A Low Complexity Solution for Resource Allocation and SDMA Grouping in Massive MIMO Systems

Maurício, Weskley Vinicius Fernandes; Araújo, Daniel C.; Neto, Francisco Simão; Lima, F. Rafael M.; Maciel, Tarcísio F.

doi:10.1109/iswcs.2018.8491076

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…The total power is fixed and Equal Power Allocation (EPA) among SUs and among spatial subchannels is employed. Since we are using hybrid precoding, as many other works in literature [16]- [18], [20], [32], we are considering the number of available RF chains as at most 10% of the number of BS antennas. Also, we are considering that the BS serves 2 or 3 MSs per SU and cluster.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Since hybrid beamforming gained a lot of attention by allowing the use of massive MIMO, [16]- [18] propose new scheduling methods for massive MIMO under hybrid beamforming architecture. The authors in [16] propose a scheduling based on statistical CSI with the objective of system throughput maximization. The scheduler main idea is to balance the channel gain and spatial channel correlation of the selected MSs leading to a high system throughput.…”

Section: Introductionmentioning

confidence: 99%

“…The authors propose two schedulers, one based on stable matching and another based on greedy search. However, the works [16]- [18] do not consider the scheduling of multiple SUs.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

et al. 2021

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This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three partsclustering, grouping, and scheduling -and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

et al. 2021

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“…The authors of [25] evaluated the k-means algorithm, an unsupervised algorithm, to achieve adaptive modulation in wired channels, where different cable modems with varying channel conditions are clustered. The k-means algorithm was used for user group clustering in [26]. Further, the authors consider the maximization of channel capacity by selecting appropriate clusters.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G

Gopi

Magarini

Alsamhi

et al. 2021

Drones

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The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.

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“…Another approach, used in [6], [7], first clusters all MSs of the system with spatially correlated channels and afterwards schedules MS from different clusters on a same resource. In [8], the authors investigate a SDMA grouping problem in MU MIMO in the context of 5G networks. Their approach consists in a partitioning process based on K-means to split MSs into spatially compatible clusters.…”

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SDMA Grouping for 5G Systems

Costa¹,

Maciel²

2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this study, we investigate the problem of maximization of the total data rate of a multiuser multiple input multiple output (MU-MIMO) system. We evaluate a solution to the space-division multiple access (SDMA) grouping problem that is composed of two parts. In the first part, we partition mobile stations (MSs) into clusters of spatially correlated MSs. In the second part, we schedule MSs from different clusters to build an SDMA group so that the multiuser (MU) interference is minimized. Looking for an implementation with reduced hardware cost, we employ a hybrid beamforming scheme and analyze its impact in terms of total data rate. The analog precoder is based on the channel information obtained from clustering while different digital precoding components are considered, namely zero-forcing (ZF) and maximum ratio transmission (MRT). The simulation results indicate that the combination of a proper partition of MSs into clusters and the suitable scheduling of MSs provides a technique able to exploit spatial compatibility more effectively and reduces inter-cell interference. Moreover, when the ZF is considered as digital precoding scheme, there is an increase of the total data rate.

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A Low Complexity Solution for Resource Allocation and SDMA Grouping in Massive MIMO Systems

Cited by 12 publications

References 19 publications

A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G

SDMA Grouping for 5G Systems

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