Energy-efficient massive MIMO system: Exploiting user location distribution variation

Abuibaid, Mohammed; Aldırmaz-Çolak, Sultan

doi:10.1016/j.aeue.2016.11.013

Cited by 28 publications

(21 citation statements)

References 17 publications

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“…The authors optimized the number of BS antennas to maximize EE, where a uniform data rate was guaranteed for all UEs. [23] has investigated the EE improvement in the downlink of a multi-cell massive MIMO system under different user location distribution. The authors proposed a new resource allocation scheme to optimize the number of active BS antennas based on the variation of UEs' location without QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

et al. 2020

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“…In most of the literature, the performance of M-MIMO is evaluated in terms of the ergodic capacity, where the bit error rate (BER) and modulation do not take part [4]- [7], [12]- [15]. However, other more practical performance evaluations could be used, such as the BER, pair-wise error probability (PEP) or outage probability [16]- [19]. Furthermore, those performance measures are a function of the signal-to-interference plus noise ratio (SNIR) [20], [21].…”

Section: Introductionmentioning

confidence: 99%

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

et al. 2019

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New perspectives for wireless communications have brought new techniques, such as a very large number of antennas at a base station (BS) serving multiple user terminals (UTs) with a single antenna each, known as massive MIMO (M-MIMO). M-MIMO linear detectors, such as maximal-ratio combining (MRC), zero-forcing (ZF) or minimum-mean-square error (MMSE) can achieve excellent performance with low complexity due to the channel hardening property. However, imperfect channel estimation produces a penalty in the performance. An average bit error rate (BER) performance analysis over time-invariant channel is presented for M-MIMO systems under imperfect channel estimation in contrast with most of M-MIMO literature that uses the ergodic capacity approach. Closed-form expressions and bounds to evaluate the average BER are derived for MRC, ZF and MMSE detectors in a unicellular environment considering M -QAM modulation. Furthermore, an expression to evaluate the normalized signal-to-noise ratio (E b /N 0 ) penalty due to the imperfect channel estimation is presented. Montecarlo numerical simulations are used to verify the tightness of the derived equations which are a function of the number of BS antennas, number of users, coherence time interval, number of pilot symbols and the E b /N 0 of pilot and data symbols used for channel estimation and data detection.INDEX TERMS BER, imperfect channel estimation, massive MIMO, maximal-ratio combining, minimummean-square error, zero-forcing.

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“…The EE is also maximized by the transmitting RF chain configuration [23]. The resource allocation schemes show [24] significant role in the optimization of EE with optimal antennas at the BS. The area energy efficiency (AEE) improved by a realistic power consumption model reported [25] when the number of BS antennas reaches of the infinite.…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

Rayi¹,

Prasad²

2019

IJIES

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Massive Multi-Input and Multi-Output (MIMO) antenna system is considered as the key technology to improve both spectral efficiency (SE) and energy efficiency (EE) for 5G wireless systems. This paper evaluates the impact of perfect and imperfect channel state information (CSI) on the SE. The maximum SE of 200 bits/s/Hz achieved in a multi-cell scenario and its limiting factors were explored. We mainly demonstrated linear precoding algorithms such as zero-forcing (ZF), maximum ratio combing (MRC), and minimum mean square error (MMSE) for SE analysis. The explicit SE and EE expressions were derived from Shannon capacity calculations for single and multi-cell scenario. The proposed ZF and MRC precoding schemes were observed with SEs of 185 bit/s/Hz, and 180 bits/s/Hz respectively with pilot reuse factor with λ=7. The simulation results were validated by comparing the SE versus the number of base station (BS) antennas with receiving schemes by using MATLAB 2018a.

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Energy-efficient massive MIMO system: Exploiting user location distribution variation

Cited by 28 publications

References 17 publications

Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

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