Novel User Scheduling Schemes Based on Nonlinear Precoding for Multiuser MIMO Systems

2012 ◽  
Vol 195-196 ◽  
pp. 270-276 ◽  
Author(s):  
Cun Yi Zhang ◽  
Mu Qing Wu ◽  
Run Qian Chen ◽  
Guo Dong Ma
Keyword(s):  
Mimo Systems ◽  
Geometric Mean ◽  
Multiple Input Multiple Output ◽  
Computation Complexity ◽  
User Scheduling ◽  
Processing Scheme ◽  
Scheme Design ◽  
Input Multiple Output ◽  
The Impact ◽  
Ber Performance

This paper proposes a novel greedy user ordering algorithm for the multiuser multiple-input multiple-output (MIMO) systems employing block diagonal geometric mean decomposition method and Tomlinson-Harashima precoding (THP). Theoretical analysis and computer simulations illustrate its low computation complexity relative to the optimal user ordering achieving by brute search over all the possible ordering permutations resulting in extremely high computation complexity. Meanwhile the bit error rate (BER) performance of the proposed algorithm is very close to the optimal user ordering. Moreover, in order to mitigate the impact of users with smaller sub-channel gains to the whole systems BER performance, a joint pre-processing scheme design of adaptive data streams reduction and greedy user ordering (ADSR-GUO) is proposed. By means of choosing different values for the controlling factor, we can obtain different system sum-rate and BER performance to satisfy different quality-of-service (QoS) requirements.

scholarly journals Massive MIMO-NOMA Networks with Imperfect SIC: Design and Fairness Enhancement

2020 ◽  
Author(s):  
Arthur Sousa de Sena ◽  
Pedro Nardelli
Keyword(s):  
Outage Probability ◽  
Power Allocation ◽  
Interference Cancellation ◽  
Massive Mimo ◽  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
Input Multiple Output ◽  
The Impact

This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.

scholarly journals Efficient Power Allocation for LDPC-Coded MIMO Systems

2011 ◽  
pp. 127-133
Author(s):  
Layak Ali Sd ◽  
K. Kishan Rao ◽  
M. Sushanth Bab
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Mimo Systems ◽  
Geometric Mean ◽  
Multiple Input Multiple Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Coded Systems ◽  
Efficient Power ◽  
Q Function

In this papers an efficient ordering scheme for an ordered successive interference cancellation detector is determined under the bit error rate minimization criterion for multiple-input multiple-output(MIMO) communication systems using transmission power control. From the convexity of the Q-function, we evaluate the choice of suitable quantization characteristics for both the decoder messages and the received samples in Low Density Parity Check (LDPC)-coded systems using M-QAM schemes. We derive the ordering strategy that makes the channel gains converge to their geometric mean. Based on this approach, the fixed ordering algorithm is first designed, for which the geometric mean is used for a constant threshold using correlation among ordering results.

scholarly journals MIMO-OFDM equaliser for spatial multiplexing transmission modes

2010 ◽  
Vol 8 ◽  
pp. 81-85 ◽  
Author(s):  
P. Beinschob ◽  
U. Zölzer
Keyword(s):  
Channel Coding ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Spatial Multiplexing ◽  
Performance Loss ◽  
Low Snr ◽  
Input Multiple Output ◽  
Spatial Interference ◽  
The Impact ◽  
Ber Performance

Abstract. In search for faster and more reliable communication, multiple-input multiple-output (MIMO) in conjuction with Orthogonal Frequency Division Multiplexing (OFDM) are subject of extensive research. In spatial multiplexing transmission an instantaneous rise of data rates governed by the number of transmit antennas can be realised. The system performance depends highly on signal-to-interference-plus-noise ratios (SINR) at the receiver. The receiver's equaliser is supposed to maximize the SINR by mitigating the spatial interference and thus separating the transmitted signals. For this problem several solutions exist such as linear and nonlinear, per subcarrier or OFDM symbol-based. An overview of common algorithms is given and complexity is discussed. Bit error rate (BER) performance evaluations are presented. Another aspect is the impact of the equalisation strategy on the performance of bit-interleaved soft information-based channel coding schemes. As a representative, LDPC codes are chosen. Simulation results show a significant BER performance loss for symbol decision-based equalisers compared to the uncoded performance. To overcome this problem a modification of the Maximum Likelihood algorithm is proposed which yields good performance for low SNR applications.

