Uplink Spectral and Energy Efficiency of Cell-Free Massive MIMO With Optimal Uniform Quantization

Bashar, Manijeh; Ngo, Hien Quoc; Cumanan, Kanapathippillai; Burr, Alister G.; Xiao, Pei; Björnson, Emil; Larsson, Erik G.

doi:10.1109/tcomm.2020.3028305

Cited by 59 publications

(38 citation statements)

References 34 publications

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“…A consequence of these aspects is a completely different user signalto-interference-plus noise (SINR) expression compared to the system-level analyses of CoMP and C-RAN. This motivated a separate set of system-level analyses [11]- [18] for cell-free mMIMO with finite fronthaul capacity as briefly outlined below.…”

Section: A Related Workmentioning

confidence: 99%

“…A network-centric approach that achieves this goal is proposed in [12], [15]. However, from the perspective of scalability and distributed implementation, a user-centric architecture is preferred where a user selects its set of serving APs [18]- [25]. To the best of our knowledge, the downlink performance of the usercentric cell-free architecture with finite fronthaul capacity has not been studied in the literature yet.…”

Section: A Related Workmentioning

confidence: 99%

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Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective

Parida¹,

Dhillon²

2022

Preprint

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In this work, we analyze the downlink performance of a cell-free massive multiple-input-multipleoutput system with finite capacity fronthaul links between the centralized baseband unit and the access point (APs). Conditioned on the user and AP locations, we first derive an achievable rate for a randomly selected user in the network that captures the effect of finite fronthaul capacity as a compression error.From this expression, we establish that for the traditional cell-free architecture where each AP serves all the users in the network, the achievable rate becomes zero as the network size grows. Hence, to have a meaningful analysis, for the traditional architecture, we model the user and AP locations as two independent binomial point processes over a finite region and provide an accurate theoretical result to determine the user rate coverage. For a larger (possibly infinite) network, we consider a user-centric architecture where each user in the network is served by a specified number of nearest APs that limits the fronthaul load. For this architecture, we model the AP and user locations as two independent Poisson point processes (PPPs). Since the rate expression is a function of the number of users served by an AP, we statistically characterize the load in terms of the number of users per AP. As the exact derivation of the probability mass function of the load is intractable, we first present the exact expressions for the first two moments of the load. Next, we approximate the load as a negative binomial random variable through the moment matching method. Using the load results along with appropriate distance distributions of a PPP, we present an accurate theoretical expression for the rate coverage of the typical user. From the analyses we conclude that for the traditional architecture the average system sum-rate is a quasi-concave function of the number of users. Further, for the user-centric architecture, there exists an optimal number of serving APs that maximizes the average user rate.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective

Parida¹,

Dhillon²

2022

Preprint

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“…It can obtain from (35) that the tracking error z 1 can converge to an arbitrarily small range around the origin by adjusting parameters properly. From (33), it determines that z i , θ, D are bounded. Since z 1 is bounded, then x 1 is confirmed as bounded.…”

Section: Stability Analysismentioning

confidence: 99%

“…When the control input has quantized constraints, modeling the quantizer as a discontinuous mapping from the continuous domain to the discrete set is a common method. According to different quantization functions selected, it usually can be divided into uniform quantizers, 32,33 log quantizers, 34,35 and hysteresis quantizers. 36 For instance, Zhou et al 36 investigate a class of strict-feedback nonlinear systems, where the system states are taken quantized values based on a hysteresis quantizer.…”

Section: Introductionmentioning

confidence: 99%

Adaptive quantized control for uncertain nonlinear systems with unknown control directions

Sun

et al. 2021

Intl J Robust & Nonlinear

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This study reports adaptive quantized control for a class of uncertain strict-feedback nonlinear systems with unknown control directions. Combining backstepping technique and Lyapunov stability theory, a systematic analysis method is designed. Based on the disintegration of the hysteresis quantizer, a Nussbaum-based scheme can be developed to get over the obstacle of the quantized input signal and unknown control directions. Moreover, the number of adaptive laws is small, thereby reducing the computational burden. Then we testify the boundedness of all signals in the closed-loop system. Besides, the tracking error can converge to an arbitrarily small domain of origin. Finally, a simulation example is provided to verify the feasibility of the control scheme.

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“…3) The L-MMSE detector can be implemented in a distributed manner, as each AP uses the complex conjugate of the channel estimates in a distributed approach [14]. 4) The L-MMSE detector can facilitate flexible functional splits in cell-free massive MIMO [15].…”

Section: B Why Local Minimum Mean Square Error (L-mmse) Detection?mentioning

confidence: 99%

Limited-Fronthaul Cell-Free Massive MIMO With Local MMSE Receiver Under Rician Fading and Phase Shifts

Bashar

Xiao

Tafazolli

et al. 2021

IEEE Wireless Commun. Lett.

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A cell-free Massive multiple-input multiple-output (MIMO) system is considered, where the access points (APs) are linked to a central processing unit (CPU) via the limited-capacity fronthaul links. It is assumed that only the quantized version of the weighted signals are available at the CPU. The achievable rate of a limited fronthaul cell-free massive MIMO with local minimum mean square error (MMSE) detection is studied. We study the assumption of uncorrelated quantization distortion, which is commonly used in literature. We show that this assumption will not affect the validity of the insights obtained in our work. To investigate this, we compare the uplink per-user rate with different system parameters for two different scenarios; 1) the exact uplink per-user rate and 2) the uplink per-user rate while ignoring the correlation between the inputs of the quantizers. Finally, we present the conditions which imply that the quantization distortions across APs can be assumed to be uncorrelated.

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Uplink Spectral and Energy Efficiency of Cell-Free Massive MIMO With Optimal Uniform Quantization

Cited by 59 publications

References 34 publications

Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective

Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective

Adaptive quantized control for uncertain nonlinear systems with unknown control directions

Limited-Fronthaul Cell-Free Massive MIMO With Local MMSE Receiver Under Rician Fading and Phase Shifts

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