A Dominant Interferer Plus Mean Field-Based Approximation for SINR Meta Distribution in Wireless Networks

Abstract

This paper proposes a novel approach for computing the meta distribution of the signal-to-interferenceplus-noise ratio (SINR) for the downlink transmission in a wireless network with Rayleigh fading.The novel approach relies on an approximation mix of exact and mean-field analysis of interference (dominant interferer-based approximation) to reduce the complexity of analysis and enhance tractability.In particular, the proposed approximation omits the need to compute the first or the second moment of the SINR that is used in the beta approximation typically adopted in the literature but requires of computing the joint distance distributions. We first derive the proposed approximation based on a Poisson point process (PPP) network with a standard path-loss and Rayleigh fading and then illustrate its accuracy and operability in another four widely used point processes: Poisson bipolar network, Matérn cluster process (MCP), K-tier PPP and Poisson line Cox process (PLCP). Specifically, we obtain the SINR meta distribution for PLCP networks for the first time. Even though the proposed approximation looks simple but it shows good matching in comparison to the popular beta approximation as well as the Monte-Carlo simulations, which opens the door to adopting this approximation in more advanced network architectures.

Reliability in Post-Disaster Networks: A Novel Interference-Mitigation Strategy

“…λ T (r Ω (ω, β) ω −α+1 dβ dω. By recalling the expressions of f Z1 (z 1 ) from Corollary 2 and F Z0|Z1 (z 0 |z 1 ) from Corollary 5, the dominant-interferer-based approximation of the SINR meta distribution [24] can be computed as (23) where…”

“…Then, recalling that Z 0 < Z 1 , the dominant-interfererbased approximation of the SINR meta distribution can be obtained as [24] FPc (γ) = P(P c > γ)…”

Performance Trade-off in Inseparable Joint Communications and Sensing: A Pareto Analysis

“…Since the typical IU transmitter is located at the center of a circular network area and each IU in this network follows uniformly i.i.d., the probability density function of l can be given by f l (l) = 2πl πR 2 = 2l R 2 [18], where R is the radius of the circular network area and 0 < l ≤ R.…”

“…Let r denote the distance between the typical IU receiver and an IU transmitter. Since the receiver is located at the center of a circular area and each transmitter follows an independent and identical uniform distribution, the probability density function of r is given by f r (r) = 2πr πR 2 = 2r R 2 [18] . From the definition of the probability of coverage P C in Section II-C, we obtain P C (ζ c ) as [16],…”

“…where Since h is a random variable following an exponential distribution with mean 1, P [h 0 ≥ γ p r α 0 (σ 2 + I c )|r 0 ] in ( 12) is represented as [18] ,…”