IISRA: Inter-cell Interference Separation-based Resource Allocation for VoLTE

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1 IISRA: Inter-cell Interference Separation-based Resource Allocation for VoLTE Byungkab Jo, Taejun Park, Wonbo Lee,BoRyu, and Sunghyun Choi Department of ECE and INMC, Seoul National University, Korea EpiSys Science, USA {bkjo, tjpark, Abstract Voice over LTE (VoLTE), adopted as a standard technology by 3GPP is confronted by a number of challenges due to different Quality-of-Service (QoS) requirements as well as the Inter-Cell Interference (ICI) problem. In this paper, a novel Resource Allocation (RA) strategy for improving VoIP capacity in LTE uplink system is proposed. Our strategy improves the VoLTE performance simply by prioritizing packets according to Signal to Generating Interference plus Noise Ratio (SGINR) based metric, and at the same time, allocating the ordered packets to Resource Blocks (RBs) in a predefined way, which protects vulnerable users with low uplink received signal strength (RSS) and separates threatening users in each cell into different RBs in an orthogonal manner. Through system level simulation, we demonstrate that our resource allocation scheme outperforms commonly-used frequency reuse-based schemes by increasing the VoIP capacity by up to 2%. Keywords LTE, VoIP Capacity, Resource Allocation, Scheduling, Multi-cell environment I. INTRODUCTION Since LTE systems are designed to operate in fully packetswitched networks, Voice over Internet Protocol (VoIP) on top of LTE (VoLTE) is adopted as a standard technology by 3GPP. The VoLTE service is confronted by many challenges due to stringent QoS requirements, such as low latency and low tolerance to loss, and due to certain traffic characteristics including silence suppression and periodic small-sized packet generation. Moreover, ICI problem is critical to the system performance especially for small cell environments. Generally, the LTE uplink performance is known to be worse than the downlink performance due to mobile devices limitation, and consequently, is a bottleneck in implementing VoLTE services. We, therefore, focus on improving the uplink capacity of VoLTE services in this paper. Not many studies have been conducted for RA strategies for VoLTE while so far most papers dealing with VoIP scheduling on LTE systems focus on downlink and/or Semi-Persistent Scheduling (SPS). In [], [2], the authors propose and evaluate the SPS scheme for VoIP in LTE systems. The SPS is used as a baseline scheduling scheme in our work. However, there are still many problems beyond it to enhance the VoIP capacity, because allocating resources in the frequency domain along with SPS is an open problem. In [3], a simple concept of user priority based scheduling and a frequency-domain resource allocation method for Orthogonal Frequency Division Multiple Access (OFDMA)-based systems are presented. They, however, only deal with Time Division Duplex (TDD) OFDMA systems, which have different characteristics from Frequency Division Duplex (FDD) based systems. In [4], the authors introduce and evaluate several VoIP RA algorithms based on the SPS scheme on LTE uplink systems. However, they do not present the methodology about how to mitigate ICI among different cells, which can be an important issue considering realistic channel condition in multi-cell environments. Fractional Frequency Reuse (FFR) is a widely-used mechanism to mitigate ICI. Although the FFR scheme effectively mitigates ICI, the spectral efficiency becomes worse because it prohibits the User Equipments (UEs) which are allocated to a certain subband from using other subbands even when the traffic load of the other subbands is relatively low. To tackle this problem, various alternative schemes such as dynamic FFR [5] and soft frequency reuse (SFR) [6] have been introduced. These schemes try to mitigate the limitation of FFR by dynamically adjusting the borders of subbands (dynamic FFR) and making the subbands which are allocated to innercell UEs partly overlapped among neighboring cells (SFR). These schemes still need strict classification of UEs (e.g., typically according to UEs location) at least on a transmit time interval (TTI), and it leads to a low spectral efficiency. In [7], which inspired our work, a strategy aiming at mitigating ICI as well as the ICI fluctuation based on FFR is proposed. In this work, packets, which are prioritized based on remaining delay bound, are firstly allocated to the confined frequency band, and then allocated to other bands where other neighboring cells have higher