Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

Saatlou, O.; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1109/access.2019.2959711

Cited by 9 publications

(6 citation statements)

References 44 publications

(84 reference statements)

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“…6, the SE for traffic scenario 1 is improved for 5 PRBs per MS (i.e., 21 bps/Hz), since in this case an increased number of MSs can be supported compared to the case of 15 PRBs per MS. Results are aligned with the ones presented in [14] and [15], where SE reaches 22 bps/Hz. This is also the case with the work presented in [45], when maximal ratio combining transmission is assumed.…”

Section: Performance Evaluationmentioning

confidence: 79%

“…In this context, closed-form expressions were derived. In [15], a resource allocation method was proposed in order to maximize the SE of a single-cell m-MIMO time-division duplex (TDD) system. The presented results indicate that SE can be significantly improved at the high signal to noise region by allocating more power to data symbols for a given total power budget.…”

Section: A Related Workmentioning

confidence: 99%

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A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

et al. 2022

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The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the MLassisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

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Section: Performance Evaluationmentioning

confidence: 79%

Section: A Related Workmentioning

confidence: 99%

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

et al. 2022

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“…It attained better SE with minimized iteration count. Saatlou et al [43] have handled the problem of SE maximization in a MU-MIMO DL system, in which a BS was equipped with vast antenna count. The accuracy as well as the computational simplicity was tested via the simulations.…”

Section: A Related Workmentioning

confidence: 99%

Research Track towards Spectral Efficiency and Energy Efficiency in Massive MIMO Communication System: 5G to 6G Scope

Nimmagadda

Bethala

2022

Preprint

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The limitation of global bandwidth has inspired the exploration as well as study of wireless access technology called massive MIMO. It is one of the primary technologies for the future networks that cluster the antennas together at the receiver and transmitter for offering high Energy Efficiency (EE) and Spectral Efficiency (SE) with the help of simple processing. Attaining a clear concept regarding the massive MIMO system for handling the primary conflicts is important for the better deployment of 5G to 6G networks. The main intention of this paper is to make a detailed review on SE and EE maximization for massive MIMO systems to transform from 5G to 6G networks. This review plans for studying a set of research contributions for past 10 years. The comprehensive analysis on the “key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems” is also presented here. It also explains the fundamental challenges associated with the “channel estimation, precoding, pilot contamination, user scheduling, signal detection, and EE” in a massive MIMO system. Finally, the critical open research conflicts that show the future research in massive MIMO systems for 5G to 6G networks are also be expedited.

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“…Fortunately, references [11]- [19] come up with a practical solution, called beamforming training (BT), that allows users to obtain DL CSI in a massive MIMO system. In particular, by using BT, the length of DL pilots is determined by the number of users and independent of the number of antennas at the BS.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, by using BT, the length of DL pilots is determined by the number of users and independent of the number of antennas at the BS. In [11], [12], the rate performance of a singlecell multiuser massive MIMO system with the BT scheme has been studied and [11] proposes a resource allocation strategy to maximize the spectral efficiency for MRT or ZF precoder. In [13], [14], the authors study a multicell multiuser massive MIMO system under Rayleigh fading using BT and derive achievable rate expressions when the BS uses MRT and ZF processing.…”

Section: Introductionmentioning

confidence: 99%

On the Energy Efficiency of Multi-Cell Massive MIMO With Beamforming Training

2020

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This paper is concerned with a multi-cell downlink (DL) massive multiple-input multipleoutput (MIMO) system operating over spatially correlated Rician fading channels. We not only consider estimating channel state information (CSI) at the base station (BS), but also adopt beamforming training (BT) to obtain CSI at the users. With maximum-ratio transmission (MRT) or zero-forcing (ZF) employed at the BS to process the transmit signals, the paper derives closed-form expressions of the sum spectral efficiency for both cases: with and without BT. Based on the obtained closed-form expressions, we investigate the effect of the DL pilot length on the system performance for MRT and ZF precoding and with or without BT. Moreover, when the DL pilot length falls within different ranges, we find out whether using BT leads to better system performance. To address the energy efficiency (EE) maximization problem under the constraints of a given sum spectral efficiency and a maximum total DL transmit power, we transform the problem into a geometric program (GP), which can be solved more efficiently. In particular, we develop one iterative power allocation algorithm for the system with BT scheme. Simulation and numerical results demonstrate that the proposed power allocation algorithm can improve the system EE. Numerical results also show that when the BS uses MRT precoding, the sum spectral efficiency in the high signal-to-noise ratio (SNR) region is much improved with BT. INDEX TERMSMassive MIMO, spatially correlated Rician fading, beamforming training, energy efficiency.

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Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

Cited by 9 publications

References 44 publications

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

Research Track towards Spectral Efficiency and Energy Efficiency in Massive MIMO Communication System: 5G to 6G Scope

On the Energy Efficiency of Multi-Cell Massive MIMO With Beamforming Training

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