Capacity of wireless networks under SINR interference constraints

Abstract

A fundamental problem in wireless networks is to estimate their throughput capacity -given a set of wireless nodes and a set of connections, what is the maximum rate at which data can be sent on these connections. Most of the research in this direction has focused either on random distributions of points, or has assumed simple graph-based models for wireless interference. In this paper, we study the capacity estimation problem using a realistic Signal to Interference Plus Noise Ratio (SINR) model for interference, on arbitrary wireless networks without any assumptions on node distributions. The problem becomes much more challenging for this setting, because of the non-locality of the SINR model. Recent work by Moscibroda et al. (IEEE INFOCOM 2006, ACM MobiHoc 2006 has shown that the throughput achieved by using SINR models can differ significantly from that obtained by using graph-based models. In this work, we develop polynomial time algorithms to provably approximate the throughput capacity of wireless network under the SINR model.

Maximal Packing with Interference Constraints

“…[12]). Now, given the optimal solution H for the SDR, we propose below a randomization algorithm to obtain a solution (v) for the unrelaxed problem (6). Now, we obtain the bounds relating σ (optimal value of the unrelaxed problem) and ρ (optimal value of the SDR) using the proposed randomization algorithm.…”

“…In [4], [5], a related problem of sensor selection, i.e., selecting K out of N sensors that minimize the error in estimating network parameters is studied and solutions are proposed using several frameworks such as convex optimization, hypothesis testing, experiment design, compressed sensing and sparse signal recovery etc. Another related problem is the signal-to-interference-and-noise ratio (SINR) maximization problem wherein the SINR at each node is maximized [6]- [8] using techniques from semi-definite programming and graph theory. However, the above methods assume that the maximum number of nodes is fixed and the interference amongst the selected nodes is optimised.…”

Quantum versus simulated annealing in wireless interference network optimization

“…There are two major methods for solving network interference: the first one is the graph based model that solves the Weighted Maximum Independence Set (WMIS) problem on a conflict graph 23 24 25 26 27 , the other optimizes a geometric-based SINR 28 29 30 31 . The former is sometimes argued as being an overly idealistic assumption; however, the WMIS problem is still involved in the latter model 32 and is of interest in SA versus QA; thus we mainly consider the former model for simplicity.…”

Joint Dimensioning of Outdoor Heterogeneous Radio Access Networks (HetNet) using Monte Carlo Simulation

“…where P stands for the power of the incoming signal of interest, I for the interference (baneful signal),and the noise is denoted by (Figure 1) [6]. Due to the adaptive coding and modulation of the LTE mobile network, different SINR values are assigned to different SE ( Figure 2).…”