A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS

To satisfy the demand for higher data rate while maintaining the quality of service, a dense long-term evolution (LTE) cells environment is required. This imposes a big challenge to the network when it comes to performing handover (HO). Cell selection has an important influence on network performance, to achieve seamless handover. Although a successful handover is accomplished, it might be to a wrong cell when the selected cell is not an optimal one in terms of signal quality and bandwidth. This may cause significant interference with other cells, handover failure (HOF), or handover ping-pong (HOPP), consequently degrading the cell throughput. To address this issue, we propose a multiple-criteria decision-making method. In this method, we use an integrated fuzzy technique for order preference by using similarity to ideal solution (TOPSIS) on S-criterion, availability of resource blocks (RBs), and uplink signal-to-interference-plus-noise ratio. The conventional cell selection in LTE is based on S-criterion, which is inadequate since it only relies on downlink signal quality. A novel method called fuzzy multiple-criteria cell selection (FMCCS) is proposed in this paper. FMCCS considers RBs utilization and user equipment uplink condition in addition to S-criterion. System analysis demonstrates that FMCCS managed to reduce handover ping-pong and handover failure significantly. This improvement stems from the highly reliable cell-selection technique that leads to increased throughput of the cell with a successful handover. The simulation results show that FMCCS outperforms the conventional and cell selection scheme (CSS) methods

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

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

A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS

To satisfy the demand for higher data rate while maintaining the quality of service, a dense long-term evolution (LTE) cells environment is required. This imposes a big challenge to the network when it comes to performing handover (HO). Cell selection has an important influence on network performance, to achieve seamless handover. Although a successful handover is accomplished, it might be to a wrong cell when the selected cell is not an optimal one in terms of signal quality and bandwidth. This may cause significant interference with other cells, handover failure (HOF), or handover ping-pong (HOPP), consequently degrading the cell throughput. To address this issue, we propose a multiple-criteria decision-making method. In this method, we use an integrated fuzzy technique for order preference by using similarity to ideal solution (TOPSIS) on S-criterion, availability of resource blocks (RBs), and uplink signal-to-interference-plus-noise ratio. The conventional cell selection in LTE is based on S-criterion, which is inadequate since it only relies on downlink signal quality. A novel method called fuzzy multiple-criteria cell selection (FMCCS) is proposed in this paper. FMCCS considers RBs utilization and user equipment uplink condition in addition to S-criterion. System analysis demonstrates that FMCCS managed to reduce handover ping-pong and handover failure significantly. This improvement stems from the highly reliable cell-selection technique that leads to increased throughput of the cell with a successful handover. The simulation results show that FMCCS outperforms the conventional and cell selection scheme (CSS) methods