BER performance of uplink massive MIMO with low-resolution ADCs

Azizzadeh, Azad; Mohammadkhani, Reza; Makki, Seyed Vahab Al‐Din

doi:10.1109/iccke.2017.8167895

Cited by 10 publications

(13 citation statements)

References 12 publications

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“…For simplicity, we assume single-antenna users in the following formulations although these can be easily extended to multiple-antenna users. The baseband received signal vector at the BS can be expressed as follows [12]…”

Section: Interference Cancellation In Fbmc/oqammentioning

confidence: 99%

MU-Massive MIMO for UWA Communication

Bocus

Doufexi

Agrafiotis

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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The present paper considers the application of massive multiple-input multiple-output (MIMO) to a multiuser (MU) underwater acoustic (UWA) communication system. In the scope to achieve a high throughput within a limited acoustic bandwidth, we consider filterbank multicarrier (FBMC) modulation for waveform shaping instead of the widely used orthogonal frequency division multiplexing (OFDM). We assess the performance of the system in a 1 km simulated time-varying underwater acoustic channel (UAC) in terms of bit error rate (BER), packet error rate (PER) and maximum achievable throughput. The transmission scenario consists of four users, each one equipped with multiple transmitting hydrophones. We show that the implementation of massive MIMO at the receiver allows all the users to use the same frequency bandwidth to transmit their information reliably to the receiver. Therefore, a high spectral efficiency and throughput can be achieved, making the transmission of multimedia data such as high quality real-time video a reality.

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Section: Interference Cancellation In Fbmc/oqammentioning

confidence: 99%

MU-Massive MIMO for UWA Communication

Bocus

Doufexi

Agrafiotis

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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“…Full digital massive MIMO deployment is difficult to realize because highresolution analog-to-digital converters (ADCs) produce primary power consumption. Owing to its favorable property of low cost and low power consumption, lowresolution ADCs (1∼4 bits) have also been worth paying attention [2,3,4]. Reference [2] proves that an appropriate quantization range limit (clipping) can relieve the distortion in a single-input single-output OFDM system.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the performance of single-carrier modulation in a massive MIMO system with lowresolution ADCs is analyzed while OFDM is assumed in this letter. In [4], it is shown that a minimum mean square error (MMSE) algorithm can achieve better BER using low-resolution ADCs in a hybrid analog-digital system with a large number of BS antennas. MMSE detection demands a large amount of computational complexity for matrix inversion while it achieves near-optimal performance.…”

Section: Introductionmentioning

confidence: 99%

Detection schemes for massive MIMO system with low-resolution ADCs

Gao

Yukitoshi

2019

IEICE ComEX

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In a full-digital massive multi-user MIMO system, maximal-ratio combining (MRC) can obtain more considerable diversity gain while interstream interference (ISI) and multi-user interference (MUI) can be canceled using minimum mean square error (MMSE) algorithm. This letter evaluates the throughputs of detection schemes for different antenna numbers in the massive MIMO system with low-resolution analog-to-digital converters (ADCs). The letter makes a comparison between MRC and MMSE under quantization range limit. Numerical results show that MRC achieves better system performance with lower implementation complexity as the number of antennas increases.

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“…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.…”

mentioning

confidence: 99%

“…Numerous numerical studies are carried out. 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%

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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BER performance of uplink massive MIMO with low-resolution ADCs

Cited by 10 publications

References 12 publications

MU-Massive MIMO for UWA Communication

MU-Massive MIMO for UWA Communication

Detection schemes for massive MIMO system with low-resolution ADCs

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

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