Abstract
In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. The algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. Then, using the PGD and the classical ZF as well as the QR-decomposition- (QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). The PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can significantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as efficient means of estimating the transmitted signals in high-load massive MIMO systems.
Highlights
IntroductionMassive multiple-input-multiple-output (massive MIMO) systems have been proposed to improve the quality of signal transmission in wireless communications
In recent years, massive multiple-input-multiple-output systems have been proposed to improve the quality of signal transmission in wireless communications
We develop our idea called the parallel group detection (PGD) algorithm in [12] for high-load massive MIMO systems. e algorithm is built by dividing the equivalent extended form of high-load massive MIMO system into two smaller load subsystems, which can be detected in parallel by utilizing the classical detectors
Summary
Massive multiple-input-multiple-output (massive MIMO) systems have been proposed to improve the quality of signal transmission in wireless communications. In order to improve BER performance of linear detectors in high-load massive MIMO systems, Post Detection Sparse Error Recovery (PDSR) algorithm [10] can be applied. E empirical simulations and complexity evaluations show that the proposed detectors can significantly improve the BER performance of massive MIMO systems compared to those of the classical linear detectors and the QRD while their complexities are at the practical levels. They are the good candidates for signal recovery in high-load massive MIMO systems. Notations: C denotes set of complex numbers; Q is the slicing operation, which slices the received signals to the nearest values in the set of integer numbers corresponding to the QAM constellation; IN is a N × N identity matrix, and 1N is a N × 1 one vector, whose elements are all 1; (▪)T and (▪)H are the transpose and Hermitian transpose operations; E[▪] and ⊗ denote the expectation operation and Kronecker product, respectively; and P† is the pseudoinverse of matrix P and [▪] is the round operation
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