Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation

Liu, Tianle; Tong, Jun; Guo, Qinghua; Xi, Jiangtao; Yu, Yanguang; Xiao, Zhitao

doi:10.1109/twc.2019.2920129

Cited by 54 publications

(49 citation statements)

References 53 publications

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“…Since there is only one branch (with the MMSE ordering), there is no need to compute the ML metric. Following [3,12], its power consumption is estimated as…”

Section: Power Consumption By Signal Detectionmentioning

confidence: 99%

“…The receiver power consumption also includes the power consumption of the ADCs. The power consumed by a single ADC is estimated by [12]…”

Section: Adc Power Consumptionmentioning

confidence: 99%

“…Generally, an ADC with high-resolution leads to smaller quantization errors compared with a low-resolution ADC but also greater power consumption (48). In this paper, we assume a moderate value of FOM = 1432.1 fJ per conversion step [12,47] to model ADCs. Note that the power consumption due to ADC is independent of the detection algorithm applied.…”

Section: Adc Power Consumptionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the previous studies on MIMO detection [13][14][15][16][17][18][19][23][24][25][26][27] that focus mainly on the transmission power required, this work provides a new perspective for study the detectors in realistic scenarios under the EE criterion. Our work also differs from the previous studies on EE [3,5,12,33] where the capacity, i.e., the upper bound of the achievable rate under Gaussian signaling, is considered, as the actual power consumption for practical modulation, signal processing schemes and reliability requirement is evaluated in our studies.…”

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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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“…Since there is only one branch (with the MMSE ordering), there is no need to compute the ML metric. Following [3,12], its power consumption is estimated as…”

Section: Power Consumption By Signal Detectionmentioning

confidence: 99%

“…The receiver power consumption also includes the power consumption of the ADCs. The power consumed by a single ADC is estimated by [12]…”

Section: Adc Power Consumptionmentioning

confidence: 99%

Section: Adc Power Consumptionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

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Entropy‐Enhanced One‐Bit Compressive Sensing for DOA Estimation

Liao,

You,

Xiao

2024

Information‐Theoretic Radar Signal Processing

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Hybrid Grey Wolf Optimization with Cuckoo Search‐based optimal channel estimation for energy efficient massive multiple input multiple output

Indumathi¹,

Rani²

2022

Int J Communication

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Summary The massive multiple input multiple output (mMIMO) provides reliable base station (BS) for the mobile users (MUs) with CSI (channel state information) and jointly offers spectral efficiency (SE) and energy efficiency (EE). Conversely, because of the existence of multiple transceivers at both the transmitter and receiver side, the channel estimation (CE) issue is increasingly complex and expensive in terms of hardware and energy utilization. Hence, we have proposed an effective Hybrid Grey Wolf Optimization with Cuckoo Search (GWO‐CS) based optimal channel estimation for developing energy efficient mMIMO. The proposed GWO‐CS selects the optimal channel by jointly optimizing the spectral efficiency and reduces the SINR (signal to interference plus noise ratio). Experimental analysis of the performance of the proposed approach is carried out using existing approaches. The results obtained show that, the EE of the proposed GWO‐CS based CE provides 15–37 Mbits for varying quantization bits, 22.5–25 Mbits for different user equipment (UE) ranges and 7–24 Mbits for various SE. However, the existing approaches fail to provide such EE and this proves the efficiency of proposed approach.

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Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation

Cited by 54 publications

References 53 publications

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

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

Entropy‐Enhanced One‐Bit Compressive Sensing for DOA Estimation

Hybrid Grey Wolf Optimization with Cuckoo Search‐based optimal channel estimation for energy efficient massive multiple input multiple output

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