Dynamic Fractional Frequency Reuse Based On An Improved Water-Filling For Network MIMO

In Long Term Evolution-Advanced (LTE-A) systems, Inter-cell Interference (ICI) is a prominent limiting factor that affects the performance of the systems, especially at the cell edges. Based on the literature, Fractional Frequency Reuse (FFR) methods are known as efficient interference management techniques. In this report, the proposed Dynamic Fractional Frequency Reuse (DFFR) technique improved the capacity and cell edge coverage performance by 70% compared to the Fractional Frequency Reuse (FFR) technique. In this study, an improved power allocation method was adopted into the DFFR technique to reach the goal of not only reducing the ICI mitigation at the cell edges, but also improving the overall capacity of the LTE-A systems. Hence, an improved water-filling algorithm was proposed, and its performance was compared with that of other methods that were considered. Through the simulation results and comparisons with other frequency reuse techniques, it was shown that the proposed method significantly improved the performance of the cell edge throughput by 42%, the capacity by 75%, and the coverage by 80%. Based on the analysis and numerical expressions, it was concluded that the proposed DFFR method provides significant performance improvements, especially for cell edge users

An Improved-Water Filling Algorithm Power Allocation for DFFR Network MIMO

In wireless systems, interference is a major factor that limits the total network capacity. Power allocation is one of the effective techniques that has garnered interest in Network MIMO system and improved the efficiency of wireless systems. This study presents the development of a new power allocation algorithm based on water filling. This algorithm combines the Dynamic Fractional Frequency Reuse (DFFR) with a Network MIMO to maximise the performance of cell edge users. Simulation results show that the proposed algorithm provides more ergodic capacity, compared with the existing power allocation strategies. In addition, it improves the network throughput, while ensuring fairness for cell edge users in the LTE-A system. When the total transmit power is 100W, the proposed algorithm offers 50% capacity, 37.5% throughput and 38% fairness advantage over the conventional water-filling algorithm

Distributed resource allocation for inter cell interference mitigation in irregular geometry multicell networks

Extensive increase in mobile broadband applications and proliferation of smart phones and gadgets require higher data rates of wireless cellular networks. However, limited frequency spectrum has led to aggressive frequency reuse to improve network capacity at the expense of increased Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been acknowledged as an effective ICI mitigation scheme but in irregular geometric multicellular network, ICI mitigation poses a very challenging issue. The thesis developed a decentralized ICI mitigation scheme to improve both spectral and energy efficiency in irregular geometric multicellular networks. ICI mitigation was realized through Distributed Resource Allocation (DRA) deployed at the cell level and region level of an irregular geometric cell. The irregular geometric cell consists of a minimum of four regions comprising three sectors and a central region. DRA at the cell level is defined as Multi Sector DRA (MSDRA), and at the region level is defined as Distributed Channel Selection and Power Allocation (DCSPA). MSDRA allocates discrete power to every region in a cell based on Game Theory and Regret Learning Process with correlated equilibrium as the optimum decision level. The DCSPA allocates power to every channel in a region based on non-coalesce liquid droplet phenomena by selecting optimum channels in a region and reserving appropriate power for the selected channels. The performance was evaluated through simulation in terms of data rate, spectral efficiency and energy efficiency. The results showed that MSDRA significantly improved cell data rate by 58.64% and 37.92% in comparision to Generalized FFR and Fractional Frequency Reuse-3 (FFR-3) schemes, respectively. The performance of MSDRA at the cell level showed that its spectral and energy efficiency improved 32% and 22%, respectively in comparison to FFR-3. When the number of sectors increased from three to four, data rate was improved by 30.26% and for three to six sectors, it was improved by 56.32%. The DCSPA further improved data rate by 41.07% when compared with Geometric Water Filling, and 86.46% in comparison to Asynchronous Iterative Water Filling. The DCSPA enhanced data rate achieved in MSDRA by 15.6%. Overall, DRA has shown to have significant improvement in data rate by 53.6%, and spectral efficiency by 38.10% as compared to FFR-3. As a conclusion, the DRA scheme is a potential candidate for Long Term Evaluation – Advanced, Fifth Generation networks and can be deployed in future heterogeneous irregular geometric multicellular Orthogonal Frequency Division Multiple Access networks

Spectrally and Energy Efficient Radio Resource Management for Multi-Operator Shared Networks

Commercial mobile communication systems are mainly based on licensed frequency spectrum, and the license is very expensive as the spectrum is a sparse wireless resource. Therefore, sharing this wireless resource is an essential requirement not only at the present but also in the future considering trends like connectivity for everybody and everything. In this thesis, we study the sharing of wireless resources with different approaches for realizing fair, efficient, and predictable sharing solutions in a controlled manner. The efficient use of wireless channel resources is an important target to reduce the costs of network operation and deployment. To achieve this, we need practical scheduling algorithms for wireless resources, out of which several of them will be presented and analyzed in this work. Different optimization frameworks for the spectral efficiency utility are presented, with an individual focus on guaranteeing resource or rate fairness among the operators in a network with shared radio resources. Thus, the presented proposals will help the mobile network operators to overcome the issues of losing network control and traceability of used wireless resources in a shared environment. Besides this, emerging vertical industries, such as automotive, healthcare, industry 4.0, internet of things (IoT) industries will put a certain burden on the wireless networks asking for guaranteed service level requirement from the mobile network operators. In this regard, this thesis provides the necessary methods addressing these challenges with the help of scheduling methods which are based on the joint optimization of spectral and energy efficiency. Thus, wireless networks will be enabled as a service function in a controlled and scalable way for new emerging markets. Furthermore, the presented solutions t well with the requirements of fifth generation (5G) network slicing

Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases