Fractional Frequency Reuse for Hierarchical Resource Allocation in Mobile WiMAX Networks
© T. Ali-Yahiya and H. Chaouchi. 2010
Received: 30 July 2009
Accepted: 8 February 2010
Published: 10 May 2010
We propose a frequency planning based on zone switching diversity scheme for multicell OFDMA mobile WiMAX networks. In our approach, we focus on the use of Fractional Frequency Reuse (FFR) for guaranteeing the quality of service for the different service flows in the system. We investigate an architecture that coordinates the allocation of resources in terms of slots (the basic allocation unit in time and frequency domain in an OFDMA frame) between the Radio Resource Controller (RRC) and the Radio Resource Agent (RRA) which resides in the Base Station (BS). The proposed algorithm attempts to capture three types of diversity, namely, mutual interference diversity, traffic diversity, and selective fading channel diversity. As a consequence, the proposed algorithm for slot allocation makes a trade-off between maximizing overall throughput of the system while guaranteeing the Quality of Service (QoS) requirements for a mixture of real-time and non-real-time service flows under different diversity configurations. Our algorithm is evaluated under various cell configurations and traffic models. The results reveal important insights on the trade-off between cell interference suppression and QoS assurance.
One of the major concerns in mobile WiMAX (based on IEEE 802.16e standard) networks is the optimization of radio resource utilization, which can be enhanced by the frequency reuse when multicells scenarios are deployed. This is due to the use of Orthogonal Frequency Division Multiplex (OFDMA) for the inherent robustness to time dispersion of the radio channel .
Mobile WiMAX supports Orthogonal Frequency Division Multiple Access (OFDMA) communication system where frequency reuse of one is used, that is, all cells/sectors operate on the same frequency channel to maximize spectral efficiency. However, due to heavy cochannel interference (CCI) in frequency reuse one deployment, MSs at the cell edge may suffer degradation in connection quality. With mobile WiMAX, MSs operate on subchannels, which only occupy a small fraction of the whole channel bandwidth; the cell edge interference problem can be easily addressed by appropriately configuring subchannel usage without resorting to traditional frequency planning .
Resource allocation in multicell OFDMA networks has been developed in several works using Fractional Frequency Reuse (FFR). However, only few contributions have explicitly taken into account the nature of application being either real-time or non real-time. For example, authors in [3–6] proposed dynamic resource allocation scheme for guarantying QoS requirements while maximizing the whole throughput of the system. However, both schemes work only for non real-time application References [7–9] introduced the Radio Network Controller (RNC) to control a cluster of Base Station (BSs) in the multicell OFDMA system and to allocate resources in a distributed way however, these schemes allocate resources in the RNC without taking into account the reallocation scheme at each BS for coordinating resource according to the FFR. Authors in [10, 11] proposed a local resource allocation the BSs in a random way without taking into consideration the RNC. Thus the BS has not a global view about the adjacent cells in the system, leading to inefficient resource allocation.
In this paper, we propose a radio resource allocation scheme for multicell OFDMA downlink mobile WiMAX systems. Our scheme consists firstly of a hierarchical architecture based on message exchanges between Radio Resource Agent (RRA) at the Base Stations (BS) and Radio Resource Controller (RRC) which controls a cluster of BSs. The RRC coordinates the Intercell Interference (ICI) considering the types of service flows (SFs) and their Quality of Service (QoS) requirements at superframe level, whereas BSs allocate slots in each cell at frame level in a fair way using slot reallocation strategy between MSs at inner cell and outer ring cell.
The rest of paper is organized as follows. In Section 2, Subchannalizestion and Zone Switching Diversity in Mobile WiMAX are introduced. Section 3 describes the system model. Section 4 presents our hierarchical approach for resource allocation. Sections 5 and 6 present our simulation results and conclude the paper.
2. Zone Switching Diversity Overview in Mobile WiMAX
The mobile WiMAX physical layer is based on OFDMA which divides the very high rate data stream into multiple parallel low rate data streams. Each smaller data stream is then mapped to individual data subcarrier and modulated using some Phase Shift Keying Quadrature Amplitude Modulation (QPSK, 16-QAM, and 64-QAM) .
However, the available subcarriers may be divided into several groups of subcarriers called subchannels. Subchannels may be constituted using either contiguous subcarriers or subcarriers pseudorandomly distributed across the frequency spectrum. Subchannels formed using distributed subcarriers provide more frequency diversity . This permutation can be represented by Partial Usage of Subcarriers (PUSC) and Full Usage of Subcarriers (FUSC) modes. The subchannelization scheme based on contiguous subcarriers in mobile WiMAX is called Band Adaptive Modulation and Coding (AMC). Although frequency diversity is lost, band AMC allows system designers to exploit multiuser diversity, allocating subchannels to users based on their frequency response .
3. Proposed Design for Mobile WiMAX Network Architecture
Even though the WiMAX Forum specified an architecture for resource allocation for mobile WiMAX systems , functions related to resource allocation using fractional frequency reuse (FRR) are not described in such architecture. Therefore, in this section we propose new functionalities to be added to this architecture in order to enable a hierarchical approach for managing resources using the concept of FFR.
