VoLTE cell capacity estimation using AMR‐WB codecs and considering VAF, SID, packet bundling, and TTI bundling

Abstract

SummaryThe Voice over Long‐Term Evolution (VoLTE) cell capacity notably has an influence on Call Admission Control (CAC) in Long‐Term Evolution (LTE) network. In literature, many techniques have been studied to estimate the LTE cell capacity, but most of them are throughput‐based physical layer (PHY layer) estimate. These estimates do not consider delay requirement, transmission loss, and protocol overhead. In this paper, we have proposed novel analytical models for estimating the VoLTE cell capacity in terms of number of users by considering available physical resource blocks (PRBs) in each bandwidth, voice traffic‐based characteristics such as voice activity factor (VAF), silent insertion descriptor (SID), and radio‐specific techniques such as packet bundling and transmission time interval (TTI) bundling. Our developed analytical models are then used to investigate the effect of modulation and coding scheme (MCS) and Adaptive Multi‐RateWideband (AMR‐WB) voice codec on VoLTE cell capacity. Results show that higher order modulation scheme with lower bit voice codec accommodates maximum number of VoLTE users. Further, the impact of VAF, SID, packet bundling, and TTI bundling on VoLTE cell capacity is investigated in different radio conditions. Finally, using our proposed model, the effect of hybrid automatic repeat request (HARQ) retransmission on VoLTE cell capacity is investigated.

Analysis of Mission Critical Services Radio Access Network Capacity Limitations Over 5G

“…An estimation of VoLTE capacity per cell has been calculated and considered in [14] and [15], where only data Physical Resources Blocks (PRBs) based analysis is developed, without considering possible limitations due to control channels. 5G capacity calculations have been indirectly introduced in [16], presenting control channel resources and some simplified VoNR capacity calculations while comparing results with VoLTE.…”

“…Each CCE is composed by multiple Resource Element Groups (REGs), in turn made up of groupings of REs, which number vary for LTE and 5G. To simplify the calculations, a relationship between MCS and AL has been determined as a function of whether the radio conditions are optimal (22)(23)(24)(25)(26)(27), average (15)(16)(17)(18)(19)(20)(21), bad (7)(8)(9)(10)(11)(12)(13)(14) or very bad (0-6). The corresponding MCS-AL mapping is defined in [18], as 2 for average conditions, 4 for bad, and 8 for very bad.…”

Behavior Decision Model With Situation Assessment for Intelligent Vehicles Based on Vehicle-to-Everything Information

“…With V2X technology, RSUs can collect information about vehicles on the road network. The mobile edge computing unit can assess the capacity and update the condition of each road segment in real-time ( 19 ). Connected vehicles can receive the updated road network capacity and optimize the navigation information in time so that they can choose a better lane to avoid congested segments of urban roads and travel efficiently.…”