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When the base station has downlink channel status information (CSI), the huge potential of large-scale multiple input multiple output (MIMO) in frequency division duplex (FDD) mode can be fully exploited. In this paper, we propose a deep-learning-based joint channel estimation and feedback framework to realize channel estimation and feedback in massive MIMO systems. Specifically, we use traditional channel design rather than end-to-end methods. Our model contains two networks. The first network is a channel estimation network, which adopts a double loss design, and can accurately estimate the full channel information while removing channel noises. The second network is a compression and feedback network. Inspired by the masked token transformer, we propose a learnable mask token method to obtain excellent estimation and compression performance. The extensive simulation results and ablation studies show that our method outperforms state-of-the-art channel estimation and feedback methods in both separate and joint tasks., 9 pages, 8 figures

In massive multiple-input multiple-output (mMIMO) systems, the technique of using low-resolution analog-to-digital converters (ADCs) to quantize received signals can, significantly reduce the receiver hardware cost as well as circuit power consumption., However, it incurs quantization noise in the digital domain which degrades the quality, of signal reception. In particular, for 1- to 3-bit resolution quantizers, communication, systems become very nonlinear and non-Gaussian. Quantization noise can be correlated to the received signal as well as the thermal noise. These are critical issues that, can fundamentally change the way of receiver design and optimization, and thus they, must be rigorously studied., The first contribution of this thesis lies in the use of Hermite polynomials to study, the linear approximation model of low-resolution quantized mMIMO signal reception., Unlike other linear approximation models in the literature that assume the quantization, noise to be white Gaussian and uncorrelated to the received signal, the proposed model, termed second-order Hermite expansion (SOHE), uses the second-order Hermite kernel, to describe the quantization noise and the first-order Hermite kernel for the signal, linear response. Novel mathematical theorems are established to explain key stochastic, characteristics of the quantization noise. The SOHE theory results in an enhanced, LMMSE channel equalizer which considerably improves the equalization performance, particularly for mMIMO receivers using 2-bit quantizers., Another key contribution lies in an intensive study of the well-known dither effect, of low-resolution quantizers, which explains the phenomenon of constructive noise in, the mMIMO signal reconstruction. This study leads to a fundamental rethinking of, mMIMO signal transmission strategy, operating signal-to-noise ratio (SNR) setup and, more fundamentally the threshold optimization for low-resolution signal quantization., All of these provide theoretical support for the optimization of low-resolution quantized, mMIMO., Finally, an outlook of major technical challenges and future direction of the lowresolution mMIMO research is presented.

In this paper, single-input multiple-output (SIMO) system when employing massive binary array-receiver has been investigated while constructive noise has been observed in the single user system to detect the higher-order QAM modulated signals. To fully understand the interesting phenomenon, mathematical model has been established and analyzed in this paper. Theorems of the signal detectability are studied to understand the best operating signal-to-noise ratio (SNR) range based on the error behaviours of the single user SIMO system. Within the observation and analysis, a novel new multiuser SIMO with binary array-receiver structure has been proposed and can be considered as a solution to deal with the high complexity problem that the traditional model has when using maximum likelihood (ML) detection. The key idea of this approach is to set up the multiuser multiple-input multiple-output (MIMO) model into a frequency division multiple access (FDMA) scenario and regard each user as single user SIMO to achieve the goal of decreasing the exponentially increased complexity of ML detection method to the number of users. It is shown by numerical results that each user in this system can achieve a promising error behaviour in the specific best operating SNR range.