Detecção Ótima por Grupos em Sistemas com Transmissão em Blocos

Maza, Byron; Neto, Raimundo Sampaio; Medina, César A.

doi:10.14209/sbrt.2012.131

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…formance between non-linear and linear receivers. Figure 1 shows the structure of the GD-ML receiver, which consists of three steps [27], [28]: 1) linear transformation (LT) of the received vector, 2) division of the result into groups; 3) application of ML detection to each group.…”

Section: Gd-ml Is a Technique With Intermediate Complexity And Per-mentioning

confidence: 99%

Average Vector-Symbol Error Rate Closed-Form Expression for ML Group Detection Receivers in Large MU-MIMO Channels With Transmit Correlation

et al. 2020

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In this paper, we derive a closed-form expression for the evaluation of the average vectorsymbol error rate (VER) of group detection followed by maximum-likelihood (GD-ML) receivers in large multiuser multiple-input multiple-output (MU-MIMO) systems with transmit side correlated Rayleigh channels. We assume M antennas at the base station (BS), N closely-located, single-antenna user equipment (UEs) with load factor λ = N M , and N 1; consequently, we evaluate the performance of GD-ML receivers as the load factor grows to unity. The derived expression requires a negligible correlation at the receive side of the communication channel. Hence, from a practical point-of-view, when considering scenarios with a large number of UEs, the derived analytical expression is generally more applicable for systems with a distributed massive number of BS antennas. Numerical results are provided to validate our derived expression. We observe that the GD-ML with N u group size achieves a diversity order proportional to M − N + N u. Moreover, we show that for small group sizes, the analytical and simulation results remain close, and at moderate to high signal-to-noise ratio (SNR), the derived expression very closely matches the simulations, whereas this match becomes perfect as the users' side correlation increases. We also demonstrate that GD-ML outperforms the zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers, in terms of VER; where for high λ, GD-ML exploits the maximum spatial multiplexing gain. Moreover, in terms of floating-point operations (FLOPs), we show that GD-ML receivers have almost the same complexity as ZF and MMSE where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation. INDEX TERMS Group detection (GD), maximum likelihood (ML) detection, multiple-input multiple-output (MIMO), vector-error rate VER.

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Section: Gd-ml Is a Technique With Intermediate Complexity And Per-mentioning

confidence: 99%