Mobile Information Systems

Volume 2016, Article ID 8267407, 11 pages

http://dx.doi.org/10.1155/2016/8267407

## A Fast Self-Planning Approach for Fractional Uplink Power Control Parameters in LTE Networks

^{1}Ingeniería de Comunicaciones, Universidad de Málaga, Campus de Teatinos S/N, 29071 Málaga, Spain^{2}Ericsson, Calle de la Retama 1, 28045 Madrid, Spain

Received 22 July 2016; Accepted 19 September 2016

Academic Editor: Jung-Ryun Lee

Copyright © 2016 J. A. Fernández-Segovia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Uplink Power Control (ULPC) is a key feature of mobile networks. Particularly, in LTE, Physical Uplink Shared Channel (PUSCH) performance strongly depends on Uplink Power Control configuration. In this work, a methodology for the self-planning of uplink Fractional Power Control (FPC) settings is presented. Values for nominal power and channel path-loss compensation factor are proposed. The method is designed for the planning and operational (replanning) stages. A very fast solution for FPC setting can be achieved by the combination of several simple solutions obtained by assuming some simplifications. First, the FPC planning problem is formulated analytically on a cell basis through the combination of multiple regular scenarios built on a per-adjacency basis from a live scenario. Secondly, detailed inspection of the FPC parameter values aims to identify the most important variables in the scenario impacting optimal FPC settings. Finally, regression equations can be built based on those key variables for a simple FPC parameter calculation, so computational costs are extremely reduced. Results show that network performance with the proposed FPC parameter settings is good when compared with typical FPC configurations from operators.

#### 1. Introduction

Mobile Communication Networks have experienced strong evolution in the last years. The development of new radio access technologies has increased network capacity and quality significantly, especially with the UMTS Long-Term Evolution (LTE) [1]. Simultaneously, the appearance of the so-called* smartphones* has changed the traffic behavior carried by mobile networks, where data transmission (and not voice calls) is the traffic benchmark, and, as a consequence, data transmission enhancement has been the main focus in present networks [2]. These factors have strengthened the role of network planning when there is a desire to improve overall network performance. Before the network deployment stage, network planning aims to get the best network performance in a concrete scenario. Trade-off between network capacity and coverage is the most limiting factor for network planning [3].

Regardless of the network radio access technology, proper network planning allows the operator to identify key network areas, which eases proper network dimensioning and enables the prediction of future bottlenecks. Thus, network planning is useful to avoid or, at least, delay subsequent capital investments [4, 5]. The growth of application data traffic has led to changes in network planning approaches trying to predict how the user experience is. Whereas former approaches have focused on network performance indicators, user-centric statistics are now the preference (e.g., average and cell-edge user throughput) [6].

Cellular network planning can be divided depending on the network system to be planned: core and Radio Access Network (RAN). While the core network planning relies mainly on dimensioning processes, RAN planning comprises radio dimensioning and radio parameter configuration [3, 7, 8]. Network operators do not usually take advantage of radio parameter configuration due to the inherent complexity of finding optimal parameter settings for every cell in the network. Thus, operators usually set radio parameters to some network-wide values recommended by vendors, which work reasonably well in most cases, but some additional network improvement is discarded. To revert this situation, the 3rd Generation Partnership Project (3GPP) has defined the requirements for the automation of planning, optimization, and self-healing in mobile networks [9]. As a result, network self-planning has been identified as an important process in Self-Organizing Networks (SON) now [5, 10, 11] and in the future [12].

Power Control (PC) is one of the most impacting algorithms in network performance. Fractional Power Control (FPC) has been selected for the Physical Uplink Shared Channel (PUSCH) in LTE [13, 14]. Consequently, FPC algorithm controls LTE uplink performance, which makes its configuration an ideal candidate for self-planning purposes. In fact, the variability of radio conditions such as propagation losses and interference level makes it difficult to set an optimal value for FPC parameters. For this reason, network operators use safe network-wide recommended values. As a consequence, suboptimal performance is achieved by the network. Hence, even if this can be solved in the operational stage, the provision of proper initial FPC settings would be valuable for network operators. Additionally, the research of low computational complexity methodologies for FPC self-planning is of high interest in the development of SON algorithms.

