1

Inter-Cell Interference Coordination for Highly Mobile Users in LTE-Advanced Systems Shady S. Khalifa Student Member, IEEE,, Haitham S. Hamza Member, IEEE, Khaled Elsayed Senior Member, IEEE Cairo University Giza, Egypt 12311 [email protected], [email protected], [email protected] Abstract— How good is the performance of the existing Inter-Cell Interference Coordination (ICIC) schemes when dealing with users moving at high speeds? In this paper, we evaluate a number of existing schemes under high user mobility conditions. Then, we propose a dynamic decentralized ICIC scheme that requires no apriori frequency planning. The proposed scheme minimizes the amount of data needs to be exchanged among base stations. The scheme uses the Harmony Search (HS) algorithm in order to rapidly generate a more accurate User-to-Channel allocation matrix to cope with high user mobility. We also propose power control and channel restriction strategies to minimize the power consumption and inter-cell interference. A key advantage in the proposed scheme is that its computations are independent of the number of users and cells in the network. Accordingly, it can be deployed in large networks with large number of users. Extensive simulations demonstrate that, with a slight degradation in fairness, the proposed scheme provides 18% throughput improvements to edge users without penalizing other users. In addition, the use of the power control and restriction strategies has led to a 22% reduction in power consumption. Keywords-LTE-Adv; ICIC; Harmony Search; High Mobility

I.

INTRODUCTION

In 3GPP Long Term Evolution (LTE) systems, downlink transmission is based on Orthogonal Frequency Division Multiple Access (OFDMA). By orthogonal allocation of the OFDMA sub-carriers, intra-cell interference can be avoided. However, inter-cell interference (ICI) still presents a challenge that considerably limits the system performance. ICI causes serious degradation of the users’ throughput, especially for user equipments (UEs) located at the cell edge due to the reuse of the same channel for different users in neighboring cells. Intercell interference coordination (ICIC) has been investigated as a key technology to alleviate the overall impact of interference in LTE systems, and hence, improve system performance and increase bit rates for cell edge users. The high-speed mobility of UEs in the LTE systems (350km/h [1]) leads to large channel variations and continuous changing and uneven distribution of the load. Coupled with the requirements for supporting very high transmission rates (300Mbps [1]), channel variations and changing load pose a real challenge on finding the most efficient allocation for the limited resources. Dynamic ICIC schemes have emerged as a more efficient and realistic solution as opposed to the conventional static schemes. However, channel assignment problem in dynamic ICIC schemes is known to be NP-hard [2]. Accordingly, several heuristics have been proposed to solve the channel

assignment problem in a computational efficient manner, such as: game theory [3], integer programming [4-6], graph coloring [2], water filling [7, 8], and genetic algorithms [2]. To perform network-wide ICIC, user’s channel information needs to be exchanged between neighboring cells so that each base station (i.e., eNB) can make an informative decision. In reality, the amount of information needs to be exchanged will be prohibitively large. Moreover, in realistic systems, the LTE X2 interface has a non-negligible latency [8], resulting in an additional delay, especially in very fast fading environments. Thus, minimizing the amount of data exchanged between eNBs is essential in order to allow more low-cost frequent exchange of information. Such frequent exchanges allow for capturing more accurate information about interference avoidance and partial fading diversity gains in the network [8]. As a result, a better decision for allocation problem can be made, especially in the emerging systems that support high user mobility. In this paper, we propose a novel dynamic decentralized ICIC scheme based on the concept of Harmony Search (HS) algorithm [10]. An important feature of the proposed approach is that it does not require a centralized controller and only makes use of minimum amount of information exchange between eNBs. A fundamental advantage in the proposed scheme is that its computations are independent of the number of cells and users in the system, making it attractive for deployment in large networks. To the best of our knowledge, this is the first time to adopt the notion of HS in solving the channel allocation problem in the LTE-Advanced systems. The HS algorithm and its variants have been applied to several problems in wireless networks including the spectrum allocation problem in cognitive radios [11, 12], and subcarrier and power allocation in cognitive OFDMA downlink [13, 14]. Results reported in the literature show that the HS provides fast and better quality solutions to the resource allocation problems than other optimization algorithms [11, 15]. II.

