System-Level Analysis of Mobile Cellular Networks Considering Link Unreliability

Call-Level Performance Sensitivity in Cellular Networks

“…Finally, the mathematical model used to consider link unreliability is based on the proposed call interruption process proposed in (Rodríguez-Estrello et al, 2009 with the j-th handed-off cell (for j = 0, 1, 2, …) until the instant the call would be interrupted due to the wireless link unreliability assuming that the mobile terminal has neither successfully completed his call nor has been handed off to another cell. The RVs used to represent this time are X i (j) (for j = 0, 1, 2, …).…”

“…The RVs used to represent this time are X i (j) (for j = 0, 1, 2, …). These RVs are assumed to be independent and phase-type distributed (Rodríguez-Estrello et al, 2009;Rodríguez-Estrello et al, 2010). Relationships between the different time interval variables defined in this section are illustrated in Fig.…”

“…The development of analytically tractable teletraffic models for performance evaluation of mobile cellular networks under more realistic assumptions has been the concern of recent works (Corral-Ruíz et al 2010;2005, Fang b;Kim & Choi 2009;Pattaramalai, 2009;Rico-Páez et al, 2007;Rodríguez-Estrello et al, 2009;Rodríguez-Estrello et al, 2010;Wang & Fan, 2007;Yeo & Yun, 2002;Zeng et al, 2002). The general conclusion of those works is that, in order to capture the overall effects of cellular shape, cellular size, users' mobility patterns, wireless channel unreliability, handoff schemes, and characteristics of new applications, most of the time interval variables (i.e., those used for modeling time duration of different events in telecommunications -for example, cell dwell time, residual cell dwell time, unencumbered interruption time, unencumbered service time) need to be modeled as random variables with general distributions.…”

Modeling and performance analysis for mobile cognitive radio cellular networks

“…To realistically capture the overall effects of users' mobility and traffic flow characteristics of a real system, the use of general distributions for modeling UST and CDT has been highlighted in previous works [8][9][10][11][12]. These time variables are of primary importance as they can be used to derive other key performance metrics such as new call blocking, handoff failure, and call forced termination probabilities, as well as inter-cell handoff arrival rate and system Erlang capacity.…”

“…(e) Finally, channel holding time statistics and the effects of the relative value of the mean secondary service time to the mean primary channel holding time, users' mobility, the use of spectrum handoff (SH), and the primary channel utilization factor on the maximum Erlang capacity of the system are investigated. Traffic modeling and call-level performance evaluation of wireless cellular communication systems have been widely investigated in the literature [9,10,15,16] (and the references therein). Different performance metrics have been investigated (channel holding time probability distribution, completion probability, forced termination probability, new call blocking probability, among others); however, most published works on traffic modeling in wireless cellular communication systems have considered exponentially distributed UST.…”