An accurate performance analysis of an FFR scheme in the downlink of cellular systems under large-shadowing effect
© Pijcke et al.; licensee Springer. 2013
Received: 22 July 2011
Accepted: 15 January 2013
Published: 22 February 2013
We address the problem of analyzing the performances of interference-limited cellular networks in large-scale shadowing environments. Focusing on the fractional frequency reuse (FFR) framework, we examine how to optimally assign mobile users in a cell either to the full frequency reuse part or to the orthogonal part of the FFR band. Instead of using traditional Monte Carlo simulations, which do not provide sufficiently accurate results under important shadowing, we propose a fast and accurate numerical method. We consider a fast-fading environment and we use the ergodic capacity as the performance measure. Applying a distributed power control and scheduling strategy, we examine both cases where access points have knowledge of partial- or full-channel state information (CSI); for the latter, we also propose an approximated waterfilling strategy. The interest of our method lies in the fact that it allows for a fast and accurate analysis of the performances of FFR. In addition, it takes into account a broad range of shadowing environments.
Since the invention of digital networks, efforts for providing higher capacity and higher quality of service have never stopped. With the emergence of new wireless networks that aim at providing multimedia service everytime and everywhere, the need for higher spectral efficiency has never been so high [1, 2]. In order to achieve these goals, aggressive reuse of the whole frequency spectrum throughout the network is mandatory. As a consequence, today’s systems have to make intensive use of intercell interference mitigation techniques, including power control, opportunistic scheduling, FFR, MIMO... [3–9].
As the term ‘fractional’ implies, FFR attempts to find a compromise between the full frequency reuse scenario, which allocates the entire available bandwidth to each cell but at the expense of a maximum level of intercell interference, and the partial frequency reuse scenario, which provides zero intercell interference by orthogonally sharing the bandwidth among adjacent cells . The idea behind FFR has come from the observation that user terminals (UTs) within a cell are not equivalent from an intercell interference point of view: due to the path-loss effect, cell-edge users are far more sensitive to it than users located near the center of the cell.
A key aspect in FFR design is for the access points (APs) to appropriately decide, for each UT, whether it has to be scheduled within the full frequency reuse part of the FFR band or within the orthogonal part of the FFR band. In this article, we investigate this decision issue based on the knowledge of the users channel gains.
Generally, the performances of FFR are evaluated by Monte Carlo simulations and/or empirical measurements [8, 11–13]. A recent contribution  has tackled the problem by following an analytical approach, however using some simplifying assumptions (very basic power control at the AP, no shadowing effect). Although the Monte Carlo simulation technique is a very common and practical tool, it does not provide sufficiently accurate results in the case where the random phenomenon included in the simulation model exhibits a heavy-tailed behavior. This is due to the fact that for heavy-tailed distributions, large amplitudes are associated a very low probability, so they rarely appear in the simulation process, despite their great impact on system performance . In wireless networks, the heavy-tailed behavior of the interference power originates from the cumulative effect of the shadowing phenomenon in interfering links (sum of log-normal distributions) [16, 17], and this makes accurate and fast simulations very challenging in such environments.
In this article, we propose an accurate and fast numerical method to determine the performances of FFR in terms of ergodic capacity, in a multicell network suffering from path loss, shadowing and Rayleigh fading. In particular, we examine how the UT should be assigned to the FFR band, based on its channel gain. Unlike other studies that assume a fixed shadowing standard deviation (see e.g., [11, 13], where σdB=8 dB), we consider it as a variable parameter (σdB∈ [0,10] dB) in order to accurately analyze its influence on the performances of FFR.
The rest of the article is organized as follows. In Section 2, we present the system model. In Section 3, we derive the methodology of the numerical method that we use to provide a fast and accurate simulation of the performances of FFR. Numerical results are analyzed in Section 4.
