LTE networks' main challenge is to efficiently use the available spectrum, and to provide satisfying quality of service for mobile users. However, using the same bandwidth among adjacent cells leads to occurrence of Inter-cell Interference (ICI) especially at the cell-edge. Basic interference mitigation approaches consider bandwidth partitioning techniques between adjacent cells, such as frequency reuse of factor m schemes, to minimize cell-edge interference. Although SINR values are improved, such techniques lead to significant reduction in the maximum achievable data rate. Several improvements have been proposed to enhance the performance of frequency reuse schemes, where restrictions are made on resource blocks usage, power allocation, or both. Nevertheless, bandwidth partitioning methods still affect the maximum achievable throughput. In this proposal, we intend to perform a comprehensive survey on Inter-Cell Interference Coordination (ICIC) techniques, and we study their performance while putting into consideration various design parameters. This study is implemented throughout intensive system level simulations under several parameters such as different network loads, radio conditions, and user distributions. Simulation results show the advantages and the limitations of each technique compared to frequency reuse-1 model. Thus, we are able to identify the most suitable ICIC technique for each network scenario.
When several radio access technologies (e.g., HSPA, LTE, WiFi and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a Semi-Markov Decision Process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization. Index Terms-Radio access technology selection, semi-Markov decision process, reinforcement learning, heterogeneous cellular networks. I. INTRODUCTION T HE demand for high-quality and high-capacity radio networks is continuously increasing. It has been reported that global mobile data traffic grew by 81 percent in 2013 [1]. Furthermore, monthly mobile traffic is forecast to surpass 15 exabytes by 2018, nearly 10 times more than in 2013 [1]. Along with this impressive growth, mobile operators are urged to intelligently invest in network infrastructure. They also need to reconsider their flat-rate pricing models [2], seeking positive return-on-investment.
International audienceIn heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize network performance and capacity, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the Radio Access Technology (RAT) selection, a key CRRM functionality, and proposes a hybrid decision framework that dynamically integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS information. Our hybrid approach involves two interdependent decision-making processes. The first one, on the network side, consists in deriving appropriate network information so as to guide user decisions in a way to meet operator objectives. The second one, where individual users combine their needs and preferences with the signaled network information, consists in selecting the RAT to be associated with in a way to maximize user utility. We first focus on the user side and present a satisfaction-based multi-criteria decision-making method. By avoiding inadequate decisions, our algorithm outperforms existing solutions and maximizes user utility. Further, we introduce two heuristic methods, namely the staircase and the slope tuning policies, to dynamically derive network information in a way to enhance resource utilization. The performance of each decision-making process, on network and user sides, is evaluated separately through extensive simulations. A comparison of our hybrid approach with six different RAT selection schemes is also presented
Mobile network operators are facing the challenge to increase network capacity and satisfy the growth in data traffic demands. In this context, Long Term Evolution (LTE) networks, LTE-Advanced networks, and future mobile networks of the Fifth Generation seek to maximize spectrum profitability by choosing the frequency reuse-1 model. Due to this frequency usage model, advanced radio resource management and power allocation schemes are required to avoid the negative impact of interference on system performance. Some of these schemes modify resource allocation between network cells, while others adjust both resource and power allocation. In this article, we introduce a cooperative distributed interference management algorithm, where resource and power allocation decisions are jointly made by each cell in collaboration with its neighboring cells. Objectives sought are: increasing user satisfaction, improving system throughput, and increasing energy efficiency. The proposed technique is compared to the frequency reuse-1 model and to other state-of-the-art techniques under uniform and non-uniform user distributions and for different network loads. We address scenarios where throughput demands are homogeneous and non-homogeneous between network cells. System-level simulation results demonstrate that our technique succeeds in achieving the desired objectives under various user distributions and throughput demands.
One major concern for operators of Long Term Evolution (LTE) networks is mitigating inter-cell interference problems. Inter-Cell Interference Coordination (ICIC) techniques are proposed to reduce performance degradation and to maximize system capacity. It is a joint resource allocation and power allocation problem that aims at controlling the trade-off between resource efficiency and user fairness. Traditional interference mitigation techniques are Fractional Frequency Reuse (FFR) and Soft Frequency Reuse (SFR). FFR statically divides the available spectrum into reuse-1 and reuse-3 portions in order to protect cell-edge users, while SFR reduces downlink transmission power allocated for cell-center resources to protect vulnerable users in the neighboring cells. However, these static techniques are not adapted to non-uniform user distribution scenarios, and they do not provide guarantees on throughput fairness between user equipments. In this paper, we introduce a non-cooperative dynamic ICIC technique that dynamically adjusts resource block allocation according to user demands in each zone. We investigate the impact of this technique on throughput distribution and user fairness under non-uniform user distributions, using an LTE downlink system level simulator. Simulation results show that the proposed technique improves system capacity, and increases throughput fairness in comparison with reuse-1 model, FFR and SFR. It does not require any cooperation between base stations of the LTE network.
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