Rahul Thakur

Breathe to Save Energy: Assigning Downlink Transmit Power and Resource Blocks to LTE Enabled IoT Networks

Undoubtedly, the Internet of Things (IoT) is the next big revolution in the field of wireless communication networks. IoT is an invisible network, which connects the physical world to the virtual world. Seamless Internet connectivity is essential between these two worlds for IoT to become a reality. In this aspect, long-term evolution advanced (LTE-A) is a promising technology, which meets the requirements of IoT. However, an exponential increase in the number of IoT devices will increase the energy consumption at base stations in LTE-A. Therefore, in this letter, we study the downlink energy efficiency aspect of LTE-A in the IoT networks. We propose a power control and resource block allocation scheme called breathing for an IoT network. Simulation results have shown that breathing performs better than the traditional maximum power allocation scheme and greedy power reduction scheme in terms of energy efficiency, system throughput, and system blocking.

An energy efficient cell selection scheme for femtocell network with spreading

Use of femtocells for indoor and office environment has proved to be an effective solution to handle ever increasing mobile data demands. Femtocell helps improving network capacity in an energy efficient manner without significantly burdening the operator with huge capital and operational expenditure. Since extremely dense femtocell deployments are expected in near future, it is of interest to look into their energy efficiency measures. In this paper, we analyse the energy efficiency aspect of cell selection scheme for femtocell networks. Cell selection scheme defines the criteria on which mobile users associate themselves with base stations. Hence, it plays a crucial role in system load balancing and total energy consumption. We suggest a unique cell selection scheme that assigns mobile users to femtocell base stations considering the capacity improvement obtained per unit increase in transmit power. Our proposed scheme shows an improvement in network performance in terms of both system capacity and energy efficiency. Additionally, we suggest the use of power spreading over subchannels to keep overall interference minimum while maximizing spectrum utilization.

Improving Delay and Energy Efficiency of Vehicular Networks Using Mobile Femto Access Points

A vehicular network with Road Side Units (RSUs) provides an efficient way to connect vehicles even on the move. However, due to high deployment and maintenance cost of RSUs, it is necessary to use fewer RSUs, such that the total cost is minimized. It is suggested that cellular networks, such as Long-Term Evolution (LTE), are capable of fulfilling the demands posed in vehicular network scenarios. Availability of high bandwidth, large coverage area, and low latency are some of the advantages of cellular networks, which help in overcoming the challenges of high-speed vehicular communication. In this paper, we propose a maiden approach to analyze the performance of a vehicular network with cellular infrastructure as a backbone. For this, we use mobile Femto Access Points (FAPs) as relays in place of RSUs. We model the network using

An Energy Efficient Cell Selection Framework for Femtocell Networks With Limited Backhaul Link Capacity

M/M/m

Coverage and Rate Analysis for Facilitating Machine-to-Machine Communication in LTE-A Networks Using Device-to-Device Communication

queue and compare the delay and throughput performance with traditional IEEE 802.11p vehicular networks. We also formulate an optimization problem and propose a subchannel power control algorithm to handle increased co-channel interference, which emerges due to high mobility of vehicles in the network. Our suggested approach shows improvement in terms of delay, throughput, and energy efficiency. The results are verified using extensive simulations.

Design and stochastic geometric analysis of an efficient Q-Learning based physical resource block allocation scheme to maximize the spectral efficiency of Device-to-Device overlaid cellular networks

Dense deployment of femtocells improves the network capacity without significantly burdening the operator with huge capital and operational expenditure. However, extremely dense deployment of femtocells comes with the cost of increased cochannel interference and higher energy consumption. Additionally, femtocells burden the existing ADSL/broadband lines by using them as backhaul to connect to the cellular core network. A cell selection scheme defines the criteria on which mobile users associate themselves with base stations. This criteria may include received signal quality, available bandwidth, and energy consumption. Hence, cell selection plays a crucial role in system capacity, load balancing, and energy consumption. In this paper, we provide a framework for femtocell deployment in the urban scenario where interference, energy consumption, and backhaul capacity are major concerns. We first model the energy consumption of base stations, user equipment, and wired backhaul links. Then, we propose an efficient spectrum and power allocation technique to mitigate interference and improve bandwidth utilization in the uplink and downlink, respectively. Finally, we suggest a novel QoS-aware cell selection scheme that assigns mobile users to femtocells considering the capacity improvement obtained per unit increase in energy consumption. The proposed cell selection scheme incorporates the energy consumption of the wired backhaul links and their limited capacity constraint into the cell selection criteria. Our proposed spectrum and power allocation technique, when combined with the proposed cell selection, leads to a significant reduction in energy consumption without any deterioration in the system capacity. Simulation results confirm that our proposed framework has the potential to significantly improve the energy efficiency of the network.