2 Multicell downlink transmission model
We consider the downlink of a synchronous discrete-time 19-cell cellular network having the 2D hexagonal layout depicted in Figure 1. We assume a unit-gain omnidirectional SISO (single input, single output) antenna pattern, both for the fixed APs and the mobile UTs. The UTs are supposed to be uniformly and independently distributed over the service area. Users belonging to the same cell are considered orthogonal, which gives rise to a wireless channel primarily limited by intercell interference, especially at the cell edges (as we do not consider sectorization in this article, there is no intersector interferencea). This model corresponds, e.g., to a TDMA system. In this context, the FFR scheme that we consider is a mixture of the two following patterns for allocating the available frequency spectrum across the network:
a partial frequency reuse pattern, denoted FR3, with reuse factor 3 (N=6 interferers). For this pattern, three groups of APs are active simultaneously on three orthogonal bands;
the full frequency reuse pattern, denoted FR1, where all APs in the network transmit at the same time using the whole frequency band (N=18 intercell interferers).
Here x0(m) represents the information symbol intended to UT0 and x n (m), n≠0, the n th interfering symbol (this symbol is sent from AP n to its respective user UT n ). The coefficient h n (m) denotes the instantaneous channel gain from AP n to UT0. Each channel is subject to additive white Gaussian noise w0(m), with variance W.
where , with c being the speed of light, f, the operating frequency, and d0, a reference distance for the antenna far-field; and γ represents the attenuation coefficient. The Rayleigh fading gain Hf,n is modeled by an exponential distribution with rate parameter equal to 1, i.e., . The shadowing gain Hs,n is modeled by a unit-mean lognormal distribution parameterized by what is called the shadowing standard deviation σdB (usually expressed in dB) which captures the importance of the shadowing phenomenon (e.g., a σdB=0 dB scenario represents a wireless channel subject to fading only). Since r.v.’s Hf,n and Hs,n are independent of each other, and since , from (1) we have , which reflects the fact that the Rayleigh fading and shadowing components cause the actual gain H N to fluctuate about its mean value Hpl,n.
We now introduce the power control and scheduling strategy that we consider in this article. In what follows, we first assume a distributed environment, i.e., each AP has access to some (local) CSI provided by its respective mobile users only. According to what type of CSI data are fed back from UTs to their serving APs, we consider two different scenarios: a partial (statistical) CSI scheme (for which only path losses are available at the APs), and a full-CSI scheme (APs have knowledge of instantaneous channel gains). Since channel information is available only at the cell levelc, we consider a distributed power control and scheduling strategy. A channel inversion-type power control is applied within each cell, based on the mean information (i.e., the path losses Hpl,n) available at the APs: the power emitted at each time slot by AP n is , where Pmax is the maximum power available at the AP, and P∗ is the target power at the mobile user. To this power control scenario we associate the resource-fair opportunistic scheduling policy proposed (and proved to be sum-rate optimal) in : each cell ranks its users by (say decreasing) order of channel gain and assigns the best users to the available time slots, regardless of channel gains in other slots.
In Section 3.1, we present the ergodic capacity as the performance measure. In Section 3.2, we consider the partial-CSI scheme. We examine the full-CSI scheme in Section 3.3, where we propose an approximated waterfilling-in-time strategy.
3.1 The ergodic capacity as the performance measure
where I is the total interference power received by UT0, that can be written . Since no closed-form expression exists for the ergodic capacity (3), it must be computed numerically. The traditional approach consists in performing Monte Carlo simulations. However, because the shadowing phenomenon exhibits a heavy-tailed behavior for large values of the shadowing standard deviation σdB, efficient simulations in such environments are very challenging. Hence the need for a method to quickly and accurately compute the ergodic capacity that takes into account the importance of the shadowing.
To propose such a method, let us closely examine (3). First of all, we investigate the impact of the power control and scheduling policy on the interference power gain I. Recall that the distributed scheduling policy consists, for each cell, in ranking its users by decreasing order of mean channel gain Hpl,n, and in assigning them, in that order, an available time slot . We now make an important assumption over the transmit power. Because of the scheduling policy, each user has a mean channel gain Hpl,n contained in a range which (statistically) becomes more and more restricted as the number of users per cell grows. In this article, we only consider a framework in which the number of users per cell is high. So, applying the scheduling policy described previously, it can be expected that the channels between users scheduled at the same time and their serving APs have about the same power gains: Hpl,n≈Hpl,m, ∀n≠m. Since the power control is based on a constant target (received) power P∗, we can reasonably assume that, at each time slot, each AP transmits at the same power: P n =P, ∀n. In Appendix 1, we evaluate how this transmit power assumption is impacted by the total number of users.
