scholarly journals On Channel Estimation for Rician fading with the phase-shift in Cell-Free Massive MIMO system

2021 ◽  
Author(s):  
Jamal Amadid ◽  
Mohamed boulouird ◽  
Abdelfettah Belhabib ◽  
Abdelouhab Zeroual
Keyword(s):  
Phase Shift ◽  
Channel Estimation ◽  
Communication Systems ◽  
Mean Squared Error ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Rician Fading ◽  
Mmse Estimator

Abstract Channel estimation (CE) is a crucial phase in wireless communication systems, especially in cell-free (CF) massive multiple input multiple output (M-MIMO) since it is a dynamic wireless network. Therefore, this work is introduced to study CE for CF M-MIMO system in the uplink phase, wherein the performance of different estimators are evaluated, discussed, and compared in various situations. We assume the scenario in which each access point has prior knowledge of the channel statistics. The phase-aware-minimum mean square error (PA-MMSE) estimator, the non-phaseaware-MMSE (NPA-MMSE) estimator, and the least-squares estimator are the three estimators which are exploited in this work. Besides, we consider the Rician fading channel in which the line-of-sight path is realized with a phase-shift that models the users’ mobility where the considered phase-shift follows a uniform distribution. On the other hand, the mean-squared error metric is employed in order to evaluate the performance of each estimator, where an analytical and simulated result is provided for the PA-MMSE estimator and the NPA-MMSE estimator in order to assert our numerical results.

scholarly journals Improved Pilot Selection for Channel Estimation of Polar Codes in Wireless 5G

2019 ◽  
Vol 9 (2) ◽  
pp. 4978-4983
Keyword(s):  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Poor Performance ◽  
Polar Codes ◽  
Selection Scheme ◽  
Polar Code ◽  
Pilot Selection

Fading channels learning about polar codes is great prominence. Knowledge of polar codes is very important while they are applied to the wireless communication systems. In fading Channels the communication through channel estimation which is an essential step for communication. The structure is constructed by a set of information bits for both systematic polar code and non-systematic polar code and the information set recognized frozen bits. In fading channels uneven pilot selection scheme and even pilot selection scheme are two pilot selection schemes are considered for polar codes. There is an improvement in decoding performance of polar codes using these selection schemes. In this choosing of coded symbols treated as pilots is a replacement of insertion of pilots. Polar codes have poor performance in fixed domain. So the EPS selection scheme can be active for tracing or channel estimation. The structure of polar code encoding is acapable structure and pilot selection is grave since whole selections cannot use the existing structure again. By conjoining the above advantages, pilot signals are selected without any addition from outside and insertion of pilot symbols impartial to estimation of the channel. Leveraging this, the DM-BS scheme is applyto multiple input multiple output (MIMO) system in frequency selective fading channel.

scholarly journals A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

2022 ◽  
Author(s):  
Dariel Pereira-Ruisánchez ◽  
Óscar Fresnedo ◽  
Darian Pérez-Adán ◽  
Luis Castedo
Keyword(s):  
Reinforcement Learning ◽  
Phase Shift ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Joint Optimization ◽  
Mimo Communication ◽  
Continuous State ◽  
Policy Gradient ◽  
Sum Rate

<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

scholarly journals Precise Channel Estimation Approach for a mmWave MIMO System

2020 ◽  
Vol 10 (12) ◽  
pp. 4397 ◽  
Author(s):  
Prateek Saurabh Srivastav ◽  
Lan Chen ◽  
Arfan Haider Wahla
Keyword(s):  
Channel Estimation ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Low Rank ◽  
High Signal ◽  
Formidable Challenge ◽  
Input Multiple Output ◽  
Efficient Recovery

Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.

scholarly journals A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

2022 ◽  
Author(s):  
Dariel Pereira-Ruisánchez ◽  
Óscar Fresnedo ◽  
Darian Pérez-Adán ◽  
Luis Castedo
Keyword(s):  
Reinforcement Learning ◽  
Phase Shift ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Joint Optimization ◽  
Mimo Communication ◽  
Continuous State ◽  
Policy Gradient ◽  
Sum Rate

<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

On Channel Estimation for Rician Fading with the Phase-Shift in Cell-Free Massive MIMO System

2021 ◽  
Author(s):  
Jamal Amadid ◽  
Mohamed Boulouird ◽  
Abdelfettah Belhabib ◽  
Abdelouhab Zeroual
Keyword(s):  
Phase Shift ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo System ◽  
Rician Fading

scholarly journals Investigation of error performance in network coded MIMO-VBLAST wireless communication systems

2019 ◽  
Vol 70 (4) ◽  
pp. 273-284
Author(s):  
Ali Farzamnia ◽  
Ngu War Hlaing ◽  
Lillian Eda Kong ◽  
Manas Kumar Haldar ◽  
Tohid Yousefi Rezaii
Keyword(s):  
Phase Shift ◽  
Wireless Communication ◽  
Bit Error Rate ◽  
Error Rate ◽  
Communication Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Phase Shift Keying ◽  
Shift Keying

Abstract Paper aims to enhance the performance of bit error rate (BER) in wireless communication based on the multiple-input multiple-output (MIMO) system of vertical Bell laboratories layered space-time (VBLAST) algorithm. The VBLAST algorithm uses zero-forcing (ZF) and the minimum mean square error (MMSE) to evaluate the BER of wireless communication. MIMO VBLAST techniques function as an adaptive filter and can minimize the interference and multipath fading in the received signal of the channel. Physical layer network coding (PNC) is a new technique used to exploit the spatial diversity of the MIMO VBLAST system to improve the throughput and performance of wireless communication. The bit-error-rate (BER) of proposed VBLAST MIMO with PNC with binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation over the additive white Gaussian noise and Rayleigh fading channel are analyzed. The performance of both BPSK and QPSK modulation in two and four antennas are compared. From the simulation results, it was found that the proposed scheme MIMO VBLAST PNC has a 45.2 % higher BER performance compared to the traditional MIMO scheme with an increase in the BER using MMSE and ZF respectively in both two and four antennas.

scholarly journals Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

2021 ◽  
Author(s):  
Chang Liu ◽  
Xuemeng Liu ◽  
Derrick Wing Kwan Ng ◽  
Jinhong Yuan
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Prior Probability ◽  
Minimum Mean Square Error ◽  
Residual Learning ◽  
Residual Noise ◽  
Spatial Features ◽  
Multidimensional Integration ◽  
Mmse Estimator ◽  
Reflecting Surface

<div>Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.</div>

scholarly journals Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

2021 ◽  
Author(s):  
Chang Liu ◽  
Xuemeng Liu ◽  
Derrick Wing Kwan Ng ◽  
Jinhong Yuan
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Prior Probability ◽  
Minimum Mean Square Error ◽  
Residual Learning ◽  
Residual Noise ◽  
Spatial Features ◽  
Multidimensional Integration ◽  
Mmse Estimator ◽  
Reflecting Surface

<div>Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.</div>

scholarly journals Location-Aided Uplink Transmission for User-Centric Cell-Free Massive MIMO Systems: A Fairness Priority Perspective

2022 ◽  
Author(s):  
Chen Wei ◽  
Kui Xu ◽  
Zhexian Shen ◽  
Xiaochen Xia ◽  
Wei Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Estimation Accuracy ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output ◽  
User Centric ◽  
Assignment Scheme

Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme based on a novel proposed metric representing inter-user interference to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which the normalized local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while the normalized maximum ratio (MR) combining is adopted for the remaining APs. A new closed-form SE expression using the normalized MR combining is derived and a novel metric to indicate the UE fairness is also proposed. Moreover, the max-min fairness (MMF) power control algorithm is utilized to further ensure uniformly good service to the UEs. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. Furthermore, although the proposed location-aided uplink combining scheme is not always the best in terms of the per-UE SE, it can provide the more fairness among UEs and can achieve a good trade-off between the average SE and computational complexity.

scholarly journals MMSE-NP-RISIC-Based Channel Equalization for MIMO-SC-FDE Troposcatter Communication Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Zedong Xie ◽  
Xihong Chen ◽  
Xiaopeng Liu ◽  
Yu Zhao
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Intersymbol Interference ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Channel Equalization ◽  
Single Input Single Output ◽  
Single Carrier ◽  
Single Output ◽  
The Impact

The impact of intersymbol interference (ISI) on single-carrier frequency-domain equalization with multiple input multiple output (MIMO-SC-FDE) troposcatter communication systems is severe. Most of the channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the noise-predictive (NP) MMSE-based and the residual intersymbol interference cancellation (RISIC) equalization in the single input single output (SISO) system, we focus on the combination of both equalization schemes mentioned above. After extending both of them into MIMO system for the first time, we introduce a novel MMSE-NP-RISIC equalization method for MIMO-SC-FDE troposcatter communication systems. Analysis and simulation results validate the performance of the proposed method in time-varying frequency-selective troposcatter channel at an acceptable computational complexity cost.

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