Energy efficiency analysis of single-cell massive MIMO under pilot contamination

Amin, Umer; Anwar, Shahzad; Khan, Waseem S.; Khan, Saqib Ishaq

doi:10.1109/ibcast.2016.7429958

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Cited by 3 publications

(2 citation statements)

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“…The MMIMO network EE has been optimized under the Zero-Forcing (ZF) precoding scheme [21], [22], [27], and the Maximum Ratio Combining (MRC) precoding scheme [34], [35]. The focus of this paper is on a more complex ZF precoder (see Sec.…”

Section: ) State-of-the-art Solutionmentioning

confidence: 99%

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Hoffmann

Kryszkiewicz

2021

IEEE Access

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Section: ) State-of-the-art Solutionmentioning

confidence: 99%

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Hoffmann

Kryszkiewicz

2021

IEEE Access

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“…The EE of the downlink (DL) mm-wave massive MIMO communication can be improved by employing hybrid-beam forming schemes [7] with reduced interference from other user terminals (UT)s. To the best of our knowledge, the achievable rate of massive MIMO mainly depends on perfect channel state information (CSI) available at the UT and the BS. However, acquiring the perfect CSI is highly limited by the coherence time and pilot contamination (PC) [8,9] in large antenna systems. The impact of interference with PC is presented in [10] on the cells and UTs.…”

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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Optimal Energy Efficiency for Multi-Cell Massive MIMO Systems with Pilot Scheduling

Zahoor

Dou

Ayub

2020

2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)

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Energy efficiency analysis of single-cell massive MIMO under pilot contamination

Cited by 3 publications

References 20 publications

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

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

Optimal Energy Efficiency for Multi-Cell Massive MIMO Systems with Pilot Scheduling

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