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

A Cognitive Radio-Based Energy-Efficient System for Power Transmission Line Monitoring in Smart Grids

The research in industry and academia on smart grids is predominantly focused on the regulation of generated power and management of its consumption. Because transmission of bulk-generated power to the consumer is immensely reliant on secure and efficient transmission grids, comprising huge electrical and mechanical assets spanning a vast geographic area, there is an impending need to focus on the transmission grids as well. Despite the challenges in wireless technologies for SGs, cognitive radio networks are considered promising for provisioning of communications services to SGs. In this paper, first, we present an IEEE 802.22 wireless regional area network cognitive radio-based network model for smart monitoring of transmission lines. Then, for a prolonged lifetime of battery finite monitoring network, we formulate the spectrum resource allocation problem as an energy efficiency maximization problem, which is a nonlinear integer programming problem. To solve this problem in an easier way, we propose an energy-efficient resource-assignment scheme based on the Hungarian method. Performance analysis shows that, compared to a pure opportunistic assignment scheme with a throughput maximization objective and compared to a random scheme, the proposed scheme results in an enhanced lifetime while consuming less battery energy without compromising throughput performance

Applications of Game Theory and Microeconomics in Cognitive Radio and Femtocell Networks

Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.1 yea