Performance Analysis of Heterogeneous Feedback Design in an OFDMA Downlink with Partial and Imperfect Feedback

Current OFDMA systems group resource blocks into subband to form the basic feedback unit. Homogeneous feedback design with a common subband size is not aware of the heterogeneous channel statistics among users. Under a general correlated channel model, we demonstrate the gain of matching the subband size to the underlying channel statistics motivating heterogeneous feedback design with different subband sizes and feedback resources across clusters of users. Employing the best-M partial feedback strategy, users with smaller subband size would convey more partial feedback to match the frequency selectivity. In order to develop an analytical framework to investigate the impact of partial feedback and potential imperfections, we leverage the multi-cluster subband fading model. The perfect feedback scenario is thoroughly analyzed, and the closed form expression for the average sum rate is derived for the heterogeneous partial feedback system. We proceed to examine the effect of imperfections due to channel estimation error and feedback delay, which leads to additional consideration of system outage. Two transmission strategies: the fix rate and the variable rate, are considered for the outage analysis. We also investigate how to adapt to the imperfections in order to maximize the average goodput under heterogeneous partial feedback.Comment: To appear in IEEE Trans. on Signal Processin

An Analytical Framework for Heterogeneous Partial Feedback Design in Heterogeneous Multicell OFDMA Networks

The inherent heterogeneous structure resulting from user densities and large scale channel effects motivates heterogeneous partial feedback design in heterogeneous networks. In such emerging networks, a distributed scheduling policy which enjoys multiuser diversity as well as maintains fairness among users is favored for individual user rate enhancement and guarantees. For a system employing the cumulative distribution function based scheduling, which satisfies the two above mentioned desired features, we develop an analytical framework to investigate heterogeneous partial feedback in a general OFDMA-based heterogeneous multicell employing the best-M partial feedback strategy. Exact sum rate analysis is first carried out and closed form expressions are obtained by a novel decomposition of the probability density function of the selected user's signal-to-interference-plus-noise ratio. To draw further insight, we perform asymptotic analysis using extreme value theory to examine the effect of partial feedback on the randomness of multiuser diversity, show the asymptotic optimality of best-1 feedback, and derive an asymptotic approximation for the sum rate in order to determine the minimum required partial feedback.Comment: To appear in IEEE Trans. on Signal Processin

LTE performance evaluation with realistic channel quality indicator feedback

In the context of mobile communications, the availability of new services and mobile applications along with the constant evolution in terminals run up the need of higher data rates. In order to fulfill such expectations, mobile operators are continually optimizing and upgrading their networks. The Long Term Evolution (LTE) of the Universal Mobile Telecommunications System (UMTS) seems to be the path to follow in a very short term. The objective of this project is to study the behaviour of the radio resource assignment in LTE under realistic conditions. The scheduling is a key process in the functioning of the radio interface. Thus, two types of schedulers can be identified, the opportunistic, where the scheduler considers the state of the radio channel to make the best allocation possible, and the non-opportunistic, where the allocation has no knowledge of the radio channel‘s state. As the opportunistic option adapts to the radio channel conditions it requires the transmission of a certain level of signalling from users informing about how the channel evolves along time. One of the objectives of this project is to evaluate the system performance under different degrees of feedback. To do this, different CQI reporting methods have been programmed and simulated. So, to achieve this objective it is obvious that a second one is necessary: program and simulate in a more realistic way the LTE radio channel. The followed methodology has been fundamentally the programming of different mathematical models and algorithms, as well as its simulation. In concrete, one of the main tasks in this work has been to extent a software platform of the research group Wicomtec to obtain more realistic results through dynamic simulations over a dynamic radio channel

Planning and dynamic spectrum management in heterogeneous mobile networks with QoE optimization