RELATED WORK

Interference avoidance schemes for ICIC provides frequency reuse planning algorithms that can be used by network elements to restrict or allocate certain resources among users in different cells. The main objective of these algorithms is to increase the SINR, and hence, allow the system to support as many users as possible. Various avoidance (allocation) techniques have been studied in the literature under various traffic conditions and network

2 structures. Along with the well-known static ICIC schemes (e.g., Reuse-1 and Reuse-3), a number of dynamic schemes were proposed to achieve network-wide ICIC with no aprior frequency planning. This section presents a brief overview about some recent dynamic schemes. Interested readers can refer to a comprehensive survey of various ICIC avoidance schemes in [16]. In [17], M. Rahman et al. proposed a scheme that shares the computations between a central entity and eNBs. Each eNB creates a wish-list of RBs to be restricted in its neighboring cells. Then, the central entity solves the restriction requests for all eNBs and returns a decision to the eNBs to apply locally. The scheme is dependent on the number of users, number of coordinated cells, and the number of RBs requested to be restricted, which may limit the usability of this scheme for only small networks. In [18], D. Kimura et al. proposed a distributed dynamic ICIC scheme where cell-center bands dynamically adapt (shrink/expand) depending on user behavior, cell load, and interference situation. In this scheme, no central controller is used and only communication between eNBs is required. However, the scheme suffers from the “fake” unavailability of edge-RBs, as each eNB can only selects a pre-determined number of RBs as edge-bands regardless the number of edgeUEs. This limits the usability of the scheme in networks with irregular cell shapes and large number of edge users. With centralized controller as in [17], the schemes become often too heavy for implementations as all the interference information has to be gathered at the central entity. Accordingly, the system performance may degrade as only low rate information exchange is applicable in practice [8]. In [17, 18], equal static power allocation to edge-channels is used to reduce the computations. However, allocating different power can achieve higher spectral efficiency by allocating the same channel in different cells using different power levels. In addition, a lower ICI can be achieved by reducing the power levels of the dominating interferers. Moreover, power waste can be reduced by exploiting the tradeoffs between over allocating power to some channels while under allocating power to others. III.

SYSTEM MODEL

The LTE-Advanced OFDMA downlink transmission in a multi-cellular network with I cells is considered in this paper. A. User Classification An eNB is located at the centre of each cell and allocates downlink resources in the time and frequency domains to each of the Ui active users with . Users in each cell are divided into center and edge UEs using an adaptive Bandwidth Proportionality SINR threshold that guarantees that the number of users in each class is proportional to the percentage of RBs allocated to the user’s class. Thus, the set of edge UEs becomes smaller, leaving more RBs for them to use, which in turn enhances the fairness value. For instance, in Soft Frequency Reuse (SFR), 1/3 of the bandwidth is allocated

to edge UEs, and hence, the Bandwidth Proportionality SINR threshold guarantees that only 1/3 of the UEs are classified as edge UEs. B. Throughput Calculation The total bandwidth B is divided into J channels (each of 12 orthogonal subcarriers occupying a total of 180 kHz). Time is divided into slots (0.5ms each). Each RB represents a single channel for the duration of one time slot. One or more RBs can be allocated to a user at a time. Each RB is assigned exclusively to one user at any point of time within a given cell; however, neighboring cells may use the same RB at the same time. Each cell utilizes all system channels and operates with total transmission power . The signal carrying the payload is transmitted by only one eNB. Signals coming from other eNBs are considered as ICI. The signal to interference plus noise power ratio (SINR) of the uth user allocated to the jth channel in the ith cell is given by:

where,

is the channel gain between the uth user and the ith

eNB using the jth channel. is the transmission power th allocated to the j channel by the ith eNB to serve the uth user. is the additive white noise power. The achievable rate on the jth RB for the uth UE in the ith cell is given by: where C(·) is the adaptive modulation and coding (AMC) function that maps the SINR to rate. The modulation schemes range from the robust low rate QPSK scheme to the high rate but more error prone 64-QAM scheme. IV.

