## What is/are Mimo Detection?

Mimo Detection - For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc.^{[1]}GS MIMO detection that combines feedback from turbo decoding has been proposed.

^{[2]}The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

^{[3]}Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the Alternating Direction Method Of Multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.

^{[4]}Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding.

^{[5]}In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless systems.

^{[6]}In this paper, the enhanced channel hardening-exploiting message passing (ECHEMP) algorithm is proposed for large-scale MIMO detection to improve the decoding performance with less complexity cost, so as to a better decoding tradeoff.

^{[7]}The proposed MBM-mMIMO detection technique exploits the upper bound on support recovery error and iteratively minimizes the residual error associated with the estimated transmit vector.

^{[8]}Several algorithms for MIMO detection for spatially-multiplexed signals have been developed till today.

^{[9]}With the expansion of the quantity of send recieving wires, the complexity of MIMO detection increases exponentially.

^{[10]}Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature.

^{[11]}However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem.

^{[12]}The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa.

^{[13]}MIMO detection is one of the key technologies for MIMO system designs.

^{[14]}An additional step is introduced in the cell-free Massive MIMO processing: each AP in the uplink locally implements soft MIMO detection and then shares the resulting bit log-likelihoods on the front-haul link.

^{[15]}In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers.

^{[16]}In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations.

^{[17]}This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation.

^{[18]}Compared with the traditional MIMO detection schemes, the MOMS scheme is more robust in the sparse multipath channel scenarios, and can obtain most spatial degrees of freedom (DoF) in terms of antenna aperture and channel geometry.

^{[19]}Using computer simulations, it is shown that the error performance can be significantly improved and computational complexity reduced compared to those of state-of-the-art MIMO detection schemes.

^{[20]}Though machine learning-based MIMO detection techniques outperform conventional symbol detection techniques, in large user massive MIMO, they suffer from maintaining an optimal bias-variance trade-off to yield optimal performance from an individual model.

^{[21]}A novel architecture structuring MIMO Detection with a Trellis search is presented.

^{[22]}In this paper, a stochastic bio-inspired meta-heuristic algorithm is proposed for large MIMO detection.

^{[23]}In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems.

^{[24]}The MIMO detection algorithm is the key for design of MIMO receiver.

^{[25]}Linear detectors such as Minimum Mean Square Error (MMSE), Zero Forcing are applied for large scale MIMO detection because of their low computational cost, but has the least performance.

^{[26]}Hence, a deep learning based efficient MIMO detection approach is proposed in this paper.

^{[27]}Simulation results based on MIMO detections are presented to confirm the convergence gain brought by the proposed Gibbs sampling schemes.

^{[28]}Considering this challenging, but realistic practical scenario, various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate (BER) performance trade-off.

^{[29]}The MIMO detection complexity increases significantly along with the number of antennas.

^{[30]}In spite of the existence of various different SDRs for the MIMO detection problem in the literature, very little is known about the relationship between these SDRs.

^{[31]}The synthesis and post-layout simulation results show that the proposed GSM-MIMO detection chip achieves a better normalized throughput, hardware efficiency, and energy efficiency than a previous SM-MIMO detector chip.

^{[32]}Using the total degradations (TD) criterion, an appropriate quiescent point is determined to decrease the impact of non-linearity level, and hence the performances of different MIMO detection methods are compared.

^{[33]}In this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO detection, including minimal residual (MINRES) algorithm, generalized minimal residual (GMRES) algorithm, and conjugate residual (CR) algorithm.

^{[34]}In this paper, a novel and robust GSM-MIMO detection algorithm are proposed based on artificial bee colony optimization with mutation operator.

^{[35]}In practical massive MIMO detection, besides the influence of the algorithm’s own characteristics on the detection results, the hardware circuits also affect the efficiency of signal detection.

^{[36]}This research presents the performance of Gibbs sampling iterative MIMO detection and decoding scheme with maximum ratio combining (MRC).

^{[37]}In this paper, a low-complexity massive MIMO detection is first proposed based on approximate EP, which relieves the computational complexity of the exact EP while maintaining the good performance.

^{[38]}The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem.

^{[39]}For MIMO detections, the K-Best algorithm has been widely applied for multiple-antenna wireless communications.

^{[40]}Neural network has been applied into MIMO detection problem and has achieved the state-of-the-art performance.

^{[41]}Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement.

^{[42]}For MIMO detection, a supervised DLNN, which is designed, trained and evaluated using a Keras library and TensorFlow, is implemented in this MDM optical transmission system.