3.1. Radio Resource Allocation Model
We propose hierarchical approach for resource allocation for this architecture, and we add new information elements concerning SF types, their QoS requirements in terms of data rate, their channel qualities, and so forth. These information elements are collected by the RRA from all MSs which are in the inner cell or in the outer ring cell and then feedback to the RRC. The RRC utilizes such information to calculate the soft reuse factor in each cell. Then it sends its decision to the RRA of each cell, such decision includes the specific set of slots assigned to the MSs in the outer ring and in the inner cell. Upon receiving the decision, the RRA at the BS will make the actual pairing between slots and MSs based on their actual traffic load and employ a policy for load distributing among the MSs when it is necessary. Thus, depending on our architecture, information exchanged between RRA and RRC can be either information reporting procedures which is used for delivery of BS radio resource indicators from the RRA to the RRC or decision-support procedures from RRC to RRA which is used for communicating decision that may be used by the BS for resource allocation.
3.2. Link Model
3.3. Problem Formulation
4. Hierarchical Resource Allocation Approach—HRAA
We propose in this section a Hierarchical Resource Allocation Approach (HRAA) at both the RRC and the BS. The cooperation between both the RRC and the BSs is necessary since each BS has to provide information to its associated RRC. Message exchanges between RRC and BS enable RRC to decide how to allocate resources among all the BSs in the system.
4.1. Resource Allocation at RRC
where is the average traffic rate for connection . In essence, this allocation exploits multiuser diversity by allocating more slots to the SFs with better channels. For instance, let us assume that the average traffic rate of all connection is the same, then the factor is equal to one. A connection with relatively good channel conditions, that is, its , will initially allocate two or more slots. On the other hand, a MS with relatively bad channel conditions will initially allocate only one slot. The role of weighting factor is to weight the allocation proportional to SF's average rate.
The next step to be achieved by the RNC is slot assignment among MSs at the inner and outer ring cell. The RNC performs the assignment first for the MSs in the outer ring then the MSs in the inner cell. Each MS has one SF, that is, there is one-to-one mapping between an MS and its SF through a connection. Since UGS has strict QoS constraints, therefore, we prioritize it over all other types by allocating first the best slots to it. We proceed in slot allocation as follows.
(2)Calculate the number of slots for each SF as in (10).
(4)Allocate the residual slots with to the remaining SFs prioritizing the real-time SFs (rtPS and ertPS) over the others. First allocate the best slots to rtPS and ertPS until their maximum sustained traffic rates are achieved. Then, allocate the slots to nrtPS up to their maximum sustained traffic rate. The algorithm of resource allocation is described in Algorithm 1.
Algorithm 1: Resource allocation at RNC
and ertPS over nrtPS
4.2. Resource Allocation at the BS
At this level of resource allocation, each BS receives its assignment information concerning slot offset for each MS in the inner and outer ring cell. Accordingly,each BS will do the following steps to assure fairness and a good level of satisfaction for each SF in terms of data rate (see Algorithm 2).
Check the level of satisfaction for each MS in terms of number of slots.
Initiate the set of the dissatisfied MSs associated with rtPS, ertPS, and nrtPS in both inner and outer ring cell. The dissatisfaction of these MSs is due to the insufficient resource (slots) as the allocation for rtPS, ertPS, and nrtPS is done with the maximum data rate for the outer ring MSs.
Reallocate the slots to guarantee the minimum reserved traffic for all dissatisfied SFs. This is done by searching the slots already allocated to the satisfied MSs and reallocating them to the dissatisfied ones starting by rtPS and ertPS SFs. If this reallocation does not lead to a violation of minimum reserved data rate for the satisfied MSs, then the reallocation will continue until all the SFs are satisfied.
Algorithm 2: Resource allocation at BS
throughput in that slot
5. Numerical Results
In this section, we present simulation results to illustrate the performance of our proposed algorithms. We use certified system parameters proposed by WiMAX Forum in order to simulate realistic environment and wireless communication system in Mobile WiMAX .
5.1. Simulation Environment
Subcarrier frequency spacing
Number of null/guard band subcarriers
Number of pilot subcarriers
Number of used data subcarriers
DL/UL frame ratio
OFDM symbol duration Number
Data OFDM symbols in 5 ms
QPSK, 16-QAM, 64-QAM
Number of MSs
UGS maximum traffic rate
rtPS traffic rate
5 Kbps–384 Kbps
nrtPS traffic rate
0.01 Mbps–100 Mbps
6-tap Rayleigh Fading
5.2. Simulation Results
Performance is measured first in terms of cell throughput, which is the total throughput divided by the number of cells in the system. Moreover, we consider three different allocation strategies which are then compared. The first one is uncoordinated in the sense that there is no RRC and the resource allocation is based on local information, we refer to this method as "Random" as slots are allocated randomly among MSs. The second scheme is a coordinated allocation where the RRC algorithm is executed every superframe but once each BS receives the slots assignments from the RRC then it follows these recommendations and no slot reallocation takes place which is referred as to "RRC+BS". Finally, the third scheme considers both RRC and BS for load distributing among MSs, this is referred to as "RRC+LD".
In this paper, we proposed a slot allocation scheme for multicell OFDMA mobile WiMAX system using zone switching diversity method. Based on our scheme we proposed an architecture in which resources are allocated in a hierarchical way. By using fractional frequency reuse in our scheme, QoS requirements for the different SFs in the inner and outer ring cell are guaranteed. Our scheme does not only coordinate the inner-cell interference but also utilizes opportunistic scheduling to increase the overall throughput of the system while guaranteeing QoS needs in terms of delay for ertPS SFs and packet loss rate for rtPS SFs. In our future work we will include MIMO technology in our scheme of resource allocation.
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