THE PROPOSED SCHEME

Considering the various drawbacks discussed in section II, and in order to ensure practicality of the proposed scheme, the following objectives and guidelines are considered for designing the proposed scheme:  Autonomous and fast adaptation: Resource allocation should be performed only at the eNB level, without the need of a central coordinator for rapid adaptation to the variation in the number of users and in their channels and power requirements.  Computationally efficient: The algorithm should be independent of the number of users and cells, in order to be suitable for use in crowded cells and large networks.  RB Power manipulation: The algorithm should be able to assign different power levels to the different RBs based on the obtained instantaneous channel conditions. The idea is to reuse the same frequency spectrum at spatially separated locations as the signal power falls off with distance. Also, the most efficient way to increase edge UEs rate is to allocate more power instead of more bandwidth as they suffer from low SINR on all RBs due to the presence of ICI and path-loss.

3 A. Data Exchange Strategy Achieving fast adaptation to the varying channel conditions requires minimizing the data exchange between eNBs. We adapt a modified version of the data exchange strategy presented in [18]. Similar to [18] each eNB sends only its calculated weights, instead of all of channel information of its users, to the neighboring cells on regular intervals. In our strategy, the weight of a cell with respect to a neighboring cell represents the number of all users in the cell (not only edge users as presented in [18]) for which the power of the signal received from the serving cell is less than the power of the signal received from the neighboring cell. By taking the center users into consideration when computing the weight in our strategy, the proposed scheme can adapt to highly moving users and prevents assigning very high power to edge users in one cell that can affect a center user in some neighboring cell. The weight denotes cell i weight with respect to neighboring cell k as calculated at cell i using:

where is the set of UEs in the ith cell. H(x) is a unit step function, H(x) = 1 only if x > 0. Otherwise, H(x) = 0. is the received power by the uth user from the kth neighboring cell and is the received power by the uth user from the ith cell. In the example shown in Fig. 1, the serving cell (cell 0) calculates the weights of neighboring cells. A comparison of the received-power values from cells 0 and 1 indicates a user count at the cell edge of 2, resulting in weight . Similarly, the serving cell calculates the weights of other neighboring cells ( ), and each of the neighboring cells calculates the weights for their neighbors.

Weight update messages are transmitted every 10 ms with no retransmission policy on drop. Every update message is time-stamped, thus, eNBs use the update message with the latest time-stamp to calculate the average weights. Smaller average weights indicate that the serving cell would be least affected by interference from the other cell, and that the other cell as well will be least affected by interference from the serving cell. Thus, the serving cell can allocate more common channels to this neighbor. In the example shown in Fig. 1, after cell 1 sends , cell 2 sends and cell 3 sends to the serving cell. The serving cell calculates the average weights ( , ). Accordingly, the serving cell allocates more common channels to cell 3 as it is the least affected and affecting one. Repeating this process across all neighboring cells enables the allocation of minimum common frequency bands to cell-edge UEs. Along with minimizing the amount of information to be exchanged between eNBs using the concept of weights, the proposed scheme uses the Harmony Search (HS) algorithm for its computational efficiency to rapidly calculate optimized UE/Power-to-channel allocation updates. The sequence diagram given in Fig. 2 illustrates the full sequence of operations performed in the proposed scheme with one neighbor. Neighbor cell 1

Edge-UE in cell 0

Reference Signal

Measure Received Power Report Received Power Calculate Weights (w0,1) Exchange Calculated Weight (w0,1) Exchange Calculated Weight (w1,0)

Neighbor Cell 2 w2,0 = 2

Serving cell 0

Reference Signal

Calculate Average Weights ( )

Neighbor Cell 1

Neighbor Cell 3

w1,0 = 3

w3,0 = 1

Run HS algorithm to allocate RBs/Power to UEs Allocation Info. Transmit Data Receive Data

Serving Cell 0

w0,1 = 2 w0,2 = 4 w0,3 = 3

Figure 1. Weights calculation example

Smaller weights indicate that the serving cell would be least affected by interference from the other cell. However, this weigh does not reflect the effect of the interference caused by the serving cell on the UEs served by the neighboring cell. Thus, each eNB periodically exchanges the weights it calculated with its neighbors over the X2 interface. Average weight is calculated to reflect both the effect of the serving cell interference on the neighbor’s UEs and the effect of the neighboring cell interference on the UEs of the serving cell. represents the average weight between the ith and kth cells, and is given by

Figure 2. Sequence of operations of the proposed scheme with one neighbor.