Resource allocation and cell selection framework for LTE-Unlicensed femtocell networks

With a wide range of applications, Machine-to-Machine (M2M) communication has become an emerging technology for connecting generic machines to the Internet. To ensure ubiquity in connections across all machines, it is necessary to have a standard infrastructure, such as 3GPP LTE-A network infrastructure, that facilitates such type of communications. However, owing to the huge scale of ?> machines to be deployed in near future and the nature of data transactions, ensuring ubiquitous connections among all the machines will be difficult. Solutions that not only maintain connectivity but also route machine data in a cost effective manner are the need of the hour. In this context, it has been suggested that Device-to-Device (D2D) communication can play a very important role in expanding network coverage and routing the data between source-destination machine pairs. In this paper, we conduct a feasibility study to highlight the impact of multi-hop D2D communication in increasing the network coverage and average rate of a Machine Type Communication (MTC) device. We present a stochastic geometry based framework to analyze the coverage probability and average data rate of a three-hop M2M network deployed along with User Equipments (UEs) and conduct extensive simulations to study the system performance. Our simulation results show that the three-hop M2M network formed from out-of-range MTC devices and UEs can significantly improve the coverage and average rate of the entire network. Due to the mobility of users in the network, design of robust routing mechanisms in such a time evolving network becomes difficult. Hence, we suggest the use of space-time graph built from the predicted user locations to design a cost efficient multi-hop D2D topology that enables routing of MTC data to its destination.

An Efficient Physical Resource Block Assignment for Dense Femtocell Networks

Device-to-Device (D2D) communication empowers direct communication between two proximal users and helps in offloading network traffic. It is considered as one of the most promising 3GPP Long Term Evolution (LTE)-Advanced technologies for improving the spectrum and energy efficiency of Proximity-based Services (ProSe). However, due to interference posed by the D2D transmitters to the primary cellular users, intelligent resource allocation techniques need to be incorporated for the betterment of the overall system. Overlay D2D communication avoids the interference between cellular and D2D users by dedicating resources for D2D users. With this approach, efficient use of the dedicated Physical Resource Blocks (PRBs) among D2D users is still a concern. To handle the issue of optimum spectral efficiency, efficient allocation of spectrum resources is essential among D2D users. For the first time, we address this problem and propose a PRB allocation scheme that maximizes the PRB utilization among D2D users. The proposed scheme spreads the PRB requirement of a D2D pair over multiple PRBs while reducing the transmit power over each PRB, thereby reducing the overall interference and improving the spectral efficiency of the network. Extensive simulations demonstrate that our scheme does better than the conventional PRB allocation scheme (of assigning PRBs based on their data rate demand), by increasing the overall throughput and energy efficiency of the network. To analytically evaluate the proposed PRB allocation scheme, we present a stochastic geometry based framework to analyze the coverage and average rate of the network, and conclude that the link SINR of the D2D pairs is actually improved on employing our proposed scheme which results in high coverage and overall rate of the system. Finally, we propose a Q-Learning algorithm that learns the optimum number of PRBs to dedicate for overlay D2D communication based on the PRB demand of the cellular and D2D users. Extensive simulations reveal that the proposed algorithm efficiently allocates PRBs to the D2D pairs when compared to the conventional static scheme of dedicating fixed number of PRBs.

An efficient preamble compression for multi clock-rate sampling wireless devices

Abstract The availability of enormous bandwidth in 5 GHz WiFi band has advocated its use for the LTE communication. For this, LTE-Unlicensed (LTE-U) technology is standardized in 3GPP Release 12. The use of coexistence mechanism such as Listen-Before-Talk (LBT) can help protecting the quality of service of mobile users operating in the WiFi band by guaranteeing them channel access. Additionally, allocation of wireless resources to each mobile user in the WiFi band should be controlled to facilitate interference-free transmissions to other mobile users in the same band. In this paper, we propose two resource allocation techniques for femtocell networks which efficiently allocate resources such as spectrum and transmit power among competing mobile users in the LTE and WiFi bands. For the LTE band, we propose the use of spreading based spectrum and power allocation, and model the throughput of femtocells using a non-cooperative game theoretic framework. To improve the co-existence performance of femtocells operating in the WiFi band, we propose a Q-leaning based resource allocation technique for femtocell transmit power and duty cycle calculation. In a scenario where multiple base stations are available for user-base station association, considering only the signal strength to make association decision is not an optimal approach, specially when user load and available resources at base stations vary significantly with time. Considering this, we suggest a cell selection scheme that assigns mobile users to femtocells considering the bitrate they are expected to receive in both LTE and WiFi bands. Our resource allocation techniques, when combined with the proposed cell selection scheme, show a minimum improvement of 23.4% in network throughput and 19.6% reduction in energy consumption. This, in turn, also improves the energy efficiency of the network by at least 35%.

Power consumption modeling of femto-assisted cellular networks using Renewal Reward Process

Femtocells have proved to be an effective solution for handling the ever increasing demands for wireless data without incurring additional deployment costs. Deployment of these low cost, miniature base stations not only improves network robustness but also facilitates efficient location specific services for mobile users. However, dense deployment of femtocells comes with the cost of increased interference. To handle this additional interference without significantly affecting spectrum efficiency, smart assignment of wireless resources is necessary among femtocells. In this paper, we suggest a technique to intelligently reuse the available wireless resources among interfering femtocells so as to improve spectrum reuse and energy efficiency. Additionally, the suggested technique also shows improvement in system blocking for all possible deployment scenarios. Obtained results are verified using extensive simulations.