Achievable uplink rates for massive MIMO with coarse quantization

Mollén, Christopher; Choi, Junil; Larsson, Erik G.; Heath, Robert W.

doi:10.1109/icassp.2017.7953406

Cited by 35 publications

(29 citation statements)

References 21 publications

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“…The 1-bit A/D converter case is of particular interest as it allows operation without automatic gain control (AGC), which simplifies hardware complexity. With N -bit quantization, N > 1, corresponding results can be found in [23], and when N grows eventually the capacity formulas for the un-quantized case [3, Ch. 3] are rediscovered.…”

Section: B Coarse and Lean Convertorsmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency.Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices.

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Section: B Coarse and Lean Convertorsmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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“…4a the uncoded BER with QPSK and ZF for the single-input single-output (SISO) case (i.e, when U = 1 and B = 1) as a function of the SNR and the number of DAC bits. 13 We compare simulated BER values with the analytical BER in (57) and note that the rounding approximation is accurate over the entire range of SNR values. We note that the diagonal approximation become 13 In the SISO-OFDM case, ZF precoding reduces to channel inversion.…”

Section: A Power Spectral Densitymentioning

confidence: 99%

“…13 We compare simulated BER values with the analytical BER in (57) and note that the rounding approximation is accurate over the entire range of SNR values. We note that the diagonal approximation become 13 In the SISO-OFDM case, ZF precoding reduces to channel inversion. more accurate as the number of DAC bits increase.…”

Section: A Power Spectral Densitymentioning

confidence: 99%

Linear Precoding With Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink

Jacobsson

Durisi

Coldrey

et al. 2019

IEEE Trans. Wireless Commun.

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We consider the downlink of a massive multiuser (MU) multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with low-resolution digital-toanalog converters (DACs). In contrast to most existing results, we assume that the system operates over a frequency-selective wideband channel and uses orthogonal frequency division multiplexing (OFDM) to simplify equalization at the user equipments (UEs). Furthermore, we consider the practically relevant case of oversampling DACs. We theoretically analyze the uncoded bit error rate (BER) performance with linear precoders (e.g., zero forcing) and quadrature phase-shift keying using Bussgang's theorem. We also develop a lower bound on the informationtheoretic sum-rate throughput achievable with Gaussian inputs, which can be evaluated in closed form for the case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet accurate, expressions for the distortion caused by low-precision DACs, which can be used to establish lower bounds on the corresponding sum-rate throughput. Our results demonstrate that, for a massive MU-MIMO-OFDM system with a 128-antenna BS serving 16 UEs, only 3-4 DAC bits are required to achieve an uncoded BER of 10 −4 with a negligible performance loss compared to the infinite-resolution case at the cost of additional out-of-band emissions. Furthermore, our results highlight the importance of taking into account the inherent spatial and temporal correlations caused by low-precision DACs.

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“…The number of quantization levels is given by the ADC bit resolution such that the distortion decreases whereas the power consumption and complexity increase with the ADC bit resolution. In [34][35][36][37], the selection of the ADC bit resolution is studied to maximize performance in terms of BER, SE, and power consumption, showing that Massive MIMO can provide high SE with low-resolution ADCs. Here, low ADC bit resolution refers to values of up to 4-5 bits which is considerably lower than standard ADCs used in 3G and 4G BS deployments which have around 15 bits.…”

Section: Hardware Designmentioning

confidence: 99%

Exploring Alternative Massive MIMO Designs : Superimposed Pilots and Mixed-ADCs

Verenzuela

2020

Linköping Studies in Science and Technology. Dissertations

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This is a Swedish Doctor of Philosophy Dissertation. The Doctor of Philosophy degree comprises 240 ECTS credits of postgraduate studies.Cover: A Massive MIMO network where the stairs in the buildings represent the different ADC bit resolutions in a mixed-ADC design. The two merging signals from cellular phones illustrate the superposition of pilot and data signals, corresponding to the superimposed pilots design. This cover was painted by Andrea Britto Moreno with design input from the author. Exploring Alternative Massive MIMO Designs AbstractThe development of information and communication technologies (ICT) provides the means for reaching global connectivity that can help humanity progress and prosper. This comes with high demands on data traffic and number of connected devices which are rapidly growing and need to be met by technological development. Massive MIMO, where MIMO stands for multiple-input multiple-output, is a fundamental component of the 5G wireless communication standard for its ability to provide high spectral and energy efficiency, SE and EE, respectively. The key feature of this technology is the use of a large number of antennas at the base stations (BSs) to spatially multiplex several user equipments (UEs).In the development of new technologies like Massive MIMO, many design alternatives need to be evaluated and compared in order to find the best operating point with a preferable tradeoff between low cost and complexity. In this thesis, two alternative designs for signal processing and hardware in Massive MIMO are studied and compared with the baseline operation in terms of SE, EE, and power consumption. The first design is called superimposed pilot (SP) transmission and is based on superimposing pilot and data symbols to eliminate the need to reserve dedicated time-frequency resources for pilots. This allows more data to be transmitted and supports longer pilot sequences that, in turn, reduce pilot contamination. The second design is mixed analog-to-digital converters (ADCs) and it aims at balancing the SE performance and the power consumption cost by allowing different ADC bit resolutions across the BS antennas.The results show that the Massive MIMO baseline, when properly optimized, is the preferred choice in standard deployments and propagation conditions. However, the SP alternative design can increase the SE compared to the baseline by using the Massive-MIMO iterative channel estimation and decoding (MICED) algorithm proposed in this dissertation. In particular, the SE gains are found in cases with high mobility, high carrier frequencies, or high number of spatially multiplexed UEs. For the mixed-ADCs alternative design, improvements in the SE and EE compared to the Massive vii MIMO baseline can be achieved in cases with distributed BS antennas where interference suppression techniques are used. ix 3.2.2 Optimal Tradeoff between HWIs and BS Antennas . . 82 3.2.3 Optimal HWI Allocation under i.i.d. Rayleigh Fading 85 3.2.4 Optimal HWI Allocation under Correlated Rayleigh Fading and LSF...

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Achievable uplink rates for massive MIMO with coarse quantization

Cited by 35 publications

References 21 publications

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Linear Precoding With Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink

Exploring Alternative Massive MIMO Designs : Superimposed Pilots and Mixed-ADCs

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