Optimising energy efficiency and spectral efficiency in multi-tier heterogeneous networks:performance and tradeoffs

The exponential growth in the number of cellular users along with their increasing demand of higher transmission rate and lower power consumption is a dilemma for the design of future generation networks. The spectral efficiency (SE) can be improved by better utilisation of the network resources at the cost of reduction in the energy efficiency (EE) due to the enormous increase in the network power expenditure arising from the densification of the network. One of the possible solutions is to deploy Heterogeneous Networks (HetNets) consisting of several tiers of small cell BSs overlaid within the coverage area of the macrocells. The HetNets can provide better coverage and data rate to the cell edge users in comparison to the macrocells only deployment. One of the key requirements for the next generation networks is to maintain acceptable levels of both EE and SE. In order to tackle these challenges, this thesis focuses on the analysis of the EE, SE and their tradeoff for different scenarios of HetNets. First, a joint network and user adaptive selection mechanism in two-tier HetNets is proposed to improve the SE using game theory to dynamically re-configure the network while satisfying the user's quality-of-service (QoS) requirements. In this work, the proposed scheme tries to offload the traffic from the heavily loaded small cells to the macrocell. The user can only be admitted to a network which satisfies the call admission control procedures for both the uplink and downlink transmission scheme. Second, an energy efficient resource allocation scheme is designed for a two-tier HetNets. The proposed scheme uses a low-complexity user association and power allocation algorithm to improve the uplink system EE performance in comparison to the traditional cellular systems. In addition, an opportunistic joint user association and power allocation algorithm is proposed in an uplink transmission scheme of device to device (D2D) enabled HetNets. In this scheme, each user tries to maximise its own Area Spectral Efficiency (ASE) subject to the required Area Energy Efficiency (AEE) requirements. Further, a near-optimal joint user association and power allocation approach is proposed to investigate the tradeoff between the two conflicting objectives such as achievable throughput and minimising the power consumption in two-tier HetNets for the downlink transmission scheme. Finally, a multi-objective optimization problem is formulated that jointly maximizes the EE and SE in two-tier HetNets. In this context, a joint user association and power allocation algorithm is proposed to analyse the tradeoff between the achievable EE and SE in two-tier HetNets. The formulated problem is solved using convex optimisation methods to obtain the Pareto-optimal solution for the various network parameters

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective

Resource Allocation for Next Generation Radio Access Networks

Driven by data hungry applications, the architecture of mobile networks is moving towards that of densely deployed cells where each cell may use a different access technology as well as a different frequency band. Next generation networks (NGNs) are essentially identified by their dramatically increased data rates and their sustainable deployment. Motivated by these requirements, in this thesis we focus on (i) capacity maximisation, (ii) energy efficient configuration of different classes of radio access networks (RANs). To fairly allocate the available resources, we consider proportional fair rate allocations. We first consider capacity maximisation in co-channel 4G (LTE) networks, then we proceed to capacity maximisation in mixed LTE (including licensed LTE small cells) and 802.11 (WiFi) networks. And finally we study energy efficient capacity maximisation of dense 3G/4G co-channel small cell networks. In each chapter we provide a network model and a scalable resource allocation approach which may be implemented in a centralised or distributed manner depending on the objective and network constraints

Improved interference management techniques for multi-cell multi-user MIMO systems

