## What is/are Mimo Receiver?

Mimo Receiver - 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.^{[1]}The two main components of an M-MIMO receiver are a detector and a decoder.

^{[2]}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.

^{[3]}In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together.

^{[4]}Therefore, it achieves remarkable complexity reduction and exhibits significant performance advantages compared to the existing quantized SC-MIMO receivers from the literature.

^{[5]}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.

^{[6]}In this work, a new, novel MIMO technique for simultaneously achieving multiplexing and diversity gains as well as completely eliminating any processing at the MIMO receiver, leading to advantages such as low complexity and low power consumption, is proposed.

^{[7]}

## minimum mean squared

In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture.^{[1]}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.

^{[2]}

## mean squared error

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.^{[1]}

## Massive Mimo Receiver

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]}A polar-coded massive MIMO receiver that supports a length-1024 rate-1/2 polar code, 128 receive antennas, and eight users is designed and implemented, and delivers a throughput of 7.

^{[4]}Drawing from a recent work on negative noise correlation in quantization and statistics, we propose a novel antithetic dithered 1-bit massive MIMO receiver architecture and develop efficient channel estimation algorithms that exploit the natural and induced negative correlated noise in the system.

^{[5]}Building upon a recently proposed system model taking into account the reverberation (clutter) produced by the radar system at the massive MIMO receiver, we provide a theoretical analysis, in terms of a lower bound on the achievable uplink (UL) spectral efficiency (SE) and in terms of the mutual information of the cellular massive MIMO system, showing that for large number of antennas at the base station the radar clutter effects can be suppressed.

^{[6]}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.

^{[7]}Methods: The existing research work namely Noise and Relevancy aware Low Complexity Detection (NRLCD) algorithm for massive MIMO receiver utilizes normalized cross correlation based pruning strategy to viably evacuate uncorrelated signals.

^{[8]}

## Multiuser Mimo Receiver

In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI.^{[1]}The use of SQLC allows grouping the users in pairs and halves the number of streams to be managed by the multiuser MIMO receiver.

^{[2]}

## Hybrid Mimo Receiver

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.^{[1]}, an adaptive ADC bit allocation method and an MMSE-based VAMP, are proposed for mm Wave communications of the hybrid MIMO receiver architecture.

^{[2]}

## Conventional Mimo Receiver

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 Receiver

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]}The considered low-resolution MIMO receiver implies that the received signals simultaneously are processed by the 1-bit ADCs and the comparator network, where the latter is composed of several simple comparators with binary outputs.

^{[2]}

## mimo receiver architecture

In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together.^{[1]}, an adaptive ADC bit allocation method and an MMSE-based VAMP, are proposed for mm Wave communications of the hybrid MIMO receiver architecture.

^{[2]}Drawing from a recent work on negative noise correlation in quantization and statistics, we propose a novel antithetic dithered 1-bit massive MIMO receiver architecture and develop efficient channel estimation algorithms that exploit the natural and induced negative correlated noise in the system.

^{[3]}