Variable List Detection for Multiuser MIMO Systems

Arevalo, Leonel; Lamare, Rodrigo C. de; Sampaio‐Neto, Raimundo

doi:10.1109/tvt.2016.2586942

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…We here consider three types of detectors, i.e., the MMSE detector, the LR-SIC detector, and a modified multi-branch LR-SIC detector (MMB-LR-SIC), as example detectors of different performance and complexity. Among them, the MMSE detector is a representative linear option; the MMB-LR-SIC is adapted from the variable list detector (VLD) of [41], representing a sophisticated detector with good BER performance; and LR-SIC has complexity and performance in between MMB-LR-SIC and MMSE. The study of this paper can also be extended to other signal detectors.…”

Section: Mimo Detectionmentioning

confidence: 99%

“…When the system size increases, the performance of LR-SIC still exhibits a gap to the maximum likelihood (ML) detector, as has shown in [41,43]. In [41], VLD (variable list detection) is proposed, which, in addition to the MMSE ordering-based LR-SIC as described above, also applies LR-SIC detectors with different randomly generated ordering to produce multiple estimates of the same transmitted symbol. The VLD allows the size of the candidate sets to be variable and hence the resulting complexity can vary over symbols, which can be high for certain channel uses.…”

Section: Modified Multi-branch Lr-sic (Mmb-lr-sic)mentioning

confidence: 99%

“…Denote these multiple estimates in the LR domain as z l , l = 1, 2, · · · , L, each corresponding to a specific ordering. One best estimate is selected according to the ML criterion in the LR domain [41] as…”

Section: Modified Multi-branch Lr-sic (Mmb-lr-sic)mentioning

confidence: 99%

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On the Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Lattice Reduction Aided Detectors

et al. 2020

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As an effective technology for boosting the performance of wireless communications, massive multiple-input multiple-output (MIMO) systems based on symmetric antenna arrays have been extensively studied. Using low-resolution analog-to-digital converters (ADCs) at the receiver can greatly reduce hardware costs and circuit complexity to further improve the energy efficiency (EE) of the system. There are significant research on the design of MIMO detectors but there is limited study on their performance in terms of EE. This paper studies the effect of signal detection on the EE in practical systems, and proposes to apply several signal detectors based on lattice reduction successive interference cancellation (LR-SIC) to massive MIMO systems with low-precision ADCs. We report results on their achievable EE in fading environments with typical modeling of the path loss and detailed analysis of the power consumption of the transceiver circuits. It is shown that the EE-optimal solution depends highly on the application scenarios, e.g., the number of antennas employed, the cell size, and the signal processing efficiency. Consequently, the signal detector must be properly selected according to the application scenario to maximize the system EE. In addition, medium-resolution ADCs should be selected to balance their own power consumption and the associated nonlinear distortion to maximize the EE of system. Symmetry 2020, 12, 406 2 of 20 Significant work has been done on the symbol detection in massive MIMO systems with low-precision ADCs. A class of techniques recover the transmitted symbols directly from the nonlinear model. In [13], iterative decision feedback receiver is studied for quantized MIMO systems. In [14], message passing dequantization (MPDQ) is applied to achieve multiuser detection for systems employing single-bit quantization. In [15], generalized approximate message passing is employed for massive MIMO with low-resolution ADCs. On the other hand, the tremendous detection algorithms designed for MIMO systems with ideal ADCs [16][17][18][19][20][21][22] can also be directly applied to quantized systems by using a linearized additive quantization noise model (AQNM). This will result in certain loss in the effective signal-to-noise ratio (SNR) but may offer solutions with moderate complexity, which are feasible for practical applications. Among the candidate techniques, the MMSE detector represents a linear option while lattice reduction-aided successive interference cancellation (LR-SIC) [23][24][25][26][27][28] provides nonlinear solutions with good tradeoff between performance and complexity for channels with large coherence blocks. The use of low-complexity, energy-efficient ADCs is also foreseen to be appealing in case of energy-constrained receive sides, such as cluster heads in massive MIMO-based wireless sensor networks (WSNs), as studied in [29][30][31][32].In general, more advanced detectors lead to improved bit error rate (BER) performance for MIMO systems (with either ideal or low-resolution ADCs) bu...

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Section: Mimo Detectionmentioning

confidence: 99%

Section: Modified Multi-branch Lr-sic (Mmb-lr-sic)mentioning

confidence: 99%

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On the Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Lattice Reduction Aided Detectors

et al. 2020

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“…Este critério foi empregado com sucesso em outros trabalhos promovidos pelo grupo de sistemas de comunicações do Centro de Estudos em Telecomunicações (CETUC) da PUC-Rio, como os realizados em [73] e [74].…”

Section: Conclusõesunclassified