Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Design of VLC and heterogeneous RF/VLC systems for future generation networks: an algorithmic approach

Visible light communication (VLC) has attracted significant research interest within the last decade due in part to the vast amount of unused transmission bandwidth in the visible light spectrum. VLC is expected to be part of future generation networks. The heterogeneous integration of radio frequency (RF) and VLC systems has been envisioned as a promising solution to increase the capacity of wireless networks, especially in indoor environments. However, the promised advantages of VLC and heterogeneous RF/VLC systems cannot be realized without proper resource management algorithms that exploit the distinguishing characteristics between RF and VLC systems. Further, the problem of backhauling for VLC systems has received little attention. This dissertation’s first part focuses on designing and optimizing VLC and heterogeneous RF/VLC systems. Novel resource allocation algorithms that optimize the sum-rate and energy efficiency performances of VLC, hybrid, and aggregated RF/VLC systems while considering practical constraints like illumination requirements, inter-cell interference, quality-of-service requirements, and transmit power budgets are proposed. Moreover, a power line communicationbased backhaul solution for an indoor VLC system is developed, and a backhaul-aware resource allocation algorithm is proposed. These algorithms are developed by leveraging tools from fractional programming (i.e., Dinkelbach’s transform and quadratic transform), the multiplier adjustment method, matching theory, and multi-objective optimization. The latter part of this dissertation examines the adoption of emerging beyond 5G technologies, such as intelligent reflecting surfaces (IRSs) and reconfigurable intelligent surfaces (RISs), to overcome the limitations of VLC systems and boost their performance gains. Novel system models for IRSs-aided and RISs-aided VLC systems are proposed, and metaheuristic-based algorithms are developed to optimize the configurations of the IRSs/RISs and, consequently, the performance of VLC systems. Extensive simulations reveal that the proposed resource allocation schemes outperform the considered benchmarks and provide performance close to the optimal solution. Furthermore, the proposed system models achieve superior performance compared to benchmark system models

Distributed radio resource allocation in wireless heterogeneous networks

This dissertation studies the problem of resource allocation in the radio access network of heterogeneous small-cell networks (HetSNets). A HetSNet is constructed by introducing smallcells(SCs) to a geographical area that is served by a well-structured macrocell network. These SCs reuse the frequency bands of the macro-network and operate in the interference-limited region. Thus, complex radio resource allocation schemes are required to manage interference and improve spectral efficiency. Both centralized and distributed approaches have been suggested by researchers to solve this problem. This dissertation follows the distributed approach under the self-organizing networks (SONs) paradigm. In particular, it develops game-theoretic and learning-theoretic modeling, analysis, and algorithms. Even though SONs may perform subpar to a centralized optimal controller, they are highly scalable and fault-tolerant. There are many facets to the problem of wireless resource allocation. They vary by the application, solution, methodology, and resource type. Therefore, this thesis restricts the treatment to four subproblems that were chosen due to their significant impact on network performance and suitability to our interests and expertise. Game theory and mechanism design are the main tools used since they provide a sufficiently rich environment to model the SON problem. Firstly, this thesis takes into consideration the problem of uplink orthogonal channel access in a dense cluster of SCs that is deployed in a macrocell service area. Two variations of this problem are modeled as noncooperative Bayesian games and the existence of pure-Bayesian Nash symmetric equilibria are demonstrated. Secondly, this thesis presents the generalized satisfaction equilibrium (GSE) for games in satisfaction-form. Each wireless agent has a constraint to satisfy and the GSE is a mixed-strategy profile from which no unsatisfied agent can unilaterally deviate to satisfaction. The objective of the GSE is to propose an alternative equilibrium that is designed specifically to model wireless users. The existence of the GSE, its computational complexity, and its performance compared to the Nash equilibrium are discussed. Thirdly, this thesis introduces verification mechanisms for dynamic self-organization of Wireless access networks. The main focus of verification mechanisms is to replace monetary transfers that are prevalent in current research. In the wireless environment particular private information of the wireless agents, such as block error rate and application class, can be verified at the access points. This verification capability can be used to threaten false reports with backhaul throttling. The agents then learn the truthful equilibrium over time by observing the rewards and punishments. Finally, the problem of admission control in the interfering-multiple access channel with rate constraints is addressed. In the incomplete information setting, with compact convex channel power gains, the resulting Bayesian game possesses at least one pureBayesian Nash equilibrium in on-off threshold strategies. The above-summarized results of this thesis demonstrate that the HetSNets are amenable to self-organization, albeit with adapted incentives and equilibria to fit the wireless environment. Further research problems to expand these results are identified at the end of this document

Interference Aware Cognitive Femtocell Networks

Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks. Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision. This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference

Power control with Machine Learning Techniques in Massive MIMO cellular and cell-free systems