One major limiting factor for wireless communication systems is the limited available bandwidth for cellular networks. Current technologies like Long Term Evolution (LTE) and LTE-Advanced (LTE-A) have standardised a frequency reuse factor of 1 to enable more channel resources in each cell. Also multi-layer networks that consist of overlapping macro cells and small cells like pico cells, micro cells and femto cells have also been used to improve the capacity of the cellular network system. These multi-layer networks are known as heterogeneous networks or HetNets while the single layer, traditional cellular systems are referred to as homogeneous networks or HomoNets. Several interference management systems and techniques have been proposed in the past to deal with the effect of inter-cell interference (ICI) (i.e., the interference from a macro cell base station (BS) to a macro cell user in another macro cell) and inter-user interference (IUI) (i.e., the interference of another user's data signal to a given user within the same cell on the same time and frequency slot). Interference cancellation techniques such as beamforming, uses transmit pre-coders and receive beam-formers to limit the effect of interference. The interference alignment strategy ensures that the interference is aligned into a given subspace and leaves a residual subspace free for the desired signal. Coordinated scheduling/beam-forming and coordinated multi-point transmission (CoMP) have also been used to limit the interference within the cellular network. For HetNets, interference avoidance techniques based on radio resource management (RRM) have been used to limit the effect of interference within the network and improve the attainable system capacity. This thesis investigates the challenges of two main interference management techniques and proposes methods to alleviate these issues without impeding the expected performance already attained. The main techniques considered for HomoNets and HetNets are: CoMP transmission under the interference cancellation technique and resource block allocation (RBA) under the interference avoidance technique. The setbacks for the well known CoMP transmission strategy are high data overhead, energy consumption and other associated costs to the network provider. Further investigations were carried out and a joint selection of transmit antennas for the users was proposed with the main aim of preserving or exceeding the already achieved gains but obtaining a further reduction in the data overhead. Fully distributed RBA solutions are required, especially since future networks tend to become self-organising networks (SON). Another major consideration in choosing the resource blocks (RBs) for the users in each cell is the RBA metric. Since the capacity of the cell is dependent on the sum-rate of the users, it is important to consider the maximisation of the sum-rate or sum-SINR (i.e the sum signal to interference and noise ratio) when assigning RBs to users. The RBA technique aims to choose the RBs such that the interference within the cell is avoided. To achieve this, a RBA metric is required to obtain the qualification matrix before allocating RBs to the users. Many authors in the past have proposed several metrics for RBA but avoided any RBA metric that required a direct estimation of the interference power on each RB for each user's allocation. This is because the SINR or interference power on each RB for any user can only be obtained with pre-knowledge of already occupied RBs in neighbouring cells. In this thesis, two distributed RBA solutions based on a direct interference estimation was proposed to obtain the required qualification matrix for the RBA under the HomoNet and HetNet system models. The gains and advantages obtained are shown and analysed using the obtained simulation results. The issue of interference coupled with limited available channels remains a major limiting factor for HetNets. Therefore, it is very important to develop techniques that maximise the utilisation of available bandwidth for each cell while minimising possible interference from neighbouring cells. Finally, this thesis considers and investigates a possible joint solution using both interference avoidance and interference mitigation techniques. Hence two solutions are proposed: joint RBA plus beam-forming and joint RBA plus CoMP transmission, to further mitigate the high interference in HetNets. The simulation results have shown significantly improved system performance especially for a highly dense HetNet

Fast converging robust beamforming for downlink massive MIMO systems in heterogenous networks

Massive multiple-input multiple-output (MIMO) is an emerging technology, which is an enabler for future broadband wireless networks that support high speed connection of densely populated areas. Application of massive MIMO at the macrocell base stations in heterogeneous networks (HetNets) offers an increase in throughput without increasing the bandwidth, but with reduced power consumption. This research investigated the optimisation problem of signal-to-interference-plus-noise ratio (SINR) balancing for macrocell users in a typical HetNet scenario with massive MIMO at the base station. The aim was to present an efficient beamforming solution that would enhance inter-tier interference mitigation in heterogeneous networks. The system model considered the case of perfect channel state information (CSI) acquisition at the transmitter, as well as the case of imperfect CSI at the transmitter. A fast converging beamforming solution, which is applicable to both channel models, is presented. The proposed beamforming solution method applies the matrix stuffing technique and the alternative direction method of multipliers, in a two-stage fashion, to give a modestly accurate and efficient solution. In the first stage, the original optimisation problem is transformed into standard second-order conic program (SOCP) form using the Smith form reformulation and applying the matrix stuffing technique for fast transformation. The second stage uses the alternative direction method of multipliers to solve the SOCP-based optimisation problem. Simulations to evaluate the SINR performance of the proposed solution method were carried out with supporting software-based simulations using relevant MATLAB toolboxes. The simulation results of a typical single cell in a HetNet show that the proposed solution gives performance with modest accuracy, while converging in an efficient manner, compared to optimal solutions achieved by state-of-the-art modelling languages and interior-point solvers. This is particularly for cases when the number of antennas at the base station increases to large values, for both models of perfect CSI and imperfect CSI. This makes the solution method attractive for practical implementation in heterogeneous networks with large scale antenna arrays at the macrocell base station.Dissertation (MEng)--University of Pretoria, 2018.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Cloud Based Small Cell Networks: System Model, Performance Analysis and Resource Allocation

