## What is/are Mimo Receivers?

Mimo Receivers - Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed learning-based MIMO receivers.^{[1]}For practicality, a generic power consumption model for massive MIMO receivers, including ADC resolution, symbol detection, and receiver circuits, is considered.

^{[2]}Unfortunately, this application is limited because all the channel information over the transmission link must be transmitted to MIMO receivers for complete crosstalk compensation, which indicates difficulty in applying MIMO to the transmission path via optical switches.

^{[3]}This work serves as a design guide to determine the selection of A-MIMO, TA-MIMO, or C-MIMO receivers depending on the misalignment conditions for a particular underwater application.

^{[4]}Interestingly, we find that the pilot power that minimizes the MSE of the data symbols does not depend on the number of antennas and that the new linear MMSE receiver outperforms previously proposed MIMO receivers when the autocorrelation coefficient of the channel is high.

^{[5]}The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented.

^{[6]}Therefore, it achieves remarkable complexity reduction and exhibits significant performance advantages compared to the existing quantized SC-MIMO receivers from the literature.

^{[7]}Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task.

^{[8]}Our numerical results demonstrate that the proposed BiLiMO receiver operating with a bit budget of one bit per sample achieves target recovery performance which approaches that of costly MIMO radars operating with unlimited resolution ADCs, while substantially outperforming MIMO receivers operating only in the digital domain under the same bit limitations.

^{[9]}In this study, we propose a low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for such low-resolution MIMO receivers.

^{[10]}Antenna impedance matching significantly affects the channel capacity of compact MIMO receivers.

^{[11]}It has been shown that adopting variable-resolution (VR) ADCs in Ma-MIMO receivers can improve performance with Mean Squared Error (MSE) and throughput while providing better EE.

^{[12]}This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm.

^{[13]}An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition.

^{[14]}Numerical results show that by appropriately setting the number of hidden neurons, the ELM achieves higher spectral efficiency and smaller BER, with fewer floating-point operations than the conventional linear MIMO receivers, namely the minimum mean squared error and maximum ratio receivers.

^{[15]}The goal of this paper is to establish performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models.

^{[16]}The proposed receivers are analyzed and compared with the standard Alamouti MIMO receiver as a reference and also compared with the non-iterative, basic turbo iterative and non-progressive iterative MIMO receivers.

^{[17]}It is known that lattice-reduction-aided (LRA) MIMO receivers can achieve full spatial diversity.

^{[18]}Compared with the existing minimum mean squared error (MMSE) based L-MUD and the generic MU-MIMO receivers, numerical simulations demonstrate valuable tradeoffs between the permissible user overloading, required number of radio frequency chains (RFCs), computational efforts, and bit error rate.

^{[19]}Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.

^{[20]}Use of low resolution ADCs has been proposed as a means to decrease power consumption in MIMO receivers.

^{[21]}An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition.

^{[22]}Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.

^{[23]}

## Massive Mimo Receivers

For practicality, a generic power consumption model for massive MIMO receivers, including ADC resolution, symbol detection, and receiver circuits, is considered.^{[1]}An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition.

^{[2]}The goal of this paper is to establish performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models.

^{[3]}An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition.

^{[4]}

## Conventional Mimo Receivers

Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.^{[1]}Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.

^{[2]}

## Resolution Mimo Receivers

The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented.^{[1]}In this study, we propose a low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for such low-resolution MIMO receivers.

^{[2]}

## mimo receivers adopt

Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.^{[1]}Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners.

^{[2]}