The interference power thus simplifies to . We now define the interference power gain—which is denoted G—as being the sum of the channel power gains between the interested user UT0 and the N interferers, i.e., . In this expression, G is a function of UT0’s location through the distances r n (see (2)). However, it has been shown in  that the impact of UT0’s position on G is negligible, so G can be approximated by (the path loss Hpl,n of the interfering link between AP n and UT0 is then replaced by a mean value). Having introduced this interference power gain, we can further write I=P G. To characterize G’s statistical law, we make use of the closed-form expression, parameterized by σdB, that has been proposed in . In the next place, we evaluate the expression of the power received by UT0 (see (3)), i.e., P0H0=P H0. Using (1), the gain H0 can be decomposed as H0=Hpl,0Hf,0Hs,0. Since an analytical formula is available in the literature  for the probability law , we can further write: H0=Hpl,0Hfs,0.
Fast and accurate calculation of (4) is made possible by the use of available analytical probability laws for both r.v.’s Hfs,0 and G; this calculation is computed numerically by estimating the sample mean in (4). This makes our method of great interest as compared with traditional time-consuming Monte Carlo simulations.
where K is a constant. Using (4) in association with (5), we are able to determine a mean network capacity by averaging the position of mobile users in the cell: this capacity corresponds to the average network capacity when there is an infinite number of users in each cell (U→∞). Note that, for the clarity of the illustrations, we express the mean power gain Hpl,0 in dB; the expression of its associated pdf can be easily derived from (5).
3.2 Partial (statistical) CSI
Again, we can see the great interest of our numerical method: the mathematical expectation (6) can be calculated quickly and accurately—by numerical computation of the mean in (6)—because we have at our disposal, for both frequency reuse patterns FR1 and FR3, analytical expressions for the probability laws of the r.v.’s Hfs,0 and G, parameterized by the shadowing standard deviation σdB. Plotting the ergodic capacities for FR1 and FR3, namely and , as functions of Hpl,0, may then exhibit an intersection point, each side of which is applied one of the FR1 or FR3 frequence reuse patterns. This intersection point also helps us classify users in the appropriate FFR band.
Using the ergodic capacity (6) and the pdf (5), we can also determine what the average gain is of swapping from FR1 to FR3 near the cell border, where users suffer from severe interference. This gain can be calculated by comparing the ergodic capacity for the FR1 pattern with the ergodic capacity for the FR3 pattern, which we denote by and , respectively. Finally, if we consider the FFR area, we can associate to this potential gain the ratio of users concerned with this policy; note that this last ratio grows exponentially as Hpl,0 diminishes.
3.3 Full-CSI: approximated waterfilling strategy
We then consider the full-CSI scheme for which the APs have access to the instantaneous channel gains of their respective users. From the knowledge of the instantaneous channel conditions, we are able to propose a more efficient power allocation strategy in order to optimize the network capacity. We concentrate on what is known as the waterfilling-in-time strategy, since it is the optimal solution with respect to capacity optimization under an average transmit power constraint when both the AP and the mobile user have perfect and instantaneous knowledge of channel conditions .
We now closely analyze the complexity of the optimization problem (7)–(9). By carefully examining (7), we highlight an important issue: the waterfilling strategy in cell 0 obviously depends upon the waterfilling procedures performed in other (interfering) cells. Indeed, the power P n emitted by AP n results from the waterfilling strategy of cell n, and it therefore depends on the instantaneous channel gain Hfs,n between AP n and UT n . However, since we have chosen for a distributed power control and scheduling policy, a cell only has access to local information, namely, the actual channel gains of its own users. As such, the optimization problem (7)–(9) thus reveals too complex to solve and the waterfilling strategy needs to be approximated.
where L and J are both functions of Hpl,0. As mentioned before, our method here also allows for a fast and accurate (numerical) computation of the ergodic capacity (17) because the probability laws of Hfs,0 and G can be expressed analytically .
where the ergodic capacities Cerg(Hpl,0) and can be computed easily, respectively, from (6) and (17) (computation of the optimal value is intractable and meaningless in a realistic scenario). And, as for the partial-CSI scheme, we are interested in determining a potential point of intersection of the capacity curves and , for both the frequency reuse patterns FR1 and FR3, and for different values of the shadowing standard deviation σdB. This intersection point also allows us to assign mobile users either to the FR1 band or to the FR3 band on the basis of their path losses.
