## 1 Introduction  
Massive multiple-input multiple-output (MIMO) systems employing symmetric antenna arrays can be used to improve the spectrum efficiency (SE) of wireless communications [1; 2; 3; 4]. In a typical massive MIMO system, analog-to-digital converters (ADCs) with symmetric quantization levels are used to convert the real and imaginary parts of the received signal before further processing. The power consumption of ADCs increases exponentially with the quantization bits. Therefore, low-precision ADCs have been widely considered recently for massive MIMO systems [5]. Significant studies have been carried out on the SE of massive MIMO with linear detectors [6; 7; 8; 9]. In [7; 10], the uplink SE of a maximum ratio combining (MRC) receiver is analyzed for massive MIMO with low-precision ADCs. The SE is analyzed for the minimum mean squared error (MMSE) receiver in [11], and it is concluded that the large arrays can compensate for the loss of low-resolution quantization. Based on the power consumption model in [3], Ref. [12] analyzes the energy efficiency (EE) of successive interference cancellation (SIC) receiver with low-precision ADCs. 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) but also higher computational complexity and higher power consumption. It is challenging to compare the different schemes and choose the one that yields the best balance between them. This work attempts to address this challenge by applying the EE as a criterion to compare several linear and nonlinear detectors. We focus on the MMSE and LR-SIC detectors due to their potential in achieving good tradeoff between performance and complexity but the work can be extended to other detectors easily. The contributions can be summarized as follows:  
1. We investigate the implementation of LR-SIC detectors and their improvements for massive MIMO systems with low-resolution ADCs;
2. We analyze the transmission power required for achieving a target BER, the power required for signal detection, and also the power consumption due to the ADCs given the system configuration;
3. We analyze and compare the EE for several different MIMO detectors under a universal system-level power consumption model, and discuss the influence of the number of antennas, the ADC resolution and also the signal processing algorithms on the performance. Numerous numerical studies are carried out.