Intercell interference mitigation in long term evolution (LTE) and LTE-advanced

University of Technology Sydney. Faculty of Engineering and Information Technology.Bandwidth is one of the limited resources in Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks. Therefore, new resource allocation techniques such as the frequency reuse are needed to increase the capacity in LTE and LTE-A. However, the system performance is severely degraded using the same frequency in adjacent cells due to increase of intercell interference. Therefore, the intercell interference management is a critical point to improve the performance of the cellular mobile networks. This thesis aims to mitigate intercell interference in the downlink LTE and LTE-A networks. The first part of this thesis introduces a new intercell interference coordination scheme to mitigate downlink intercell interference in macrocell-macrocell scenario based on user priority and using fuzzy logic system (FLS). A FLS is an expert system which maps the inputs to outputs using “IF...THEN” rules and an aggregation method. Then, the final output is obtained through a deffuzifaction approach. Since this thesis aims to mitigate interference in downlink LTE networks, the inputs of FLS are selected from important metrics such as throughput, signal to interference plus noise ratio and so on. Simulation results demonstrate the efficacy of the proposed scheme to improve the system performance in terms of cell throughput, cell edge throughput and delay when compared with reuse factor one. Thereafter, heterogeneous networks (HetNets) are studied which are used to increase the coverage and capacity of system. The focus of the next part of this thesis is picocell because it is one of the important low power nodes in HetNets which can efficiently improve the overall system capacity and coverage. However, new challenges arise to intercell interference management in macrocell-picocell scenario. Three enhanced intercell interference coordination (eICIC) schemes are proposed in this thesis to mitigate the interference problem. In the first scheme, a dynamic cell range expansion (CRE) approach is combined with a dynamic almost blank subframe (ABS) using fuzzy logic system. In the second scheme, a fuzzy q-learning (FQL) approach is used to find the optimum ABS and CRE offset values for both full buffer traffic and video streaming traffic. In FQL, FLS is combined by q-learning approach to optimally select the best consequent part of each FLS rule. In the third proposed eICIC scheme, the best location of ABSs in each frame is determined using Genetic Algorithm such that the requirements of video streaming traffic can be met. Simulation results show that the system performance can be improved through the proposed schemes. Finally, the optimum CRE offset value and the required number of ABSs will be mathematically formulated based on the outage probability, ergodic rate and minimum required throughput of users using stochastic geometry tool. The results are an analytical formula that leads to a good initial estimate through a simple approach to analyse the impact of system parameters on CRE offset value and number of ABSs

Dynamic Almost Blank Subframe Scheme for Enhanced Intercell Interference Coordination in LTE-A Heterogeneous Networks

In LTE-A heterogeneous network, traffic load may be distributed unequally because the transmission power of macro eNodeB (eNB) is higher than pico eNB. To address the coverage problems resulting from nodes with different transmission powers, cell range expansion (CRE) technique has been proposed as a cell selection technique. However, in this case, the intercell interference (ICI) problem can occur on both data and control channels when users connect to pico eNB. To mitigate ICI problem, a new dynamic almost blank subframe (ABS) scheme is proposed in this paper. In this scheme, a fuzzy logic system is deployed to monitor the system performance and then obtain the required number of ABSs. Simulation results show that the cell throughput and user throughput can be improved using the proposed dynamic ABS scheme

On/Off Macrocells and Load Balancing in Heterogeneous Cellular Networks

The rate distribution in heterogeneous networks (HetNets) greatly benefits from load balancing, by which mobile users are pushed onto lightly-loaded small cells despite the resulting loss in SINR. This offloading can be made more aggressive and robust if the macrocells leave a fraction of time/frequency resource blank, which reduces the interference to the offloaded users. We investigate the joint optimization of this technique - referred to in 3GPP as enhanced intercell interference coordination (eICIC) via almost blank subframes (ABSs) - with offloading in this paper. Although the joint cell association and blank resource (BR) problem is nominally combinatorial, by allowing users to associate with multiple base stations (BSs), the problem becomes convex, and upper bounds the performance versus a binary association. We show both theoretically and through simulation that the optimal solution of the relaxed problem still results in an association that is mostly binary. The optimal association differs significantly when the macrocell is on or off; in particular the offloading can be much more aggressive when the resource is left blank by macro BSs. Further, we observe that jointly optimizing the offloading with BR is important. The rate gain for cell edge users (the worst 3-10%) is very large - on the order of 5-10x - versus a naive association strategy without macrocell blanking