priority. However, there are still limitations on utilizing the system bandwidth and packet prioritization rule for separating ICI. Therefore, we propose a novel resource allocation strategy to overcome such limitations for VoLTE traffic. The rest of the paper is organized as follows. In Section II, we describe the problem formulation and system modeling. In Section III, the proposed resource allocation strategy, IISRA, is explained in detail. Then, the proposed scheme is evaluated via system level simulations in Section IV. The paper concludes in Section V. II. A. VoIP Framework SYSTEM MODEL In this paper, we adopt the VoIP model and parameter set mostly from [8]. The state of each VoIP session alternates between active state and silent state. VoIP packets of 4 bytes are generated every 2 ms in the active states and Silence Insertion Descriptor (SID) packets of 5 bytes are generated every 6 ms in the inactive states. Detailed VoIP parameters including duration of each state, codec, packet sizes, and intervals are shown in Table I, and the 2-state voice

2 2 TABLE I: VoIP model and parameters [8]. Parameter Characterization PDF of a state duration f(x) =λe λx, for x, /λ =2(s) Codec RTP AMR 2.2 kbps Encoder frame length 2 ms Voice activity factor (VAF) 5 % SID payload 5 bytes (5 bytes + header) Protocol overhead bits + padding (RTP pre-header) with compressed header 4 bytes (RTP/UDP/IP) 2 bytes (RLC/security), 2 bytes (CRC) Total voice payload 4 bytes (AMR 2.2) Fig. : An example of Semi-Persistent Scheduling (SPS). activity model presented in [8] is adopted. The VoIP capacity is determined by the number of VoIP users, where at least 95% of the users are satisfied. A user is satisfied if the user experiences the packet error rate under 2% [9]. B. VoIP Scheduling in LTE Uplink In a conventional dynamic scheduling, both initial transmissions and retransmissions of VoIP packets are scheduled dynamically (i.e., RBs-UEs allocation can be changed for every transmission opportunity). Therefore, the benefits of frequency selective scheduling (or resource allocation), i.e., achieving higher spectral efficiency by allocating resources to the UEs with relative good channel quality, can be fully exploited. In dynamic scheduling, however, a large number of downlink control channels (i.e., Physical Downlink Control Channel or PDCCH) should be consumed to support a large number of VoIP UEs, and it can become a bottleneck. For that reason, 3GPP has adopted the SPS as a scheduling method for VoLTE. The concept of the SPS can be explained as follows: persistent scheduling for initial VoIP packet transmissions in active state and dynamic scheduling for both VoIP packet retransmissions and SID transmissions. The resource allocation via SPS is illustrated in Fig.. Suppose UE and UE 2 are allocated to resources for their initial VoIP packet transmissions in the first subframe. If the initial transmissions of VoIP packets in the active state fail, retransmissions via Hybrid Automatic Repeat request (HARQ) occur in 8 ms interval via control channel signaling (i.e., dynamic allocation). For the retransmissions, the dynamic scheduling is applied. The second VoIP packets of the UEs are generated in 2 ms after the first packet transmissions. At this time, the UEs are allocated to the same frequency/time resources for the initial transmissions as those used for the initial transmissions of their first packets (i.e., persistent allocation). C. System Model and Resource Allocation Principle In this paper, we consider LTE uplink systems in multicell environments, where each cell site has one enodeb(enb) and three sectored cells with sector antennas. Each sector antenna covers one of three cells and has an antenna pattern as specified in []. The resource allocation unit is a pair of Resource Blocks (RBs) spanning one subframe of ms in the time domain and 2 sub-carriers in the frequency domain as described in []. As a basic scheduling method, we adopt SPS with synchronous adaptive HARQ. In every Transmission Time Interval (TTI) of ms, the persistently scheduled packets are allocated first according to their previously allocated RB positions in the frequency domain, and the retransmission packets can be dynamically allocated to the rest of RBs. D. Uplink Fractional Power Control The path-loss estimation should be done for estimating uplink path-loss in order to compensate it. According to [2], [3], the downlink pathloss estimate calculated in the UE is used for the uplink power control. It is based on the observation in [] that the path-loss difference between downlink and