4 Numerical results
In this section, we present numerical results related to the analysis of the FFR framework under different frequency reuse scenarios and for different values of the shadowing standard deviation. In Section 4.1, we first describe the simulation parameters. In Section 4.2, we compare results of the ergodic capacity as a function of the path loss under both the FR1 and FR3 patterns and we compute the potential gain of applying the FFR technique in different wireless channels. In Section 4.3, we improve the ergodic capacity results by performing the approximated waterfilling policy described in Section 3.2.
4.1 Simulation parameters
We use the following simulation parameters. We consider a system functioning at 1 GHz. We fix the cell radius to R=700 m, the far-field distance to d0=10 m, and the attenuation coefficient to γ=3.2, which corresponds to a typical urban environment, as described by the COST-231 reference model . We consider a wireless channel that is subject to a large-scale shadowing phenomenon that may vary in a [0,10] dB range. The maximum power available at the access point is Pmax=5·10−3 W and the target received power is P∗=10−12 W. The additive white Gaussian noise has variance W=10−15 W. As stated previously (see Section 3.1), the number of users per cell is considered to be infinite.
4.2 Partial-CSI scheme
because in the FR1 scenario, the whole frequency band is used while this resource is divided in three orthogonal bands in the FR3 scenario. Next, we see that all capacity curves slightly take down on the left side of a fixed gain denoted . This results from the maximum power constraint applied at the APs: is the demarcation point between the fully power controlled area (right side) and the area where APs emit at their maximum (saturated) power (left side); it follows immediately that .
Mean ergodic capacities and for the partial-CSI scheme and for different values of the shadowing standard deviation σ dB
We come to the conclusion that, for the partial-CSI scheme, the FFR technique may increase the average ergodic capacity for users located near the cell border, in light or moderate shadowing environments. But, more importantly, besides this capacity increase, what really makes FFR of great interest is the fact that it may concern a non negligible proportion of mobile users, especially for small values of the shadowing standard deviation.
4.3 Full-CSI scheme
From these observations, we can conclude that applying FFR is of no interest with respect to network capacity when APs perfectly know their local channel conditions (full-CSI scheme), though other simulation scenarios may lead to different conclusions.
5 Conclusion and future study
In this article, we investigated the problem of evaluating the performances of FFR in large-scale shadowing environments. Instead of using Monte Carlo simulations or empirical measurements, we proposed a numerical method to easily compute the ergodic capacity in a fast-fading environment. Using a distributed power control and scheduling policy, we examined both cases where APs have access to partial- or full-CSI. We showed that our method allows for a fast and accurate analysis of the performances of FFR. In addition, it takes into account a broad range of shadowing environments.
The results presented in this article only hold for the simulation conditions specified; they may differ for other simulation scenarios. So a future article will pertain to applying the proposed method to other types of network (e.g., ad hoc networks, …) and using different simulation conditions (user distribution in the cell, transmit or target power, attenuation coefficient, …). Another perspective is to consider sectorization, which induces intersector interference and leads to totally different models for the power gains as well as for the interference. Yet another perspective concerns the analysis of the network performances in a slow fading environment using other performance measures, like the outage capacity or the outage probability.
Validity of the assumption over the transmit power
In this Appendix, we analyze our assumption over the transmit power. First we investigate how the total number of users impacts the transmit power within one cell and we evaluate our assumption in the case where all cells have the same number of users. Then we examine the case where the total number of users varies among cells.
Same number of users per cell
Maximum values of the standard deviation , as a proportion of the [0,1]-dynamics of P u, for a total of U = 10, U = 100, and U =1000 users in a cell
Variability in the number of users per cell
Proportion of scheduled users for which Δ <10 % and Δ <1 %
proportion of scheduled users
A lower bound for the ergodic capacity
aTaking sectorization into consideration would indeed lead to a totally different interference model (see e.g., ).bSince we focus on power gains only, the term power will then be omitted in subsequent paragraphs.cNote that considering a full-CSI scheme at the network level would not be a realistic scenario because of the enormous amount of information that a central unit would have to manage.dThe notation [x]+ means max(0,x).eIn what follows, we write P0(Hfs,0) instead of P0 to emphasize the fact that, for a fixed value of Hpl,0, P0 depends on the instantaneous channel state Hfs,0.
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