^{[43]}The implementation of MIMO detection techniques become a difficult missionas the computational complexity increases with the number of transmitting antenna and constellation size.

^{[44]}In this paper, we propose a novel information updating scheme for EP MIMO detection algorithm to achieve high performance with low complexity.

^{[45]}The main contributions of this paper include first, a proposal of newly developed iterative interference cancellation technique with parallel processing for MDL-impact mitigation, second, a comparative study on performance of MIMO detection schemes, and third, a demonstration of the longest dense SDM transmission over multicore (MC)-FMF.

^{[46]}Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade.

^{[47]}We propose a deep-learning approach for the joint MIMO detection and channel decoding problem.

^{[48]}Thus, an efficient iterative matrix inversion based on the hyper-power (HP) method is proposed for massive MIMO detection.

^{[49]}Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity.

^{[50]}

## Massive Mimo Detection

Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding.^{[1]}Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature.

^{[2]}However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem.

^{[3]}This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation.

^{[4]}In practical massive MIMO detection, besides the influence of the algorithm’s own characteristics on the detection results, the hardware circuits also affect the efficiency of signal detection.

^{[5]}In this paper, a low-complexity massive MIMO detection is first proposed based on approximate EP, which relieves the computational complexity of the exact EP while maintaining the good performance.

^{[6]}Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade.

^{[7]}Thus, an efficient iterative matrix inversion based on the hyper-power (HP) method is proposed for massive MIMO detection.

^{[8]}Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity.

^{[9]}

## Scale Mimo Detection

In this paper, the enhanced channel hardening-exploiting message passing (ECHEMP) algorithm is proposed for large-scale MIMO detection to improve the decoding performance with less complexity cost, so as to a better decoding tradeoff.^{[1]}Linear detectors such as Minimum Mean Square Error (MMSE), Zero Forcing are applied for large scale MIMO detection because of their low computational cost, but has the least performance.

^{[2]}

## Iterative Mimo Detection

In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers.^{[1]}This research presents the performance of Gibbs sampling iterative MIMO detection and decoding scheme with maximum ratio combining (MRC).

^{[2]}

## Joint Mimo Detection

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem.^{[1]}We propose a deep-learning approach for the joint MIMO detection and channel decoding problem.

^{[2]}

## mimo detection algorithm

Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the Alternating Direction Method Of Multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.^{[1]}Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding.

^{[2]}In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless systems.

^{[3]}In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers.

^{[4]}The MIMO detection algorithm is the key for design of MIMO receiver.

^{[5]}In this paper, a novel and robust GSM-MIMO detection algorithm are proposed based on artificial bee colony optimization with mutation operator.

^{[6]}In this paper, we propose a novel information updating scheme for EP MIMO detection algorithm to achieve high performance with low complexity.

^{[7]}Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade.

^{[8]}Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity.

^{[9]}Moreover, simulation results of adopting various mMIMO detection algorithms are presented.

^{[10]}

## mimo detection problem

In spite of the existence of various different SDRs for the MIMO detection problem in the literature, very little is known about the relationship between these SDRs.^{[1]}The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem.

^{[2]}Neural network has been applied into MIMO detection problem and has achieved the state-of-the-art performance.

^{[3]}

## mimo detection technique

The proposed MBM-mMIMO detection technique exploits the upper bound on support recovery error and iteratively minimizes the residual error associated with the estimated transmit vector.^{[1]}Though machine learning-based MIMO detection techniques outperform conventional symbol detection techniques, in large user massive MIMO, they suffer from maintaining an optimal bias-variance trade-off to yield optimal performance from an individual model.

^{[2]}The implementation of MIMO detection techniques become a difficult missionas the computational complexity increases with the number of transmitting antenna and constellation size.

^{[3]}

## mimo detection scheme

Compared with the traditional MIMO detection schemes, the MOMS scheme is more robust in the sparse multipath channel scenarios, and can obtain most spatial degrees of freedom (DoF) in terms of antenna aperture and channel geometry.^{[1]}Using computer simulations, it is shown that the error performance can be significantly improved and computational complexity reduced compared to those of state-of-the-art MIMO detection schemes.

^{[2]}The main contributions of this paper include first, a proposal of newly developed iterative interference cancellation technique with parallel processing for MDL-impact mitigation, second, a comparative study on performance of MIMO detection schemes, and third, a demonstration of the longest dense SDM transmission over multicore (MC)-FMF.

^{[3]}

## mimo detection method

This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation.^{[1]}Using the total degradations (TD) criterion, an appropriate quiescent point is determined to decrease the impact of non-linearity level, and hence the performances of different MIMO detection methods are compared.

^{[2]}