Performance Scaling Law for Multicell Multiuser Massive MIMO

Zhang, Cheng; Jing, Yindi; Huang, Yongming; Yang, Liu

doi:10.1109/tvt.2017.2731820

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…In Fig. 2, the average training length of the IT-SU scheme in Algorithm 1 is shown for N RF = 1 and α = 4, 8 3 . First, it can be seen that the derived average training length in Theorem 1 well matches the simulation.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Interleaved Training and Training-Based Transmission Design for Hybrid Massive Antenna Downlink

Zhang

Jing

Huang

et al. 2018

IEEE J. Sel. Top. Signal Process.

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In this paper, we study the beam-based training design jointly with the transmission design for hybrid massive antenna single-user (SU) and multiple-user (MU) systems where outage probability is adopted as the performance measure. For SU systems, we propose an interleaved training design to concatenate the feedback and training procedures, thus making the training length adaptive to the channel realization. Exact analytical expressions are derived for the average training length and the outage probability of the proposed interleaved training. For MU systems, we propose a joint design for the beam-based interleaved training, beam assignment, and MU data transmissions. Two solutions for the beam assignment are provided with different complexity-performance tradeoff. Analytical results and simulations show that for both SU and MU systems, the proposed joint training and transmission designs achieve the same outage performance as the traditional full-training scheme but with significant saving in the training overhead.Index Terms-Hybrid massive antenna system, outage probability, beam training, beam assignment.Cheng Zhang received the B.Eng. degree from

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Interleaved Training and Training-Based Transmission Design for Hybrid Massive Antenna Downlink

Zhang

Jing

Huang

et al. 2018

IEEE J. Sel. Top. Signal Process.

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“…In massive MIMO systems, power scaling laws describe how fast the transmission power can decrease with the increasing of the number of antennas while maintaining certain performance levels [55]. For example, [56] investigates massive MIMO relay networks with imperfect channel state information, co-channel interference.…”

Section: F Impact Of Transmit Power Controlmentioning

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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“…Proof: Based on the central limit theorem [5],z H iz j / √ cM ∼ CN (0, 1) when M → ∞. For β I , from (4) and (29) we have…”

Section: Appendix B: the Proof Of Theoremmentioning

confidence: 99%

Performance Analysis for Massive MIMO Downlink With Low Complexity Approximate Zero-Forcing Precoding

Zhang

Jing

Huang

et al. 2018

IEEE Trans. Commun.

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Zero-forcing (ZF) precoding plays an important role for massive MIMO downlink due to its near optimal performance. However, the high computation cost of the involved matrix inversion hinders its application. In this paper, we adopt the first order Neumann series (NS) for a low-complexity approximation. By introducing a relaxation parameter jointly with one selected user's interference to others into the precondition matrix, we propose the identity-plus-column NS (ICNS) method. By further exploiting the multi-user diversity gain via choosing the user with the largest interference to others, the ordered ICNS method is also proposed. Moreover, the sum-rate approximations of the proposed ICNS method and the competitive existing identity matrix based NS (INS) method are derived in closed-form, based on which the performance loss of ICNS due to inversion approximation compared with ideal ZF and its performance gain over INS are explicitly analyzed for three typical massive MIMO scenarios.Finally, simulations verify our analytical results and also show that the proposed two designs achieve better performance-complexity tradeoff than ideal ZF and existing low-complexity ZF precodings for practical large antenna number, correlated channels and not-so-small loading factor. Index TermsMassive MIMO, precoding, low complexity, sum-rate analysis, Neumann series expansion. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Part of this work has been submitted to

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Performance Scaling Law for Multicell Multiuser Massive MIMO

Cited by 19 publications

References 32 publications

Interleaved Training and Training-Based Transmission Design for Hybrid Massive Antenna Downlink

Interleaved Training and Training-Based Transmission Design for Hybrid Massive Antenna Downlink

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

Performance Analysis for Massive MIMO Downlink With Low Complexity Approximate Zero-Forcing Precoding

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