On frame bases (every 10ms), each eNB solves the UE/Power-to-channel assignment problem individually using the information collected from its UEs and neighboring eNBs. The objective function carried out by the ith eNB is minimizing the use of the same channels by edge users in neighboring cells:

In all cells, the UE/Power-to-channel assignment employed at any given time should always result in having the sum of the number of channels allocated to users less than or equal the total number of channels available :

4 The total power used in all channels must be less than or equal the maximum available eNB transmission power : Each eNB tries also to minimize the number of unsatisfied UEs given by the Soft Constraint: where is the set of channels allocated to the uth user. is the required rate of the uth user. = {0,1} represents the usage of the jth channel in the ith cell. = 1 only if the jth th channel is being used in the i cell. B. Harmony Search Mapping In the proposed scheme, Harmony Search (HS) algorithm is utilized to rapidly calculate the optimized UE/Power allocation updates by solving Eq. (5). The traditional HS proposed in [10] was extended to optimize two decision variables, namely, (1) UE to be allocated to each RB, and (2) power to be allocated to each RB. In the proposed scheme, each musical instrument corresponds to a RB. The list of cords/keys of an instrument corresponds to the active UEs in the cell. The range of pitches/tones of a cord/key corresponds to the power levels (See Section IV.C). Musical harmony between all instruments corresponds to the UE/Power to RB assignment matrix. Finally, audience’s aesthetics correspond to the cost of the assignment matrix based on Eq. (5). The HS algorithm is initialized by creating a Harmony Memory (HM) of size Harmony Memory Size (HMS). The initial HM consists of a number of random Harmonies (possible solutions) and their corresponding costs by Eq. (5). After the HM initialization, the algorithm iterates until it reaches the Maximum Improvisation (MI) limit. At each iteration, the algorithm introduces a single new Harmony that replaces the worst Harmony in the HM. For each instrument (RB) in the new Harmony, the new chord/key (UE) and pitch/tone (power level) can be selected from the HM with a probability of HM Consideration Rate (HMCR). Otherwise; they are generated randomly from the range of valid chords/keys and pitches/tones with a probability of 1-HMCR. If the new chord/key and pitch/tone were selected from the HM, then there is a probability of Pitch Adjustment Rate (PAR) to adjust the pitch/tone (allocated power). At the final iteration, the best Harmony (assignment matrix) is presented as the solution. C. Power Control Strategy The proposed power control strategy is carried out by attempting to allocate more power to a UE that has not yet reached its required rate. The increments start by attempting to allocate 1.25X of the default power (

), and keep

incrementing by a step of 0.25 until either the throughput of the UE increases or the power value of 3X is reached. To reduce ICI, the scheme attempts to minimize the allocated power to an UE that has satisfied its required rate without causing it to become unsatisfied. The scheme attempts to

allocate 0.5X of the default power then keeps incrementing by a step of 0.1 until the UE becomes satisfied again. D. Channel Restriction Strategy To maximize the system throughput, each cell altruistically restricts channels based on the newly proposed Selfishness Index (SI) parameter, where . The higher the value of the index, the more the scheme becomes selfish and prefers allocating channels to its users rather than restricting them to enhance the quality of the channel in the neighboring cells. The strategy states that a channel is restricted if or . For instance, if SI=10 and there is an UE (u) with a rate requirement of 500 Kbps, then channel j can be allocated to UE u only if . The upper bound guarantees that the high achieving channels are allocated to UEs with high rate requirements to prevent the waste of “good” channels. The lower bound, on the other hand, guarantees that UEs are allocated their highest achieving channels to minimize the number of channels per UE in order to allow for allocating those channels to other UEs that can achieve better rates, or preventing their usage to minimize ICI. E. Algorithm Computational Complexity The computational complexity of the proposed algorithm is a function of the constant MI iterations on the HM used to generate new Harmonies. Each new Harmony requires iterating on all J instruments (Channels), assigning chords/keys (UEs) randomly, thus, is independent of number of UE, and sets the pitches/tones (Power) according to the power control strategy. The cost of each of the MI iterations is O(J). Thus, the overall complexity of the new scheme is O(MI × J), and hence, it is independent of the number of users, cells, and power levels. V.

