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

Fair resource allocation with interference mitigation and resource reuse for LTE/LTE-A femtocell networks

Joint consideration of interference, resource utilization, fairness, and complexity issues is generally lacking in existing resource allocation schemes for Long-Term Evolution (LTE)/LTE-Advanced femtocell networks. To tackle this, we employ a hybrid spectrum allocation approach whereby the spectrum is split between the macrocell and its nearby interfering femtocells based on their resource demands, whereas the distant femtocells share the entire spectrum. A multiobjective problem is formulated for resource allocation between femtocells and is decomposed using a lexicographic optimization approach into two subproblems. A greedy algorithm of reasonably low complexity is proposed to solve these subproblems sequentially. Simulation results show that the proposed scheme achieves substantial throughput and packet loss improvements in low-density femtocell deployment scenarios while performing satisfactorily in high-density femtocell deployment scenarios with substantial complexity and overhead reduction. The proposed scheme also performs nearly as well as the optimal solution obtained by exhaustive search

On Supporting Legacy and RF Energy Harvesting Devices in Two-Tier OFDMA Heterogeneous Networks

Future devices are likely to have the capability to harvest energy from Radio Frequency (RF) signals. In this paper, we consider such Energy Harvesting (EH) devices operating in a two-tier Orthogonal Frequency Division Multiple Access (OFDMA) based Heterogeneous Network (HetNet). Critically, we investigate how such EH devices can be supported along side non-RF harvesting or legacy devices. Our aim is to minimize the downlink sum transmit power of both femto and macro base-stations and ensure legacy and EH devices receive a given data rate and amount of energy, respectively. Critically, we study sub-carrier and power allocation to both types of devices, and investigate novel questions related to interference, which reduces network capacity but improves the amount of harvested energy by EH devices. To study these questions, we formulate a Mixed-Integer Non Linear Program (MINLP) and propose three linear approximations to the MINLP where devices are either assigned one or multiple sub-carriers. Numerical results show that EH devices will not affect network capacity if they can harvest sufficient energy from data transmissions to legacy devices. In addition, if multiple sub-carriers can be assigned to devices, our results show that the sum transmit power decreases by approximately 15% as compared to assigning a single sub-carrier to these devices

Geometric frequency reuse for irregular cellular networks

PhD ThesisThis thesis uniquely addresses challenges of bandwidth management in cellular networks. The need for enhanced frequency assignment strategies in Long term evolution (LTE) systems arises due to the limiting e ects of intercell interference (ICI). In this study, the realistic scenario of irregular network coverage patterns is considered, and in addition, Heterogeneous cellular networks (HetNets). Firstly, extensive analysis using simulations is presented for static frequency reuse (FR) techniques in irregular Homogeneous (single-tier) cellular networks. Investigation was carried out over several network positional and deployment layouts. Second, a model is developed for irregular networks by de ning frameworks for their location parameters and relationships, FR bandwidth and power assignment, and the probability of interference in partitioned FR schemes. A novel Geometric FR (GeoFRe) algorithm is then proposed for single-tier networks with random BS placements. Third, an optimization framework based on user fairness is proposed and implemented for single-tier networks based on the concept of virtual UEs in di erent BS regions. Finally, a framework for HetNets is presented where macro and small BS deployments have imperfect coverage grid patterns. Performance analysis is then carried out for two implementations of the Soft FR (SFR) algorithm. Results from this research provide detailed analysis on impact of BS irregularity on UE performance under FR schemes, a simpli ed framework for modelling irregular macro BS, an improved FR model, accurate computations for the area of irregular network coverage patterns for intelligent bandwidth assignment, an optimization framework to improve user fairness (and edge UE performance) in single-tier networks and an FR model with performance analysis for irregular Het- Nets.National Information Technology Development Agency (NITDA) and Federal University of Technology Minna, both in Nigeria for o ering me scholarship and support

