Load-Aware Energy Efficient Adaptive Large Scale Antenna System

Pramudito, Wahyu; Alsusa, Emad; Al-Dweik, Arafat; Hamdi, Khairi Ashour; So, Daniel K. C.; Marzetta, Thomas L.

doi:10.1109/access.2020.2991381

Cited by 8 publications

(4 citation statements)

References 35 publications

(39 reference statements)

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“…We can distinguish between original massive MIMO beamforming models from [31], with recent application in [32], or the recent (new) massive MIMO beamforming models from [33].…”

Section: B Signal Modelmentioning

confidence: 99%

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Spatial and Spectral Resource Allocation for Energy-Efficient Massive MIMO 5G Networks

Marwaha

Jorswieck

Liu

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user (UE) to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support UEs with large rate requirements via spatial multiplexing and assigning each UE maximum available PRBs.

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“…We can distinguish between original massive MIMO beamforming models from [31], with recent application in [32], or the recent (new) massive MIMO beamforming models from [33].…”

Section: B Signal Modelmentioning

confidence: 99%

“…It should be noted that the SINR expression in[31],[32] and[33] considers pilot contamination and intra-cell interference, which, however, are not accounted for in this work and hence the expressions have been adapted accordingly. However, pilot contamination can be included via lower channel gains or additional noise terms.…”

mentioning

confidence: 99%

Spatial and Spectral Resource Allocation for Energy-Efficient Massive MIMO 5G Networks

Marwaha

Jorswieck

Liu

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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show abstract

“…Considering both rate and latency requirements, the work in [167] has also recently investigated the power consumption minimisation problem in multi-cell mMIMO networks, by implementing cell DTX (see Section VI-A) in conjunction with precoding and antenna selection. The authors have considered a multiple frame optimisation window, and have proposed a strategy to select the proper precoding technique for each transmission frame such that the total transmission time and latency are minimised.…”

Section: Antenna-domain Energy Saving Solutionsmentioning

confidence: 99%

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

Liu¹,

Domenico²,

Piovesan³

et al. 2021

Preprint

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Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low latency communications.In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -from a theoretical and a practical viewpoint-the main energy efficiency enabling features that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key technology features, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -and the status on-green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.

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“…networks [4], wireless network virtualization [5], advanced antenna systems [6], [7], and efficient error correction coding [8]. Furthermore, great advancements have been achieved in terms of multiple access technologies such as the non-orthogonal multiple access (NOMA), which may enhance the spectral efficiency with respect to orthogonal multiple access (OMA), by allowing multiple users to share the transmission resources simultaneously.…”

mentioning

confidence: 99%

Efficient Bit Loading Algorithm for OFDM-NOMA Systems With BER Constraints

Assaf

Al-Dweik

Elmoursi

et al. 2022

IEEE Trans. Veh. Technol.

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This paper considers applying bit loading to multicarrier non-othogonal multiple access (NOMA) systems.The objective is to maximize the system total throughput while satisfying the users' individual quality of service (QoS) constraints. Although bit loading is generally an NP-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, we propose in this paper an efficient bit loading algorithm for multicarrier NOMA systems and compares the complexity and throughput with OMA. The obtained results show that NOMA has virtual cognition and hybrid modes of operation, NOMA/OMA, that enables NOMA to outperform OMA by 100% for the two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high computational complexity of bit error rate (BER) computation for NOMA. The obtained results show that the NOMA throughput depends on which user is loaded first, and on the initially loaded bits.

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Load-Aware Energy Efficient Adaptive Large Scale Antenna System

Cited by 8 publications

References 35 publications

Spatial and Spectral Resource Allocation for Energy-Efficient Massive MIMO 5G Networks

Spatial and Spectral Resource Allocation for Energy-Efficient Massive MIMO 5G Networks

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

Efficient Bit Loading Algorithm for OFDM-NOMA Systems With BER Constraints

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