SIMULATION RESULTS AND ANALYSIS

A. Simulation Setup Simulations were performed using the WINNER - Phase II (WIM2) shadowing and fading models [19] to generate a radio channel realization for a metropolitan suburban environment. Initially, UEs are randomly dropped and configured to dynamically move with random speeds between 0 m/s (stationary) and 100 m/s (on a speedy train) in random directions (for a certain UE, the speed and direction are constant throughout the simulation). Three hexagonal cell layout of 500 m radius each was considered, wherein each cell is equipped with an eNB with an omnidirectional antenna located at the cell centre. The bandwidth B is 20 MHz and the number of channels is 100. Total transmission power in each cell is 40W, and the additive white noise power is −114dBm/Hz. Full buffer traffic model was considered for all users as it represents the worst case from the ICIC performance assessment perspective. Handover was executed at 3dB. Statistics are collected in the 3 cells over the time duration of 1000 frames. For the proposed HS, the values of the HMS, MI, HMCR and PAR were set to 200, 200, 0.5 and 0.5, respectively.

5 Performance of the proposed scheme is compared with that of four reference schemes, namely, Reuse-1, Reuse-3, PFR and SFR, along with the dynamic decentralized Kimura scheme [18] presented in section II. Proportional Fairness (PF) user Scheduling proposed in [20] is used by other schemes while the proposed scheme uses HS. Given that the default channel power is (

) and to satisfy Eq. (5), in

Reuse-1 all 100 channels are allocated . In Reuse-3, all 33 channels are allocated . In PFR, the 67 centre channels are allocated , while the 11 edge channels are allocated . In SFR and Kimura, the 67 centre channels are allocated , while the 33 edge channels are allocated . B. Performance Analysis Fig. 3 depicts the Cumulative Distribution Function (CDF) of the Time-Average UE Throughput (TATP) under high mobility conditions. With no ICI in case of Reuse-3, achieved rate of channels depends solely on the path-loss. Thus, with PF Scheduling almost all UEs have an equal TATP as it appears from the steep slope of the Reuse-3 curve shown in Fig. 3. However, under heavy load and large number of users, an UE time share in a channel becomes smaller as it shares the resources with other UEs. Using only 1/3 of the bandwidth per cell decreases the UE time share further. Thus, Reuse-3 achieves the worst TATP for all UEs. Kimura, SFR, and PFR schemes use, respectively, 1/3, 1/3 and 1/9 of the bandwidth for edge UEs. Both Kimura and SFR schemes can achieve higher edge TATP than that obtained under the PFR scheme. This is due to the availability of more resources to edge UEs. Kimura distributed algorithm might decide to use a group of RBs as edge RBs in more than one cell causing an ICI increase. Thus, it achieves edge TATP less than that of SFR. Kimura, SFR, and PFR schemes achieve almost the same center TATP as they all dedicate 2/3 of the bandwidth to center UEs. It can be noticed from Fig. 3 that under heavy load and large number of users, the amount of allocable resources has a larger effect on the TATP than that of eliminating ICI for both center and edge UEs.

However, as both Reuse-1 and SFR use the same power for center RBs, Reuse-1 achieves higher center TATP since more RBs are available for center UEs. Similar to Reuse-1, the proposed scheme does not dedicate any portion of the allocable bandwidth to any user class, thus edge RBs are dynamically redefined every frame. However, unlike Reuse-1 and similar to Kimura, not only the information fed back from the cell UEs to the eNB is used, but also, the weights exchanged between eNBs is used to optimize the RBs allocation in order to minimize ICI. This in turn leads to a higher edge TATP for the proposed scheme compared to any of the other schemes. Similar to Reuse-3 and PFR, the new scheme restricts channels in some cells to further minimize the ICI. However, unlike Reuse-3 and PFR, the proposed scheme does the restrictions dynamically based on the SI, which prevents stalling due to the unavailability of channels. Similar to SFR and PFR, the proposed scheme uses different power levels for different RBs. However, unlike PFR and SFR, power levels are determined dynamically for each RB-UE allocation with the objective of increasing the SINR for unsatisfied users and decreasing the power consumption for the satisfied users. Interestingly, the proposed scheme combines the advantages of Kimura scheme, Reuse-1, Reuse-3, SFR and PFR, as it provides, simultaneously, very good throughputs for both edge and center UEs . However, the proposed scheme achieves a slightly lower fairness as it appears from the less steep slope of the curve shown in Fig.3. This is expected as, unlike the PF scheduling used by other schemes, the proposed algorithm attempts to satisfy the largest amount of users as formulated in the constrain given in Eq. (8).

