Handoff Decision for Multi-user Multi-class Traffic in MIMO-LTE-A Networks

AbstractLTE-A networks do not have a central controlling system or node and is made up of several networking technologies. Handover is a method to assure that users can move freely within a network without losing the network connection. Thus, handoff is important in LTE-A to maintain the quality of service. But, handoffs in LTE-A face numerous issues like rapid change in network topology, failure in calls maintenance, etc. Thus, making efficient handoff decision is important. So, in this paper we develop a vertical handoff decision model on the basis of the utility model such that the handoff occurs only to the suitable cells in order to avoid any problem in maintaining the network connectivity

Performance improvement of vertical handoff algorithms for QoS support over heterogenuous wireless networks

During the vertical handoff procedure, handoff decision is the most important step that affects the normal working of communication. An incorrect handoff decision or selection of a non-optimal network can result in undesirable effects such as higher costs, poor service experience, degrade the quality of service and even break off current communication. The objective of this paper is to determine the conditions under which vertical handoff should be performed in heterogeneous wireless networks. In this paper, we present a comprehensive analysis of different vertical handoff decision algorithms. To evaluate tradeoffs between their performance and efficiency, we propose two improved vertical handoff decision algorithm based on Markov Decision Process which are referred to as MDP_SAW and MDP_TOPSIS. The proposed mechanism assists the terminal in selecting the top candidate network and offer better available bandwidth so that user satisfaction is effectively maximized. In addition, our proposed method avoids unbeneficial handoffs in the wireless overlay networks

Mobile Networks

The growth in the use of mobile networks has come mainly with the third generation systems and voice traffic. With the current third generation and the arrival of the 4G, the number of mobile users in the world will exceed the number of landlines users. Audio and video streaming have had a significant increase, parallel to the requirements of bandwidth and quality of service demanded by those applications. Mobile networks require that the applications and protocols that have worked successfully in fixed networks can be used with the same level of quality in mobile scenarios. Until the third generation of mobile networks, the need to ensure reliable handovers was still an important issue. On the eve of a new generation of access networks (4G) and increased connectivity between networks of different characteristics commonly called hybrid (satellite, ad-hoc, sensors, wired, WIMAX, LAN, etc.), it is necessary to transfer mechanisms of mobility to future generations of networks. In order to achieve this, it is essential to carry out a comprehensive evaluation of the performance of current protocols and the diverse topologies to suit the new mobility conditions

A hybrid intelligent model for network selection in the industrial Internet of Things

Industrial Internet of Things (IIoT) plays an important role in increasing productivity and efficiency in heterogeneous wireless networks. However, different domains such as industrial wireless scenarios, small cell domains and vehicular ad hoc networks (VANET) require an efficient machine learning/intelligent algorithm to process the vertical handover decision that can maintain mobile terminals (MTs) in the preferable networks for a sufficient duration of time. The preferred quality of service parameters can be differentiated from all the other MTs. Hence, in this paper, the problem with the vertical handoff (VHO) decision is articulated as the process of the Markov decision aimed to maximize the anticipated total rewards as well as to minimize the handoffs’ average count. A rewards function is designed to evaluate the QoS at the point of when the connections take place, as that is where the policy decision for a stationary deterministic handoff can be established. The proposed hybrid model merges the biogeography-based optimization (BBO) with the Markov decision process (MDP). The MDP is utilized to establish the radio access technology (RAT) selection’s probability that behaves as an input to the BBO process. Therefore, the BBO determines the best RAT using the described multi-point algorithm in the heterogeneous network. The numerical findings display the superiority of this paper’s proposed schemes in comparison with other available algorithms. The findings shown that the MDP-BBO algorithm is able to outperform other algorithms in terms of number of handoffs, bandwidth availability, and decision delays. Our algorithm displayed better expected total rewards as well as a reduced average account of handoffs compared to current approaches. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model

