Service Response Time of Elastic Data Traffic in Cognitive Radio Networks

Abstract

Abstract-Quality of service (QoS) support over cognitive radio networks (CRNs) is challenging due to the random spectrum availability. Elastic data traffic is a popular service whose service response time is an important QoS parameter. We analyze the mean response time of elastic data traffic service operating over a single channel time-slotted centralized CRN under three main service disciplines, namely, shortest processing time without preemption (SPTNP), shortest processing time with preemption, and shortest remaining processing time, in comparison with the processor sharing (PS) service discipline. It is shown that the SPTNP is a better choice over the PS service discipline when the traffic load is high, and that the preemption reduces the mean response time when the data file size (service time requirement) follows a heavy tailed distribution. The response time analysis can be used for call admission control to ensure service satisfaction.Index Terms-Cognitive radio network, elastic data traffic, quality-of-service, response time, shortest processor time, shortest remaining processor time, processor sharing.I. I The spectrum underutilization [2] is becoming a major setback for the development of next generation wireless networks. Among proposed solutions, the concept of cognitive radio networks (CRNs) [3][4] has become a popular choice due to its flexibility and adaptability to use in any available frequency band. It has been well accepted within the wireless communications research community to explore the underutilized portions of the radio spectrum using new-generation smart programmable radios, without harmfully interfering with the licensed primary users (PUs). Early research studies on CRNs mainly focused on physical layer aspects such as spectrum sensing [5] and power/rate controlling [6], and link layer aspects such as channel access coordination [7]. Further, the unpredictable nature of the opportunistic channels limits the research work on CRNs to best effort services without any strict quality of service (QoS) requirements. As the demand for wireless multimedia services keeps on increasing, supporting QoS-aware services over CRNs is essential.Research over CRNs. The impact of primary user activities on traffic congestion and the economic interaction between secondary user (SU) and primary network operators are studied in [12] and [13], respectively, when the SUs are data users. This type of services does not require strict QoS as in conversational or streaming services, but has a moderate service requirement in the form of response time. The response time of a service request (file) is defined as the time elapsed from the instant it is placed by a user to the instant that the service (file transmission) is completed. A. Related WorkMost of the resource allocation/scheduling works in CRNs mainly focus on throughput optimization/fairness, and they do not deal with any specific data file length distributions or response time as a performance metric. A performance analysis of elastic traff...

RF-Spectrum Opportunities for Cognitive Radio Networks Operating Over GSM Channels

“…Tran et al [20] evaluate the average service time of delay-sensitive and delay-insensitive packets showing that the average service time of the packets transmitted by a SU not only depends on the arrival rate and size of SU's packets but also on the arrival rate and size of the PU's packets. In [21] the service time is analyzed for a single channel CRN and the analysis is extended in [22] to characterize the average service response time of elastic data (i.e., variable packet-length) when the PU activity follows an ON-OFF behavior with ON and OFF durations following exponential distributions. While the previous works only characterize the average of the service time, [23] derived expressions for the distribution of the packet service time when the SUs are traffic-saturated and fixed-length packets are transmitted.…”

Discrete-Time Analysis of Cognitive Radio Networks with Nonsaturated Source of Secondary Users

“…For details see [2]. The vast majority of the models developed for studying CR networks, and specifically for spectrum sensing, assume that busy and idle times follow an exponential distribution (or geometric in discrete time); for example, see [14,15] and all the above references to sensing studies. Using measurements, the authors of [16] showed that the channel idle time can be modeled by a lognormal distribution.…”

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

“…The vast majority of models that have been developed for studying CR networks, and in particular for studying spectrum sensing, assume that the durations of both the busy and idle times are governed by the exponential distribution (or its discrete time counterpart: geometric), see for example [18,19] and all the references given above for sensing related studies. However, this assumption is not backed with empirical evidence.…”