Figure 4. Edge UE throughput Vs aggregate system throughput for different number of users/cell with 3Mbps/user offered load.

Figure 3. CDF of the time-average UE throughput for 30 users/cell at 3Mbps/user offered load. The 5% throughput presents edge-UEs throughput.

Interestingly, both Reuse-1 and SFR schemes achieved the same edge TATP. This is because SFR has a limited number of edge RBs, but it uses higher power for edge RBs, while Reuse1 has more RBs available for edge UEs but it uses less power.

Fig. 4 presents a closer look at the performance of the various schemes under different number of UEs. As expected, the general trend is that as number of UEs U increases so does the Aggregated system Throughput (ATP). This is due to the increase in multi-user diversity achieved by the channel-aware scheduler, while the edge TATP decreases because more UEs share the same resources. For the same number of UEs, the proposed scheme always achieves higher edge TATP and system ATP. It is worth noting that at a smaller number of users (e.g., 10 users per cell), Reuse-3 achieves the highest edge TATP as expected followed by the proposed scheme, while Reuse-1 has the worst value due to the excessive ICI. These results; however, are omitted from Fig.4 for clarity. It can be deduced from comparing the performance at small and large number of UEs that, ICI effect on the edge TATP is only

6 significant when there are enough resources to serve all UEs; otherwise allocable resources size has higher significance. Fig. 5 presents the power efficiency, which is calculated by dividing the system throughput by the power consumed. As shown in the figure, the general trend for all schemes is that, as number of users U increases, so does the power efficiency. This is due to the increase in the system ATP. However, as all RBs in Reuse-3 consume the same amount of power and achieve the same rate, since there are no ICI, the system ATP and the power efficiency remain constant with the different number of users. Reuse-3 achieves very low power efficiency because of the limited allocable bandwidth per cell, which limits the maximum achievable rate. Moreover, the equal division of transmission power between all channels allocates more power than needed to some channels.

Figure 5. Power efficiency Vs aggregate system throughput for different number of users/cell with 3Mbps/user offered load.

As can be expected, Reuse-1 and SFR achieves higher power efficiency than that of Kimura scheme, as they can achieve higher system ATP. Interestingly, the PFR also achieves higher power efficiency than that of Kimura scheme while it has always achieved lower system ATP. Our analysis of the Kimura scheme shows that, on average, 25% of the edge RBs are used by more than one cell with equal high power, which results in high ICI, and hence, the Kimura algorithm allocates more RBs to the UEs to satisfy their required rate leading to power consumption larger than that of PFR which has isolated edge RBs. As shown in Fig. 5, for the same number of UEs, the proposed scheme has significant higher power efficiency and system ATP than that of all other schemes for all U. The main factors leading to this high power efficiency are the power control and channel restriction strategies. With power control, the proposed scheme avoids the problem that the Kimura scheme has, where the proposed scheme allows some RBs to be allocated to edge users in two or more neighboring cells, but with different power levels (based on each UE channel conditions), thus achieving an acceptable SINR for the UEs and lower power consumption. Also, the channel restriction strategy prevents power wasting by not allocating power to the channels that suffer from high ICI. This approach conserves power in the restricting cell while increases the RB throughput in the neighbor cell. As can be seen from Fig. 5, the curve of the proposed scheme has a steeper slope as compared to that of other schemes. This indicates that as the number of UEs increases, only small extra power is consumed, while the system ATP is significantly increased. This behavior is also

