Rate Analysis of Cell-Free Massive MIMO-NOMA With Three Linear Precoders

Rezaei, Fatemeh; Tellambura, Chintha; Tadaion, Aliakbar; Heidarpour, Ali Reza

doi:10.1109/tcomm.2020.2978189

Cited by 43 publications

(21 citation statements)

References 48 publications

(102 reference statements)

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“…Thus, large capacity improvements are possible. More antennas translate into more possible signal paths, which improves and data rate and link reliability [53], [54].…”

Section: E Large Antenna Casementioning

confidence: 99%

Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model

Wang

Rezaei

Tellambura

2020

IEEE Open J. Commun. Soc.

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Wireless powered communication networks (WPCNs) are commonly analyzed by using the linear energy harvesting (EH) model. However, since practical EH circuits are non-linear, the use of the linear EH model gives rise to distortions and mismatches. To overcome these issues, we propose a more realistic, nonlinear EH model. The model is based upon the error function and has three parameters. Their values are determined to best fit with measured data. We also develop the asymptotic version of this model. For comparative evaluations, we consider the linear and rational EH models. With these four EH models, we investigate the performance of a WPCN. It contains a multiple-antenna power station (PS), a signal-antenna wireless device (WD), and a multiple-antenna information receiving station (IRS). The WD harvests the energy broadcast by the PS in the PS-WD link, and then it uses the energy in the WD-IRS link to transfer information. We analyze the average throughput of delay-limited and delaytolerant transmission modes as well as the average bit error rate (BER) of binary phase-shift keying (BPSK) and binary differential phase-shift keying (BDPSK) over the four EH modes. As well, we derive the asymptotic expressions for the large PS antenna case and the effects of transmit power control. Furthermore, for the case of multiple WDs, we optimize energy beamforming and time allocation to maximize the minimum rate of the WDs. Finally, the performances of four EH models are validated by Monte-Carlo simulations.

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“…Thus, large capacity improvements are possible. More antennas translate into more possible signal paths, which improves and data rate and link reliability [53], [54].…”

Section: E Large Antenna Casementioning

confidence: 99%

Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model

Wang

Rezaei

Tellambura

2020

IEEE Open J. Commun. Soc.

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“…We propose an adaptive SG learning algorithm, which takes into account the gradient of the error in (18) to perform APA and has a per-antenna power constraint. Our main objective here is to propose alternatives to the OPA and UPA algorithms.…”

Section: Adaptive Power Allocationmentioning

confidence: 99%

“…The technique was combined with a clustering method based on pilot assignment and a heuristic solution for power allocation. Moreover, in a scenario which considers cell-free networks and non-orthogonal multiple access (NOMA), precoders based on MR transmission, full pilot ZF precoding and modified regularized ZF precoding [40] have been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Iterative AP selection, MMSE precoding and power allocation in cell‐free massive MIMO systems

et al. 2020

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In this work, the authors proposed the iterative access point (AP) selection (APS), linear minimum mean-square error (MMSE) precoding and power allocation techniques for cell-free massive multiple-input multiple-output (MIMO) systems. They considered the downlink channel with single-antenna users and multiple-antenna APs. They derive sum-rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming and zero-forcing schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information.

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“…Uplink processing MMSE [59,124,145,146] ZF [147] Power allocation and load balancing [148] Max-min power control [149] Pilot power control [150] Sequential linear MMSE [120] mmWave Limited fronthaul capacity [151] Power control [152] Channel modeling [153] Energy efficiency [154] Energy Efficiency [116,132,[155][156][157] Imperfect Fronthaul [158][159][160][161][162][163][164] Low Resolution ADCs [163,[165][166][167] Hardware Impairments [164,[168][169][170] Non-orthogonal [171][172][173][174][175] Multiple Access…”

Section: Topics Aspects and Referencesmentioning

confidence: 99%

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Interdonato

2020

Linköping Studies in Science and Technology. Dissertations

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Linköping 2020 This is a Swedish Doctor of Philosophy thesis. The Doctor of Philosophy degree comprises 240 ECTS credits of postgraduate studies. Cover: Illustration of the Ericsson Radio Stripes concept, co-invented by the author of this thesis. The Radio Stripes constitute a way to implement a cell-free massive MIMO system. They aim at enabling invisible, low-cost deployment of large number of distributed access points in the vicinity of the users. This is achieved by serial integration of several transmitting and receiving components into a cable (or stripe) which also provides fronthaul communication and power supply.

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Rate Analysis of Cell-Free Massive MIMO-NOMA With Three Linear Precoders

Cited by 43 publications

References 48 publications

Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model

Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model

Iterative AP selection, MMSE precoding and power allocation in cell‐free massive MIMO systems

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

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