Implementation of DRAMA in Macrocell Network

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Implementation of DRAMA in Macrocell Network AHMED Afaz Uddin1 , RAHMAN Ashiqur2, ISLAM Mohammad Tariqul3 Space Science Centre, Universiti Kebangsaan Malaysia Bangi, Selangor, Malaysia 1E-mail: [email protected] 2E-mail: [email protected] 3E-mail: [email protected]

Abstract In this paper, Modified-Dynamic Resource Allocation Management Algorithm is implemented in macrocell network. Previously, the algorithm was developed for heterogeneous network with both smallcell and macrocell network. The increasing number of network nodes in wireless cellular communication has increased the demand of proper resource allocation in heterogeneous network. Inter-cell interference is still considered as one of the most challenging issues in vast deployment of mobile base stations in urban and sub-urban areas. In LTE and LTE-A network, spectrum sharing and leasing mandated by huge number of base stations (indoor and outdoor) deployed, has an adverse effect on systems throughput and quality of service. Advances in resource management strategies has improved the interference scenarios in vast scale. A modified dynamic resource allocation management algorithm for macrocell network is proposed here to counter the intercell interference. Simulation environment is developed to study the performance of M-DRAMA for the marcoell network. The performance analysis shows that higher number of users get quality coverage at high node density. Keywords: Macrocell, Heterogeneous DRAMA, LTE, Resource Allocation

Network,

connected to the macrocell. Studies regarding proper allocation of resources are investigating the possible way to reduce the interference without compromising the bandwidth, latency, and QoS [1-5]. In article [6], A Dynamic resource allocation scheme (DRAMA) has been developed which focused on both local and centralized approach to optimize the resource and provide quality coverage to highest number of macro and femtocell users. In this paper, a Modified-Dynamic Resource Allocation Management Algorithm (MDRAMA) is developed based on the principle of DRAMA for macrocell network only. Rest of the paper follows as; M-DRAMA is section II, System Modelling in section III, Results and Discussion in section IV and Conclusion in section V. II. M-DRAMA In dense network, users give the highest priority to that serving cell that provides better coverage [7]. When the number of users under a macro-cell increases, the throughput of the each user decreases. Since the capacity of the macrocell is constant and it is shared by higher number of users. On the other hand, assigning users who are far from the macrocell will drag high path-loss that will end up in a less effective solution of resource

I. INTRODUCTION Along with the advance of modern multimedia devices, the demand of high data speed and voice call has increased massively over the last few years. Moreover, cellular operators are providing more and more multimedia contents and VoIP services are getting in operation along with the higher number of network nodes. To coup up with this high demand of active nodes, cellular operators all over the world are increasing the number of outdoor cells in large scale. As so, coverage areas of outdoor cells intersect with each other making it vulnerable for possible inter-cell interference. This co-tier interference reduces the Quality of Service (QoS) of the users and the effect is high at busy hours when a large numbers of users are The research was supported by ICT fellowship grant for higher education and research in information and communication technology under the Ministry of Post, Telecommunication and Information Technology, Bangladesh.

Fig. 1: Dense macrocell network with outdoor users

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allocation. Resource allocation for macrocell has been studied in several journals and some of them are already been tested and implemented in heterogeneous network. The M-DRAMA that has been modified for macrocell resource allocation deals with the random number of users under a group of macrocells and maximizes the average throughput of the users. This user centric approach requires a functioning area with a small group of active macrocells and random active users. The RNC (Radio network controller) defines the functioning area traffic and for a particular period based on the


















































Consider the number of active MBS users in the functioning area is N. M is the number of active MBS in the functioning area. Based on the case analysis in DRAMA, the throughput under any active macrocell is determined by;

dm Nm

Cm =

(1)

d m = N RB CRB Df log 2 (1 + aSINRm,r ) where, C m , N m N RB , C RB , Df and a are the throughput of user under mth macrocell service, number of active user under mth macrocell, number of resource block, sub-carrier per resource block, sub-carrier spacing and constant for target Bit Error Rate (BER), respectively. For maximum throughput for each user in the functioning area, the maximization problem can be stated as: 

dm

å Cm =

(2)

Nm

max

Subjected to å Cm » å Ci and å N m » N 















max

Let,

SINRm + k m = SINRi

(3)

where, k m ¥ M N where, Km is the adjacent variable to maximum SINR as the number of users are not always distributed properly within the functioning area. The value of Km is depended on the number of active users, active marcocell in the function area and the distribution of users in the particular time of the operation. Table 1 describes the brief structure of the algorithm. III. SYSTEM MODELLING

% M number of MBSs are active in the system for n=1 to N n number of users gets active and looks for the strongest cell coverage while i=0 nth user connects to mth MBS, calculate SINRm if SINRm + km SINRi+ki i=1 else Nm=Nm-1 Ni=Ni+1 (i=j: SINRj is the max SINR) end if end while end for 