Cognitive Radio Made Practical: Forward-Lookingness and Calculated Competition

Cognitive radio is more than just radio environment awareness, but more importantly the ability to interact with the environment in the best way possible. Ideally, cognitive radios will form a selfregulating society of mobile radios achieving maximum spectrum utilization. However, challenges arise as mobile radios tend to compete with one another for spectrum, generating harmful interference and damaging performance individually and for the network as a whole. In this paper, we present a framework that allows competing radios to teach and learn from each other’s action so that a desirable equilibrium can be reached. The heart of cognition to establish this is the forward-looking ability, which enables competing radios to see beyond the present time, negotiate and optimize their actions towards a more agreeable equilibrium. Technically speaking, we adopt a belief-directed game where each mobile radio, regarded as player, formulates a belief function to project how the radio environment as a whole would respond to any of its action. This model facilitates engineering of the equilibrium by different choices of the players’ belief functions. Under this model, players will negotiate naturally through a sequence of calculated competition (i.e., cycles of teaching and learning with each other). We apply this methodology to a cognitive orthogonal frequency-division multiple-access (OFDMA) radio network where mobile users are free to access any of the subcarriers and thus compete for radio resources to maximize their rates. Results reveal that the proposed negotiation-by-forward-looking competition mechanism guides users to converge to an equilibrium that benefits not only individual users but the entire network approaching the maximum achievable sum-rate

Resource allocation in future green wireless networks : applications and challenges

Over the past few years, green radio communication has been an emerging topic since the footprint from the Information and Communication Technologies (ICT) is predicted to increase 7.3% annually and then exceed 14% of the global footprint by 2040. Moreover, the explosive progress of ICT, e.g., the fifth generation (5G) networks, has resulted in expectations of achieving 10-fold longer device battery lifetime, and 1000-fold higher global mobile data traffic over the fourth generation (4G) networks. Therefore, the demands for increasing the data rate and the lifetime while reducing the footprint in the next-generation wireless networks call for more efficient utilization of energy and other resources. To overcome this challenge, the concepts of small-cell, energy harvesting, and wireless information and power transfer networks can be evaluated as promising solutions for re-greening the world. In this dissertation, the technical contributions in terms of saving economical cost, protecting the environment, and guaranteeing human health are provided. More specifically, novel communication scenarios are proposed to minimize energy consumption and hence save economic costs. Further, energy harvesting (EH) techniques are applied to exploit available green resources in order to reduce carbon footprint and then protect the environment. In locations where implemented user devices might not harvest energy directly from natural resources, base stations could harvest-and-store green energy and then use such energy to power the devices wirelessly. However, wireless power transfer (WPT) techniques should be used in a wise manner to avoid electromagnetic pollution and then guarantee human health. To achieve all these aspects simultaneously, this thesis proposes promising schemes to optimally manage and allocate resources in future networks. Given this direction, in the first part, Chapter 2 mainly studies a transmission power minimization scheme for a two-tier heterogeneous network (HetNet) over frequency selective fading channels. In addition, the HetNet backhaul connection is unable to support a sufficient throughput for signaling an information exchange between two tiers. A novel idea is introduced in which the time reversal (TR) beamforming technique is used at a femtocell while zero-forcing-based beamforming is deployed at a macrocell. Thus, a downlink power minimizationscheme is proposed, and optimal closed-form solutions are provided. In the second part, Chapters 3, 4, and 5 concentrate on EH and wireless information and power transfer (WIPT) using RF signals. More specifically, Chapter 3 presents an overview of the recent progress in green radio communications and discusses potential technologies for some emerging topics on the platforms of EH and WPT. Chapter 4 develops a new integrated information and energy receiver architecture based on the direct use of alternating current (AC) for computation. It is shown that the proposed approach enhances not only the computational ability but also the energy efficiency over the conventional one. Furthermore, Chapter 5 proposes a novel resource allocation scheme in simultaneous wireless information and power transfer (SWIPT) networks where three crucial issues: power-efficient improvement, user-fairness guarantee, and non-ideal channel reciprocity effect mitigation, are jointly addressed. Hence, novel methods to derive optimal and suboptimal solutions are provided. In the third part, Chapters 6, 7, and 8 focus on simultaneous lightwave information and power transfer (SLIPT) for indoor applications, as a complementary technology to RF SWIPT. In this research, Chapter 6 investigates a hybrid RF/visible light communication (VLC) ultrasmall cell network where optical transmitters deliver information and power using the visible light, whereas an RF access point works as a complementary power transfer system. Thus, a novel resource allocation scheme exploiting RF and visible light for power transfer is devised. Chapter 7 proposes the use of lightwave power transfer to enable future sustainable Federated Learning (FL)-based wireless networks. FL is a new data privacy protection technique for training shared machine learning models in a distributed approach. However, the involvement of energy-constrained mobile devices in the construction of the shared learning models may significantly reduce their lifetime. The proposed approach can support the FL-based wireless network to overcome the issue of limited energy at mobile devices. Chapter 8 introduces a novel framework for collaborative RF and lightwave power transfer for wireless communication networks. The constraints on the transmission power set by safety regulations result in significant challenges to enhance the power transfer performance. Thus, the study of technologies complementary to conventional RF SWIPT is essential. To cope with this isue, this chapter proposes a novel collaborative RF and lightwave power transfer technology for next-generation wireless networks

