## What is/are Meta Distribution?

Meta Distribution - We use the concept of meta distribution (MD) of the signal-to-interference ratio (SIR) to gain a complete understanding of the per-link reliability and describe the performance of two scheduling methods for data aggregation of machine type communication (MTC): random resource scheduling (RRS) and channel-aware resource scheduling (CRS).^{[1]}In contrast to the conventional performance analysis based on the coverage probability, the distribution of the conditional coverage probability (CCP), which is called signal-to-interference ratio (SIR) meta distribution, is investigated by considering the random location of the nodes as well as the underlying channel.

^{[2]}Therefore, we derive the meta distribution, which is calculated directly from the moment result of the CP, and validate its correctness by Monte Carlo simulations.

^{[3]}In this article, we focus on the analysis of the coverage probability and the meta distribution of the signal-to-interference ratio (SIR) for a LoRaWAN uplink with fractional power control (FPC).

^{[4]}Meta distributions (MDs) have emerged as a powerful tool in the analysis of wireless networks.

^{[5]}That holds for even a simple average-based performance metric—the success probability, which is a special case of the fine-grained metric, the meta distribution (MD) of the signal-to-interference ratio (SIR).

^{[6]}The meta distribution (MD) of the signal to interference ratio (SIR) extends stochastic geometry analysis from spatial averages to reveals find-grained information about the network performance.

^{[7]}In this paper, we apply the signal-to-interference ratio (SIR) meta distribution framework for a refined SIR performance analysis of HCNs, focusing on

^{[8]}Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance.

^{[9]}The meta distribution of the signal-to-interference-ratio (SIR) is an important performance indicator for wireless networks because, for ergodic point processes, it describes the fraction of scheduled links that achieve certain reliability, conditionally on the point process.

^{[10]}This paper characterizes the meta distribution of the downlink signal-to-interference ratio (SIR) attained at a typical Internet-of-Things (IoT) device in a dual-hop IoT network.

^{[11]}The meta distribution (MD) of the signal-to-interference ratio (SIR) provides fine-grained reliability performance in wireless networks modeled by point processes.

^{[12]}With the pcf in hand, we provide the tightest known approximation of the point process of interfering BSs as seen by the typical user of Type I process, which is used to derive remarkably tight expressions for the moments of the downlink signal-to-interference- ratio (SIR) meta distribution for the typical cell.

^{[13]}In particular, we provide closed-form expressions for the information coverage probability and the spatial throughput for both networks and we derive the meta distribution of the signal-to-interference-plus-noise ratio.

^{[14]}This paper focuses on the meta distributions of the signal-to-interference-plus-noise ratio (SINR) and user-perceived rate in heterogeneous cellular networks (HCNs) with multiple tiers of base stations.

^{[15]}We introduce an analytical framework for computing the distribution of the conditional coverage probability given the point process, which is referred to as the meta distribution and provides one with fine-grained information on the performance of cellular networks beyond spatial averages.

^{[16]}This paper considers a WEIT-enabled device-to-device (D2D) network with the ambient RF transmitters distributed according to a Poisson point process and focuses on the meta distribution of the transferred energy, which is the distribution of the conditional energy outage probability given the locations of the RF transmitters, to show what fraction of devices in the network satisfy the target energy outage constraint if the required transmission energy is given.

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## non orthogonal multiple

We study the meta distribution (MD) of the coverage probability (CP) in downlink non-orthogonal-multiple-access (NOMA) networks.^{[1]}We develop an analytical framework to derive the meta distribution and moments of the conditional success probability (CSP), which is defined as success probability for a given realization of the transmitters, in large-scale co-channel uplink and downlink non-orthogonal multiple access (NOMA) networks with one NOMA cluster per cell.

^{[2]}This paper presents the meta distribution analysis of the downlink two-user non-orthogonal multiple access (NOMA) in cellular networks.

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## interference plus noise

By combining stochastic geometry with queueing theory, two fundamental measures are analyzed, namely the transmission success probability and the meta distribution of signal-to-interference-plus-noise ratio (SINR).^{[1]}This work studies the signal-to-interference-plus-noise ratio (SINR) meta distribution (MD) in cellular networks with a focus on the Poisson model.

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## fine grained information

This work studies the meta distribution in a partial-NOMA network to obtain fine-grained information about the network performance.^{[1]}In order to obtain fine-grained information on the coverage performance, the meta distribution (MD) of signal-to-interference-to-noise ratio (SINR) is adopted for network performance evaluation, which provides the probability that certain percentage of satellite-relays-user links are able to reach a target SINR threshold.

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## downlink non orthogonal

In this letter, the Meta distributions (MDs) of the secrecy rate in downlink non-orthogonal multiple access (NOMA) systems with randomly located eavesdroppers are investigated.^{[1]}

## Sinr Meta Distribution

Under a general Nakagami fading model, we derive the upper bounds for the $b$ -moments of the conditional signal-to-interference-noise ratio (SINR) distributions for the two modes given the network realization, and further calculate the upper bounds of SINR meta distributions (MDs).^{[1]}We derive a tractable expression for the SINR meta distribution, and verify its accuracy via simulations.

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