The radio and network planning and optimisation are continuous processes that do not end after the network has been launched. To achieve the best trade-offs, especially between quality and costs, operators make use of several coverage and capacity enhancement methods. The research from this thesis proposes methods such as the implementation of cell zooming and Relay Stations (RSs) with dynamic sleep modes and Carrier Aggregation (CA) for coverage and capacity enhancements. Initially, a survey is presented on ubiquitous mesh networks implementation scenarios and an updated characterization of requirements for services and applications is proposed. The performance targets for the key parameters, delay, delay variation, information loss and throughput have been addressed for all types of services. Furthermore, with the increased competition, mobile operator’s success does not only depend on how good the offered Quality of Service (QoS) is, but also if it meets the end user’s expectations, i.e., Quality of Experience (QoE). In this context, a model for the mapping between QoS parameters and QoE has been proposed for multimedia traffic. The planning and optimization of fixed Worldwide Interoperability for Microwave Access (WiMAX) networks with RSs in conjunction with cell zooming has been addressed. The challenging case of a propagation measurement-based scenario in the hilly region of Covilhã has been considered. A cost/revenue function has been developed by taking into account the cost of building and maintaining the infrastructure with the use of RSs. This part of the work also investigates the energy efficiency and economic implications of the use of power saving modes for RSs in conjunction with cell zooming. Assuming that the RSs can be switched-off or zoomed out to zero in periods when the traffic exchange is low, such as nights and weekends, it has been shown that energy consumption may be reduced whereas cellular coverage and capacity, as well as economic performance may be improved. An integrated Common Radio Resource Management (iCRRM) entity is proposed that implements inter-band CA by performing scheduling between two Long Term Evolution – Advanced (LTE-A) Component Carriers (CCs). Considering the bandwidths available in Portugal, the 800 MHz and 2.6 GHz CCs have been considered whilst mobile video traffic is addressed. Through extensive simulations it has been found that the proposed multi-band schedulers overcome the capacity of LTE systems without CA. Result shown a clear improvement of the QoS, QoE and economic trade-off with CA

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate

5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions

Sustainable scheduling policies for radio access networks based on LTE technology

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIn the LTE access networks, the Radio Resource Management (RRM) is one of the most important modules which is responsible for handling the overall management of radio resources. The packet scheduler is a particular sub-module which assigns the existing radio resources to each user in order to deliver the requested services in the most efficient manner. Data packets are scheduled dynamically at every Transmission Time Interval (TTI), a time window used to take the user’s requests and to respond them accordingly. The scheduling procedure is conducted by using scheduling rules which select different users to be scheduled at each TTI based on some priority metrics. Various scheduling rules exist and they behave differently by balancing the scheduler performance in the direction imposed by one of the following objectives: increasing the system throughput, maintaining the user fairness, respecting the Guaranteed Bit Rate (GBR), Head of Line (HoL) packet delay, packet loss rate and queue stability requirements. Most of the static scheduling rules follow the sequential multi-objective optimization in the sense that when the first targeted objective is satisfied, then other objectives can be prioritized. When the targeted scheduling objective(s) can be satisfied at each TTI, the LTE scheduler is considered to be optimal or feasible. So, the scheduling performance depends on the exploited rule being focused on particular objectives. This study aims to increase the percentage of feasible TTIs for a given downlink transmission by applying a mixture of scheduling rules instead of using one discipline adopted across the entire scheduling session. Two types of optimization problems are proposed in this sense: Dynamic Scheduling Rule based Sequential Multi-Objective Optimization (DSR-SMOO) when the applied scheduling rules address the same objective and Dynamic Scheduling Rule based Concurrent Multi-Objective Optimization (DSR-CMOO) if the pool of rules addresses different scheduling objectives. The best way of solving such complex optimization problems is to adapt and to refine scheduling policies which are able to call different rules at each TTI based on the best matching scheduler conditions (states). The idea is to develop a set of non-linear functions which maps the scheduler state at each TTI in optimal distribution probabilities of selecting the best scheduling rule. Due to the multi-dimensional and continuous characteristics of the scheduler state space, the scheduling functions should be approximated. Moreover, the function approximations are learned through the interaction with the RRM environment. The Reinforcement Learning (RL) algorithms are used in this sense in order to evaluate and to refine the scheduling policies for the considered DSR-SMOO/CMOO optimization problems. The neural networks are used to train the non-linear mapping functions based on the interaction among the intelligent controller, the LTE packet scheduler and the RRM environment. In order to enhance the convergence in the feasible state and to reduce the scheduler state space dimension, meta-heuristic approaches are used for the channel statement aggregation. Simulation results show that the proposed aggregation scheme is able to outperform other heuristic methods. When the aggregation scheme of the channel statements is exploited, the proposed DSR-SMOO/CMOO problems focusing on different objectives which are solved by using various RL approaches are able to: increase the mean percentage of feasible TTIs, minimize the number of TTIs when the RL approaches punish the actions taken TTI-by-TTI, and minimize the variation of the performance indicators when different simulations are launched in parallel. This way, the obtained scheduling policies being focused on the multi-objective criteria are sustainable. Keywords: LTE, packet scheduling, scheduling rules, multi-objective optimization, reinforcement learning, channel, aggregation, scheduling policies, sustainable