Communications

To meet the demands for the explosive growth of mobile data rates and scarcity of spectrum resources in the near future,the terahertz(THz)band has widely been regarded as a key enabler for the upcoming beyond fifth-generation(B5G)wireless communications.An accurate THz channel model is crucial for the design and deployment of the THz wireless communication systems.In this paper,a three-dimensional(3-D)dynamic indoor THz channel model is proposed by means of combining deterministic and stochastic modeling approaches.Clusters are randomly distributed in the indoor environment and each ray is characterized with consideration of molecular absorption and diffuse scattering.Moreover,we present the dynamic generation procedure of the channel impulse responses(CIRs).Statistical properties are investigated to indicate the non-stationarity and feasibility of the proposed model.Finally,by comparing with delay spread and K-factor results from the measurements,the utility of the proposed channel model is verified.

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.