Robust Planning and Operation of Multi-Cell Homogeneous and Heterogeneous Networks

Abstract

In this work, we propose a robust planning tool that allocates power statically in homogeneous and heterogeneous cellular networks with non-regular base station (BTS) placement, to mitigate interference and improve overall performance. Each BTS will use the total available spectrum, but it will divide it into multiple sub-bands, and each BTS will transmit with a specific pre-computed power on each sub-band. We refer to such a power allocation as a power map. Our offline planning tool computes a robust power map for a given topology, by solving a non-convex, non-linear optimization problem, through simple transformations, based on geometric programming. The power map is computed based solely on the network topology, and it is made available to all BTSs that use it throughout the network operation to perform scheduling using a fast quasi-optimal online algorithm that we propose. We evaluate our planning tool for different homogeneous and heterogeneous networks (HetNets), first in a static setting where scheduling is performed optimally and then in a dynamic setting when scheduling is performed with our online scheduler. Results show that our solution significantly outperforms a classical equal power/fixed frequency reuse scheme in terms of sum-rate, by up to 30% in homogeneous networks and by up to 70% in HetNets.

A Vector-Based Approach for Dimensioning Small Cell Networks in Millimeter-Wave Frequencies

“…One example is presented in [6] where the coverage and service quality of the Klaten city were improved by adding as few additional BSs as possible using a cell planning software. The acquired coverage, traffic throughput and delay vary with deploying schemas just like the works reported by [7], [8]. The authors are with the School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: jmzhang@bupt.edu.cn; drzhang@bupt.edu.cn; sunjj@bupt.edu.cn).…”

An optimized small‐cell planning procedure for Heterogeneous Network to improve network energy efficiency

“…The cell planning procedure reported in Özcan et al 11 considers heterogeneous and homogeneous networks for statistical power allocation to overcome the interference and enhance the network performance. This allows the base station to utilize the available spectrum as multiple sub‐bands with precomputed specific power.…”

Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks

“…The simulator then calculates the Signal-to-Interference-plus-noise ratio (SINR) at each possible user location for a given realisation of the BS-to-user channels. The task is then repeated sufficiently large number of times to generate a user location-dependent probabilistic description of a desired performance metric [3]. For example, one can obtain location-specific coverage probability (i.e., the probability that the SINR at a given location surpasses a predefined threshold) and location-specific average rates via this method.…”

“…The algorithm initiates with one new BS (for loop initiation at line 4). If the RoI is discretised into a N × N square grid 3 , then the algorithm considers all N 2 possible placements of the new BS (for loop at line 26). For every location, its associated coverage manifold is obtained by feeding the network topology to a trained CNN-AE (line 29).…”

“…If the obtained fraction does not exceed a certain threshold for all of the possible new deployment scenarios (line 14), the Algorithm increases the number of deployable new BSs by one (progress in the for loop at line 4) and moves to the next stage. 3 In section II-B, we have taken N = 64.…”