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

Abstract

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...

Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling

“…Total bits successfully transmitted by consuming a Joule of energy is called EE measured as bits-per-Joule [4]. In massive MIMO systems, EE is dependent on many factors, i.e., spectral efficiency, network architecture, power consumption by the entire system, and transmission protocol [4][5][6][7][8][9]. Existing literature provides mathematical modeling of mentioned parameters to enhance area throughput and EE.…”

Evaluation of different quantization resolution levels on the BER performance of massive MIMO systems under different operating scenarios

“…As a result of the interuser interference among the users, the signal symbols detector is used to evaluate the vector of the transmitted signal symbols (x) from the vector of the received signal symbols (y). In this work, we have adopted the LMMSE signal symbols detector from the other state-of-the-art detectors because of its advantages, including [7], [16]…”

“…The power consumed by high-quantization resolution level ADCs represents the essential part of the total power consumed by the other circuit components of the RF chain [14], [15]. In parallel, the utilization of high-quantization resolution level ADCs contributes significantly to increasing the hardware costs and complexities and reducing quantization errors in the massive MIMO systems [16]- [18]. The power consumption in the ADCs decreases exponentially with quantization resolution levels (quantization bits) and linearly with sampling frequencies (sampling rates) [19].…”

“…In addition, the use of the perfect CSI-based linear ZFSIC signal symbols detector is able to improve the EE of the system more than the perfect CSIbased linear ZF signal symbols detector. The authors in [16] investigated the role of using low-quantization resolution levels ADCs with the linear minimum mean squared error (LMMSE) and nonlinear lattice reduction-based successive interference cancellation (LR-SIC) signal symbols detectors in improving the EE in the massive MIMO systems under different operating scenarios. Their results showed that the bit error rate (BER) performance using the LR-SIC signal symbols detectors-based receivers is better than that of LMMSE signal symbols detectors-based receivers and that the EE of the system depends on the number of antennas, cell size, and signal processing efficiency employed at the BS.…”

Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

“…In [37], the authors proposed an LR-SIC receiver for the massive MIMO system with low-precision ADCs. The prime focus of this paper to select the proper receiver system to make the system more energy efficient.…”