بهینه سازی برای حفظ تعادل بار با استفاده از یادگیری Q فازی برای نسل بعدی شبکه های بی سیم
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5806||2013||11 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 4, March 2013, Pages 984–994
Load balancing is considered by the 3GPP as an important issue in Self-Organizing Networks due to its effectiveness to increase network capacity. In next generation wireless networks, load balancing can be easily implemented by tuning handover (HO) margins, achieving a decrease in call blocking. However, call dropping can be increased as a negative effect of the HO-based load balancing, because users usually are handed over to cells where the radio conditions are worse. In this work, a Fuzzy Logic Controller (FLC) optimized by the fuzzy Q-Learning algorithm is proposed for the load balancing problem, with the aim of decreasing call blocking in congested cells, while at the same time restricting call dropping in neighboring cells according to the network policy. In particular, two different approaches for the FLC optimization are evaluated in this work, highlighting that one of the proposed methods allows to accurately preserve the call quality constraint during the load balancing, while the other can adapt to network variations. Results show that the optimized FLC provides a notable reduction in call blocking while preserving call dropping under the operator constraint.
During the last years, cellular networks have experienced a large increase in size and complexity. Generally, network planning provides proper dimensioning of radio resources during the design phase of the Radio Access Network. However, as traffic demand changes over time, both traffic demand and network resource dimensioning become misaligned, thus leading to an inefficient use of resources. To cope with such a problem in a cost-effective manner, self-optimizing techniques remains as the best solution rather than adding new resources. Load balancing is a major issue addressed in the field of Self-Organizing Networks (SONs), whose main objective is the reduction of operational effort and cost in networks, (Döttling and Viering, 2009 and GPP TR 36.902, 2009). SONs consist of a set of principles and concepts that can be applied on advanced real-world networks in order to automate the operation and improve network quality. SONs functions are classified into three related terms (INFSO-ICT-216284 SOCRATES, 2008): Self-Configuration, Self-Optimization and Self-Healing. The first term, Self-Configuration, attempts to automate network deployment and parameter configuration, e.g., when a new resource is added to the existing infrastructure. Self-Healing is related to failure detection, diagnosis and mitigation to cope with major service outages. Lastly, Self-Optimization aims to dynamically adapt network parameters to improve network quality. Load balancing is tackled here as a Self-Optimizing task that can be solved by tuning specific network parameters, like those involved in the handover (HO) process. The concept of SON is widely addressed in next-generation networks, being part of the specification of the wireless technology known as Long-Term Evolution (LTE) (Rinne & Tirkkonen, 2010). In addition, load balancing, as part of SON, has been recognized as one of the key enablers in LTE-advanced (3GPP TR 36.912, 2011). The load balancing problem can be solved by sharing traffic between adjacent cells. The HO process is responsible for transferring an ongoing call from one cell to another. By adjusting HO parameters settings, the service area of a cell can be modified to send users to neighboring cells. Thus, the size of the congested cell is reduced while adjacent cells increase in size taking users from the congested cell edge. As a result of a better matching between the spatial distribution of traffic demand and network resources, more users could be accepted in the crowded area so that the call blocking probability would be reduced (Luna-Ramírez, Toril, Fernández-Navarro, & Wille, 2011). A significant research effort has been devoted in the literature to the load balancing problem. Studies are technology-dependent: GERAN (Luna-Ramírez et al., 2011), GSM/UMTS (Tölli and Hakalin, 2002 and Pillekeit et al., 2004), UMTS (Li, Fan, Yang, & Gu, 2005) and LTE (Lobinger et al., 2010, Nasri and Altman, 2007, Kwan et al., 2010, Wang et al., 2010 and Zhang et al., 2010). Some of the previous references are focused on inter-system load balancing, where propagation conditions are not necessarily a problem because the coverage areas of different radio technologies usually are overlapped. In the case of intra-system load balancing, when a user is handed over to another cell, the propagation conditions could get worse if the user is located near the cell edge because of the interference caused by neighboring cells. In LTE, the previous references analyze the performance of load balancing mostly in terms of achievable throughput for data services, neglecting the dropping rate, which is an important performance indicator in real-time traffic (voice, video, etc.). Also it is noted that controlling those quality indicators (e.g., dropping rate) for real-time traffic is not considered by the previous references as only performance evaluation is carried out without any further impact on the indicators. In Kwan et al. (2010), it is remarked that the trade-off between enhanced blocking and degraded dropping can be adjusted by restricting the maximum achievable HO margin. However, further study of load balancing effects on quality indicators for real-time traffic would be necessary. Concerning the second topic of the paper, automatic self-tuning implemented by FLCs has been widely applied to mobile network parameter optimization in many references, (Luna-Ramírez et al., 2008, Werner et al., 2007 and ZahirAzami et al., 2003). In Toril and Wille (2008), traffic sharing in GERAN is addressed by designing an FLC which iteratively adjusts the HO margins to minimize the call blocking in the network. In Rodríguez, de la Bandera, Muñoz, and Barco (2011), the dynamic load balancing in a realistic urban scenario is tackled by applying an FLC. All these references prove that FLCs are very useful for automatic network parameter optimization. Fuzzy Logic benefits come from their ability to translate human knowledge into a set of basic rules, which represent the mapping of the input to the output in linguistic terms. Such rules are derived from the knowledge and experience of a human expert of the system. In cellular networks, the experience is extracted from network operators, who have to manually adjust many network parameters. However, knowledge usually is not always available and different strategies have been developed to adapt or refine rules, e.g. through learning using neural networks, evolutionary computing or reinforcement learning. Some of these techniques have been successfully applied in wireless network optimization problems, such as in Çalhan and Çeken (2010). Q-Learning is a reinforcement learning method particularly appropriate for learning from interaction, when it is often