In cloud-based small cell networks (C-SCNs), radio resource allocation at the base station (BS) is moved to a cloud data centre for centralised optimisation. In the centre, multiple processors referred to as the cloud computational unit (CCU), is used for the optimisation. As the cell size and networks become respectively smaller and denser, the number of BSs to be optimised grows exponentially, resulting in high computational complexity and latency at CCUs. This thesis propose belief propagation (BP) based power allocation schemes for C-SCNs that can be used for any network optimisation objectives such as energy efficiency at the centre and BSs; and spectral efficiency (SE). The computation for the schemes is done in parallel, leading to very low latency and computational complexity with increasing number of BSs. The transmission-latency depends on the number of bits used to quantise the received signal from terminals at the remote radio head (RRH). The computational-latency depend on the speed of resource allocation procedure at the CCU. BP based joint SE and latency optimisation scheme that compute the optimum terminal’s uplink power and number of quantisation bits for each RRHs. The results indicate a significant reduction in transmission and computational-latencies compared to other schemes. This thesis further investigates a user association (UA) to the BS and subcarrier allocation (SCA) where a BS allocates different number of SC to different users associated to it. In jointly optimising the UA and SCA, the Sharpe Ratio (SR) is used as the utility function, which is defined as the ratio between the mean of user achievable rates to its standard deviation. Thus, the achieved user rates will be closer to each other, leading to a fair network access. By using binary BP algorithm, the results show that the achievable user rates are doubled in comparison with other schemes

Resource Allocation in Energy Cooperation Enabled 5G Cellular Networks

PhD thesisIn fifth generation (5G) networks, more base stations (BSs) and antennas have been deployed to meet the high data rate and spectrum efficiency requirements. Heterogeneous and ultra dense networks not only pose substantial challenges to the resource allocation design, but also lead to unprecedented surge in energy consumption. Supplying BSs with renewable energy by utilising energy harvesting technology has became a favourable solution for cellular network operators to reduce the grid energy consumption. However, the harvested renewable energy is fluctuating in both time and space domains. The available energy for a particular BS at a particular time might be insufficient to meet the traffic demand which will lead to renewable energy waste or increased outage probability. To solve this problem, the concept of energy cooperation was introduced by Sennur Ulukus in 2012 as a means for transferring and sharing energy between the transmitter and the receiver. Nevertheless, resource allocation in energy cooperation enabled cellular networks is not fully investigated. This thesis investigates resource allocation schemes and resource allocation optimisation in energy cooperation enabled cellular networks that employed advanced 5G techniques, aiming at maximising the energy efficiency of the cellular network while ensuring the network performance. First, a power control algorithm is proposed for energy cooperation enabled millimetre wave (mmWave) HetNets. The aim is to maximise the time average network data rate while keeping the network stable such that the network backlog is bounded and the required battery capacity is finite. Simulation results show that the proposed power control scheme can reduce the required battery capacity and improve the network throughput. Second, resource allocation in energy cooperation enabled heterogeneous networks (Het- Nets) is investigated. User association and power control schemes are proposed to maximise the energy efficiency of the whole network respectively. The simulation results reveal that the implementation of energy cooperation in HetNets can improve the energy efficiency and the improvement is apparent when the energy transfer efficiency is high. Following on that, a novel resource allocation for energy cooperation enabled nonorthogonal multiple access (NOMA) HetNets is presented. Two user association schemes which have different complexities and performances are proposed and compared. Following on that, a joint user association and power control algorithm is proposed to maximise the energy efficiency of the network. It is confirmed from the simulation results that the proposed resource allocation schemes efficiently coordinate the intra-cell and inter-cell interference in NOMA HetNets with energy cooperation while exploiting the multiuser diversity and BS densification. Last but not least, a joint user association and power control scheme that considers the different content requirements of users is proposed for energy cooperation enabled caching HetNets. It shows that the proposed scheme significantly enhances the energy efficiency performance of caching HetNets

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments