Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape

Self-Supervised and Invariant Representations for Wireless Localization

In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive MIMO systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes

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

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-based positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.Comment: 32 pages, Added references in section 2,3; Added explanations for some academic terms; Corrected typos; Added experiments in section 5, previous results unchanged; is under review for possible publicatio

Semantic Communication Systems for Speech Transmission

Semantic communications could improve the transmission efficiency significantly by exploring the semantic information. In this paper, we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit or symbol level. Particularly, we design a deep learning (DL)-enabled semantic communication system for speech signals, named DeepSC-S. In order to improve the recovery accuracy of speech signals, especially for the essential information, DeepSC-S is developed based on an attention mechanism by utilizing a squeeze-and-excitation (SE) network. The motivation behind the attention mechanism is to identify the essential speech information by providing higher weights to them when training the neural network. Moreover, in order to facilitate the proposed DeepSC-S for dynamic channel environments, we find a general model to cope with various channel conditions without retraining. Furthermore, we investigate DeepSC-S in telephone systems as well as multimedia transmission systems to verify the model adaptation in practice. The simulation results demonstrate that our proposed DeepSC-S outperforms the traditional communications in both cases in terms of the speech signals metrics, such as signal-to-distortion ration and perceptual evaluation of speech distortion. Besides, DeepSC-S is more robust to channel variations, especially in the low signal-to-noise (SNR) regime