#### 2. Related Work and Contribution

There is a wide background regarding FPC performance in LTE uplink. First, a performance analysis of open-loop FPC is presented in [15, 16], whereas closed-loop behavior was analyzed in [17–19]. Moreover, more sophisticated Power Control schemes for LTE were assessed in later studies [20, 21]. In those schemes, interference or load conditions were taken into consideration.

A sensitivity analysis of FPC parameters is performed in [22]. The analysis relies on system-level simulations and the results suggest a suboptimal parameter configuration for noise-limited and interference-limited scenarios. Obviously, the overall problem solution is not as simple, since it is a nonseparable and nonlinear large-scale optimization problem. However, it is the start point in the search of more complex solutions. In [23], the FPC parameter settings problem in a single cell is formulated as a classical optimization problem, where average user throughput and cell throughput are taken as figures of merit for the optimization process. An extension of this analysis to a multicell scenario is done in [24] by formulating FPC as a noncooperative game model where a heuristic iterative optimization algorithm solves the problem. More conscientiously, a self-planning method for selecting the best parameter settings in FPC on a per-cell basis in an irregular LTE scenario is proposed in [25]. It is based on an exhaustive search approach using Taguchi’s method over a system-level simulator. There are other studies considering tuning algorithms for the network operational stage. These self-tuning algorithms, which have been conceived for the operational stage, can also be applied in the planning stage, provided that a system model is available (e.g., a system-level simulator). For instance, a self-tuning algorithm is proposed in [26] to control interference by performing dynamical adjustment of nominal power parameter based on the overload indicator [27]. Likewise, a self-tuning algorithm for FPC is proposed in [28] based on fuzzy-reinforcement learning techniques. Most of these self-tuning algorithms need iterative evaluation of the system model for many different parameter settings, thus emulating the realistic network behavior. As a consequence, this iterative process is adequate for live networks, where performance measurements are provided. However, this is not the case of network planning, where computational cost increases with the complexity of the implemented system model. For this reason, most existing FPC planning methods rely on simple analytical models, which eases scalability and performance assessment.

A more computationally efficient planning method is presented in [29]. The method relies on an analytical model that predicts the influence of the nominal power and path-loss compensation factor on call acceptance probability for a previously defined spatial user distribution. A suboptimal solution for these parameters is computed by a local greedy search algorithm. In the same way, a computationally efficient method for self-planning Uplink Power Control parameters in LTE is presented in [30]. This method proposes a heuristic algorithm that can handle irregular scenarios at a low computational complexity. For this purpose, the parameter planning problem in a cell is formulated analytically through the combination of multiple regular scenarios built on a per-adjacency basis. However, in [30], Nonfractional Power Control is considered, assuming total propagation losses compensation.

To the authors’ knowledge, few of the previous references handle irregular scenarios at a low computational cost and none of them propose some simple model with the aim of getting near-optimal FPC parameter values depending on scenario details.

In this paper, a fast method for the self-planning of FPC parameters in LTE uplink is proposed. The self-planning method determines the nominal PUSCH power, , and the path-loss compensation, , parameters in FPC. Similar to [30], to deal with scenario irregularities, the parameter setting problem is solved by the aggregation of multiple scenarios defined on a per-adjacency basis. Moreover, with the aim of minimizing computational complexity, solutions provided by the self-planning method are further analyzed and a simple model for the estimation of FPC parameter values is proposed.

Unlike [30], the decision variables in this work are and , instead of and uplink cell load, . The approach in [30] is suitable for the planning stage, when performance measurements (PMs) are not available and the maximum cell loads are still design variables. However, it is limited to some first vendor releases, where was a system constant ( = 1). In contrast, the approach proposed here is conceived for the operational stage, when input parameters can be taken from network PMs. Thus, is an input parameter taken from statistics of Physical Resource Block (PRB) utilization ratio in the network management system. Likewise, FPC is considered here (). Moreover, the optimization criterion is different from that used in [30].

The main contributions of this work are (a) a sensitivity analysis of FPC parameter solutions in a realistic network implemented over a system-level simulator, (b) a thorough analysis of how FPC parameter values are related to LTE scenario characteristics and the identification of the most significant scenario parameters affecting FPC setting, and (c) a highly computationally efficient methodology to configure FPC parameters. The rest of the paper is organized as follows. In Section 3, the system model is outlined. The self-planning algorithm is presented in Section 4. Performance assessment is carried out in Section 5. Finally, concluding remarks are given in Section 6.

