Spectral-energy efficiency trade-off for next-generation wireless communication systems

The data traffic in cellular networks has had and will experience a rapid exponential rise. Therefore, it is essential to innovate a new cellular architecture with advanced wireless technologies that can offer more capacity and enhanced spectral efficiency to manage the exponential data traffic growth. Managing such mass data traffic, however, brings up another challenge of increasing energy consumption. This is because it contributes into a growing fraction of the carbon dioxide (CO2) emission which is a global concern today due to its negative impact on the environment. This has resulted in creating a new paradigm shift towards both spectral and energy efficient orientated design for the next-generation wireless access networks. Acquiring both improved energy efficiency and spectral efficiency has, nonetheless, shown to be a difficult goal to achieve as it seems improving one is at the detriment to the other. Therefore, the trade-off between the spectral and energy efficiency is of paramount importance to assess the energy consumption in a wireless communication system required to attain a specific spectral efficiency. This thesis looks into this problem. It studies the spectral-energy efficiency tradeoff for some of the emerging wireless communication technologies which are seen as potential candidates for the fifth generation (5G) mobile cellular system. The focus is on the orthogonal frequency division multiple access (OFDMA), mobile femtocell (MFemtocell), cognitive radio (CR), and the spatial modulation (SM). Firstly, the energy-efficient resource allocation scheme for multi-user OFDMA (MU-OFDMA) system is studied. The spectral-energy efficiency trade-off is analysed under the constraint of maintaining the fairness among users. The energy-efficient optimisation problem has been formulated as integer fractional programming. We then apply an iterative method to simplify the problem to an integer linear programming (ILP) problem. Secondly, the spectral and energy efficiency for a cellular system with MFemtocell deployment is investigated using different resource partitioning schemes. Femtocells are low range, low power base stations (BSs) that improve the coverage inside a home or office building. MFemtocell adopts the femtocell solution to be deployed in public transport and emergency vehicles. Closed-form expressions for the relationships between the spectral and energy efficiency are derived for a single-user (SU) MFemtocell network. We also study the spectral efficiency for MU-MFemtocells with two opportunistic scheduling schemes. Thirdly, the spectral-energy efficiency trade-off for CR networks is analysed at both SU and MU CR systems against varying signal-to-noise ratio (SNR) values. CR is an innovative radio device that aims to utilise the spectrum more efficiently by opportunistically exploiting underutilised licensed spectrum. For the SU system, we study the required energy to achieve a specific spectral efficiency for a CR channel under two different types of power constraints in different fading environments. In this scenario, interference constraint at the primary receiver (PR) is also considered to protect the PR from harmful interference. At the system level, we study the spectral and energy efficiency for a CR network that shares the spectrum with an indoor network. Adopting the extreme-value theory, we are able to derive the average spectral efficiency of the CR network. Finally, we propose two innovative schemes to enhance the capability of (SM). SM is a recently developed technique that is employed for a low complexity multipleinput multiple-output (MIMO) transmission. The first scheme can be applied for SU MIMO (SU-MIMO) to offer more degrees of freedom than SM. Whereas the second scheme introduces a transmission structure by which the SM is adopted into a downlink MU-MIMO system. Unlike SM, both proposed schemes do not involve any restriction into the number of transmit antennas when transmitting signals. The spectral-energy efficiency trade-off for the MU-SM in the massive MIMO system is studied. In this context, we develop an iterative energy-efficient water-filling algorithm to optimises the transmit power and achieve the maximum energy efficiency for a given spectral efficiency. In summary, the research presented in this thesis reveals mathematical tools to analysis the spectral and energy efficiency for wireless communications technologies. It also offers insight to solve optimisation problems that belong to a class of problems with objectives of enhancing the energy efficiency

Radio network management in cognitive LTE-Femtocell Systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell base stations, a large portion of the assigned spectrum is used sporadically leading to underutilisation of valuable frequency resources. Novel spectrum access techniques are necessary to solve these current spectrum inefficiency problems. Therefore, spectrum management solutions should have the features to improve spectrum access in both temporal and spatial manner. Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered to be the key technology in this research in order to increase the spectrum efficiency. This is an effective solution to allow a group of Secondary Users (SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at no interference. The core aim of this thesis is to develop new cognitive LTE-femtocell systems that offer a 4G vision, to facilitate the radio network management in order to increase the network capacity and further improve spectrum access probabilities. In this thesis, a new spectrum management model for cognitive radio networks is considered to enable a seamless integration of multi-access technology with existing networks. This involves the design of efficient resource allocation algorithms that are able to respond to the rapid changes in the dynamic wireless environment and primary users activities. Throughout this thesis a variety of network upgraded functions are developed using application simulation scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system models are not restricted in the considered networks, but rather have a wider applicability to be used in other technologies. This thesis mainly investigates three aspects of research issues relating to the efficient management of cognitive networks: First, novel spectrum resource management modules are proposed to maximise the spectrum access by rapidly detecting the available transmission opportunities. Secondly, a developed pilot power controlling algorithm is introduced to minimise the power consumption by considering mobile position and application requirements. Also, there is investigation on the impact of deploying different numbers of femtocell base stations in LTE domain to identify the optimum cell size for future networks. Finally, a novel call admission control mechanism for mobility management is proposed to support seamless handover between LTE and femtocell domains. This is performed by assigning high speed mobile users to the LTE system to avoid unnecessary handovers. The proposed solutions were examined by simulation and numerical analysis to show the strength of cognitive femtocell deployment for the required applications. The results show that the new system design based on cognitive radio configuration enable an efficient resource management in terms of spectrum allocation, adaptive pilot power control, and mobile handover. The proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture. Eventually, this research shows that certain architectures fulfilling spectrum management requirements are implementable in practice and display good performance in dynamic wireless environments which recommends the consideration of CR systems in LTE and femtocell networks

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner