Analyzing Uplink SINR and Rate in Massive MIMO Systems Using Stochastic Geometry

A significant burden on wireless networks is brought by the uploading of user-generated contents to the Internet by means of applications such as the social media. To cope with this mobile data tsunami, we develop a novel multipleinput multiple-output (MIMO) network architecture with randomly located base stations (BSs) a large number of antennas employing cache-enabled uplink transmission. In particular, we formulate a scenario, where the users upload their content to their strongest base stations (BSs), which are Poisson point process (PPP) distributed. In addition, the BSs, exploiting the benefits of massive MIMO, upload their contents to the core network by means of a finite-rate backhaul. After proposing the caching policies, where we propose the modified von Mises distribution as the popularity distribution function, we derive the outage probability and the average delivery rate by taking advantage of tools from the deterministic equivalent (DE) and stochastic geometry analyses. Numerical results investigate the realistic performance gains of the proposed heterogeneous cacheenabled uplink on the network in terms of cardinal operating parameters. For example, insights regarding the BSs storage size are exposed. Moreover, the impacts of the key parameters such the file popularity distribution, and the target bitrate are investigated. Specifically, the outage probability decreases if the storage size is increased, while the average delivery rate increases. In addition, the concentration parameter, defining the number of files stored at the intermediate nodes (popularity), affects directly the proposed metrics. Furthermore, a higher target rate results in higher outage because fewer users obey this constraint. Also, we demonstrate that a denser network decreases the outage and increases the delivery rate. Hence, the introduction of caching at the uplink of the system design ameliorates the network performance.

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“…Then, we start with the numerator of the SINR. We insert in (16) the expression of the MRC decoder given by (15). We have 8 1…”

Section: Appendix B Proof Of Propositionmentioning

confidence: 99%

“…where (a) follows after substituting the estimated channel given in (15), while (b) is obtained by means of Lemma 2, since the covariance of q llk is δ 2 j P ljk β ljk + σ 2 K I M . We continue with the first term in the denominator of the SINR including the estimation error.…”

Section: Appendix B Proof Of Propositionmentioning

confidence: 99%

Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks

Papazafeiropoulos

Ratnarajah

2018

IEEE Trans. Wireless Commun.

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“…The stochastic geometry model attracts more and more attention. For example, it has been applied to spectral efficiency analysis of cooperative BS systems [24], capacity analysis of heterogeneous network [25], capacity analysis of DAS [26], and capacity analysis of massive MIMO systems [27]. However, in cooperative BSs or DAS, the closed expressions obtained by using…”

Section: B System-level Spectral Efficiencymentioning

confidence: 99%

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

WANG¹,

YOU²,

Yu³

et al. 2015

Sci. Sin.-Inf.

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To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation. Index TermsThe fifth generation mobile communication, massive MIMO, large-scale distributed antenna systems, spectral efficiency, channel state information acquisition, multi-user MIMO, resource allocation.

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“…Our results are presented in the cases with an exclusion ball model as in [13]. We also consider fractional power control in the uplink transmission to compensate for a fraction of the path loss and to mitigate the near-far problem from intra-cell interference.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Downlink Massive MIMO Networks: A Stochastic Geometry Approach

Sadeghabadi

Azimi-Abarghouyi

Makki

et al. 2020

IEEE Trans. Wireless Commun.

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Massive multiple-input multiple-output (MIMO) is recognized as a promising technology for the next generation of wireless networks because of its potential to increase the spectral efficiency. In initial studies of massive MIMO, the system has been considered to be perfectly synchronized throughout the entire cells. However, perfect synchronization may be hard to attain in practice. Therefore, we study a massive MIMO system whose cells are not synchronous to each other, while transmissions in a cell are still synchronous. We analyze an asynchronous downlink massive MIMO system in terms of the coverage probability and the ergodic rate by means of the stochastic geometry tool. For comparison, we also obtain the results for the synchronous systems. In addition, we investigate the effect of the uplink power control and the number of pilot symbols on the downlink ergodic rate, and we observe that there is an optimal value for the number of pilot symbols maximizing the downlink ergodic rate of a cell. Our results also indicate that, compared to the cases with synchronous transmission, the downlink ergodic rate is more sensitive to the uplink power control in the asynchronous mode.

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Analyzing Uplink SINR and Rate in Massive MIMO Systems Using Stochastic Geometry

Cited by 88 publications

References 32 publications

Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks

Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

Asynchronous Downlink Massive MIMO Networks: A Stochastic Geometry Approach

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