Energy-efficient optimal power allocation in integrated wireless sensor and cognitive satellite terrestrial networks

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint

Increased energy efficiency in LTE networks through reduced early handover

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Long Term Evolution (LTE) is enormously adopted by several mobile operators and has been introduced as a solution to fulfil ever-growing Users (UEs) data requirements in cellular networks. Enlarged data demands engage resource blocks over prolong time interval thus results into more dynamic power consumption at downlink in Basestation. Therefore, realisation of UEs requests come at the cost of increased power consumption which directly affects operator operational expenditures. Moreover, it also contributes in increased CO2 emissions thus leading towards Global Warming. According to research, Global Information and Communication Technology (ICT) systems consume approximately 1200 to 1800 Terawatts per hour of electricity annually. Importantly mobile communication industry is accountable for more than one third of this power consumption in ICT due to increased data requirements, number of UEs and coverage area. Applying these values to global warming, telecommunication is responsible for 0.3 to 0.4 percent of worldwide CO2 emissions. Moreover, user data volume is expected to increase by a factor of 10 every five years which results in 16 to 20 percent increase in associated energy consumption which directly effects our environment by enlarged global warming. This research work focuses on the importance of energy saving in LTE and initially propose bandwidth expansion based energy saving scheme which combines two resource blocks together to form single super RB, thereby resulting in reduced Physical Downlink Control Channel Overhead (PDCCH). Thus, decreased PDCCH overhead helps in reduced dynamic power consumption up to 28 percent. Subsequently, novel reduced early handover (REHO) based idea is proposed and combined with bandwidth expansion to form enhanced energy ii saving scheme. System level simulations are performed to investigate the performance of REHO scheme; it was found that reduced early handover provided around 35% improved energy saving while compared to LTE standard in 3rd Generation Partnership Project (3GPP) based scenario. Since there is a direct relationship between energy consumption, CO2 emissions and vendors operational expenditure (OPEX); due to reduced power consumption and increased energy efficiency, REHO subsequently proven to be a step towards greener communication with lesser CO2 footprint and reduced operational expenditure values. The main idea of REHO lies in the fact that it initiate handovers earlier and turn off freed resource blocks as compare to LTE standard. Therefore, the time difference (Transmission Time Intervals) between REHO based early handover and LTE standard handover is a key component for energy saving achieved, which is estimated through axiom of Euclidean geometry. Moreover, overall system efficiency is investigated through the analysis of numerous performance related parameters in REHO and LTE standard. This led to a key finding being made to guide the vendors about the choice of energy saving in relation to radio link failure and other important parameters

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications

Localisation of sensor nodes with hybrid measurements in wireless sensor networks

Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution

LPcomS: Towards a Low Power Wireless Smart-Shoe System for Gait Analysis in People with Disabilities

Gait analysis using smart sensor technology is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. In this thesis, we present a low power wireless smart-shoe system (LPcomS) to analyze different functional postures and characteristics of gait while walking. We have designed and implemented a smart-shoe with a Bluetooth communication module to unobtrusively collect data using smartphone in any environment. With the design of a shoe insole equipped with four pressure sensors, the foot pressure is been collected, and those data are used to obtain accurate gait pattern of a patient. With our proposed portable sensing system and effective low power communication algorithm, the smart-shoe system enables detailed gait analysis. Experimentation and verification is conducted on multiple subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this thesis

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

A comparison analysis of ble-based algorithms for localization in industrial environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

On the energy efficiency of spatial modulation concepts

Spatial Modulation (SM) is a Multiple-Input Multiple-Output (MIMO) transmission technique which realizes low complexity implementations in wireless communication systems. Due the transmission principle of SM, only one Radio Frequency (RF) chain is required in the transmitter. Therefore, the complexity of the transmitter is lower compared to the complexity of traditional MIMO schemes, such as Spatial MultipleXing (SMX). In addition, because of the single RF chain configuration of SM, only one Power Amplifier (PA) is required in the transmitter. Hence, SM has the potential to exhibit significant Energy Efficiency (EE) benefits. At the receiver side, due to the SM transmission mechanism, detection is conducted using a low complexity (single stream) Maximum Likelihood (ML) detector. However, despite the use of a single stream detector, SM achieves a multiplexing gain. A point-to-point closed-loop variant of SM is receive space modulation. In receive space modulation, the concept of SMis extended at the receiver side, using linear precoding with Channel State Information at the Transmitter (CSIT). Even though receive space modulation does not preserve the single RF chain configuration of SM, due to the deployed linear precoding, it can be efficiently incorporated in a Space Division Multiple Access (SDMA) or in a Virtual Multiple-Input Multiple-Output (VMIMO) architecture. Inspired by the potentials of SM, the objectives of this thesis are the evaluation of the EE of SM and its extension in different forms of MIMO communication. In particular, a realistic power model for the power consumption of a Base Station (BS) is deployed in order to assess the EE of SM in terms of Mbps/J. By taking into account the whole power supply of a BS and considering a Time Division Multiple Access (TDMA) multiple access scheme, it is shown that SM is significantly more energy efficient compared to the traditional MIMO techniques. In the considered system setup, it is shown that SM is up to 67% more energy efficient compared to the benchmark systems. In addition, the concept of space modulation is researched at the receiver side. Specifically, based on the union bound technique, a framework for the evaluation of the Average Bit Error Probability (ABEP), diversity order, and coding gain of receive space modulation is developed. Because receive space modulation deploys linear precoding with CSIT, two new precoding methods which utilize imperfect CSIT are proposed. Furthermore, in this thesis, receive space modulation is incorporated in the broadcast channel. The derivation of the theoretical ABEP, diversity order, and coding gain of the new broadcast scheme is provided. It is concluded that receive space modulation is able to outperform the corresponding traditional MIMO scheme. Finally, SM, receive space modulation, and relaying are combined in order to form a novel virtual MIMO architecture. It is shown that the new architecture practically eliminates or reduces the problem of the inefficient relaying of the uncoordinated virtual MIMO space modulation architectures. This is undertaken by using precoding in a novel fashion. The evaluation of the new architecture is conducted using simulation and theoretical results

Bidirectional LiFi Attocell Access Point Slicing Scheme

LiFi attocell access networks will be deployed everywhere to support diverse applications and service provisioning to various end-users. The LiFi infrastructure providers will need to offer LiFi access points (APs) resources as a service. This, however, requires a research challenge to be solved to dynamically and effectively allocate resources among service providers (SPs) while guaranteeing performance isolation among them and their respective users. This paper introduces an autonomic resource slicing (virtualization) scheme, which realizes autonomic management and configuration of virtual APs, in a LiFi attocell access network, based on SPs and their users service requirements. The developed scheme comprises of traffic analysis and classification, a local AP controller, downlink and uplink slice resources manager, traffic measurement, and information collection modules. It also contains a hybrid medium access protocol and an extended token bucket fair queueing algorithm to support uplink access virtualization and spectrum slicing. The proposed resource slicing scheme collects and analyzes the traffic statistics of the different applications supported on the slices defined in each LiFi AP and distributes the available resources fairly and proportionally among them. It uses a control algorithm to adjust the minimum contention window of user devices to achieve the target throughput and ensure airtime fairness among SPs and their users. The developed scheme has been extensively evaluated using OMNeT++. The obtained results show various resource slicing capabilities to support differentiated services and performance isolation