uplink is negligible in terms of the uplink power control accuracy. The downlink path-loss is calculated by the UE averaging the measured instantaneous path-loss based on reference signal received power (RSRP) [3]. E. Uplink Channel Estimation Principle Basically, an enb estimates the channel quality by measuring Sounding Reference Signal (SRS) transmitted by UEs. The enb may configure a UE to transmit SRS periodically, and the periodicity is from 2 to 32 ms. The SRS spans the whole or part of system bandwidth (i.e., referred to as SRS bandwidth ). We assume in this paper that the SRS bandwidth is four RBs, which is the smallest SRS bandwidth supported in LTE systems [2]. The uplink pathloss estimation may not be accurate by measuring the SRS from a UE transmitting the larger SRS bandwidth because it may be confronted by the P max constraint, while in case of using the smallest SRS bandwidth, UEs transmit several SRSs to provide channel information covering the whole system bandwidth in a frequency hopping method. We also assume that periodicity for the SRS transmission is 32 ms, which is long enough for all active UEs in a cell to transmit their SRSs. III. IISRA: PROPOSED RESOURCE ALLOCATION STRATEGY In this section, we propose our strategy, called Inter-cell Interference Separation-based Resource Allocation (IISRA), which effectively improves VoIP capacity by mitigating the ICI and optimizing the frequency selective scheduling gain. In VoIP, the number of RBs which are allocated to each packet is small. Using this characteristic, IISRA separates each cell s major interference sources into different frequency regions which are assigned to each cell of a cell site. By applying our simple prioritization strategy, the ICI is effectively mitigated between cells while not strictly limiting cells frequency resource usage into a confined area.

3 3 A. Basic Strategy The basic strategy of IISRA is divided into two steps: ) Packet Prioritization: the scheduler determines the scheduling priority among candidate packets waiting for transmission according to their packet generation interval; 2) Frequency Allocation Prioritization: After prioritization, each packet is allocated to the frequency resources in turn in a predefined way. B. Packet Prioritization Rule We first protect the cell-edge UEs which are located in the region with low RSS values. We also separate the UEs giving high ICI. Considering these two factors leads us to an idea to leverage the concept of the SGINR [4] to separate the UEs with low RSS and high ICI generation among neighboring cells. The ICI separation strategy of IISRA reduces the probability of UEs with low RSS to be affected by the high ICI generating UEs from neighboring cells. We, in this paper, use the following SGINR formula motivated by [4]. Definition. The SGINR of UE k s packet is defined as: SGINR k = RSS UL,k ICI max,k + N, () where RSS UL,k is the uplink RSS value from UE k, ICI max,k is the maximum ICI among those generated by UE k as detailed later, and N is the white Gaussian noise term. According to the 3GPP standard [5], each UE is supposed to report the measured RSRP of neighboring enbs to its serving enb in a regular basis. With this measurement reports of UEs, the schedulers in their serving enbs can recognize each UE s second closest neighboring cell and the downlink RSRP from the neighboring cell. Therefore, the serving enb can learn the biggest victim cell caused by the UE s uplink transmission. Since the term ICI max,k is a value which only the neighboring enbs can measure, we substitute it with the reported downlink RSRP of the recognized victim cell. The downlink RSRP information can be effectively used because UEs with higher RSRP values have a higher probability of generating higher inter-cell interference to the neighboring cell. As a result, the main packet prioritization metric should be the SGINR values of the packets in an ascending order, i.e., the packet of a UE with the lowest SGINR has the highest priority. However, if we stick to only the SGINR metric when we make priority order of packets, the retransmission packets or allocation failed packets of UEs with relatively high SGINR values will keep having a lower priority. It can result in the consecutive transmission failures of those packets. Therefore, we take additional priority metrics into consideration. We first classify packet transmission failures into three cases: (i) failure of resource allocation for the initial packet transmission, which is to be persistently scheduled, (ii) failure of resource allocation for the SID packets