Constructing Knowledge Map for MIMO-OFDM Clustered Channel Estimation

Channel knowledge map (CKM) exploits environ-ment information to assist channel estimation during communi-cation. For clustered channels, which represent a typical type ofwireless propagation environment, there has been no researchdevoted to designing an appropriate CKM to enhance theirestimation. To exploit environment information for clusteredchannel, improve channel estimation accuracy and reduce pilotoverhead, we propose ClusterCKM, a CKM providing the rangeof clustered multipath parameters for any pair of transmitter-receiver links in the region of interest. Firstly, we construct Clus-terCKM through estimating the spatial range of scatterer clustersfrom historical channel information. From these spatial range ofscatterer clusters, ClusterCKM infers the range of multipathparameters for the target link. Furthermore, a ClusterCKM-based channel estimation algorithm is developed to utilize theparameter range provided by ClusterCKM. Simulation resultsshow that, more accurate channel estimation can be achievedand pilot overhead can also be reduced by ClusterCKM and theClusterCKM-based estimation algorithm.

Channel Correlation Matrix Extrapolation Based on Roughness Calibration of Scatterers

To estimate the channel correlation matrix (CCM) in areas where channel information cannot be collected in advance, this paper proposes a way to spatially extrapolate CCM based on the calibration of the surface roughness parameters of scatterers in the propagation scene. We calibrate the roughness parameters of scene scatters based on CCM data in some specific areas. From these calibrated roughness parameters, we are able to generate a good prediction of the CCM for any other area in the scene by performing ray tracing. Simulation results show that the channel extrapolation method proposed in this paper can effectively realize the extrapolation of the CCM between different areas in frequency domain, or even from one domain to another.

A Dynamic Resource Scheduling Algorithm Based on Traffic Prediction for Coexistence of eMBB and Random Arrival URLLC

In this paper, we propose a joint design for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and random low-latency communication (URLLC) with different transmission time intervals (TTI): an eMBB scheduler operating at the beginning of each eMBB TTI to decide the coding redundancy of eMBB code blocks, and a URLLC scheduler at the beginning of each mini-slot to perform immediate preemption to ensure that the randomly arriving URLLC traffic is allocated with enough radio resource and the eMBB traffic keeps acceptable one-shot transmission successful probability and throughput. The framework for schedulers under hybrid-TTI is developed and a method to configure eMBB code block based on URLLC traffic arrival prediction is implemented. Simulations show that our work improves the throughput of eMBB traffic without sacrificing the reliablity while supporting randomly arriving URLLC traffic.

Permittivity Estimation in Ray-tracing Using Path Loss Data based on GAMP

In this paper, we propose a modified Generalized Approximate Message Passing (GAMP) algorithm to estimate permittivity parameters using path loss data in ray-tracing model.