This PhD thesis presents a comprehensive investigation into power control (PC) optimization in cellular (CL) and cell-free (CF) massive multiple-input multiple-output (mMIMO) systems using machine learning (ML) techniques. The primary focus is on enhancing the sum spectral efficiency (SE) of these systems by leveraging various ML methods. To begin with, it is combined and extended two existing datasets, resulting in a unique dataset tailored for this research. The weighted minimum mean square error (WMMSE) method, a popular heuristic approach, is utilized as the baseline method for addressing the sum SE maximization problem. It is compared the performance of the WMMSE method with the deep Q-network (DQN) method through training on the complete dataset in both CL and CF-mMIMO systems. Furthermore, the PC problem in CL/CF-mMIMO systems is effectively tackled through the application of ML-based algorithms. These algorithms present highly efficient solutions with significantly reduced computational complexity [3]. Several ML methods are proposed for CL/CF-mMIMO systems, tailored explicitly to address the PC problem in CL/CF-mMIMO systems. Among them are the innovative proposed Fuzzy/DQN method, proposed DNN/GA method, proposed support vector machine (SVM) method, proposed SVM/RBF method, proposed decision tree (DT) method, proposed K-nearest neighbour (KNN) method, proposed linear regression (LR) method, and the novel proposed fusion scheme. The fusion schemes expertly combine multiple ML methods, such as system model 1 (DNN, DNN/GA, DQN, fuzzy/DQN, and SVM algorithms) and system model 2 (DNN, SVM-RBF, DQL, LR, KNN, and DT algorithms), which are thoroughly evaluated to maximize the sum spectral efficiency (SE), offering a viable alternative to computationally intensive heuristic algorithms. Subsequently, the DNN method is singled out for its exceptional performance and is further subjected to in-depth analysis. Each of the ML methods is trained on a merged dataset to extract a novel feature vector, and their respective performances are meticulously compared against the WMMSE method in the context of CL/CF-mMIMO systems. This research promises to pave the way for more robust and efficient PC solutions, ensuring enhanced SE and ultimately advancing the field of CL/CF-mMIMO systems. The results reveal that the DNN method outperforms the other ML methods in terms of sum SE, while exhibiting significantly lower computational complexity compared to the WMMSE algorithm. Therefore, the DNN method is chosen for examining its transferability across two datasets (dataset A and B) based on their shared common features. Three scenarios are devised for the transfer learning method, involving the training of the DNN method on dataset B (S1), the utilization of model A and dataset B (S2), and the retraining of model A on dataset B (S3). These scenarios are evaluated to assess the effectiveness of the transfer learning approach. Furthermore, three different setups for the DNN architecture (DNN1, DNN2, and DNN3) are employed and compared to the WMMSE method based on performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the research evaluates the impact of the number of base stations (BSs), access points (APs), and users on PC in CL/CF-mMIMO systems using ML methodology. Datasets capturing diverse scenarios and configurations of mMIMO systems were carefully assembled. Extensive simulations were conducted to analyze how the increasing number of BSs/APs affects the dimensionality of the input vector in the DNN algorithm. The observed improvements in system performance are quantified by the enhanced discriminative power of the model, illustrated through the cumulative distribution function (CDF). This metric encapsulates the model's ability to effectively capture and distinguish patterns across diverse scenarios and configurations within mMIMO systems. The parameter of the CDF being indicated is the probability. Specifically, the improved area under the CDF refers to an enhanced probability of a random variable falling below a certain threshold. This enhancement denotes improved model performance, showcasing a greater precision in predicting outcomes. Interestingly, the number of users was found to have a limited effect on system performance. The comparison between the DNN-based PC method and the conventional WMMSE method revealed the superior performance and efficiency of the DNN algorithm. Lastly, a comprehensive assessment of the DNN method against the WMMSE method was conducted for addressing the PC optimization problem in both CL and CF system architectures. In addition to, this thesis focuses on enhancing spectral efficiency (SE) in wireless communication systems, particularly within cell-free (CF) mmWave massive MIMO environments. It explores the challenges of optimizing SE through traditional methods, including the weighted minimum mean squared error (WMMSE), fractional programming (FP), water-filling, and max-min fairness approaches. The prevalence of access points (APs) over user equipment (UE) highlights the importance of zero-forcing precoding (ZFP) in CF-mMIMO. However, ZFP faces issues related to channel aging and resource utilization. To address these challenges, a novel scheme called delay-tolerant zero-forcing precoding (DT-ZFP) is introduced, leveraging deep learning-aided channel prediction to mitigate channel aging effects. Additionally, a cutting-edge power control (PC) method, HARP-PC, is proposed, combining heterogeneous graph neural network (HGNN), adaptive neuro-fuzzy inference system (ANFIS), and reinforcement learning (RL) to optimize SE in dynamic CF mmWave-mMIMO systems. This research advances the field by addressing these challenges and introducing innovative approaches to enhance PC and SE in contemporary wireless communication networks. Overall, this research contributes to the advancement of PC optimization in CL/CF-mMIMO systems through the application of ML techniques, demonstrating the potential of the DNN method, and providing insights into system performance under various scenarios and network configurations

A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented

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