or retransmission packets, and (iii) packet transmission failure due to channel errors. We refer to Case (i) as the initial allocation failure and to Cases (ii) and (iii) as the transmission failure. According A low priority packet might fail to get allocated resources due to the lack of resources at a given TTI. to the number of failures of each case above, we define the packet prioritization metric as follows: Definition 2. The packet prioritization metric, M k, is defined as: M k = SGINR k (in db) ω re,k N re,k ω af,k N af,k, (2) where ω re,k and ω af,k are weighting factors, and N re,k and N af,k are the numbers of retransmissions and initial allocation failures of packet k, respectively. We observe that the initial allocation failures are more critical than the transmission failures. This comes from the property of VoIP and SPS. VoIP packets are generated every 2 ms and each packet should be transmitted within 5 ms, as specified in [6] assuming the target end-to-end delay below 2 ms for mobile-to-mobile communications. In SPS, newly generated packets by a UE in the active state are persistently scheduled using the same frequency resources every 2 ms. Therefore, additional delay due to the initial allocation failure causes delay to all the subsequent new packets. That reduces the delay budget of all the packets for retransmissions. Considering this, we give more weight to the initial allocation failures than the transmission failures by setting ω af,k and ω re,k to one and ten respectively. Optimal weighting factors should depend on the environment, and we leave it as future work. The metric M k is used for sorting the scheduling priorities of packets in a descending order. ω re,k and ω af,k are the tuning parameters which are in the range of the maximum difference of SGINRs so that SGINR metric mainly decides priority. C. Frequency Allocation Prioritization Rule After the packet prioritization is done, the scheduler sets priority for frequency allocation. ) Subband Division and Operations in HP band: First, the whole system bandwidth is divided into three subbands. Here we name the three subbands as HP band, MP band and LP band /footnotehigh Priority band, Mid Priority band, Low Priority band. Each cell of a cell site is assigned to HP band in an orthogonal manner, e.g., 8 RBs per cell with 5 MHz system bandwidth 2. Then, the allocation starts from the center of the HP band. spreads out to the outer resources in HP band and the traverse direction alternates from right to left and vice versa. This allocation rule is depicted in Fig. 2. 2) Operations in other subbands: When the frequency resources in the HP band depletes, allocation in other subbands begins. The scheduler allocates each packet to the resources in a way that the packets avoid being allocated in the other subband which is the biggest interference victim cell s HP band. We call the avoided subband as LP band. InFig.3, the third cell of cell site C and second cell of cell site E are the biggest victims of uplink signal from UE a and UE b respectively. Then LP band of UE a and UE b are Subband 2 and Subband 3 respectively, i.e., HP band of the third cell and second cell, which end up being less interfered from UE a and UE b s signals respectively. We also define Subband 2 and Subband 3 as MP band of UE a and UE b respectively. 2 5 MHz system is composed of 25 RBs in the frequency domain, and in this paper we assume that RB is allocated for control channel

4 4 Cell site X Fig. 2: Frequency allocation prioritization; in the figure, Pkt#n represents that the corresponding packet is given the nth priority according to the packet prioritization metric. C 3 I max a D A Fig. 3: Comparison of interference toward neighbors by UE locations. In Fig. 2, when UE b s packet priority index is, UE b is allocated to its MP band, i.e., Subband 3. and UE a is allocated to Subband 2 for the same reason. 3) Interference Separation: The numbers in the boxes in Fig. 2 represent the packet indexes which are sorted by the packet prioritization metric. The lower the number is, the higher scheduling priority is given. When the packet is allocated to resources in MP band and LP band, the resource traversing order is reversed to the order in its HP band, i.e., from the outer positions to the center position. The reason why doing in this way is to allocate the cell edge UE s packet, which usually has a high priority index, into the RB position, where the cell center UE s packet is allocated in the neighboring cell, which usually has a low priority. Then, frequency resources for the high interfering UE are overlapped with the resources for the less vulnerable UE in the neighboring cell, and hence, the interference can be effectively separated. 