A simplified QRD-M signal detection algorithm for MIMO-OFDM systems

Abstract

QR Decompositon with an M-algorithm (QRD-M) has good performance with low complexity, which is considered as a promising technique in Multiple-Input Multiple-Output (MIMO) detections. This paper presented a simplified QRD-M algorithm for MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. In the proposed scheme, each surviving path is expanded only to partial branches in order to carry out a limited tree search. The nodes are expanded on demand and sorted in a distributed manner, based on the novel expansion scheme which can pre-determine the children's ascending order by their local distances. Consequently, the proposed scheme can significantly decrease the complexity compared with conventional QRD-M algorithm. Hence, it is especially attractive to VLSI implementation of the high-throughput MIMO-OFDM systems. Simulation results prove that the proposed scheme can achieve a performance very close to the conventional QRD-M algorithm.

Low-Complexity QRD-M with Path Eliminations in MIMO-OFDM Systems

“…For further comparisons, Table 3 represents the comparisons for the complexity ratio using 16-QAM modulation in 4 × 4 system. In Table 3, a simplified QRD-M [6], combined QRD-M and Kalman filter [7], a pseudo-inverse-based QRD-M [8], and an advanced QRD-M [9] are compared with the conventional QRD-M and the proposed QRD-M. The error performances for the conventional QRD-M, existing algorithms, and the proposed QRD-M are almost the same.…”

“…In order to justify the better performance for the proposed QRD-M, the complexity for several QRD-M algorithms is compared with the proposed QRD-M. The complexity for the proposed QRD-M is lower than several QRD-M algorithms in [6][7][8][9][10][11][12] because the proposed QRD-M eliminates many unnecessary survival paths due to adaptively calculated thresholds at all layers considering the used modulation scheme. For further comparisons, the complexity for the E-DFE in [16] which has poor error performance is also compared with the proposed QRD-M.…”

“…The main reason for the high complexity of the QRD-M is that it contains unnecessary survival paths among the M survival paths. Several detections were developed to reduce the complexity of the conventional QRD-M in [6][7][8][9][10][11][12][13][14][15]. Among several detections, an adaptive QRD-M was developed in [10], a modified tree structure QRD-M was developed in [11], 2 of 14 and a lattice reduction (LR)-aided adaptive QRD-M was developed in [12].…”

Multiuser neural network detectors for large scale MIMO OFDM over Rayleigh Winner‐II channel

“…But the maximum likelihood (ML) detectors and nonlinear detectors are capable of detecting the multipath signals which transmitted through the wireless environment and difficult to implement due to its high complexity. 23 Thus, by taking into consideration of high complexity and performance behavior, nonlinear MUDs, such as, successive interference cancelation (SIC), 20,21 parallel interference cancelation (PIC), 20 sphere decoding (SD), [23][24][25] and QR decomposition (QRD) [26][27][28] and its modified techniques are used. However, all these MUDs are not capable in detecting the multiuser signals in overloaded and rank deficient situations, in which the number of antenna terminals are more as compared to the users.…”

“…MLP MUD model for supervised learning problems, used back propagation algorithms for estimating the weights, and bias to attenuate the error. 25,26 RBF model apply nonlinear function on its input before its classification and the dimension of the feature vector may be increased. A nonlinearly separable feature becomes highly separable than in low dimension.…”