#### 3. Fractional Power Control in LTE Uplink

Three physical channels are defined for LTE uplink: Physical Random Access Channel (PRACH), Physical Uplink Shared Channel (PUSCH), and Physical Uplink Control Channel (PUCCH) [31]. Attending to LTE standards, Uplink Power Control feature applies to PUSCH and PUCCH [14]. Specifically, PUSCH is used to transmit user data and control information for active users. Uplink Power Control (ULPC) behavior for PUSCH is defined aswhere is the maximum User Equipment (UE) transmit power, is the channel path-loss compensation factor, are the propagation losses, is the number of PRBs assigned to the UE, and is a dynamic term that depends on the selected modulation scheme and power control commands sent by the eNodeB (eNB).

As shown in (1), transmit power depends on three terms: the basic open-loop operating point, dynamic offset which represents closed-loop corrections, and a multiplicative factor depending on the bandwidth. It must be noted that, in open-loop behavior, the parameter () represents the fraction of PL which are compensated by the UE to guarantee the nominal PUSCH power, . Thus, when path-loss compensation factor is different from one, ULPC is known as Fractional Power Control (FPC).

In this work, the system model is the same as the one used in [30]; that is, uplink Signal over Interference and Noise Ratio (SINR) is based in the emulation of the uplink scheduler proposed there.

#### 4. Self-Planning Algorithm

In this section, a self-planning methodology for FPC parameters, namely, and , is described. General considerations regarding the algorithm are first explained in Section 4.1. The algorithm operation for regular scenarios is described in Section 4.2, and the algorithm extension for irregular scenario is performed in Section 4.3. Finally, a detailed analysis of FPC solutions is approached in Section 4.4, with the aim of building a multivariate linear regression model with the most significant scenario parameters.

##### 4.1. General Consideration

Mobile network performance is usually experienced as a trade-off between coverage and capacity so both characteristics cannot be optimized separately. This trade-off is known as the Coverage and Capacity Optimization (CCO) SON use case defined by 3GPP in [32]. Network coverage and capacity are usually measured with cell-edge user and cell-average throughput statistics, respectively [6, 33].

Self-planning of FPC parameter is a particular way to approach the CCO problem. On the one hand, changing in a cell impacts coverage and capacity of cell and its surrounding neighbors. High values force the UEs connected to cell to transmit with higher power, increasing interference in adjacent cells. However, received signal in cell is higher, and, thus, SINR (and, as a consequence, cell throughput) is increased. Conversely, low values decrease transmit power for UEs in the cell, reducing interference in adjacent cells, which favors coverage of surrounding cells at the expense of reducing coverage and capacity of the considered cell. Regarding parameter, different settings impact similarly the UE transmit power, so it can be also used to manage interference between cells.

In any case, and best settings are both very influenced by the particular topology and radio propagation conditions in the network scenario. This is especially important when irregular scenarios (which are majority) are considered. A cell-based FPC configuration can reach the best network performance by adapting and settings to every cell environment. As a consequence, the resulting CCO problem is a nonseparable multivariable optimization problem in which all cells are jointly optimized. In other words, the solution space is , where and are the number of possible values for and parameters and is the number of cells to be planned. The large size of the solution space prevents the use of exact algorithms, which are substituted by heuristic algorithms, for example, Taguchi’s method [25], greedy search [29], or simulated annealing.

In this work, the methodology described in [30] is reproduced to reduce the algorithm search space. The global multivariate optimization problem is divided into independent bivariate subproblems. The following subsections describe the optimization process in a regular scenario and then the extension to irregular scenarios and later an analysis of the algorithm solution that allows optimizing computational complexity by a regression model.

##### 4.2. Regular Scenario

To design the self-planning algorithm, a sensitivity analysis of FPC parameters is carried out over a simple regular scenario. This regular scenario consists in seven trisectorial sites, specifically one central site surrounded by six adjacent sites, as shown in Figure 1. In such scenario, FPC parameters and are configured uniformly in all cells. Then, parameters are separately swept and coverage and capacity indicators are measured in one cell of the centre site. The rest of the simulation parameters are shown in Table 1.