Energy efficiency in wireless communication

This era would probably be recognized as the information age, hence as a paramount milestone in the progress of mankind, by the future historians. One of the most significant achievements of this age is, making it possible to transmit and receive information effectively and reliably via wireless radio technology. The demand of wireless communication is increasing in a never-resting pace, imposing bigger challenge not only on service providers but also on innovators and researches to innovate out-of-the-box technologies. These challenges include faster data communication over seamless, reliable and cost effective wireless networks, utilizing the limited physical radio resources as well as considering the environmental impact caused by the increasing energy consumption. The ever-expanding wireless communication infrastructure is withdrawing higher energy than ever, raising the need for finding more efficient systems. The challenge of developing efficient wireless systems can be addressed on several levels, starting from device electronics, up to the network-level architecture and protocols. The anticipated gains of achieving such efficiency is the key feature of extending mobile devices' battery life and reducing environmental and economic impacts of wireless communication infrastructure. Therefore energy efficient designs are urgently needed from both environmental and economic aspects of wireless networks. In this research, we explore the field of energy efficiency in MAC and Physical layers of wireless networks in order to enhance the performance and reliability of future wireless networks as well as to reduce its environmental footprint. In the first part of this research, we analyse the energy efficiency of two mostly used modulation techniques, namely MQAM and MFSK, for short range wireless transmissions, up to a few $100$s of meters, and propose optimum rate adaptation to minimize the energy dissipation during transmissions. Energy consumed for transmitting the data over a distance to maintain a prescribed error probability together with the circuit energy have been considered in our work. We provide novel results for optimal rate adaptation for improved energy efficiency. Our results indicate that the energy efficiency can be significantly improved by performing optimal rate adaptation given the radio and channel parameters, and furthermore we identify the maximum distance where optimal rate adaptation can be performed beyond which the optimum rate then becomes the same as the minimum data rate. In the second part of this research, we propose energy efficient algorithm for cellular base stations. In cellular networks, the base stations are the most energy consuming parts, which consume approximately $60-80\%$ of the total energy. Hence control and optimization of energy consumption at base stations should be at the heart of any green radio engineering scheme. Sleep mode implementation in base stations has proven to be a very good approach for the energy efficiency of cellular BSs. Therefore, we have proposed a novel strategy for improving energy efficiency on ternary state transceivers for cellular BSs. We consider transceivers that are capable of switching between sleep, stand-by and active modes whenever required. We have modelled these ternary state transceivers as a three-state Markov model and have presented an algorithm based on Markov model to intelligently switch among the states of the transceivers based on the offered traffic whilst maintaining a prescribed minimum rate per user. We consider a typical macro BS with state changeable transceivers and our results show that it is possible to improve the energy efficiency of the BS by approximately $40\%$ using the proposed MDP based algorithm. In the third part of this research, we propose energy efficient algorithm for aerial base stations. Recently aerial base stations are investigated to provide wireless coverage to terrestrial radio terminals. The advantages of using aerial platforms in providing wireless coverage are many including larger coverage in remote areas, better line-of-sight conditions etc. Energy is a scarce resource for aerial base stations, hence the wise management of energy is quite beneficial for the aerial network. In this context, we study the means of reducing the total energy consumption by designing and implementing an energy efficient aerial base station. Sleep mode implementation in base stations (BSs) has proven to be a very good approach for improving the energy efficiency; therefore we propose a novel strategy for further improving energy efficiency by considering ternary state transceivers of aerial base stations. Using the three state model we propose a Markovian Decision process (MDP) based algorithm to switch between the states for improving the energy efficiency of the aerial base station. The MDP based approach intelligently switches between the states of the transceivers based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our simulation results show that there is a around $40\%$ gain in the energy efficiency when using our proposed MDP algorithm together with the three-state transceiver model for the base station compared to the always active mode. We have also shown the energy-delay trade-off in order to design an efficient aerial base station. In the final part of our work, we propose a novel energy efficient handover algorithm, based on Markov decision process (MDP) for the two-tier LTE network, towards reducing power transmissions at the mobile terminal side. The proposed policy is LTE backward-compatible, as it can be employed by suitably adapting a prescribed SNR target and standard LTE measurements. Simulation results reveal that compared to the widely adopted policy based on strongest cell and another energy efficient policy, our proposed policy can greatly reduce the power consumption at the LTE mobile terminals. Most of our works presented in this dissertation has been published in conference proceeding and some of them are currently undergoing a review process for journals. These publications will be highlighted and identified at the end of the first chapter of this dissertation

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

Resource management in QoS-aware wireless cellular networks

2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost