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 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 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 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.

An Adaptive Multiple LS Channel Estimation Method for MIMO System

2012 ◽  
Vol 239-240 ◽  
pp. 1084-1088
Author(s):  
Jin Lun Chen
Keyword(s):  
Numerical Simulation ◽  
Channel Estimation ◽  
Communication Systems ◽  
Mimo System ◽  
Estimation Method ◽  
Linear Least Squares ◽  
Mimo Communication ◽  
Channel Estimate ◽  
Wide Range ◽  
Multiple Input

Accurate channel estimation plays a key role in multiple-input and multiple-output (MIMO) communication systems. In this paper, we firstly discuss the popular linear least squares (LS) channel estimation and the multiple LS (MLS) channel estimation. Then an adaptive multiple LS (AMLS) channel estimate approach is proposed. Using numerical simulation, it is found that the proposed estimation method outperforms LS and MLS for a wide range of training SNRs.

Research on UCD Precoding for MIMO System

2012 ◽  
Vol 459 ◽  
pp. 620-623
Author(s):  
Hong He ◽  
Tao Li ◽  
Tong Yang ◽  
Lin He
Keyword(s):  
Communication Systems ◽  
Mimo System ◽  
Geometric Mean ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Error Correcting Codes ◽  
Decomposition Scheme ◽  
Input Multiple Output ◽  
Uniform Channel ◽  
Channel Decomposition

This article mainly describes a new technique of multiple-input multiple-output (MIMO) communication systems based on the recent communication demand. This technique, by pre-coding CSI (the channel state information) at the transmitter, is based on UCD (Uniform Channel Decomposition) algorithm for MIMO system. By Uniform Channel decomposition of channel matrix, the algorithm can decompose a MIMO downlink channel into multiple identical sub-channels. The power allocation applied to each sub channel in MIMO system is identical, and the MIMO channel’s capacity isn’t reduce when the SNR (Signal Noise Ratio) is low. The simulations show that the UCD scheme has a better performance than GMD (Geometric Mean Decomposition) scheme even without the use of error-correcting codes, and the Symbol Error Rate (SER) of UCD algorithm is lower than GMD’s at the same SNR. Consequently, MIMO system gets a better interference performance by UCD algorithm.

scholarly journals Mitigating the Interference Caused by Pilot Contamination in Multi-cell Massive Multiple Input Multiple Output Systems Using Low Density Parity Check Codes in Uplink Scenario

2020 ◽  
Vol 37 (6) ◽  
pp. 1061-1074
Author(s):  
Lokesh Bhardwaj ◽  
Ritesh Kumar Mishra
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Low Density ◽  
Pilot Contamination ◽  
Parity Check ◽  
Low Density Parity Check ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Sum Rate

The effects of pilot contamination (PC) on the performance of multi-cell multi-user massive multiple input multiple output (MC-MU-m-MIMO) system in uplink has been analyzed in this article. In a multi-cell scenario, the channel estimation (CE) at the desired cell using pilot reuse to avoid significant overhead results in poor CE due to PC. The improvement in degraded performance due to the effect of PC has been shown using low Density Parity Check (LDPC) codes. The comparative analysis of performance in terms of variation in bit error rate (BER) with the signal to noise ratio (SNR) for LDPC coded and uncoded information blocks of users has been shown when the number of cells sharing the same frequency band is varied. Further, the expression for sum-rate has been derived and its variation with the number of base station (BS) antennas has also been shown. The simulated results have shown that the LDPC coded scheme performs better than the uncoded counterpart and the sum-rate capacity increases when the strength of channel coefficients between the BS antennas of the desired cell and the users of remaining cells is less.

scholarly journals Sum-Rate of Multi-User MIMO Systems with Multi-Cell Pilot Contamination in Correlated Rayleigh Fading Channel

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 573 ◽  
Author(s):  
Menghan Wang ◽  
Dongming Wang
Keyword(s):  
Rayleigh Fading ◽  
Gaussian Approximation ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Moment Generating Function ◽  
Base Station ◽  
Pilot Contamination ◽  
Input Multiple Output ◽  
Sum Rate

This paper presents some exact results on the sum-rate of multi-user multiple-input multiple-output (MU-MIMO) systems subject to multi-cell pilot contamination under correlated Rayleigh fading. With multi-cell multi-user channel estimator, we give the lower bound of the sum-rate. We derive the moment generating function (MGF) of the sum-rate and then obtain the closed-form approximations of the mean and variance of the sum-rate. Then, with Gaussian approximation, we study the outage performance of the sum-rate. Furthermore, considering the number of antennas at base station becomes infinite, we investigate the asymptotic performance of the sum-rate. Theoretical results show that compared to MU-MIMO system with perfect channel estimation and no pilot contamination, the variance of the sum-rate of the considered system decreases very quickly as the number of antennas increases.

The impact of MIMO Communication on Non-Frequency Selective Channels Performance

2011 ◽  
pp. 100-114
Author(s):  
Andreas Ahrens ◽  
César Benavente-Peces
Keyword(s):  
Communication Systems ◽  
Mimo System ◽  
Quality Criteria ◽  
Single Input Single Output ◽  
Mimo Communication ◽  
Single Output ◽  
Transmission Modes ◽  
Input Multiple Output ◽  
Frequency Selective ◽  
Frequency Selective Channels

This chapter reviews the basic concepts of multiple-input multiple-output (MIMO) communication systems and analyses their performance within non-frequency selective channels. The MIMO system model is established and by applying the singular value decomposition (SVD) to the channel matrix, the whole MIMO system can be transformed into multiple single-input single-output (SISO) channels having unequal gains. In order to analyze the system performance, the quality criteria needed to calculate the error probability of M-ary QAM (Quadrature Amplitude Modulation) are briefly reviewed and used as reference to measure the improvements when applying different signal processing techniques. Bit and power allocation is a well-known technique that allows improvement in the bit-error rate (BER) by managing appropriately the different properties of the multiple SISO channels. It can be used to balance the BER’s in the multiple SISO channels when minimizing the overall BER. In order to compare the various results, the efficiency of fixed transmission modes is studied in this work regardless of the channel quality. It is demonstrated that only an appropriate number of MIMO layers should be activated when minimizing the overall BER under the constraints of a given fixed date rate.

scholarly journals Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7094
Author(s):  
Jaehee Lee ◽  
Jaewoo So
Keyword(s):  
Reinforcement Learning ◽  
Computational Complexity ◽  
Power Allocation ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Spectrum Efficiency ◽  
Q Learning ◽  
User Pairing ◽  
Input Multiple Output ◽  
Radio Resources

In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).

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