User Oriented Transmit Antenna Selection in Massive Multi-User MIMO SDR Systems

Zhong, Shida; Feng, Haogang; Zhang, Peichang; Xu, Jiajun; Yuan, Tao; Huo, Yongkai

doi:10.3390/s20174867

Cited by 9 publications

(16 citation statements)

References 27 publications

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“…2. The Maximum Energy Efficiency Estimator block is firstly supplied by the number of active users (Ibrahim Salah et al 2021;Mabrook et al 2019) served by the cell (Zhong et al 2020) using the base ( 4) station database or using spectrum sensing techniques (Mabrook et al 2019(Mabrook et al , 2020. Then, Eq.…”

Section: Impact Of Multiple Bs Antennas and Usersmentioning

confidence: 99%

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

et al. 2022

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Given that, the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is determined according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

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Section: Impact Of Multiple Bs Antennas and Usersmentioning

confidence: 99%

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

et al. 2022

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“…However, MRT can be costly compared to TAS and silencing at the implementation side, but this scheme is being implemented in practice [ 25 ]. Meanwhile, in the case of a limited feedback system avoiding the complexity compared to MRT, TAS provides significant performance gains [ 42 , 43 ].…”

Section: System Modelmentioning

confidence: 99%

Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes

Kharel

López

Alves

2021

Sensors

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This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.

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“…The block diagram of the proposed system is depicted in Figure (2). The Maximum Energy Efficiency Estimator block is firstly supplied by the number of active users [18,19] served by the cell [20] using the base station database or using spectrum sensing techniques [18,22]. Then, equation ( 4) is used to estimate Max.…”

Section: Fig2 Block Diagram Of the Proposed Adaptive Antenna Selection Systemmentioning

confidence: 99%

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

Ibrahim

Mabrook

Rahouma

et al. 2021

Preprint

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Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell MMIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

show abstract

User Oriented Transmit Antenna Selection in Massive Multi-User MIMO SDR Systems

Cited by 9 publications

References 27 publications

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

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