On node density - outage probability tradeoff in wireless networks

Abstract

Abstract-A statistical model of interference in wireless networks is considered, which is based on the traditional propagation channel model and a Poisson model of random spatial distribution of nodes in 1-D, 2-D and 3-D spaces with both uniform and non-uniform densities. The power of nearest interferer is used as a major performance indicator, instead of a traditionallyused total interference power, since at the low outage region, they have the same statistics so that the former is an accurate approximation of the latter. This simplifies the problem significantly and allows one to develop a unified framework for the outage probability analysis, including the impacts of complete/partial interference cancelation, of different types of fading and of linear filtering, either alone or in combination with each other. When a given number of nearest interferers are completely canceled, the outage probability is shown to scale down exponentially in this number. Three different models of partial cancelation are considered and compared via their outage probabilities. The partial cancelation level required to eliminate the impact of an interferer is quantified. The effect of a broad class of fading processes (including all popular fading models) is included in the analysis in a straightforward way, which can be positive or negative depending on a particular model and propagation/system parameters. The positive effect of linear filtering (e.g. by directional antennas) is quantified via a new statistical selectivity parameter. The analysis results in formulation of a tradeoff relationship between the network density and the outage probability, which is a result of the interplay between random geometry of node locations, the propagation path loss and the distortion effects at the victim receiver.

Throughput analysis of cognitive wireless networks with Poisson distributed nodes based on location information

“…The closest-interferer approximation is in fact a lower bound of the aggregate interference [14], leading then to an upper bound of the actual cognitive spatial throughput. This bound have been proved to be asymptotically equivalent to the actual values when λ → 0 [14], [33] 16 . For higher densities, the closest interferer treated as noise tends to contribute less to the aggregate interference experienced by the receivers, worsening our approximation.…”

“…For our purposes, though, the incorporation of these phenomena only complicates the mathematical formulation without giving any further insight on the network behaviour 8. This approximation is analysed in[33] and it usually applied to compute lower bounds of the interference power based on dominant interferers[14],[31]. We also discuss more about it in Section VII.…”

Interference mitigation in WSN by means of directional antennas and duty cycle control

“…a strong attention must be paid to the increasing density of sensor nodes (see also Ref. 1) in many networks, which may lead to excessive interference issues. The problem concerns networks that gather information from a specified area with random distribution of transmitting nodes operating at the same radio frequency.…”

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

“…However, the analysis results will be complicated and give fewer insights. In addition, the communication performance is generally determined by one critical interfering node, especially in low outage probability region [36]. Therefore, we focus on the one interfering node case in this work, but the performance for the multiple interfering nodes case is also presented in simulation results of Section V.…”

“…t β I (ℓ I )g βm(ℓm ) 0 f hm (h) dhf hI (g) dg 2K m (ℓ m ), γ t β I (ℓ I )g β m (ℓ m ) × exp (−g) dg(42)where (a) is from the CDF of the noncentral Chi-squared distribution and the integral term in(42) can be presented as(36) with c = 1, d = 0 e = 2K m (ℓ m ), and f = γtβI(ℓI)…”