1. New Adaptive Resource Allocation Scheme in LTE-Advanced
International Conference on Intelligent Systems Design and Applications
december , Portugal
New Adaptive Resource Allocation Scheme in LTE-Advanced
Authors:
1Radhia Khdhir,
2Kais Mnif, 2Aymen Belguith
And 1Lotfi Kamoun
LETI-University of Sfax
1ENIS, Ecole Nationale d’Ingénieurs de Sfax
2ENET’COM, Ecole Nationale d’Electronique et de Télécommunication de Sfax
2016/2017 1

2. Performance Evaluation Conclusion and perspectives
OUTLINE
Introduction
Objectives
Proposed Algorithm
Performance Evaluation
Conclusion and perspectives
I begin with an introduction, then I will discuss previous work related to the CAC proposition for LTE and LTE-A system. Later I will give a detailed description of the proposed scheduling and performance metrics and I will describe The System and simulation models. Finally I will give a conclusion and perspectives.

3. Introduction
Packet scheduling (PS) is considered as the most important step of Radio Resource Management (RRM) to have better resource utilization
Packet scheduling (PS) is developed to allocate each User Equipment (UE) a portion of the available resource (PRBs).
In this context, a new Scheduling algorithm is proposed for LTE-A.
Since radio spectrum is the most valuable resource in mobile technology, radio resource management (RRM) mechanisms are critical for the operation of a cellular network. One of the key RRM mechanisms is packet scheduling
For a better exploitation of radio resources in the fourth generation networks (4G) Long Term Evolution-Advanced and for a better guarantee of service quality requested by users, radio resources management and specifically scheduling, play a key role in reaching the objective.
In this paper, we propose a new CAC algorithm for LTE and LTE-A Uplink transmission of use the new features
CA enables both bandwidth extension and backward compatibility by combining several component carriers (CC’s).

4. Objectives The objectives of our research work is to :
Propose a new scheduling algorithm for LTE-A Systems
Take into account the system throughput, delay, and fairness in the scheduling decision

5. of PRBs for the NRT class
Proposed Algorithm
Classification
mixteTraffics
<
Trafics RT
ARAP
<
Trafics NRT Si Si
the portion of
PRBs reserved for the RT traffic in the next time
The portion of PRBs
reserved
for the RT paquet in
the actual time (TTI)
guarantee a
minimum
of reserved
PRBs for RT class
Parameter for ensure a
minimum number
of PRBs for the NRT class 2
Soutenance de Thèse

6. Proposed Algorithm
the instantaneous throughput of user i
the average throughput of user i.
Etape 2: Appliquer l’algorithme opt-tabu en dégageant la meilleure allocation. 2
Soutenance de Thèse

7. Proposed Algorithm

8. Simulation Parametrs Parameters Value System bandwidth 10 MHz
Subcarrier spacing
15 KHz
Number of subcarriers per PRB 12
Number of available PRBs 50
Transmission time interval(TTI)
1 ms
Total number of used subcarriers
600
Carrier frequency
2.5 GHz
Frame duration
10 ms
Number of users
10-70
Simulation Time
1000 TTIs
Link adaptation ACM Modulation
BPSK, QPSK,16-QAM, 64-QAM
Scheduling algorithms
RR, BCQI, AHSA and ARAP

9. Simulation Results: Average PDR of RT traffic

10. Simulation Results: Average PDR of NRT traffic
Ours A-EPSA algorithm proposed is simulated using OFDMA system with a single cell of the radius equal to 1.5 km. The total bandwidth of the system is 10 MHz where 2 adjacent CCs equal to 2 GHz frequency band are taken to be aggregated. The bandwidth for each CC is adjusted to be 5 MHz. Users are supposed to have a random and uniform distribution

11. Simulation Results: Served users
our proposed scheme and that proposed in [10]. It is clear that if we apply the proposed CAC scheme a decrease in the blocking rate is guaranteed compared to the solution proposed in [10]. The growth starts from a number of users equal to 20 for our proposed CAC algorithm. Indeed, when applying our scheme, the probability reaches a value of 27 % for NC and 25% for HC for a number of users equal to 120 compared to a blocking probability of 48% and 45% for NC and HC, respectively with the scheme CAC proposed in [10].
In Figure 5, we can observe that the application of our CAC scheme improves the values of blocking probabilities for two types of calls (HC and NC) for the NGBR traffic. In fact, using the proposed CAC scheme, the blocking probabilities reach the order of 32% and 30% for NC and HC respectively. While in [10], the achieved rates are 51% and 47% for NC and HC respectively.
It is clearly observed that the MuCSA scheme serves more than 90% among all users.

12. Simulation Results: Average system throughput
In Figure 5, we can observe that the application of our CAC scheme improves the values of blocking probabilities for two types of calls (HC and NC) for the NGBR traffic. In fact, using the proposed CAC scheme, the blocking probabilities reach the order of 32% and 30% for NC and HC respectively. While in [10], the achieved rates are 51% and 47% for NC and HC respectively.
We observe that MuCSA outperforms RR and AHSA schedulers. On the one hand, MuCSA can serve much more number of users compared to others algorithms.
MuCSA assigns PRBs to users considering the channel quality at each TTI.

13. Simulation Results: Fairness Index
The physical resource blocks utilization is the ratio of the number of allocated PRBs for the users in the system during the whole simulation time.
The result of the PRB utilization according to the number
of the UEs is shown in Figure 6. If we apply our CAC scheme, the PRB utilization can achieve 96% whereas this value is only 75% in the CAC method defined in [10]. This gain (of about 21%) is observed for simulations involving more than 120 UEs. The best use of the PRBs is due to the concept of resource allocation algorithm, which adjusts the allocation of resource intelligently.
As the number of users increases, it is expected that the fairness index decreases as more UEs compete for the same number of PRBs in the different CCs.
The maximum value of ARAP and RR curves are close. This is explained by the fact that ARAP serves the UEs according to their priorities. Then ARAP schedules the RT traffic without neglecting the NRT traffic

14. Conclusion and perspectives
We propose a new scheduling algorithm for LTE-A networks.
The simulation results show that the performance of our scheduling algorithm is better than others algorithms in terms of :
Average number of served user,
Fairness achieved versus number of UEs,
System delay,
System throughput :
In the future, it is worthy :
To study the algorithm complexity
To consider more realistic scenarios
Several cells and e-NodeBs,
Mobility.
In this paper, we introduced the operation of our A-EPSA packets scheduling algorithm which was designed for the downlink in an LTE-A network. The simulation results show that the performance measures in terms of the system throughput, average delay for GBR and NGBR traffic as well as PDR are better than those compared to the EPSA algorithm results.
Reduce the HC and NC dropping probabilities
Maximize the resource utilization in the system

15. THANK YOU FOR YOUR attention