Towards All-digital mmWave Massive MIMO: Designing around Nonlinearities

Abdelghany, Mohammed; Farid, Ali A.; Madhow, Upamanyu; Rodwell, M.J.W.

doi:10.1109/acssc.2018.8645214

Cited by 21 publications

(25 citation statements)

References 5 publications

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“…Let us consider the first case in (17), where we have l 1 = l 2 = l 3 = k which results in where we used the definitions in (8) and (19) in (a). The second and third cases in (17) can be proved similarly by using independence of data signals for different users.…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

Demir

Björnson

2020

IEEE Open J. Commun. Soc.

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This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.

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Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

Demir

Björnson

2020

IEEE Open J. Commun. Soc.

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“…We are particularly interested in a system where the LNA of BS is non-ideal. Using the baseband equivalent nonlinearity model [10], the output of LNA y LNA = [y LNA,1 , · · · , y LNA,n ] T is denoted as…”

Section: System Modelmentioning

confidence: 99%

“…where Q B (.) is the complex quantization function which consists of granular and overload regions [10]. We focus on the linear MIMO detection…”

Section: System Modelmentioning

confidence: 99%

“…1 Its specific value is determined by LNA linear gain as well as IIP3 , e.g., β 1 = 56, β 3 = −7497, V IP3 = 0.05 Volt for an LNA with 35dB linear gain and -10 dBm IIP3. The value Vsat = V IP3 /2 according to [10]. For clarity, we remove the unit Volt in V IP3 in the rest of the paper.…”

Section: System Modelmentioning

confidence: 99%

“…However, the impact of nonlinearity in large antenna array requires further studies. Also, there is a lack of investigation of the receiver side, except the work in [10], [11], where the impact of nonlinearity and ADC quantization error are analyzed. In summary, digital linearization techniques suitable for mmWave massive MIMO receivers are rarely investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

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DSP Linearization for Millimeter-Wave All-Digital Receiver Array with Low-Resolution ADCs

Han

Čabrić

2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Millimeter-wave (mmWave) communications and cell densification are the key techniques for the future evolution of cellular systems beyond 5G. Although the current mmWave radio designs are focused on hybrid digital and analog receiver array architectures, the fully digital architecture is an appealing option due to its flexibility and support for multi-user multipleinput multiple-output (MIMO). In order to achieve reasonable power consumption and hardware cost, the specifications of analog circuits are expected to be compromised, including the resolution of analog-to-digital converter (ADC) and the linearity of radio-frequency (RF) front end. Although the state-of-theart studies focus on the ADC, the nonlinearity can also lead to severe system performance degradation when strong input signals introduce inter-modulation distortion (IMD). The impact of RF nonlinearity becomes more severe with densely deployed mmWave cells since signal sources closer to the receiver array are more likely to occur. In this work, we design and analyze the digital IMD compensation algorithm, and study the relaxation of the required linearity in the RF-chain. We propose novel algorithms that jointly process digitized samples to recover amplifier saturation, and relies on beam space operation which reduces the computational complexity as compared to perantenna IMD compensation.

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