4) MCS Selection and Resource Allocation: According to the frequency allocation priority, the packet finds its appropriate RBs starting from the center of HP band. To find the optimum RA strategy, the Modulation and Coding Scheme (MCS) adaptation 3 is also performed. The scheduler first tries 3 Depending on the employed MCS, a VoIP packet of 4 bytes requires one to four RBs. b 3 I max 2 E to allocate a candidate packet with the highest MCS. If it fails to allocate because expected packet drop probability is higher than threshold, the scheduler tries the next MCS, following the frequency allocation prioritization rule. Considering that outage criteria of VoIP, i.e., at least 2% of packets from a UE should be received correctly, we set the threshold as 2%. UE k s packet drop probability, P k,drop,isdefinedas P k,drop = Pk,err n, (3) RB end Pk,err n = [ P k,err,r (γ k,r )], (4) r=rb start where P k,err is the UE k s expected packet error probability, which is an error probability for a single transmission. and n is the remaining number of transmission chances including transmission chance at the present time. P k,err,r is the rth RB s error probability, which is a function of SINR of the rth RB, γ k,r. RB start and RB end are the first and the last RB indexes allocated to UE k s packet, respectively. D. Enhancing SINR Estimation Accuracy In an interference-limited cell deployment scenario (e.g., small cells), estimating the SINR values accurately becomes more dependent on estimating the ICI. In a VoIP environment, however, each UE possesses only a small portion of resources in the whole system bandwidth. This peculiarity of VoIP packets rather than ordinary data packets makes estimating the ICI more difficult. By exploiting the fact that VoIP packet generation is periodic and the SPS is used as a basis scheme, we can enhance the SINR estimation accuracy by utilizing the 2 ms prior interference information on all the RBs. When the SPS is applied, the persistently scheduled UEs use the same frequency resource position every 2 ms. Therefore the 2 ms prior interference information can be more useful because the interference patterns according to the frequency band position are more likely to be the same as 2 ms ago. To validate our strategy, we simulate several interference reference methodologies including the 4 ms prior, which is a method referring to the 4 ms earlier interference information and the 2 ms sliding-window averaging, which utilize the averaged interference information during the 2 ms-long sliding-window. From the simulation result, we confirm that using the 2 ms prior interference information results in the best performance even though we do not show the result in this paper due to the lack of space. IV. PERFORMANCE EVALUATION In this section, the proposed IISRA is evaluated via a system level simulator developed with C++ to demonstrate that IISRA outperforms the other conventional schemes. A. Simulation Environment The simulation model and parameters are presented in Table II. We follow the system model previously presented in Section II. We use a BLER versus SINR curve obtained using the LTE uplink link-level simulator, reported in [7]. To evaluate IISRA scheme realistically, we implement the downlink and uplink reference signal generation both affected

5 5 TABLE II: Simulation environment [], [6]. Cell deployment Hexagonal grid 9 cell-site wrap-around, 3 cells per site Inter-site distance 75 m (3GPP urban macro case in []) System bandwidth 5MHz (24 RBs for PUSCH and RB for PUCCH) UE s max tx power 23 dbm (=P max) Channel model Path loss + Multipath fading + Shadowing Path loss log(distance in km) UE speed 3km/s Shadowing Log normal distribution with 8 db std. Correlation between sites/cells.5/. Noise figure 5 db (at enb) BS Receiver 2 antennas with MRC [ (Maximum Ratio Combining) enb antenna pattern G(θ) = min 2 ( ) ] θ 2 65, 2 (db) enb antenna gain 4 dbi Penetration loss db HARQ Synchronous adaptive with Chase Combining Power control min(p max,p + α PathLoss+log M ) P = 8 dbm,α =.8 Simulation time Number of subframes = 2, (2 s duration) by fadings. In our simulation, enbs generate and transmit the downlink reference signals and each UE measures them to determine which enb is the most adjacent one in terms of downlink RSRP. Likewise the periodic uplink reference signal transmission (i.e., SRS) of a UE is also implemented as explained in Section II-E. B. Comparison Schemes For the comparative