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios

Low complexity radio resource management for energy efficient wireless networks

Energy consumption has become a major research topic from both environmental and economical perspectives. The telecommunications industry is currently responsible for 0.7% of the total global carbon emissions, a figure which is increasing at rapid rate. By 2020, it is desired that CO2 emissions can be reduced by 50%. Thus, reducing the energy consumption in order to lower carbon emissions and operational expenses has become a major design constraint for future communication systems. Therefore, in this thesis energy efficient resource allocation methods have been studied taking the Long Term Evolution (LTE) standard as an example. Firstly, a theoretical analysis, that shows how improvements in energy efficiency can directly be related with improvements in fairness, is provided using a Shannon theory analysis. The traditional uplink power control challenge is re-evaluated and investigated from the view point of interference mitigation rather than power minimization. Thus, a low complexity distributed resource allocation scheme for reducing the uplink co-channel interference (CCI) is presented. Improvements in energy efficiency are obtained by controlling the level of CCI affecting vulnerable mobile stations (MSs). This is done with a combined scheduler and a two layer power allocation scheme, which is based on non-cooperative game theory. Simulation results show that the proposed low complexity method provides similar performance in terms of fairness and energy efficiency when compared to a centralized signal interference noise ratio balancing scheme. Apart from using interference management techniques, by using efficiently the spare resources in the system such as bandwidth and available infrastructure, the energy expenditure in wireless networks can also be reduced. For example, during low network load periods spare resource blocks (RBs) can be allocated to mobile users for transmission in the uplink. Thereby, the user rate demands are split among its allocated RBs in order to transmit in each of them by using a simpler and more energy efficient modulation scheme. In addition, virtual Multiple-input Multiple-output (MIMO) coalitions can be formed by allowing single antenna MSs and available relay stations to cooperate between each other to obtain power savings by implementing the concepts of spatial multiplexing and spatial diversity. Resource block allocation and virtual MIMO coalition formation are modeled by a game theoretic approach derived from two different concepts of stable marriage with incomplete lists (SMI) and the college admission framework (CAF) respectively. These distributed approaches focus on optimizing the overall consumed power of the single antenna devices rather than on the transmitted power. Moreover, it is shown that when overall power consumption is optimized the energy efficiency of the users experiencing good propagation conditions in the uplink is not always improved by transmitting in more than one RB or by forming a virtual MIMO link. Finally, it is shown that the proposed distributed schemes achieve a similar performance in bits per Joule when compared to much more complex centralized resource allocation methods