Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions

A serious gaming approach to managing interference in ad hoc femtocell wireless networks

The aim of this paper is to optimize femtocell performance by managing interference between femtocell devices and between a femtocell and a macrocell. It achieves this using a three-phase approach that involves deployment of femtocells and control of resulting connections through consideration and management of path loss, transmission power, signal strength and coverage area. Simulation experiments of the proposed three-phase approach at a local college that experiences a poor service from the macrocell predict significant improvements in femtocell performance in terms of managing both types of interference: co-tier and cross-tier, number of users who experience good service, coverage, and mitigating outage probability. The overall and individual complexity of each phase has also been considered. Our approach has been compared with some existing techniques chosen from the literature that has been reviewed and its predicted performance is significantly improved in comparison to these

Fractional frequency reused based interference mitigation in irregular geometry multicellular networks

Recent drastic growth in the mobile broadband services specifically with the proliferation of smart phones demands for higher spectrum capacity of wireless cellular systems. Due to the scarcity of the frequency spectrum, cellular systems are seeking aggressive frequency reuse, which improve the network capacity, however, at the expense of increased Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) scheme has been acknowledged as an effective ICI mitigation scheme, however, in literature FFR has been used mostly in perfect geometry network. In realistic deployment, the cellular geometry is irregular and each cell experiences varying ICI. The main objective of this thesis is to develop ICI mitigation scheme that improves spectrum efficiency and throughput for irregular geometry multicellular network. Irregular Geometry Sectored-Fractional Frequency Reuse (IGS-FFR) scheme is developed that comprises of cell partitioning and sectoring, and dynamic spectrum partitioning. The cell-partitioning and sectoring allows full frequency reuse within an irregular geometry cell. Nevertheless, the sub-regions in an irregular cell have varying coverage areas and thus demands diverse spectrum requirements. The IGSFFR scheme is designed to dynamically allocate the spectrum resources according to the traffic demands of each sub-region. An enhanced IGS-FFR has been developed to optimally allocate the spectrum resources to individual users of each sub-region. Enhanced IGS-FFR has been realized using two different approaches, Auction based Optimized IGS-FFR (AO-IGS-FFR) and Hungarian based Optimized IGS-FFR (HO-IGS-FFR). The results show that IGS-FFR has significantly improved the cell throughput by 89%, 45% and 18% and users’ satisfaction by 112%, 65.8% and 38% compared to Reuse-1, Strict-FFR and FFR-3 schemes, respectively. The findings show that the ICI mitigation in IGS-FFR is reinforced by users’ satisfaction. As the number of sectors in IGS-FFR increases from 3 to 4 and 6, the cell throughput increase by 21% and 33% because of spatial diversity exploitation along with orthogonal sub-band allocation. AO-IGS-FFR and HO-IGS-FFR have further improved the cell throughput of the basic FFR-3 by 65% and 72.2%, respectively. HO-IGS-FFR performs 7% better than the AO-IGS-FFR at the expense of 26.7% decrease in the users’ satisfaction and excessive complexity. Although, AO-IGS-FFR compromises sub-optimal bandwidth allocation, it is a low complexity scheme and can mitigate ICI with high users’ satisfaction. The enhanced IGS-FFR can be deployed in future heterogeneous irregular geometry multicellular OFDMA networks

Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

In this paper, we consider a down-link transmission of multicell virtualized wireless networks (VWNs) where users of different service providers (slices) within a specific region are served by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier and power allocation algorithm to maximize the network throughput, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor (UAF) to represent the joint sub-carrier and BS assignment as the optimization variable vector in the mathematical problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given power-allocation, Step 1 derives the optimum userassociation and subsequently, for an obtained user-association, Step 2 find the optimum power-allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Especially, for the cell-edge users, simulation results reveal a coverage improvement up to 57% and 71% for uniform and non-uniform users distribution, respectively leading to more reliable transmission and higher spectrum efficiency for VWN