performance evaluation of IISRA, the following comparison schemes are also evaluated: Proposed scheme without maximum interference information, referred to as IISRA w/o I max ; FFR schemes with protection ratio equal to.,.2, and.3, referred to as FFR., FFR.2, and FFR.3, respectively; Frequency Reuse Scheme with reuse factor equal to 3, referred to as Reuse 3 ; Two Greedy algorithm-based VoIP scheduling schemes introduced in [4], referred to as Random and LeastFit. The IISRA w/o I max is the same as IISRA except for not utilizing the generating interference information as well as the neighboring cell to which each UE gives the biggest ICI. Therefore, in this comparison scheme, ICI max,k,i.e.,the biggest ICI, in Eq. () is excluded in the packet prioritization metric in Eq. (2). Accordingly, when the HP band is not available to use, one of other two subbands is randomly selected. In FFR schemes, the protection ratio, i.e., the fraction of system bandwidth which is allocated to cell edge UEs, is set to.,.2, and.3 respectively so that the transmissions of UEs with the lowest RSS are protected. The number of dedicated resources is calculated as follows: N p = p +3p N total, (5) where N p is the number of dedicated resources for each cell, N total is the number of total resources, i.e., N total =25in 5 MHz band, and p is the protection ratio. To compare IISRA with non-ffr based schemes, we consider the Greedy algorithm based schemes [4]. Particularly, Random and LeastFit schemes are chosen which are shown to outperform the other schemes introduced in the paper. In these schemes, the scheduler recognizes available RB groups (referred to as space ) and UEs to be scheduled at every TTI. Referring to the channel quality estimated on all the spaces assuming a certain UE is allocated to those spaces, the scheduler tries different MCSs to fit the size of the transport block 4 ) for UE s packet to different spaces. In this procedure, the LeastFit algorithm chooses a UE-space set, which results in the largest difference between space length and the the UE s TB size while the Random algorithm chooses a UE-space set randomly. Note that the basic resource allocation strategy in each subband considered in the FFR schemes is based on this LeastFit algorithm. C. Simulation Results We consider the ratio of satisfied users as the main performance metric as it pertains to the widely used definition of VoIP capacity. A VoIP user is satisfied if more than 98% of VoIP packets are delivered from a UE to an enb within 5 ms. The number of users in each simulation run varies from 3 to 38 per cell. Fig. 4 shows the ratio of satisfied users. From the figure, we observe that the VoIP capacity, which is defined as the maximum number of users in a cell where more than 95% of the users are satisfied [8], [6], of IISRA is about 35 users per cell, while the other comparison schemes achieve lower capacities. We see that the performance of the FFR scheme is highly dependent on their protection ratio. According to the result, we also find that the best configuration of the protection ratio is around.2, however, IISRA still outperforms FFR.2. In the case of FFR, the optimal protection ratio may depend on environments, and hence the scheduler needs to repeatedly find and update the system parameter for the optimal protection ratio over time. IISRA, however, is designed to operate adaptively. The Greedy algorithm based schemes, namely LeastFit and Random, are located in the middle of lines. Since they do not consider ICI mitigation mechanism, it seems that they perform worse than both the FFR schemes and IISRA. We also see that the performances of Reuse 3 and FFR.3 are the worst due to their limited resource usage. The resource utilization ratio, which is defined as the average fraction of the utilized system bandwidth, is shown in Fig. 5. It is observed that FFR and Reuse 3 schemes achieve limited resource utilization compared with ICI coordination schemes. IISRA, however, utilizes resources more than FFR and Reuse 3 schemes so that it achieves better performance. In contrast, two Greedy algorithm-based schemes are shown to use more resources than the others despite their poorer performances. Note also that IISRA slightly outperforms IISRA w/o I max in terms of VoIP capacity. This proves the effectiveness of considering the generating interference as well as RSS in our RA strategy. Fig. 6 is the average ICIs over RBs with 32 UEs in a cell, which shows the ICI alignment effect of IISRA. In the 4 One or more consecutive RBs form a Transport Block (TB) containing data for one UE.

