Performance Analysis of Multi-Cell Millimeter-Wave Massive MIMO Networks With Low-Precision ADCs

Xu, Jindan; Xu, Wei; Zhang, Hua; Li, Geoffrey Ye; You, Xiaohu

doi:10.1109/tcomm.2018.2874963

Cited by 30 publications

(14 citation statements)

References 53 publications

(107 reference statements)

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“…where (12) and S denotes the feasible set of ζ. Same as H CB , an approximate solution to H NCB can be approached by implementing Algorithm 1 with ξ, S, and f (ξ, λ) being respectively substituted with ζ, S, and f (ζ, λ), where…”

Section: B Max-min He For Ncb Schemementioning

confidence: 99%

“…Given the total antenna number (i.e., LN is fixed), the CF architecture possesses more HE than the distributed counterpart does, which can be explained as the former helps shorten the end-to-end communication distances, thereby alleviating the impact of attenuation. We stress that for both CF and distributed mMIMO topologies, the simulation HE curves are plotted via Monte-Carlo simulations by inserting w CB lk = ĝ * lk and w NCB lk = ĝ * lk / ĝlk into (10), and the analytical HE curves are obtained by resorting to (11) and (12), respectively. Fig.…”

Section: B Performance Evaluationmentioning

confidence: 99%

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Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

Zhang

Xia

Zhao

et al. 2022

IEEE Internet Things J.

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In this paper, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet-of-things (IoT) networks relying on cell-free massive multiple-input multiple-output (CF-mMIMO) technology is investigated. In such a network, the access points (APs) beam radio-frequency (RF) energy toward the IoT sensors during the DL phase of wireless power transfer. Tight closed-form expressions for the DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are respectively derived, which enable us to analyze the HErate trade-offs under different precoding techniques. Apart from this, to guarantee the sensor fairness with respect to the HE and achievable rate, a max-min fairness power control algorithm based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based algorithm is able to determine the optimum of the considered max-min fairness problem in closed form and is more memory-efficient than traditional convex-solver-based counterparts. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.Index Terms-Simultaneous wireless information and power transfer (SWIPT), Internet-of-things (IoT), cell-free massive multiple-input multiple-output (CF-mMIMO), max-min power control, accelerated projected gradient (APG).

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Section: B Max-min He For Ncb Schemementioning

confidence: 99%

Section: B Performance Evaluationmentioning

confidence: 99%

Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

Zhang

Xia

Zhao

et al. 2022

IEEE Internet Things J.

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“…The corresponding performance analyses were studied in [13]- [16]. For multicell massive MIMO systems, low-resolution ADCs were considered in [17], [18]. To be specific, assuming that analog beamforming is used at the user side and MRC is employed at the base station (BS) side, Xu et al [17] derived a lower bound for the achievable uplink rate over a non-cooperative multi-cell mmWave system.…”

Section: A Prior Relevant Workmentioning

confidence: 99%

Spectral and Energy Efficiency of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels

Xiong¹,

Sun²,

Wei³

et al. 2021

Preprint

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This paper analyzes the performance of multicell massive multiple-input and multiple-output (MIMO) systems with variable-resolution analog-to-digital converters (ADCs). In such an architecture, each ADC uses arbitrary quantization resolution to save power and hardware cost. Along this direction, we first introduce a quantization-aware channel estimator based on additive quantization noise model (AQNM) and linear minimum mean-squared error (LMMSE) estimate theory. Afterwards, by leveraging on the estimated channel state information (CSI), we derive the asymptotic expressions of achievable uplink spectral efficiency (SE) over spatially correlated Rayleigh fading channels for maximal ratio combining (MRC), quantization-aware multicell minimum mean-squared error (QA-M-MMSE) combining, and quantization-aware single-cell MMSE (QA-S-MMSE) combining, respectively. During the derivations, we consider the effect of quantization errors and resort to random matrix theory to achieve the asymptotic results. Finally, simulation results demonstrate that our theoretical analyses are correct and that the proposed quantization-aware estimator and combiners are more beneficial than the quantization-unaware counterparts. Besides, based on a generic power consumption model, it is shown that low-resolution ADCs can obtain the best tradeoff between SE and energy efficiency (EE) under multicell scenarios.

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“…In particular, α θ is an increasing function of b θ and, for the non-uniform quantizer case, which will be considered in the next sections, can be approximately expressed as [35], and the corresponding values for b θ = 1, 2, 3, 4 and 5 are summarized in Table 1 (derived from [34,Table I]). This linear model for the quantization process (or its equivalent AQNM) has been extensively used in the massive MIMO and cell-free massive MIMO literature (see, among many others, [23], [25], [36]- [45] and references therein).…”

Section: B a Linear Model For The Quantization Processmentioning

confidence: 99%

“…The objective function is the achievable SINR of MS k as calculated in Section III (see, (43), (45), (47), (50), and (52)). The first constraint indicates that the power allocated to MS k must be less than or equal to the available transmit power.…”

Section: ) Bap-based Casementioning

confidence: 99%

Fronthaul-Constrained Cell-Free Massive MIMO With Low Resolution ADCs

Femenias

Riera-Palou

2020

IEEE Access

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In cell-free massive MIMO networks, a large number of distributed access points (APs) provide service to a much smaller number of mobile stations (MSs) over the same time/frequency resources. The key idea is to use a central processing unit (CPU) to manage such a densely populated network of APs. This centralization helps reducing operational costs and eases implementation of joint power control and coherent signal processing through a proper orchestration of the functional split between the CPU and the APs. Cell-free massive MIMO networks, however, are often subject to stringent capacity requirements on the fronthaul links connecting the APs to the CPU and thus, low-resolution ADCs must be used to quantize the signals shared among CPU and APs. In this paper, analytical closed-form expressions for the achievable user rates on both the uplink (UL) and downlink (DL) of a fronthaul-capacity constrained cell-free massive MIMO network using low-resolution ADCs are obtained. These expressions, jointly with the use of theoretical models characterizing the fronthaul capacity consumption of different CPU-AP functional splits, allow posing max-min fairness power control optimization problems that can be solved using standard convex optimization algorithms. Numerical results show that, under fronthaul capacity constraints, CPU-AP functional splits where the precoding/decoding schemes are implemented at the APs are clearly outperformed by those functional splits in which, thanks to sharing CSI among APs and CPU, the precoding/decoding functions are implemented at the CPU. In contrast, if the limiting factor is the resolution of the ADCs used to quantize the samples to be transmitted on the fronthaul links, the preferred CPU-AP functional splits are those in which the baseband processing is performed at the APs. Moreover, they also reveal that in such functional splits there is always an optimal range of values of the UL fronthaul capacity fraction allocated to share the CSI. INDEX TERMS Cell-free massive MIMO, capacity-constrained fronthaul, normalized conjugate beamforming, matched filtering, CPU-AP functional split, low-resolution ADCs.

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Performance Analysis of Multi-Cell Millimeter-Wave Massive MIMO Networks With Low-Precision ADCs

Cited by 30 publications

References 53 publications

Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

Spectral and Energy Efficiency of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels

Fronthaul-Constrained Cell-Free Massive MIMO With Low Resolution ADCs

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