Measurement Based Resource Allocation for Multimedia Applications

Modern networks are now capable of guaranteeing a consistent Quality of Service (QoS) to multimedia traffic streams. A number of major operating system vendors are also working hard to extend these guarantees into the end-system. In both cases, however, there remains the problem of determining a service rate sufficient to ensure the desired Quality of Service. Source modelling is not a sustainable approach in the network case and it is even less feasible to model the demands of multimedia applications. The ESPRIT Measure project is successfully using online measurement and estimation to perform resource allocation for bursty traffic in ATM networks. In this paper we consider the applicability of the same theory to resource allocation in a multimedia operating system which offers QoS guarantees to its applications

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs

Statistical multiplexing and connection admission control in ATM networks

Asynchronous Transfer Mode (ATM) technology is widely employed for the transport of network traffic, and has the potential to be the base technology for the next generation of global communications. Connection Admission Control (CAC) is the effective traffic control mechanism which is necessary in ATM networks in order to avoid possible congestion at each network node and to achieve the Quality-of-Service (QoS) requested by each connection. CAC determines whether or not the network should accept a new connection. A new connection will only be accepted if the network has sufficient resources to meet its QoS requirements without affecting the QoS commitments already made by the network for existing connections. The design of a high-performance CAC is based on an in-depth understanding of the statistical characteristics of the traffic sources

Information Technology

The new millennium has been labeled as the century of the personal communications revolution or more specifically, the digital wireless communications revolution. The introduction of new multimedia services has created higher loads on available radio resources. These services can be presented in different levels of quality of service. Namely, the task of the radio resource manager is to provide these levels. Radio resources are scarce and need to be shared by many users. The sharing has to be carried out in an efficient way avoiding as much as possible any waste of resources. The main contribution focus of this work is on radio resource management in opportunistic systems. In opportunistic communications dynamic rate and power allocation may be performed over the dimensions of time, frequency and space in a wireless system. In this work a number of these allocation schemes are proposed. A downlink scheduler is introduced in this work that controls the activity of the users. The scheduler is a simple integral controller that controls the activity of users, increasing or decreasing it depending on the degree of proximity to a requested quality of service level. The scheduler is designed to be a best effort scheduler; that is, in the event the requested quality of service (QoS) cannot be attained, users are always guaranteed the basic QoS level provided by a proportional fair scheduler. In a proportional fair scheduler, the user with the best rate quality factor is selected. The rate quality here is the instantaneous achievable rate divided by the average throughput Uplink scheduling is more challenging than its downlink counterpart due to signalling restrictions and additional constraints on resource allocations. For instance, in long term evolution systems, single carrier FDMA is to be utilized which requires the frequency domain resource allocation to be done in such a way that a user could only be allocated subsequent bands. We suggest for the uplink a scheduler that follows a heuristic approach in its decision. The scheduler is mainly based on the gradient algorithm that maximizes the gradient of a certain utility. The utility could be a function of any QoS. In addition, an optimal uplink scheduler for the same system is presented. This optimal scheduler is valid in theory only, nevertheless, it provides a considerable benchmark for evaluation of performance for the heuristic scheduler as well as other algorithms of the same system. A study is also made for the feedback information in a multi-carrier system. In a multi-carrier system, reporting the channel state information (CSI) of every subcarrier will result in huge overhead and consequent waste in bandwidth. In this work the subcarriers are grouped into subbands which are in turn grouped into blocks and a study is made to find the minimum amount of information for the adaptive modulation and coding (AMC) of the blocks. The thesis also deals with admission control and proposes an opportunistic admission controller. The controller gradually integrates a new user requesting admission into the system. The system is probed to examine the effect of the new user on existing connections. The user is finally fully admitted if by the end of the probing, the quality of service (QoS) of existing connections did not drop below a certain threshold. It is imperative to mention that the research work of this thesis is mainly focused on non-real time applications.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format