due to the restriction strategy, as with the increase of the number of user, all schemes attempt to allocate more RBs to satisfy the new users, while the proposed scheme only allocates extra RBs if this would lead to a significant throughput increase. Accordingly, with less RBs used, less power is consumed and higher achievable rate per RB is achieved. C. Sensitivity Analysis The effect of the delay of the weight update messages between eNBs on the TATP of the proposed scheme is also studied. A one frame to 5 frames delays were considered. The analysis shows that the TATP does not degrade if the delay of the X2 interface is lower than 5 frames (50ms). It is therefore shown that, the proposed method is sufficiently robust for the weights update messages delays as the next generation mobile networks backhaul must guarantee end-to-end maximum twoway delay of 10ms [21]. The SI is the key element in the proposed channels restriction strategy. In Fig. 6, the effect of the SI is evaluated. At low SI (e.g., SI=2), a channel must be able to achieve 0.5 of the UE required rate to be allocated by the proposed algorithm. Thus, at SI=2, all eNBs see that all channels will not achieve a significant rate if allocated to any UE. Thus, all eNBs decide to become generous and leave all channels to the neighboring eNBs to use, which will never happen as all eNBs behave in the same way. This will lead to zero throughput as no channels will be allocated in any cell. The best TATP is achieved with SI values between 5 and 10 as there is a large number of RBs allocated by the eNB but not large enough to cause significant ICI. With SI values above 10, each eNB becomes very selfish and prefers to allocate RBs to its UE rather than leaving them to neighboring eNBs, which results in an increase in ICI, and thus, a decrease in the TATP.

Figure 6. Effect of the SI on the UE throughput for 30 users/cell at 3Mbps/user offered load.

As shown in Fig. 7, the performance of the proposed scheme is slightly affected by the various HS algorithm paramenters (HMS, MI, HMCR, and PAR), where the UE throughput variations are only in terms of tens of kbps. However, as the computation complexity of the proposed scheme is dependent on the MI, small MI values are recommended, such as: MI=50 and HMS≥200 (dotted rectangle in Fig.7-a and 7-b). The analysis of the HMCR and PAR results shows that their best values are, respectively, 1.0 and 0.5 (dotted square in Fig.7-c and 7-d). It can be concluded from this analysis that having an initial large HM value allows for fast convergance to a good

7 solution. In addition, with large HM, better solutions can be achieved when new Harmonies are constructed from those in the HM.

[4]

[5]

[6] (a) HMCR=0.5 & PAR=0.5

(b) HMCR=0.5 & PAR=0.5

[7]

[8]

(d) HMS=200 & MI=200

(c) HMS=200 & MI=200

Figure 7. Effect of HS parameters on 5% UE throughput (left) and 95% UE throughput (right) for 30 users/cell at 3Mbps/user offered load.

The proposed scheme achieves better performance compared to all other schemes even without the best values for HMS, MI, HMCR, PAR and SI. Moreover, the new scheme performance is only slightly affected by the different values of the HS parameters and is robust to update messge delays. VI.

CONCLUSION

In this paper, we proposed a novel decentralized dynamic ICIC scheme based on Harmony Search (HS) algorithm for highly mobile users in multi-cell LTE-Advanced systems. The proposed scheme does not require any frequency planning and the inter-cell message exchange is minimized, thus the scheme is robust to the X2 interference delay and high user mobility. The proposed scheme does not require any central coordination, which further reduces the deployment cost and allows its deployment in the LTE-Advanced flat network architecture. Unlike existing dynamic ICIC schemes, the proposed scheme computations are independent of the number of users and cells in the system, making it more practical for deployment in large networks with rapidly moving users. Moreover, the performance of the scheme is only slightly affected by the values of the HS parameters. Finally, the proposed power control and channel restriction strategies have proven to reduce the power consumption and ensure a better edge throughput without impacting the overall cell throughput. ACKNOWLEDGMENT This work is part of the 4G++ project (4gpp-project.net) supported by the National Telecom Regulatory Authority of Egypt (www.tra.gov.eg).

[9]

[10]

[11]

[12]

[13]

[14]

[15] [16]

[17]

[18]

[19]

REFERENCES [1] [2] [3]

S. Sesia, I. Toufik, and M. Baker,"LTE – The UMTS Long Term Evolution: from theory to practice," Wiley publishing, 2009. M.C. Necker, "A novel algorithm for distributed dynamic interference coordination in cellular networks," in Proc. KiVS, pp. 233-238, 2011. Q. Duy La, Y. Huat Chew, and B. Soong, "An interference minimization game theoretic subcarrier allocation algorithm for OFDMA-based

[20]