Cognition-inspired 5G cellular networks: a review and the road ahead

Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics - including the cognitive and cooperative capabilities, network virtualization, and traffic offload - can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area

A fuzzy Q-learning approach for enhanced intercell interference coordination in LTE-Advanced heterogeneous networks

© 2014 IEEE. Since the transmission power of macro eNodeB (eNB) is higher than pico eNB in long term evolution-advanced heterogeneous network, the coverage area of picocell is small. In order to address the coverage problem, cell range expansion (CRE) technique has been recently proposed. However, CRE can lead to the downlink interference problem on both data and control channels when users are connected to pico eNB. In order to mitigate the downlink interference problem, a new dynamic almost blank subframe (ABS) scheme is proposed in this paper. In this scheme, a fuzzy q-learning approach is used to find the optimum ABS value. Simulation results show that the system performance can be improved through the proposed scheme

A dynamic almost blank subframe scheme for video streaming traffic model in heterogeneous networks

© 2015 IEEE. In heterogeneous network (HetNet), the coverage area of picocell is small due to transmission power difference between macro eNodeB (eNB) and pico eNB. As a result, the traffic load is distributed unequally which yields to macrocell overloading. In order to overcome this issue, cell range expansion (CRE) technique has been proposed. However, the CRE approach can affect the downlink signal quality of the offloaded users and then these users experience high downlink interference from macro eNB on their control and data channels. Therefore, such inter-cell interference coordination (ICIC) techniques are needed to realize the promised capacity and coverage. Enhanced ICIC (eICIC) is a time domain technique to mitigate interference in HetNets using almost blank subframes (ABSs). However, the static ABS value cannot support the dynamic changing of network conditions. In this paper, a dynamic ABS scheme is proposed based on Genetic Algorithm to find the best ABS value and ABS locations in a frame to mitigate interference problem between macrocell and picocells for video streaming traffic model. Exhaustive simulation results show that the proposed scheme can improve the system performance in terms of throughput, outage probability and delay

Joint Resource Optimization for Multicell Networks with Wireless Energy Harvesting Relays

This paper first considers a multicell network deployment where the base station (BS) of each cell communicates with its cell-edge user with the assistance of an amplify-and-forward (AF) relay node. Equipped with a power splitter and a wireless energy harvester, the self-sustaining relay scavenges radio frequency (RF) energy from the received signals to process and forward the information. Our aim is to develop a resource allocation scheme that jointly optimizes (i) BS transmit powers, (ii) received power splitting factors for energy harvesting and information processing at the relays, and (iii) relay transmit powers. In the face of strong intercell interference and limited radio resources, we formulate three highly-nonconvex problems with the objectives of sum-rate maximization, max-min throughput fairness and sum-power minimization. To solve such challenging problems, we propose to apply the successive convex approximation (SCA) approach and devise iterative algorithms based on geometric programming and difference-of-convex-functions programming. The proposed algorithms transform the nonconvex problems into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our algorithms converge to the locally optimal solutions that satisfy the Karush-Kuhn-Tucker conditions of the original nonconvex problems. We then extend our results to the case of decode-and-forward (DF) relaying with variable timeslot durations. We show that our resource allocation solutions in this case offer better throughput than that of the AF counterpart with equal timeslot durations, albeit at a higher computational complexity. Numerical results confirm that the proposed joint optimization solutions substantially improve the network performance, compared with cases where the radio resource parameters are individually optimized