6 6 Ratio of satisfied users Resource Utilization Ratio IISRA IISRA w/o Imax FFR. FFR.2 FFR.3 Reuse 3 LeastFit Random Number of UEs per cell Fig. 4: Ratio of satisfied users. IISRA IISRA w/o Imax FFR. FFR.2 FFR.3 Reuse 3 LeastFit Random Number of UEs per cell Fig. 5: Resource utilization ratio. figure, the average ICI levels in each cell s HP band are lower than the levels in the other two subbands. In less interfered frequency resources, more vulnerable and at the same time more threatening UEs are allocated. By doing so, the intercell interference can be effectively separated in an orthogonal manner without limiting their bandwidth usage boundaries. This is the beauty of IISRA. V. CONCLUDING REMARKS In this paper, IISRA, a novel RA strategy for VoLTE, is proposed and evaluated. IISRA has strengths in its effects on ICI separation. The ICI mitigation is achieved by separating vulnerable and threatening UEs in each cell in an orthogonal manner. This strategy has already been considered in FFR based algorithms. However, the FFR-based schemes have a limitation in that they strictly set the frequency allocation boundaries, thus limiting the resource usage efficiency. Our proposed algorithm overcomes these limitations by simply adopting packet prioritization, and frequency allocation prioritization. Interference power (mw) [Sector ] [Sector 2] RB index RB index RB index Fig. 6: Average ICI over RBs [Sector 3] Recently, the effectiveness of SPS over dynamic scheduling is being argued among many entities in industry. Those arguments, however, do not explain how to estimate the ICI as accurately as possible in VoIP traffic with multi-cell scenarios. Intuitively, we assert that IISRA with SPS will better perform than dynamic scheduling because the ICI estimation becomes a very complicated problem when dynamic scheduling is employed. The proof and evaluation will be our future work. In addition, in our work, the number of UEs in a cell is fixed and is evenly applied to all the cells. If the cell loading changes, the situation may also change. In the future, we plan to consider different cell loading scenarios. REFERENCES [] D. Jiang, H. Wang, E. Malkamaki, and E. Tuomaala, Principle and Performance of Semi-Persistent Scheduling for VoIP in LTE System, in Proc. IEEE WiCom, Shanghai, China, Sep. 27. [2] H. Wang, D. Jiang, and E. Tuomaala, Uplink capacity of VoIP on LTE system, in Proc. IEEE APCC, Bangkok, Thailand, Oct. 27. [3] Z. Wang, Y. Wang, and F. Wang, Comparison of VoIP Capacity between 3G-LTE and IEEE 82.6 m, in Proc. IEEE PIMRC, Tokyo, Japan, Sep. 29. [4] M. Mühleisen and B. Walke, Evaluation and Improvement of VoIP Capacity for LTE, in Proc. European Wireless, Pozna, Poland, Apr. 22. [5] R. Y. Chang, Z. Tao, J. Zhang, and C.-C. Kuo, A Graph Approach to Dynamic Fractional Frequency Reuse (FFR) in Multi-Cell OFDMA Networks, in Proc. IEEE ICC, Dresden, Germany, Jun. 29. [6] T. Novlan, J. G. Andrews, I. Sohn, R. K. Ganti, and A. Ghosh, Comparison of Fractional Frequency Reuse Approaches in the OFDMA Cellular Downlink, in Proc. IEEE Globecom, Miami, Florida, USA, Dec. 2. [7] J. Lafuente-Martinez, A. Hernandez-Solana, I. Guio, and A. Valdovinos, Radio resource strategies for uplink inter-cell interference fluctuation reduction in SC-FDMA cellular systems, in Proc. IEEE WCNC, Quintana-roo, Mexico, Mar. 2. [8] R-7674, LTE Physical Layer Framework for Performance Verification, Orange, 3GPP TSG-RAN WG Meeting#48, Feb. 27. [9] Y.-S. Kim, An Efficient Scheduling Scheme to Enhance the Capacity of VoIP Services in Evolved UTRA Uplink, EURASIP Journal on Wireless Communications and Networking, vol. 28, 28. [] 3GPP TR v.3., Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Frequency (RF) System Scenarios (Release ),3GPP, Jun. 22. [] 3GPP TS 36.2 v.2., Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release ), 3GPP, Jun. 2. [2] 3GPP TS v.2., Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer Procedures (Release ), 3GPP, Jun. 2. [3] R-73355, E-UTRA Uplink Power Control Parameters and Simulation Results, InterDigital Communications, 3GPP TSG-RAN WG Meeting#5, Aug. 27. [4] B. O. Lee, H. Je, I. Sohn, O.-S. Shin, and K. B. Lee, Interference- Aware Decentralized Precoding for Multicell MIMO TDD Systems, in Proc. IEEE Globecom, New Orleans, LA, USA, Nov. 28. [5] 3GPP TS 36.3 v.., Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E- UTRAN); Overall Description; Stage 2 (Release ), 3GPP, Mar. 22. [6] 3GPP TR v9.., Evolved Universal Terrestrial Radio Access (E-UTRA); Further Advancements for E-UTRA Physical Layer Aspects (Release 9), 3GPP, Mar. 2. [7] J. Blumenstein, J. C. Ikuno, J. Prokopec, and M. Rupp, Simulating the long term evolution uplink physical layer, in Proc. International Symposium ELMAR-2, Zadar, Croatia, Sep. 2.