OFDMA in downlink is robust against multi-path interference and frequency selectivity. It facilitates frequency domain scheduling, co-channel deployment and advanced MIMO techniques [8]. In this paper, a spectrum sharing two-tier OFDMA downlink network is considered by using MATLAB in an area of 800m x 800m consisting of 5 MBS. Random number of active users are distributed in the functioning area using homogeneous Poisson Point Distribution (PPP) at different frequency each time. During the selection process, users are considered to be at least 1 m apart from the MBS. The path-loss among the users and the cells are calculated by using 3GPP-LTE standard HATA small/large city model [9]. For outdoor users: PL hata (dB) = 69.55 + 26.16 log10 ( f c [MHz ])

(4)

+ [44.9 - 6.55 log10 (hMBS )] log10 d Km



Considering the number of active users under mth and ith macrocell are constant N m = Ni = k From equation (2); å SINRm » å SINRi max

Modified - Dynamic Resource Allocation Management Algorithm

- [1.56 log10 ( f c [MHz ]) - 0.8]

m=1

max 

ALGORITHM

- 13.82 log10 (hMBS ) - [1.1 log10 ( f c [MHz ]) - 0.7]hm

M

max

TABLE I.

where f c , hMBS , hm and d Km are frequency in MHz, MBS antenna height, mobile equipment height and distance between MBS and mobile equipment in Km, respectively. For outdoor path-loss scenarios in line-of-sight (LOS) state, WINNER II channel models is considered [10]. æ f [GHz ] ö ÷÷ (5) PL LOS (dB) = 18.7 log10 d m + 46.8 + 20 log10 çç c è 5.0 ø

where f c and d m are the frequency in GHz and the distance between users and MBS, respectively. For n number of macro users (outdoor users) and k number of sub-carriers, the expression of SINR is:

SINRm,k =

PM ,k Gm, M ,k N 0 Df + åF Pi ,k Gm,i ,k

where, N 0 , Df , PF ,k , PM ,k , Gm, M ,k and

(4)

Gm,i ,k are

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white noise power spectrum density, sub-carrier spacing, gain (depends on path-loss), transmitting power of neighboring MBS, transmitting power of the serving MBS, channel gain between the macro user and their serving MBS on subcarrier k and channel of macro user and neighboring MBS on sub-carrier k , respectively [11]. Time Division Duplex (TDD) Transmission mode is considered and assumed for the macro links. Nowadays, users can support searching function to distinguish between the neighboring cells for better capacity. The user can switch from a serving cell to other cell in order to increase its capacity.

affects the users that are sharing the same macrocell. Implementation of M-DRAMA divides the users based on the capacity and number of active users under the macrocells in the functioning area. It provides the average level throughput to all the users without giving any higher priority to any particular users.

III. Results and Discussion The simulations are event-based and developed according to 3GPP standards. The plotted values are an average of 1000 independent simulations. The assumed system parameter for the simulation is given in Table 1. TABLE 1 SYSTEM PARAMETERS System Parameters Cell layout Number of MBS Number of active user Inter cite distance Range of MBS Shadowing std. deviation Shadowing correlation Channel Model Channel Estimation MBS Antenna height User equipment height Frequency Bandwidth Sub-carrier spacing MBS transmission power Macro antenna gain Users antenna gain Users Noise figure Distribution time interval Random active user arrival intensity White noise power density Modulation Scheme Number of Resource block Sub-carrier per resource block Resource block size BER UE speed (Max. Doppler frequency) Scheduling algorithm

Value/Range Omni-directional grid 5 50-400 Min. 200 m 400m 8 dB 0.5 3GPP SCM Ideal 30 m 1m 2 GHz 10 MHz 15 KHz 46 dBm 13 dBi 0 dBi 9 dB 500 1.5 -174 dBm/Hz 64-QAM 50 12 180 Khz 10-6 3 Km/h (5.55 Hz) Proportional fairness (PF)

The performance of the M-DRAMA is evaluated with different number of users deployed in the functioning area. By varying the number of users, the throughput of the users is measured in comparison to the general approach of resource allocation. In the general approach, the users connect with the closest macrocell without any prior knowledge of the available resources. The cell selection is performed based on the path-loss or SINR. This process is outperformed when the number of users gets active near to one particular macrocell. This

Fig. 2: Average throughput of 200 users in CDF

Fig. 3: Average throughput of 400 users in CDF

Figure 2 and 3 shows the Average throughput in Cumulative density function (CDF) of 200 users and 400 users, respectively. In both cases, there are 5 macrocells active in the functioning area. In the regular allocation approach, the users are connected to the nearest macrocell that lacks of proper distribution of users centrally. For both 200 and 400 users, the higher number of users gets better throughput with less fluctuation in the curve. M-DRAMA ensures better throughput for higher number of users reducing the discrimination of the service among the users. One of the reasons why the throughput gets higher in the MDRAMA is that it considers the number of users assigned under each macrocell and the path-loss of the users to the neighboring macrocell. If the number of users is high enough that the path loss to reach the next available macrocell will suppress the throughput of the