Resource allocation in networks via coalitional games

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog

Resource Allocation Management of D2D Communications in Cellular Networks

To improve the system capacity, spectral performance, and energy efficiency, stringent requirements for increasing reliability, and decreasing delays have been intended for next-generation wireless networks. Device-to-device (D2D) communication is a promising technique in the fifth-generation (5G) wireless communications to enhance spectral efficiency, reduce latency and energy efficiency. In D2D communication, two wireless devices in close proximity can communicate with each other directly without pass through the Base Station (BS) or Core Network (CN). In this proposal, we identify compromises and challenges of integrating D2D communications into cellular networks and propose potential solutions. To maximize gains from such integration, resource management, and interference avoidance are key factors. Thus, it is important to properly allocate resources to guarantee reliability, data rate, and increase the capacity in cellular networks. In this thesis, we address the problem of resource allocation in D2D communication underlaying cellular networks. We provide a detailed review of the resource allocation problem of D2D communications. My Ph.D research will tackle several issues in order to alleviate the interference caused by a D2D user-equipment (DUE) and cellular-userequipment (CUE) in uplink multi-cell networks, the intra-cell and inter-cell interference are considered in this work to improve performance for D2D communication underlaying cellular networks. The thesis consists of four main results. First, the preliminary research proposes a resource allocation scheme to formulate the resource allocation problem through optimization of the utility function, which eventually reflects the system performance concerning network throughput. The formulated optimization problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non-convex and hence intractable. Thus, the original problem is broken down into two stages. The first stage is the admission control of D2D users while the second one is the power control for each admissible D2D pair and its reuse partner. Second, we proposed a spectrum allocation framework based on Reinforcement Learning (RL) for joint mode selection, channel assignment, and power control in D2D communication. The objective is to maximize the overall throughput of the network while ensuring the quality of transmission and guaranteeing low latency requirements of D2D communications. The proposed algorithm uses reinforcement learning (RL) based on Markov Decision Process (MDP) with a proposed new reward function to learn the policy by interacting with the D2D environment. An Actor-Critic Reinforcement Learning (AC-RL) approach is then used to solve the resource management problem. The simulation results show that our learning method performs well, can greatly improve the sum rate of D2D links, and converges quickly, compared with the algorithms in the literature. Third, a joint channel assignment, power allocation and resource allocation algorithm is proposed. The algorithm designed to allow multiple DUEs to reuse the same CUE channel for D2D communications underlaying multi-cell cellular networks with the consideration of the inter-cell and intra-cell interferences. Obviously, under satisfying the QoS requirements of both DUEs and CUEs, the more the number of the allowed accessing DUEs on a single CUE channel is, the higher the spectrum efficiency is, and the higher the network throughput can be achieved. Meanwhile, implementing resource allocation strategies at D2D communications allows to effectively mitigate the interference caused by the D2D communications at both cellular and D2D users. In this part, the formulated optimization problem of maximizing network throughput while guaranteeing predefined service levels to cellular users. Therefore, we propose an algorithm that solves this nonlinear mixed-integer problem in three steps wherein the first step, subchannel assignment is carried out, the second one is the power allocation, while the third step of the proposed algorithm is the resource allocation for multiple D2D pairs based on genetic algorithm. The simulation results verify the effectiveness of our proposed algorithm. Fourth, integrating D2D communications and Femtocells in Heterogeneous Networks (HetNets) is a promising technology for future cellular networks. Which have attracted a lot of attention since it can significantly improve the capacity, energy efficiency and spectral performance of next-generation wireless networks (5G). D2D communication and femtocell are introduced as underlays to the cellular systems by reusing the cellular channels to maximize the overall throughput in the network. In this part, the problem is formulated to maximize the network throughput under the QoS constraints for CUEs, DUEs and FUEs. This problem is a mixed-integer non-linear problem that is difficult to be solved directly. To solve this problem, we propose a joint channel selection, power control, and resource allocation scheme to maximize the sum rate of the cellular network system. The simulation results show that the proposed scheme can effectively reduce the computational complexity and improve the overall system throughput compared with existing well-known methods