Power saving is one of the key factors of emerging 4G mobile network as well as in IEEE 802.16e wireless metropolitan area networks (Wireless MAN). An efficient power saving mechanism is the heart for the guarantee of a long operating lifetime for a mobile subscriber station (MS), because MSs are normally driven by rechargeable batteries. It is a vital factor for Base Station (BS) of the same network. One of the most important features of 5G mobile is the extension of battery energy 10 times greater than the present days. In this context, the evaluation of duration of sleep mode of BS or MS based on traffic load of a network is now a new era of research work. In this paper, such analysis has been done based on two statistical models: Poisson’s pdf and Engset pdf. The concept of complete sharing and partitioning of user group of teletraffic engineering is applied to measure the possibility of getting leisure time of BS or MS. Both the traffic models used in the paper are applicable in both limited and unlimited user network, i.e. in micro and fem to cellular network of 4G and 5G.
Nowadays, wireless communications play an essential role in our daily life. People’s connections with their society, culture and even one another are about to change due to wireless communications’ ease availability and newer and faster mobile devices. The fourth generation (4G) of wireless standards has been specified by the International Telecommunications Union to meet the peak speed requirement of 1 Gbps for stationary connections and 100 Mbps for a mobile connection [
All types of traffic in wired and wireless network have the property of ON-OFF governed by the behavior of users. For example, HTTP traffic of web browsing, FTP traffic between two computers, E-mail traffic among users or mail-server to users, voice traffic of telephony or mobile communications and multimedia traffic of 3G and 4G mobile communications have ON-OFF state. Each of the above traffic is modeled by two-state Markov chain; where one state is ON, i.e. there are some traffic which make the channel in serving condition and in OFF state i.e. there is not traffic hence the devices are in sleep mode. In [
The size of sleep window is related with current traffic load for IEEE 802.16e in [
The entire paper is organized as: section 2 deals with both limited and unlimited user’s traffic to determine probability of data accumulation, section 3 provides the results based on statistical analysis of section 2 and section 4 concludes the entire analysis.
Let T is the length of TS (time slot) or frame for packet transmission. If l is the arrival rate of a packet for a particular user then the probability of arrival of i packets over a duration of [0, T] is expressed by Poisson’s pdf like [
If an observation time of k slots is taken and j packets arrives over [0, kT], then its probability,
If the packet number Nth is considered as the number of threshold packets, then the probability of arriving a number of packets greater than Nth over duration of k frame is expressed as:
According to [
The graphical presentation of Equation (4) is shown in
Let us consider, a subscriber station i of m users under a BS. The average arrival rate of packet of individual user are l1, l2, ∙∙∙, lm. and the corresponding length of one awake-and-sleep cycle are:
Taking,
Let us now concentrate on the traffic model of limited user which is consistent with a SS (Sample Space).
N ® number of users,
λ ® average packet arrival rate/user,
μ ® packet termination rate,
M ® Peak information rate,
m ® average bit rate of individual user.
Therefore the number of traffic channel,
The traffic model of
Applying cut equations [
Similarly,
where,
Now for entire sample space,
The blocking probability is the probability of occupancy of all traffic channels is,
Let us determine
First of all we will evaluate,
If S > 1, it then splits the users of ith SS or combines users of different SSs.
This section deals with both analytical and simulation results on “probability of data accumulation”. The analytical part of this section is done by MATHCAD 14 and the simulation work is done in Matlab 13. First of all we discuss about Poisson’s traffic in determining the profile of probability of data accumulation. Taking T = 2 ms and Nth = 40 and the variation of probability of data accumulation against K is shown in
[
and
Now the user group-1 and 2 are combined together and the arrival rate of the combined group becomes 2.6 packets/s. The value of K# are found as:
Next part of the result section deals with traffic of limited user group called Engset traffic model. Instead of call arrival rate of Erlang’s traffic (PCT-I traffic), here we have to consider packet arrival rate per user as shown in the legend of graphs of Figures 6-8. Taking: N = 50, n = 35 and T = 2 we get the profile of probability of data accumulation taking arrival rate per user as a parameter shown in
Finally we apply Monte-Carlo simulation on 10,000 random variables following exponential pdf (since we consider M/M/n/K traffic where interarrival and service time follow exponential pdf) we evaluate the profile of “probability of data accumulation greater than threshold” against K shown in
analytical and simulation results are found identical, since simulation results resembles to analytical case with less than 2% error. In this paper we mainly deal with the parameter schedulability to determine the probability of a BS (BS for uplink and SS for downlink) to enter in sleep cycle. Once the probability of entering sleep mode PS is determined, we can determine the portion of time the SS (or BS) enters in sleep mode hence we can get the “power saving factor” of [
In this paper, the probability of getting opportunity of a BS entering in sleeping mode is determined based on Poisson’s and Engset traffic model considering arrival rate of aggregate traffic of a mobile cellular network. Very close results of the simulation with the analytical model have been found in this paper. Still, there are more scopes to work on the new originating and handoff traffic separately with suitable call admission scheme to observe the individual impact on entering leisure/sleep mode of a BS. In 3G mobiles, both circuit and packet switch exist at radio and core network but 4G emphasizes on packet switching. That is why M/G/n/K traffic
instead of M/M/n/K has been considered. In near future, the similar analysis on 5G wireless network will be carried out. The research will also be extended in real system or with other simulation tools such as NS-3 or OPNET.
Mohammad Asif Hossain,Mohammad Imdadul Islam,Mohamed Ruhul Amin, (2016) Load Sensitive Power Saving Technique for 4G Mobile Network under Limited User Traffic. Communications and Network,08,79-87. doi: 10.4236/cn.2016.82009