CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimental conditions and the lack of a standardized experimental design. In addition, the performance of data-driven approaches is often compared based on empirical analysis. The lack of reproducibility and availability of standardized evaluation tools (e.g., datasets, codebases) hinder the development and progress of data-driven methods for channel estimation and wireless communication in general. In this work, we introduce an initiative to build benchmarks that unify several data-driven OFDM channel estimation approaches. Specifically, we present CeBed (a testbed for channel estimation) including different datasets covering various systems models and propagation conditions along with the implementation of ten deep and traditional baselines. This benchmark considers different practical aspects such as the robustness of the data-driven models, the number and the arrangement of pilots, and the number of receive antennas. This work offers a comprehensive and unified framework to help researchers evaluate and design data-driven channel estimation algorithms

Deep learning SIC approach for uplink MIMO-NOMA system

Abstract. Deep learning-based successive interference cancellation (DL-SIC) for uplink multiple-input multiple-output -non-orthogonal multiple access (MIMO-NOMA) system tries to optimize the users’ bit error rate (BER) and total mean square error (MSE) performance with higher order modulation schemes. The recent work of DL-SIC receiver design for users with a QPSK modulation scheme is investigated in this thesis to validate its performance as a potential alternative approach to traditional SIC receivers for NOMA users. Then, a DL-SIC receiver design for higher order modulation with less dependence on modulation order in the output layer is proposed, which enables us to decode the users with different modulation schemes. In our proposed design, we employ two deep neural networks (DNNs) for each SIC step. The system model is considered an M-antenna base station (BS) that serves two uplink users with a single antenna in the Rayleigh fading channel. The equivalent conventional minimum mean square error-based SIC (MMSE-SIC) and zero-forcing-based SIC (ZF-SIC) receivers are implemented as a baseline comparison. The simulation results showed that the BER performance of the proposed DL-SIC receiver for both users with QPSK modulation results in a 10 dB gain between BER of 10^(-2) and 10^(-3) compared to the ZF-SIC receiver. Furthermore, the performance difference between the proposed scheme and ZF-SIC is significantly high when both users transmit with 16QAM. Overall, the proposed DL-SIC receiver performs better in all signal-to-noise ratio (SNR) regions than the equivalent ZF-SIC receivers and also aids in mitigating the SIC error propagation problem. In addition, it improves the processing latency due to the benefits of the parallelized computing architecture and decreases the complexity of traditional SIC receivers

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI, Journal of Telecommunications and Information Technology, 2022, nr 4

In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results

Journal of Telecommunications and Information Technology

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