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connected (nearest) macrocell, the users will switch macrocell.

to predict the future behaviour of the macrocell for a particular number of users in the functioning area for a particular time period. This way, the resource allocation process can be more developed for dense heterogenous network. In the future studies, the posibiliy of machine learning algorithm in cell selection and resource allocation features will be studied. REFERENCES

Fig. 4: Average throughput of variable number of users

Figure 4 illustrates the average throughput of variable number of users. Since the number of active macrocell is fixed for a given time interval, the average throughput of the users decreases along the increasing number of users. However, the average throughputs of the users are always high in the M-DRAMA configuration. As because the system calculates the path-loss and the un-utilized capacity of the nearby macrocell, it increase the throughput by balancing or assigning users intelligently. The size of the functioning area depends on the number of active users in the peak hours and the capacity of the macrocells. In this paper, the cell layout is considered as omni-directional grid. Using sector antennas will increase the capacity 3 times. In that case, algorithm can be utilized more precisely for assigning users, as more sectors will be available in the functioning area. Based on the systems processing unit, the functioning area can be enlarged up to 50 macrocell that will be more optimum solution for resource allocation in macrocell coverage. However, for multicell existence in the network, which is more of a practical case, both DRAMA and M-DRAMA can be deployed centrally. In that case, the marcocell will have more option to balance by exchanging active users with the smaller cells like: picocells and femtocells. VII. CONCLUSION In this paper, Modified Dynamic Resource allocation Management Algorithm (M-DRAMA) is presented for the macrocell network which is the modified version of DRAMA designed for macro-femtocell network. The proposed algorithm distributes the resources cognitively by assigning users to each marcocell beased on their pathloss and available resources in the macrocell. Definig a functioning area, macrocells share the active users among themselves to ensure the hight possible average throughput. The simulated results show a better performance of M-DRAMA beacuase of the intelligent cell selection process over the traditional cell selection process. However, more development can be done by using both DRAMA and M-DRAMA in a multi-cell enviroment. Machine learning algorithm can be included

[1] X. Chu, Y. Wu, L. Benmesbah, and W.-K. Ling, "Resource allocation in hybrid macro/femto networks," in Wireless Communications and Networking Conference Workshops (WCNCW), 2010 IEEE, 2010, pp. 1-5. [2] C.-Y. Oh, M. Y. Chung, H. Choo, and T.-J. Lee, "Resource Allocation with Partitioning Criterion for Macro-Femto Overlay Cellular Networks with Fractional Frequency Reuse," Wireless Personal Communications, vol. 68, pp. 417-432, 2013. [3] A. U. Ahmed, F. B. Aziz, T. M. Masum, M. Jahan, M. M. Rahman, C. Reza, et al., "Resource allocation in hybrid access control femtocell network targeting inter-cell interference reduction," in Electrical Information and Communication Technology (EICT), 2013 International Conference on, 2014, pp. 1-5. [4] R. Ratasuk, D. Tolli, and A. Ghosh, "Carrier aggregation in LTE-Advanced," in Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st, 2010, pp. 1-5. [5] M. C. Ertürk, I. Güvenç, S. Mukherjee, and H. Arslan, "Fair and QoS-oriented resource management in heterogeneous networks," EURASIP Journal on Wireless Communications and Networking, vol. 2013, pp. 1-14, 2013. [6] I. E. Givoni and B. J. Frey, "A binary variable model for affinity propagation," Neural computation, vol. 21, pp. 1589-1600, 2009. [7] A. U. Ahmed, M. T. Islam, and M. Ismail, "A Review on Femtocell and its Diverse Interference Mitigation Techniques in Heterogeneous Network," Wireless Personal Communications, pp. 1-22, 2014. [8] X. Chu, Y. Wu, D. López-Pérez, and X. Tao, "On providing downlink services in collocated spectrumsharing macro and femto networks," Wireless Communications, IEEE Transactions on, vol. 10, pp. 4306-4315, 2011. [9] W. Debus and L. Axonn, "RF Path Loss & Transmission Distance Calculations," Axonn, LLC, 2006. [10] J. Meinilä, P. Kyösti, T. Jämsä, and L. Hentilä, "WINNER II channel models," Radio Technologies and Concepts for IMT-Advanced, pp. 39-92, 2009. [11] C. Bouras, V. Kokkinos, K. Kontodimas, and A. Papazois, "A simulation framework for LTE-A systems with femtocell overlays," in Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, 2012, pp. 85-90.