The latest TypeII codebook selects partial strongest angular-delay ports for the feedback of downlink channel state information (CSI), whereas its performance is limited due to the deficiency of utilizing the correlations among the port coefficients. To tackle this issue, we propose a tailored autoencoder named TypeII-CsiNet to effectively integrate the TypeII codebook with deep learning, wherein three novel designs are developed for sufficiently boosting the sum rate performance. Firstly, a dedicated pre-processing module is designed to sort the selected ports for reserving the correlations of their corresponding coefficients. Secondly, a position-filling layer is developed in the decoder to fill the feedback coefficients into their ports in the recovered CSI matrix, so that the corresponding angular-delay-domain structure is adequately leveraged to enhance the reconstruction accuracy. Thirdly, a two-stage loss function is proposed to improve the sum rate performance while avoiding the trapping in local optimums during model training. Simulation results verify that our proposed TypeII-CsiNet outperforms the TypeII codebook and existing deep learning benchmarks.

Deep learning based channel state information (CSI) acquisition and feedback in frequency division duplex systems have drawn much attention in the beyond fifth-generation (B5G) wireless systems. In this paper, we focus on exploiting the CSI codebook in B5G wireless standards with deep learning to enhance the performance of CSI acquisition and feedback. Specifically, the angular-delay-domain partial reciprocity between uplink and downlink channels is considered, and part of angular-delay-domain ports are selected for measuring and feeding back the downlink CSI, where the performance of the conventional deep learning methods is limited due to the deficiency of sparse structures. To address this issue, we propose the new paradigm of adopting deep learning to improve the performance of CSI codebook. Firstly, considering the relatively low signal-to-noise ratio of uplink channels, deep learning is utilized to refine the selection of the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to reconstruct the downlink CSI by way of deep learning based on the feedback of CSI codebook at the base station, where the information of sparse structures can be effectively leveraged. Finally, a weighted shortcut module is designed to facilitate the accurate reconstruction, and a two-stage loss function with the combination of the mean squared error and sum rate is proposed for adapting to actual multi-user scenarios. Simulation results demonstrate that our proposed angular-delay-domain port selection and CSI reconstruction paradigm can improve the sum rate performance by more than 10% compared with the standard CSI codebook and traditional deep learning benchmarks.

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.

Sparse code multiple access (SCMA) has been presented as a promising solution for uplink transmission since it enables random access so that the pre-establishment of link can be omitted. However, this randomness could cause codebook collision when different users pick the same codebook. Though the message passing algorithm (MPA) is employed to decode nonorthogonal codewords to lower the complexity, it needs an unacceptably high amount of calculation when serious collision happens, which also drastically increases the worst case latency (WCL) of access. In this paper, we propose a new decoding method dedicated to solve the high-complexity problem through simplifying the decoding factor graph. Simulation results demonstrate near optimal performance with greatly reduced complexity concerning two specific schemes of the proposed method.

This paper presents 28GHz path loss model based on cluster obtained from channel measurement campaigns with rotating platforms and directional antennas in the office and corridor environments. As the sequence number of cluster increases, the path loss exponents (PLEs) increase while shadow factors also have the growing tendency. The PLE of all-clusters is the least because of multi propagation paths, and the LOS PLEs of corridor scenario are less compared with that of office scenario because of corridor's long and narrow structure. This path loss model not only contains cluster characteristics, but also unites directional and omnidirectional model into the same framework, which to some extent improves the 5G mmWave channel model.