scholarly journals Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Mahmoud A. Albreem ◽  
Mohammed H. Alsharif ◽  
Sunghwan Kim
Keyword(s):  
Computational Complexity ◽  
Iterative Methods ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Matrix Inversion ◽  
Multiple Input ◽  
The Matrix ◽  
Input Multiple Output ◽  
The Impact

In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.

scholarly journals Massive MIMO Systems for 5G

2020 ◽  
Author(s):  
Robin Chataut ◽  
Robert Akl
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Visible Light Communication ◽  
User Scheduling ◽  
Access Technology ◽  
Input Multiple Output ◽  
Simple Processing

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues such as pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection is vital for the successful deployment of 5G and beyond networks. Some of the recent trends in massive MIMO are terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning. 

scholarly journals Efficient Detectors based on Group Detection for Massive MIMO systems

2018 ◽  
Author(s):  
Thanh-Binh Nguyen ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Tien-Dong Nguyen ◽  
Huy-Dung Han
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Bell Laboratory ◽  
Channel Matrix ◽  
Input Multiple Output ◽  
Group Detection ◽  
Ber Performance

In Multiple Input Multiple Output (MIMO) systems, the complexities of detectors depend on the size of the channel matrix. In Massive MIMO systems, detection complexity becomes remarkably higher because the dimensions of the channel matrix get much larger. In order to recover the signals in the up-link of a Massive MIMO system at reduced complexities, we first divide the system into two sub-systems. After that, we apply the Minimum Mean Square Error (MMSE) and Bell Laboratory Layer Space Time (BLAST) detectors to each subsystem, resulting in the so-called MMSE-GD and BLAST-GD detectors, respectively. To further enhance the BER performance of Massive MIMO systems under the high-load conditions, we propose two additional detectors, called MMSE-IGD and BLAST-IGD by respectively applying the conventional MMSE and BLAST on the sub-systems in an iterative manner. It is shown via computer simulation and analytical results that the proposed detectors enable the system to achieve not only higher BER performance but also low detection complexities as compared to the conventional linear detectors. Moreover, the MMSE-IGD and BLAST-IGD can significantly improve BER performance of Massive MIMO systems.

scholarly journals BER Performance Evaluation of Turbo Coded MIMO System in Nakagami Channel

2019 ◽  
Vol 8 (12) ◽  
pp. 5101-5104
Keyword(s):  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Data Communication ◽  
Block Codes ◽  
Space Time Block Codes ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Improved Performance ◽  
Ber Performance

Wireless technologies are aiming to improve data rates along with reliability using Multiple Input and Multiple Output(MIMO) systems. The major performance parameter for advanced systems is Bit Error rate (BER). Researchers are working for minimizing the BER for data communication. This paper presents the BER performance of turbo coded Multiple Input Multiple Output (MIMO) system in Nakagami channel. MIMO system is realized using Space Time Block Codes. System performance is analyzed for M-ary Quadrature Amplitude Modulation (QAM) in Nakagami channel. System is implemented using MATLAB code. 4QAM system performs better as far as BER is concerned. The implemented turbo coded system outperforms the uncoded system in case of BER performance. This system can be used for improved performance of data communication in LTE and WiMax.

scholarly journals Massive MIMO-NOMA Networks with Imperfect SIC: Design and Fairness Enhancement

2020 ◽  
Author(s):  
Arthur Sousa de Sena ◽  
Pedro Nardelli
Keyword(s):  
Outage Probability ◽  
Power Allocation ◽  
Interference Cancellation ◽  
Massive Mimo ◽  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
Input Multiple Output ◽  
The Impact

This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.

scholarly journals Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Rong Ran ◽  
Hayoung Oh
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Finite Alphabet ◽  
Multiuser Detectors ◽  
Input Multiple Output ◽  
Error Detector ◽  
Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

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