MaMIMO User Grouping Strategies: How much does it matter?

Guevara, Andrea P.; Bast, Sibren De; Pollin, Sofie

doi:10.1109/ieeeconf44664.2019.9048739

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…During postprocessing, the wireless channel of each position is treated as an independent virtual user. In this way, we can create a massive MIMO scenario with 120 users deployed on the ground [34,35]. signal.…”

Section: Methodsmentioning

confidence: 99%

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

et al. 2022

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The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected and this number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine to machine (M2M) devices will have the largest increase, representing nearly 50% of all devices in 2023 [1,2]. These devices are typically small and therefore ideal candidates to be powered wirelessly. Typical examples are healthcare monitoring, smart homes, and industrial 1

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Section: Methodsmentioning

confidence: 99%

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

et al. 2022

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“…In reality, the number of antennas is limited. To maximise coverage and favourable propagation conditions, while minimising the impact of shadowing, distributing the antennas over the targeted area is meaningful [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. When multiple distributed antennas are processed jointly and coherently, we call this a distributed massive MIMO system.…”

Section: Introductionmentioning

confidence: 99%

Densely Deployed Indoor Massive MIMO Experiment: From Small Cells to Spectrum Sharing to Cooperation

Guevara

Pollin

2021

Sensors

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Massive MIMO is a key 5G technology that achieves high spectral efficiency and capacity by significantly increasing the number of antennas per cell. Furthermore, due to precoding, massive MIMO allows co-channel interference cancellation across cells. In this work, based on experimental channel data for an indoor scenario, we analyse the impact of inter and intra-cell interference suppression in terms of spectral efficiency, capacity, user fairness and computational cost for three simulated systems under different cooperation levels. The first scenario assumes a cooperative case where eight neighbouring cells share the spectrum and infrastructure. This scenario provides the highest system performance; however, user fairness is achieved only when there is inter and intra-cell interference suppression. The second scenario considers eight cells that only share the spectrum; with full intra-cell and inter-cell interference cancellation, it is possible to achieve 32% of the optimal capacity with 20% of the computational cost in each distributed CPU, although the total computational cost per system is the highest. The third scenario considers eight independent cells operating in different frequency bands; in this case, intra-cell interference suppression leads to higher spectral efficiency compared to the cooperative case without intra-cell interference suppression.

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“…Nevertheless, with the deployment of a large number of antennas in a base station, it is possible to implement spatial multiplexing and transmit precoding or receive combining, allowing simultaneous communication between multiple users using the same bandwidth. These techniques are implemented in Massive MIMO systems which have demonstrated the increase of spectral efficiency, both theoretically [1]- [3] and experimentally [4]- [11].…”

Section: Introductionmentioning

confidence: 99%

Partial Interference Suppression in Massive MIMO Systems: Taxonomy and Experimental Analysis

et al. 2021

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Densely deploying Massive MIMO access points results eventually in cell-free systems. To achieve scalability, users should be allocated only to neighbouring access points as it is not realistic to assume that all access points serve all users. At the same time interference from other nearby users should be suppressed, as it is not practical to suppose that channel state information from all users is available for full MMSE-based interference suppression. Motivating the use of partial MMSE combining vectors. This work shows the taxonomy of proposed MMSE combining vectors for different clustering techniques, and three approaches to partially suppress interference based on 1) interferers with common access points, 2) strongest channel gain and 3) strongest eigendirections. In order to analyse the pros and cons of the three interference suppression methods, a general parameterised MMSE combining vector is proposed, that is generic enough to represent any clustering methodology and any partial interference suppression technique. The results are presented in terms of spectral efficiency and computational cost, with the aid of a dense indoor massive MIMO experiment and contrasted with an indoor simulation using the WINNER II channel model. Experimental and simulation results show that partial suppression based on shared access points is not suitable for systems with a small probability of users sharing the serving access points, as dominant interference is likely to come also from users connected to access points not serving the harmed user. Channel gain based interference suppression provides the best controllable approach in terms of spectral efficiency and computational cost. However, eigendirections-based interference suppression achieves the highest spectral efficiency, when the number of antennas per access point is high enough.

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MaMIMO User Grouping Strategies: How much does it matter?

Cited by 5 publications

References 8 publications

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

Densely Deployed Indoor Massive MIMO Experiment: From Small Cells to Spectrum Sharing to Cooperation

Partial Interference Suppression in Massive MIMO Systems: Taxonomy and Experimental Analysis

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