Optimal and practical handover decision algorithms in heteregeneous marco-femto cellular networks

Driven by the smart tablet/phone revolution and the proliferation of bandwidth hungry applications such as cloud computing and streaming video, the demand for high data rate wireless communication is increasing tremendously. In order to meet the increasing demand from subscribers, wireless operators are in the process of augmenting their macrocell network with supplemental infrastructure such as microcells, distributed antennas and relays. An alternative with lower upfront costs is to improve indoor coverage and capacity by using end-consumer installed femtocells. A femtocell is a low power, short range (up to 100 meters coverage radius) cellular wireless access point (AP), functioning in service provider owned licensed spectrum. Due to the proximity of end users to the femtocell access points, APs are able to provide higher end-user QoE and better spatial reuse of limited spectrum. Femtocells are useful in offloading the macro-cellular network as well as reducing the operating and capital expenditure costs for operators. Femtocells coexist with legacy cellular networks consisting of macrocells. In this emerging combined architecture, large number of Femtocell Application Point (FAPs) is randomly deployed in the coverage area of macro BSs. However, several problems related to MM (mobility management) and RM (resource management) in this combined architecture still remain to be solved. The ad hoc deployment of FAPs and asymmetric radio communication and call processing capabilities between macrofemto networks are the primary causes of these problems. Uncoordinated deployment of FAPs providing indoor oriented wireless access service within the macro coverage may cause severe interference problems that need to be mitigated and handled by RM/MM schemes. The MM decisions should take into account the resource constraints and UE mobility in order to prevent unnecessary or undesirable handovers towards femtocells. Ignoring these factors in MM decisions may lead to low customer satisfaction due to mismanagement of handover events in the combined macro-femto network, delayed signaling traffic and unsatisfactory call/connection quality. In order to address all of the aforementioned issues, the handover decision problem in combined femto-macro networks has been formulated as a multi-objective non-linear optimization problem. Since there are no known analytical solution to this problem, an MDP (Markov Decision Process) based heuristic has been proposed as a practical and optimal HO (handover) decision making scheme. This heuristic has been updated and improved in an iterative manner and has also been supported by a dynamic SON (Self Organizing Networks) algorithms that is based on heuristic's components. The performance results show that the final version of MDP based heuristic has signi cantly superior performance in terms offloading the macro network, minimizing the undesirable network events (e.g. outage and admission rejection) when compared to state-of-art handover algorithms

Fuzzy-logic framework for future dynamic cellular systems

There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data

Enhanced Handover Mechanism in Long Term Evolution (LTE) Networks

Femtocell is a low power base station, wireless access point designed especially for homes and small organizations. It is promising technology for operators to improve their capacity and for users to give indoor coverage. As mobile users are increasing day by day so the legacy system is unable to provide such a high data rates to all these users. In this case femtocells play a key role to offload the data traffic from macro base station. The implementation of femtocell has posed so many challenges like interference, localization, access control and mobility management. The aim of this paper is to present an enhanced algorithm for handover in Hand-In scenario. In already existing algorithms handover is decided on the basis of a single parameter but here we have simulated an algorithm that considers multiple parameters instead of a single parameter for handover. Through this algorithm, the most suitable femtocell will be selected for handover, hence number of handovers will be decreased. Simulation results show that the system performance has been improved.

Improved handoff mechanism for infiltrating user equipments in composite networks

The wireless technology and communication plays a vital role in our daily life. The end users are expecting more Quality of Experience (QOE) rather than the Quality of Service (QOS). In order to provide full signal coverage the entire cellular network coverage is divided in to small cells called as femtocells, those femtocells are covered with femtocell antennas which are very small in size compared with regular antennas. With these femtocell coverage problem is solved but when a user moves from one location to another location the user has to switch from one base station to so many base station which cannot be maintained with present handoff methods. The present hand off methods working on distance calculation approach, the proposed method is based on the velocity and device direction calculated based on GPS location toward the Base Station (BS) of the device which may ping pong handoff effect

User Behavior Aware Cell Association in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HetNets), cell association of User Equipment (UE) affects UE transmit rate and network throughput. Conventional cell association rules are usually based on UE received Signal-to-Interference-and-Noise-Ratio (SINR) without taking into account user behaviors, which can indeed be exploited for improving network performance. In this paper, we investigate UE cell association in HetNets based on individual user behavior characteristics with aim to maximize long- term expected system throughput. We model the problem as a stochastic optimization model Restless Multi-Armed Bandit (RMAB). As it is a PSPACE-hard problem, we develop a primal-dual heuristic index algorithm and the solution specifies the rule that determines which arms in the RMAB model to be selected at each decision time. According to the solution of RMAB, we propose a new cell association strategy called Index Enabled Association (IDEA). We also conduct simulation experiments to compare IDEA with conventional max-SINR cell association strategy and an existing game-based RAT selection scheme. Numerical results demonstrate the advantages of IDEA in typical scenarios

Reinforcement Learning Based Handoff for Millimeter Wave Heterogeneous Cellular Networks

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named LESH to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In LESH, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, LESH can significantly reduce the number of handoffs when compared with traditional handoff policies

Trajectory Aware Macro-cell Planning for Mobile Users

We design and evaluate algorithms for efficient user-mobility driven macro-cell planning in cellular networks. As cellular networks embrace heterogeneous technologies (including long range 3G/4G and short range WiFi, Femto-cells, etc.), most traffic generated by static users gets absorbed by the short-range technologies, thereby increasingly leaving mobile user traffic to macro-cells. To this end, we consider a novel approach that factors in the trajectories of mobile users as well as the impact of city geographies and their associated road networks for macro-cell planning. Given a budget k of base-stations that can be upgraded, our approach selects a deployment that impacts the most number of user trajectories. The generic formulation incorporates the notion of quality of service of a user trajectory as a parameter to allow different application-specific requirements, and operator choices.We show that the proposed trajectory utility maximization problem is NP-hard, and design multiple heuristics. We evaluate our algorithms with real and synthetic data sets emulating different city geographies to demonstrate their efficacy. For instance, with an upgrade budget k of 20%, our algorithms perform 3-8 times better in improving the user quality of service on trajectories in different city geographies when compared to greedy location-based base-station upgrades.Comment: Published in INFOCOM 201

The SMART handoff policy for millimeter wave heterogeneous cellular networks

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning