Novel Teletraffic Analysis of GSM/EDGE Orthogonal Sub Channel (OSC) and Application to Spectrum Re-farming

Abstract

GSM network operators are required to free up some of the spectrum allocated for voice traffic in order to serve 3G and 4G users. Re-farming 900 MHz and 1800 MHz bands into HSPA, W-CDMA (UMTS) and LTE is a challenge faced by operators with growing GSM voice traffic as 3G systems require 5 MHz bandwidth which is 25 times more bandwidth than a typical GSM channel. 4G systems (LTE) may even use 10 MHz to show superiority over the 5 MHz 3G systems. It is required to cope with the GSM voice traffic with significantly less bandwidth in order to accommodate 3/4G users. Orthogonal Sub Channel (OSC) is a novel technique offering efficient hardware utilization that can result in doubling GSM voice capacity. This is achieved by allowing two users to share the same physical radio resources. OSC can be used in phasing-out GSM networks to release significant frequency spectrum for 3/4G users. In this work, a thorough analysis on OSC performance gain is presented. The hard capacity gain offered by OSC is calculated as a function of the OSC penetration and compared with system level simulations. It is shown that hard capacity can be maintained with less number of TRXs (transceivers), and consequently, narrower spectrum. This paves the way for re-farming the released GSM spectrum into 3/4G traffic.

Applications of artificial neural networks for calculation of the Erlang B formula and its inverses

“…Given the nature of Erlang formulas, there is no inverse function of these formulas in a closed form. For this reason, research is carried out to find solutions (numerical models or approximations) for determining the inverse value of Erlang formulas 13–17 or look‐up tables are used, especially in applications related to call centers. Moreover, even the calculation of the Erlang functions for larger amounts of traffic and/or large channel number become computationally extensive and certain solutions of less computationally demanding approximations of Erlang formulas have been derived 14,18–20 …”

Određivanje Inverzne Vrednosti Erlang B Formule Primenom Veštačkih Neuronskih Mreža

“…Zahvaljući sposobnosti učenja zavisnosti između dva skupa podataka, veštačke neuronske mreže su našle široku primenu u različitim oblastima, kao što su oblasti elektronike i telekomunikacija [18]- [29]. One su najčešće primenjene za razvoj modela u slučaju da ne postoje matematički izrazi ili modela koji povezuju ulazne i izlazne parametre.…”