A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Radio resource allocation in relay based OFDMA cellular networks

PhDAdding relay stations (RS) between the base station (BS) and the mobile stations (MS) in a cellular system can extend network coverage, overcome multi-path fading and increase the capacity of the system. This thesis considers the radio resource allocation scheme in relay based cellular networks to ensure high-speed and reliable communication. The goal of this research is to investigate user fairness, system throughput and power consumption in wireless relay networks through considering how best to manage the radio resource. This thesis proposes a two-hop proportional fairness (THPF) scheduling scheme fair allocation, which is considered both in the first time subslot between direct link users and relay stations, and the second time subslot among relay link users. A load based relay selection algorithm is also proposed for a fair resource allocation. The transmission mode (direct transmission mode or relay transmission mode) of each user will be adjusted based on the load of the transmission node. Power allocation is very important for resource efficiency and system performance improvement and this thesis proposes a two-hop power allocation algorithm for energy efficiency, which adjusts the transmission power of the BS and RSs to make the data rate on the two hop links of one RS match each other. The power allocation problem of multiple cells with inter-cell interference is studied. A new multi-cell power allocation scheme is proposed from non-cooperative game theory; this coordinates the inter-cell interference and operates in a distributed manner. The utility function can be designed for throughput improvement and user fairness respectively. Finally, the proposed algorithms in this thesis are combined, and the system performance is evaluated. The joint radio resource allocation algorithm can achieve a very good tradeoff between throughput and user fairness, and also can significantly improve energy efficiency

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation

In this paper, we consider the distributive queue-aware power and subband allocation design for a delay-optimal OFDMA uplink system with one base station, $K$ users and $N_F$ independent subbands. Each mobile has an uplink queue with heterogeneous packet arrivals and delay requirements. We model the problem as an infinite horizon average reward Markov Decision Problem (MDP) where the control actions are functions of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the subband allocation Q-factor by the sum of the per-user subband allocation Q-factor and derive a distributive online stochastic learning algorithm to estimate the per-user Q-factor and the Lagrange multipliers (LM) simultaneously and determine the control actions using an auction mechanism. We show that under the proposed auction mechanism, the distributive online learning converges almost surely (with probability 1). For illustration, we apply the proposed distributive stochastic learning framework to an application example with exponential packet size distribution. We show that the delay-optimal power control has the {\em multi-level water-filling} structure where the CSI determines the instantaneous power allocation and the QSI determines the water-level. The proposed algorithm has linear signaling overhead and computational complexity $\mathcal O(KN)$, which is desirable from an implementation perspective.Comment: To appear in Transactions on Signal Processin

Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage

In this paper, we consider delay minimization for interference networks with renewable energy source, where the transmission power of a node comes from both the conventional utility power (AC power) and the renewable energy source. We assume the transmission power of each node is a function of the local channel state, local data queue state and local energy queue state only. In turn, we consider two delay optimization formulations, namely the decentralized partially observable Markov decision process (DEC-POMDP) and Non-cooperative partially observable stochastic game (POSG). In DEC-POMDP formulation, we derive a decentralized online learning algorithm to determine the control actions and Lagrangian multipliers (LMs) simultaneously, based on the policy gradient approach. Under some mild technical conditions, the proposed decentralized policy gradient algorithm converges almost surely to a local optimal solution. On the other hand, in the non-cooperative POSG formulation, the transmitter nodes are non-cooperative. We extend the decentralized policy gradient solution and establish the technical proof for almost-sure convergence of the learning algorithms. In both cases, the solutions are very robust to model variations. Finally, the delay performance of the proposed solutions are compared with conventional baseline schemes for interference networks and it is illustrated that substantial delay performance gain and energy savings can be achieved

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

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station