Advanced Radio Resource Management for Multi Antenna Packet Radio Systems

In this paper, we propose fairness-oriented packet scheduling (PS) schemes with power-efficient control mechanism for future packet radio systems. In general, the radio resource management functionality plays an important role in new OFDMA based networks. The control of the network resource division among the users is performed by packet scheduling functionality based on maximizing cell coverage and capacity satisfying, and certain quality of service requirements. Moreover, multiantenna transmit-receive schemes provide additional flexibility to packet scheduler functionality. In order to mitigate inter-cell and co-channel interference problems in OFDMA cellular networks soft frequency reuse with different power masks patterns is used. Stemming from the earlier enhanced proportional fair scheduler studies for single-input multiple-output (SIMO) and multiple-input multipleoutput (MIMO) systems, we extend the development of efficient packet scheduling algorithms by adding transmit power considerations in the overall priority metrics calculations and scheduling decisions. Furthermore, we evaluate the proposed scheduling schemes by simulating practical orthogonal frequency division multiple access (OFDMA) based packet radio system in terms of throughput, coverage and fairness distribution among users. As a concrete example, under reduced overall transmit power constraint and unequal power distribution for different sub-bands, we demonstrate that by using the proposed power-aware multi-user scheduling schemes, significant coverage and fairness improvements in the order of 70% and 20%, respectively, can be obtained, at the expense of average throughput loss of only 15%.Comment: 14 Pages, IJWM

Efficient Scheduling Algorithms for Wireless Resource Allocation and Virtualization in Wireless Networks

The continuing growth in demand for better mobile broadband experiences has motivated rapid development of radio-access technologies to support high data rates and improve quality of service (QoS) and quality of experience (QoE) for mobile users. However, the modern radio-access technologies pose new challenges to mobile network operators (MNO) and wireless device designers such as reducing the total cost of ownership while supporting high data throughput per user, and extending battery life-per-charge of the mobile devices. In this thesis, a variety of optimization techniques aimed at providing innovative solutions for such challenges are explored. The thesis is divided into two parts. In the first part, the challenge of extending battery life-per-charge is addressed. Optimal and suboptimal power-efficient schedulers that minimize the total transmit power and meet the QoS requirements of the users are presented. The second outlines the benefits and challenges of deploying wireless resource virtualization (WRV) concept as a promising solution for satisfying the growing demand for mobile data and reducing capital and operational costs. First, a WRV framework is proposed for single cell zone that is able to centralize and share the spectrum resources between multiple MNOs. Consequently, several WRV frameworks are proposed, which virtualize the spectrum resource of the entire network for cloud radio access network (C-RAN)- one of the front runners for the next generation network architecture. The main contributions of this thesis are in designing optimal and suboptimal solutions for the aforementioned challenges. In most cases, the optimal solutions suffer from high complexity, and therefore low-complexity suboptimal solutions are provided for practical systems. The optimal solutions are used as benchmarks for evaluating the suboptimal solutions. The results prove that the proposed solutions effectively contribute in addressing the challenges caused by the demand for high data rates and power transmission in mobile networks

