Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications

Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding. We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis. Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs. The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability. All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives.publishedVersio

Reliable Multicast D2D Communication over Multiple Channels in Underlay Cellular Networks

Author's accepted manuscript© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Multicast device-to-device (D2D) communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to the nearby receivers. However, due to critical challenges such as, mitigating mutual interference and unavailability of perfect channel state information (CSI), the resource allocation to multicast groups needs significant attention. In this work, we present a framework for joint channel assignment and power allocation strategy to maximize the sum rate of the combined network. The proposed framework allows access of multiple channels to the multicast groups, thus improving the achievable rate of the individual groups. Furthermore, fairness in allocating resources to the multicast groups is also ensured by augmenting the objective with a penalty function. In addition, considering imperfect CSI, the framework guarantees to provide rate above a specified outage for all the users. The formulated problem is a mixed integer nonconvex program which requires exponential complexity to obtain the optimal solution. To tackle this, we first introduce auxiliary variables to decouple the original problem into smaller power allocation problems and a channel assignment problem. Next, with the aid of fractional programming via a quadratic transformation, we obtain an efficient power allocation solution by alternating optimization. The solution for channel assignment is obtained by convex relaxation of integer constraints. Finally, we demonstrate the merit of the proposed approach by simulations, showing a higher and a more robust network throughput. Index Terms—D2D multicast communications, resource allocation, imperfect CSI, fractional programming.acceptedVersio

Forecasting Aquaponic Systems Behaviour With Recurrent Neural Networks Models

Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the machine learning life-cycle with industrial time-series data, i.e. data acquisition, pre-processing, feature engineering, architecture selection, training, and model verification.publishedVersionPaid open acces

Robust Transmit Beamforming for Underlay D2D Communications on Multiple Channels

Author´s accepted manuscript (postprint).© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.publishedVersio

Underlay Device-to-Device Communications on Multiple Channels

Author´s accepted manuscript (postprint).© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Since the spectral efficiency of wireless communications is already close to its fundamental bounds, a significant increase in spatial efficiency is required to meet future traffic demands. Device-to-device (D2D) communications provide such an increase by allowing nearby users to communicate directly without passing their packages through the base station. To fully exploit the benefits of this paradigm, proper channel assignment and power allocation algorithms are required. The main limitation of existing schemes, which restrict D2D transmitters to operate on a single channel at a time, is circumvented by the joint channel assignment and power allocation algorithm proposed in this paper. This algorithm relies on convex relaxation to efficiently obtain nearlyoptimal solutions to the mixed-integer program arising in this context. Numerical experiments corroborate the merits of the proposed scheme relative to state-of-the art alternatives.acceptedVersio

COGNITIVE RADIO AND GAME THEORY : OVERVIEW AND SIMULATION

This thesis aims to clearly describe the cognitive radio and its components and operations. Moreover, it aims on describing the expected outcome from the most common techniques that are proposed for use in cognitive radios. In addition, it describes the basic principles of game theory and some simple game models that can be used to analyze the efficiency of the optimization algorithms. Furthermore, it investigates the use of load balancing algorithm and genetic algorithm in optimizing the decision making operation in cognitive radios. Matlab software simulations were carried out and the results show the promising benefit of using those two algorithms along with game theory in optimizing the dynamic spectrum allocation process

Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications

Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding. We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis. Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs. The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability. All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives

Developing a line balancing tool for reconfigurable manufacturing systems : A tool to support investment decisions

Purpose - This thesis aims to developing a decision-making tool which fits in a reconfigurable manufacturing system (RMS) milieu used to identify whether to introduce and produce a new product into an already existing assembly line or to invest in a new assembly line. To fulfil the purpose, four research questions were developed.  Which line balancing problem-solving techniques exist in the literature? Which investment costs can be considered vital for new assembly lines as a    consequence from new product introductions? Can a decision-making tool be designed to evaluate new product introductions which considers both line balancing KPIs and investment costs in an assembly line? To what extent can criteria in the RMS theory be linked with the attributes of the    designed decision-making tool to support its applicability? Method - Literature studies were performed in order to create a theoretical foundation for the thesis to stand upon, hence enabling the possibility to answer the research questions. The literature studies were structured to focus on selected topics, including reconfigurable manufacturing systems, line balancing, and assembly line investment costs. To answer the third research question, which involved creating a decision-making tool, a single-case study was carried out. The company chosen was within the automotive industry. Data was collected through interviews, document studies and a focus group. Findings & analysis - An investigation regarding which line balancing solving-techniques suit RMS and which assembly line investment costs are critical when  introducing new products has been made. The outputs from these  investigations set the foundation for developing a decision-making tool which enables fact-based decisions. To test the decision-making tool’s compatibility with reconfigurable manufacturing systems, an evaluation against established characteristics was performed. The evaluation identified two reconfigurable manufacturing system characteristic as having a direct correlation to the decision-making tool. These characteristics regarded scalability and convertibility. Conclusions - The industrial contribution of the thesis was a decision-making tool that enables fact-based decisions regarding whether to introduce a new product into an already existing assembly line or invest in a new assembly line. The academic contribution involved that the procedure for evaluating the tool was recognized as also being suitable for testing the reconfigurable correlation with other production development tools. Another contribution regards bridging the knowledge gaps of the classifications in line balancing-solving techniques and assembly line investment costs. Delimitations - One of the delimitations in the thesis involved solely focusing on developing and analysing a decision-making tool from an RMS perspective. Hence, other production systems were not in focus. Also, the thesis only covered the development of a decision-making tool for straight assembly lines, not U-shaped lines

Active Noise Control of a Radial Fan

This thesis work aims at investigating the use of an active noise control (ANC) system on a radial fan. This was done by studying the fan structure and its potential working environment (the ducts). This includes measuring the sound levels on several positions and select suitable positions to apply the ANC system. Moreover, the tested ANC system was implemented on the ventilation system and acceptable results were obtained. Further analyses were made based on the obtained results and some explanations were derived to investigate the reason behind the ANC systems incapability to attenuate the noise generated by the fan at some frequencies