[21]

distributed systems," in IEEE Global Telecommunications Conference, 2009, pp. 1-6. M. Rahman and H. Yanikomeroglu, "Multicell downlink OFDM subchannel allocations using dynamic intercell coordination," in Proc. IEEE Global Telecommunications Conf. GLOBECOM '07., 2007, pp. 5220-5225. Sh. Zheng, H. Tian, Z. Hu, L. Chen, and J. Zhu, "QoS-guaranteed radio resource allocation with distributed inter-cell interference coordination for multi-cell OFDMA systems," in Proc. IEEE 71st Vehicular Technology Conf. (VTC 2010-Spring)., 2010, pp. 1-5. A. L. Stolyar and H. Viswanathan, "Self-organizing dynamic fractional frequency reuse for best-effort traffic through distributed inter-cell coordination," in Proc. IEEE INFOCOM 2009., 2009, pp. 1287-1295. S. Ko, H. Seo, H. Kwon, and B. Gi Lee, "Distributed power allocation for efficient inter-cell interference management in multi-cell OFDMA systems," in 16th Asia-Pacific Conference on Communications, 2010, pp. 243-248. G. Li and H. Liu, “Downlink Radio Resource Allocation for Multi-Cell OFDMA System,” IEEE Transactions on Wireless Communications, vol. 5, no. 12, pp. 3451-3459, 2006. S. Cicalo, V. Tralli, and A.I. Perez-Neira, "Centralized vs distributed resource allocation in multi-cell OFDMA systems," in IEEE 73rd Vehicular Technology Conference, 2011, pp. 1 - 6. Z. W. Geem, "State-of-the-art in the structure of harmony search algorithm," Recent Advances In Harmony Search Algorithm, vol. 270, pp. 1-10, 2010. A. S. Hamza, “A New Approach For Spectrum Allocation In Cognitive Radios Based On Harmony Search.” Faculty of Engineering, Cairo University, Egypt, September 2010. A.S. Hamza, H.S. Hamza, and M.M. Elghoneimy, “Spectrum allocation in cognitive radio networks using evolutionary algorithms,” Chapter 10 in H. Venkatarama and G.-M. Muntean (eds.), Cognitive Radio and its Application for Next Generation Cellular and Wireless Networks, Lecture Notes in Electrical Engineering 116, 2012. J. Del Ser, M. Nekane Bilbao, S. Gil-López, M. Matinmikko, and Sancho Salcedo-Sanz, “Iterative power and subcarrier allocation in rateconstrained orthogonal multicarrier downlink systems based on hybrid harmony search heuristics,” Engineering Applications of Artificial Intelligence, vol. 24, no. 5, pp. 748-756, August 2011. J. Del Ser, M. Matinmikko, S. Gil-López, and M. Mustonen, “Centralized and distributed spectrum channel assignment in cognitive wireless networks: A harmony Search approach,” Applied Soft Computing, vol. 12, no. 2, pp. 921-930, February 2012. Z. Geem, "Particle-swarm harmony search for water network design," Engineering Optimization, vol. 41, no. 4, pp. 297-311, April 2009. A.S. Hamza, S. S. Khalifa, H. S. Hamza and K. Elsayed, "A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-based Cellular Networks". IEEE Communications Surveys & Tutorials (In Press). M. Rahman and H. Yanikomeroglu, "Enhancing cell-edge performance: a downlink dynamic interference avoidance scheme with inter-cell coordination," IEEE Transactions on Wireless Communications, vol. 9, no.4, pp. 1414-1425, 2010. D. Kimura, Y. Harada, and H. Seki, "De-centralized dynamic ICIC using X2 interfaces for downlink LTE systems," in Proc. IEEE 73rd Vehicular Technology Conf. (VTC Spring)., 2011, pp. 1-5. L. Hentilä, P. Kyösti, M. Käske, M. Narandzic , and M. Alatossava. (2007, December.) MATLAB implementation of the WINNER Phase II Channel Model ver1.1 [Online]. Available: http://projects.celticinitiative.org/winner+/phase_2_model.html C. Wengerter, J. Ohlhorst and A. G. E. von Elbwart, "Fairness and throughput analysis for generalized proportional fair frequency scheduling in OFDMA," in Proc IEEE 61st Vehicular Technology Conf.. (VTC Spring)., vol.3, 2005, pp. 1903- 1907. NGMN Alliance, “NGMN Optimised Backhaul Requirements,” Next Generation Mobile Networks Alliance, August 2008, pp. 19.