impractical to obtain representative examples of desired behavior for all the situations in which the controller has to act (Sutton & Barto, 1998). Different works applying Q-Learning in wireless network optimization problems can also be found in the bibliography, (Chen et al., 2009, Nie and Haykin, 1999, Galindo-Serrano and Giupponi, 2010 and El-Alfy and Yao, 2011). The inter-system load balancing problem is addressed in Nasri, Samhat, and Altman (2007) by designing an FLC optimized by the fuzzy extension of Q-Learning algorithm in a UMTS network with WLAN hotspots. This paper investigates the Self-Optimization of a fuzzy logic controller (FLC) to solve persistent congestion problems in future wireless networks. More specifically, the congestion problem is due to an uneven spatial traffic distribution, e.g., when the center of a city becomes crowded. The optimization process is carried out by the fuzzy Q-Learning algorithm with the goal to reduce the blocking probability for voice services while the call dropping is controlled according to network operator constraints. Results are evaluated in a non-uniformly distributed traffic scenario, where the FLC is optimized to fulfill the call dropping constraint. Load balancing has an impact on the Grade-of-Service (GoS), which includes call accessibility and maintainability. As a result of a better exploitation of the system capacity, GoS is improved. Although HO-based load balancing is an effective method to share traffic in cellular networks, it may cause negative effects on call maintainability. If the HO margin is decreased, the target cell would increase the probability to be preferred than the serving cell (even if the connection quality is worse), so that some users could be handed over to the target cell. Those users, usually located in the cell edge, will experience worse radio conditions in the target cell as a result of applying such a traffic sharing technique. Thus, negative values of HO margins would increase the risk of dropping. The main contribution of this work is the design of an algorithm to control quality performance for real-time traffic during the load balancing process. Typically, studies found in the bibliography addressing the load balancing problem aim to provide more capacity to a fixed number of users in the system so that they experience higher instant throughput o lower delay, but accessibility is usually not tackled. In future networks, accessibility will be an important feature as services such as VoIP calls are expected to be widely used. In addition, the connection quality loss experienced by some users when load balancing is carried out and controllability of such an effect have not been properly addressed in the literature. This paper addresses how much the connection quality can be decreased when load balancing is carried out depending on the operator policy. Thus, flexibility and easiness from the network operator perspective is provided by simply adjusting a single parameter. Unlike the design in Nasri et al. (2007), the FLC proposed in this paper balances traffic load in a intra-system LTE scenario where call dropping becomes a key issue. In addition, HO margins of the standard HO algorithm are adjusted in this paper, instead of tuning load thresholds, as proposed in Nasri et al. (2007). In Muñoz, Barco, de la Bandera, Toril, and Luna-Ramírez (2011), the fuzzy Q-Learning algorithm is applied to the intra-system load balancing problem. However, the developed algorithm does not address the problem of the connection quality when load balancing is performed, which is the main objective of this paper. In addition, the analysis in Muñoz et al. (2011) is evaluated in a GSM network. Thus, the FLC proposed in this paper can be considered as a cost-effective solution to increase network capacity in next generation wireless networks as hardware upgrade is not necessary. The rest of the paper is organized as follows. Section 2 describes the system model for an LTE network, including the main system measurements and HO parameters. In Section 3, the structure of the proposed self-tuning scheme is presented. Section 4 explains the fuzzy Q-Learning algorithm and its application to the proposed FLC. Section 5 describes the simulation setup and discusses the results. Finally, Section 6 draws the conclusions.
نتیجه گیری انگلیسی
In this paper, the optimization of an FLC for load balancing in next generation wireless networks has been proposed, which is based on dynamically tuning HO margins. Two different optimization approaches using the fuzzy Q-Learning algorithm have been investigated. UEE approach is based on an optimization scheme that explores all the candidate FLC actions throughout the load balancing process. In this case, introducing test users into the network throughout the night or using network simulation tools are methods recommended to avoid degrading system performance when testing non-optimal actions. Once the optimal FLC configuration has been found, the FLC can be applied to the real situation. BEE is an optimization scheme that combines both exploitation and exploration to enhance performance while finding the optimal FLC actions and also to provide dynamic adaptation to system variations. The proposed methods are compared to an FLC (with no optimization) which is used as a benchmark. Simulations carried out in a non-uniformly distributed traffic scenario highlight that call blocking can be significantly reduced (from approximately 20% to 7% in the highest loaded cell), while at the same time keeping call dropping in neighboring cells under a certain level set by the operator. Simulation results show that the UEE optimization approach is a useful method to accurately preserve the call quality constraint during the load balancing by simply adjusting a call dropping threshold. It is shown that the best trade-off between CBR and CDR (given by a 7.5% of user dissatisfaction) is achieved by setting the CDR threshold to 0.06. In the case of the BEE optimization approach, performance is slightly decreased compared to UEE due to the percentage of non-optimal actions executed by the Q-Learning algorithm throughout the load balancing process (7.7% of user dissatisfaction is obtained). However, it is shown that the benefits from the BEE approach may be higher than the UEE approach because both optimization and load balancing are carried out on the same scenario at the same time, and the method can adapt to network variations. The combination of fuzzy logic with Q-Learning simplifies the task of designing the fuzzy rules of the controller. The FLC can also be considered as a cost-effective solution to increase network capacity because hardware upgrade is not necessary and manual intervention is minimized. Future work could include the application of the proposed algorithm to multimedia data services, in which other performance indicators (e.g., outage rate) would be needed to estimate the user connection quality. Finally, note that the proposed algorithms for load balancing are applicable to 4G networks, such as LTE-advanced, although LTE has been selected as a use case.