scholarly journals Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1509 ◽  
Author(s):  
Ismael Lopez ◽  
L. Pizano-Escalante ◽  
Joaquin Cortez ◽  
O. Longoria-Gandara ◽  
Armando Garcia
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Spatial Modulation ◽  
Impact Performance ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Receiver Performance ◽  
Limited Precision ◽  
Numerical Precision

This paper presents a proposal for an architecture in FPGA for the implementation of a low complexity near maximum likelihood (Near-ML) detection algorithm for a multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. Our proposal was verified in the context of a MIMO receiver. The effects of the finite length arithmetic and limited precision were evaluated in terms of their impact on the receiver bit error rate (BER). We defined the minimum fixed point word size required not to impact performance adversely for n T transmit antennas and n R receive antennas. The results showed that the proposal performed very near to optimal with the advantage of a meaningful reduction in the complexity of the receiver. The performance analysis of the proposed detector of the MIMO receiver under these conditions showed a strong robustness on the numerical precision, which allowed having a receiver performance very close to that obtained with floating point arithmetic in terms of BER; therefore, we believe this architecture can be an attractive candidate for its implementation in current communications standards.

scholarly journals Improved QRD-M Detection Algorithm for Generalized Spatial Modulation Scheme

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaorong Jing ◽  
Mingyue Wang ◽  
Wei Zhou ◽  
Hongqing Liu
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Spatial Modulation ◽  
Modulation Scheme ◽  
Transmission Scheme ◽  
Imperfect Detection ◽  
Input Multiple Output

Generalized spatial modulation (GSM) is a spectral and energy efficient multiple-input multiple-output (MIMO) transmission scheme. It will lead to imperfect detection performance with relatively high computational complexity by directly applying the original QR-decomposition with M algorithm (QRD-M) to the GSM scheme. In this paper an improved QRD-M algorithm is proposed for GSM signal detection, which achieves near-optimal performance but with relatively low complexity. Based on the QRD, the improved algorithm firstly transforms the maximum likelihood (ML) detection of the GSM signals into searching an inverted tree structure. Then, in the searching process of the M branches, the branches corresponding to the illegitimate transmit antenna combinations (TACs) and related to invalid number of active antennas are cut in order to improve the validity of the resultant branches at each level by taking advantage of characteristics of GSM signals. Simulation results show that the improved QRD-M detection algorithm provides similar performance to maximum likelihood (ML) with the reduced computational complexity compared to the original QRD-M algorithm, and the optimal value of parameter M of the improved QRD-M algorithm for detection of the GSM scheme is equal to modulation order plus one.

scholarly journals Partial Nulling Regularized Block Diagonalization Using Unfair Channel Selection for Post-Coding with Low-Complexity

2020 ◽  
Vol 10 (19) ◽  
pp. 6809
Author(s):  
Hyun-Sun Hwang ◽  
Jae-Hyun Ro ◽  
Young-Hwan You ◽  
Duckdong Hwang ◽  
Hyoung-Kyu Song
Keyword(s):  
Performance Enhancement ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Diversity Gain ◽  
Block Diagonalization ◽  
Time Diversity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
User Interference

A number of requirements for 5G mobile communication are satisfied by adopting multi-user multiple input multiple output (MU-MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MU-MIMO systems becomes controllable when the precoding scheme is used. The proposed scheme, which is one of the precoding schemes, is built on regularized block diagonalization (RBD) precoding and utilizes the partial nulling concept, which is to leave part of the IUI at the same time. Diversity gain is obtained by leaving IUI, which is made by choosing the row vectors of the channel matrix that are not nullified. Since the criterion for choosing the row vectors of the channel is the power of the channel, the number of selected row vectors of the channel for each device can be unfair. The proposed scheme achieves performance enhancement by obtaining diversity gain. Therefore, the bit error rate (BER) performance is better and the computational complexity is lower than RBD when the same data rate is achieved. When the number of reduced data streams is not enough for most devices to achieve diversity gain, the proposed scheme has better performance compared to generalized block diagonalization (GBD). The low complexity at the receiver is achieved compared to GBD by using the simple way to remove IUI.

scholarly journals Power Scaling for Spatial Modulation with Limited Feedback

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Yue Xiao ◽  
Qian Tang ◽  
Lisha Gong ◽  
Ping Yang ◽  
Zongfei Yang
Keyword(s):  
System Performance ◽  
Multiple Input Multiple Output ◽  
Main Idea ◽  
Limited Feedback ◽  
Low Complexity ◽  
Spatial Diversity ◽  
Spectrum Efficiency ◽  
Spatial Modulation ◽  
Power Scaling ◽  
Input Multiple Output

Spatial modulation (SM) is a recently developed multiple-input multiple-output (MIMO) technique which offers a new tradeoff between spatial diversity and spectrum efficiency, by introducing the indices of transmit antennas as a means of information modulation. Due to the special structure of SM-MIMO, in the receiver, maximum likelihood (ML) detector can be combined with low complexity. For further improving the system performance with limited feedback, in this paper, a novel power scaling spatial modulation (PS-SM) scheme is proposed. The main idea is based on the introduction of scaling factor (SF) for weighting the modulated symbols on each transmit antenna of SM, so as to enlarge the minimal Euclidean distance of modulated constellations and improve the system performance. Simulation results show that the proposed PS-SM outperforms the conventional adaptive spatial modulation (ASM) with the same feedback amount and similar computational complexity.

Depth-First QRD-M Detection Algorithm for MIMO Systems

2013 ◽  
Vol 347-350 ◽  
pp. 3478-3481
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han ◽  
Yu Huan Wang
Keyword(s):  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
Detection Scheme ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Maximum likelihood (ML) detection algorithm for multiple input multiple output (MIMO) systems provided the best bit error rate (BER) performance with heavy calculating complexity. The use of QR decomposition with M-algorithm (QRD-M) had been proposed to provide near-ML detection performance and lower calculating complexity. However, its complexity still grew exponentially with increasing dimension of the transmitted signal. To reduce the problem, an improved detection scheme was proposed here. After constructing the tree detecting model of MIMO systems, the ML search of one layer was done, the branch metrics were calculated and sorted, which gave an ordered set of the layer, then depth-first search were used to search the left layers with termination methods. The proposed algorithm provides near QRD-M detection performance.

Low-complexity iterative channel estimation with lattice reduction-based detection for multiple-input multiple-output systems

2014 ◽  
Vol 8 (6) ◽  
pp. 905-913 ◽  
Author(s):  
Lin Bai ◽  
Qiaoyu Li ◽  
Jinho Choi ◽  
Weixi Xing ◽  
Shengyue Dou
Keyword(s):  
Channel Estimation ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Lattice Reduction ◽  
Multiple Input ◽  
Input Multiple Output
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