30,087 research outputs found

    Efficient Device to Device Communications Underlaying Heterogeneous Networks

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    Device-to-Device communications have the great potential to bring significant performance boost to the conventional heterogeneous network by reusing cellular resources. In cellular networks, Device-to-Device communication is defined as two user equipments in a close range communicating directly with each other without going through the base station, thus offloading cellular traffic from cellular networks. In addition to improve network spectral efficiency, D2D communication can also improve energy efficiency and user experience. However, the co-existence of D2D communication on the same spectrum with cellular users can cause severe interference to the primary cellular users. Thus the performance of cellular users must be assured when supporting underlay D2D users. In this work, we have investigated cross-layer optimization, resource allocation and interference management schemes to improve user experience, system spectral efficiency and energy efficiency for D2D communication underlaying heterogeneous networks. By exploiting frequency reuse and multi-user diversity, this research work aims to design wireless system level algorithms to utilize the spectrum and energy resources efficiently in the next generation wireless heterogeneous network

    Efficient radio resource management for future generation heterogeneous wireless networks

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    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

    New Methods of Efficient Base Station Control for Green Wireless Communications

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 2. ์ด๋ณ‘๊ธฐ.This dissertation reports a study on developing new methods of efficient base station (BS) control for green wireless communications. The BS control schemes may be classified into three different types depending on the time scale โ€” hours based, minutes based, and milli-seconds based. Specifically, hours basis pertains to determining which BSs to switch on or offminutes basis pertains to user equipment (UE) associationand milli-seconds basis pertains to UE scheduling and radio resource allocation. For system model, the dissertation considers two different models โ€” heterogeneous networks composed of cellular networks and wireless local area networks (WLANs), and cellular networks adopting orthogonal frequency division multiple access (OFDMA) with carrier aggregation (CA). By combining each system model with a pertinent BS control scheme, the dissertation presents three new methods for green wireless communications: 1) BS switching on/off and UE association in heterogeneous networks, 2) optimal radio resource allocation in heterogeneous networks, and 3) energy efficient UE scheduling for CA in OFDMA based cellular networks. The first part of the dissertation presents an algorithm that performs BS switchingon/off and UE association jointly in heterogeneous networks composed of cellular networks and WLANs. It first formulates a general problem which minimizes the total cost function which is designed to balance the energy consumption of overall network and the revenue of cellular networks. Given that the time scale for determining the set of active BSs is much larger than that for UE association, the problem may be decomposed into a UE association algorithm and a BS switching on/off algorithm, and then an optimal UE association policy may be devised for the UE association problem. Since BS switching-on/off problem is a challenging combinatorial problem, two heuristic algorithms are proposed based on the total cost function and the density of access points of WLANs within the coverage of each BS, respectively. According to simulations, the two heuristic algorithms turn out to considerably reduce energy consumption when compared with the case where all the BSs are always turned on. The second part of the dissertation presents an energy-per-bit minimized radioresource allocation scheme in heterogeneous networks equipped with multi-homing capability which connects to different wireless interfaces simultaneously. Specifically, an optimization problem is formulated for the objective of minimizing the energy-per-bit which takes a form of nonlinear fractional programming. Then, a parametric optimization problem is derived out of that fractional programming and the original problem is solved by using a double-loop iteration method. In each iteration, the optimal resource allocation policy is derived by applying Lagrangian duality and an efficient dual update method. In addition, suboptimal resource allocation algorithms are developed by using the properties of the optimal resource allocation policy. Simulation results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency. The third part of the dissertation presents an energy efficient scheduling algorithm for CA in OFDMA based wireless networks. In support of this, the energy efficiency is newly defined as the ratio of the time-averaged downlink data rate and the time-averaged power consumption of the UE, which is important especially for battery-constrained UEs. Then, a component carrier and resource block allocation problem is formulated such that the proportional fairness of the energy efficiency is guaranteed. Since it is very complicated to determine the optimal solution, a low complexity energy-efficient scheduling algorithm is developed, which approaches the optimal algorithm. Simulation results demonstrate that the proposed scheduling scheme performs close to the optimal scheme and outperforms the existing scheduling schemes for CA.Abstract i List of Figures viii List of Tables x 1 Introduction 1 2 A Joint Algorithm for Base Station Operation and User Association in Heterogeneous Networks 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 UE Association Algorithm . . . . . . . . . . . . . . . . . . . . . . 14 2.5 BS Switching-on/off Algorithm . . . . . . . . . . . . . . . . . . . . 17 2.5.1 Cost Function Based (CFB) Algorithm . . . . . . . . . . . 19 2.5.2 AP Density Based (ADB) Algorithm . . . . . . . . . . . . 19 2.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Energy-per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 System Model and Problem Formulation . . . . . . . . . . . . . . . 30 3.3 Parametric Approach to Fractional Programming . . . . . . . . . . 36 3.3.1 Parametric Approach . . . . . . . . . . . . . . . . . . . . . 37 3.3.2 Double-Loop Iteration to Determine Optimal ฮธ . . . . . . . 38 3.4 Optimal Resource Allocation Algorithm . . . . . . . . . . . . . . . 39 3.4.1 Optimal Allocation of Subcarrier and Power . . . . . . . . . 41 3.4.2 Optimal Allocation of Time Fraction . . . . . . . . . . . . . 44 3.4.3 Lagrangian Multipliers Update Algorithm . . . . . . . . . . 48 3.5 Design of Suboptimal Algorithms . . . . . . . . . . . . . . . . . . 51 3.5.1 Time-Fraction Allocation First (TAF) Algorithm . . . . . . 51 3.5.2 Normalized Time-Fraction Allocation (NTA) Algorithm . . 53 3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4 Energy Efficient Scheduling for Carrier Aggregation in OFDMA Based Wireless Networks 68 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3 Energy Efficiency Proportional Fairness (EEPF) Scheduling . . . . 74 4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 78 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Conclusion 87 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . 91 References 93Docto

    FEDRESOURCE: Federated Learning Based Resource Allocation in Modern Wireless Networks

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    Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively
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