Statistical priority-based uplink scheduling for M2M communications

Currently, the worldwide network is witnessing major efforts to transform it from being the Internet of humans only to becoming the Internet of Things (IoT). It is expected that Machine Type Communication Devices (MTCDs) will overwhelm the cellular networks with huge traffic of data that they collect from their environments to be sent to other remote MTCDs for processing thus forming what is known as Machine-to-Machine (M2M) communications. Long Term Evolution (LTE) and LTE-Advanced (LTE-A) appear as the best technology to support M2M communications due to their native IP support. LTE can provide high capacity, flexible radio resource allocation and scalability, which are the required pillars for supporting the expected large numbers of deployed MTCDs. Supporting M2M communications over LTE faces many challenges. These challenges include medium access control and the allocation of radio resources among MTCDs. The problem of radio resources allocation, or scheduling, originates from the nature of M2M traffic. This traffic consists of a large number of small data packets, with specific deadlines, generated by a potentially massive number of MTCDs. M2M traffic is therefore mostly in the uplink direction, i.e. from MTCDs to the base station (known as eNB in LTE terminology). These characteristics impose some design requirements on M2M scheduling techniques such as the need to use insufficient radio resources to transmit a huge amount of traffic within certain deadlines. This presents the main motivation behind this thesis work. In this thesis, we introduce a novel M2M scheduling scheme that utilizes what we term the “statistical priority” in determining the importance of information carried by data packets. Statistical priority is calculated based on the statistical features of the data such as value similarity, trend similarity and auto-correlation. These calculations are made and then reported by the MTCDs to the serving eNBs along with other reports such as channel state. Statistical priority is then used to assign priorities to data packets so that the scarce radio resources are allocated to the MTCDs that are sending statistically important information. This would help avoid exploiting limited radio resources to carry redundant or repetitive data which is a common situation in M2M communications. In order to validate our technique, we perform a simulation-based comparison among the main scheduling techniques and our proposed statistical priority-based scheduling technique. This comparison was conducted in a network that includes different types of MTCDs, such as environmental monitoring sensors, surveillance cameras and alarms. The results show that our proposed statistical priority-based scheduler outperforms the other schedulers in terms of having the least losses of alarm data packets and the highest rate in sending critical data packets that carry non-redundant information for both environmental monitoring and video traffic. This indicates that the proposed technique is the most efficient in the utilization of limited radio resources as compared to the other techniques

Fairness-Oriented and QoS-Aware Radio Resource Management in OFDMA Packet Radio Networks: Practical Algorithms and System Performance

During the last two decades, wireless technologies have demonstrated their importance in people’s personal communications but also as one of the fundamental drivers of economic growth, first in the form of cellular networks (2G, 3G and beyond) and more recently in terms of wireless computer networks (e.g. Wi-Fi,) and wireless Internet connectivity. Currently, the development of new packet radio systems is evolving, most notably in terms of 3GPP Long Term Evolution (LTE) and LTE-Advanced, in order to utilize the available radio spectrum as efficiently as possible. Therefore, advanced radio resource management (RRM) techniques have an important role in current and emerging future mobile networks. In all wireless systems, the data throughput and the average data delay performance, especially in case of best effort services, are greatly degraded when the traffic-load in the system is high. This is because the radio resources (time, frequency and space) are shared by multiple users. Another big problem is that the transmission performance can vary heavily between different users, since the channel state greatly depends on the communication environment and changes therein. To solve these challenges, new major technology innovations are needed. This thesis considers new practical fairness-oriented and quality-of-service (QoS) -aware RRM algorithms in OFDMA-based packet radio networks. Moreover, using UTRAN LTE radio network as application example, we focus on analyzing and enhancing the system-level performance by utilizing state-of-the-art waveform and radio link developments combined with advanced radio resource management methods. The presented solutions as part of RRM framework consist of efficient packet scheduling, link adaptation, power control, admission control and retransmission mechanisms. More specifically, several novel packet scheduling algorithms are proposed and analyzed to address these challenges. This dissertation deals specifically with the problems of QoS provisioning and fair radio resource distribution among users with limited channel feedback, admission and power control in best effort and video streaming type traffic scenarios, and the resulting system-level performance. The work and developments are practically-oriented taking aspects like finite channel state information (CSI), reporting delays and retransmissions into account. Consequently, the multi-user diversity gain with opportunistic frequency domain packet scheduling (FDPS) is further explored in spatial domain by taking the multiantenna techniques and spatial division multiplexing functionalities into account. Validation and analysis of the proposed solutions is performed through extensive system level simulations modeling the behavior and operation of a complete multiuser cell in the overall network. Based on the obtained performance results, it is confirmed that greatly improved fairness can be fairly easily built in to the scheduling algorithm and other RRM mechanisms without considerably degrading e.g. the average cell throughput. Moreover, effective QoS-provisioning framework in video streaming type traffic scenarios demonstrate the effectiveness of the presented solutions as increased system capacity measured in terms of the number of users or parallel streaming services supported simultaneously by the network