Formulation, implementation considerations, and first performance evaluation of algorithmic solutions - D4.1

Deliverable D4.1 del projecte Europeu OneFIT (ICT-2009-257385)This deliverable contains a first version of the algorithmic solutions for enabling opportunistic networks. The presented algorithms cover the full range of identified management tasks: suitability, creation, QoS control, reconfiguration and forced terminations. Preliminary evaluations complement the proposed algorithms. Implementation considerations towards the practicality of the considered algorithms are also included.Preprin

Formulations and identification of algorithmic solutions for enabling opportunistic networks - M4.1

Milestone M4.1 del projecte Europeu OneFIT (ICT-2009-257385).This document contains a detailed description of the algorithms to be implemented to manage the opportunistic networks. There are defined according to the functional and system architecture (WP2) to fulfil the technical challenges. These algorithms will implemented during the WP4.2 and validated during the WP4.3Postprint (published version

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Leveraging Cognitive Radio Networks Using Heterogeneous Wireless Channels

The popularity of ubiquitous Internet services has spurred the fast growth of wireless communications by launching data hungry multimedia applications to mobile devices. Powered by spectrum agile cognitive radios, the newly emerged cognitive radio networks (CRN) are proposed to provision the efficient spectrum reuse to improve spectrum utilization. Unlicensed users in CRN, or secondary users (SUs), access the temporarily idle channels in a secondary and opportunistic fashion while preventing harmful interference to licensed primary users (PUs). To effectively detect and exploit the spectrum access opportunities released from a wide spectrum, the heterogeneous wireless channel characteristics and the underlying prioritized spectrum reuse features need to be considered in the protocol design and resource management schemes in CRN, which plays a critical role in unlicensed spectrum sharing among multiple users. The purpose of this dissertation is to address the challenges of utilizing heterogeneous wireless channels in CRN by its intrinsic dynamic and diverse natures, and build the efficient, scalable and, more importantly, practical dynamic spectrum access mechanisms to enable the cost-effective transmissions for unlicensed users. Note that the spectrum access opportunities exhibit the diversity in the time/frequency/space domain, secondary transmission schemes typically follow three design principles including 1) utilizing local free channels within short transmission range, 2) cooperative and opportunistic transmissions, and 3) effectively coordinating transmissions in varying bandwidth. The entire research work in this dissertation casts a systematic view to address these principles in the design of the routing protocols, medium access control (MAC) protocols and radio resource management schemes in CRN. Specifically, as spectrum access opportunities usually have small spatial footprints, SUs only communicate with the nearby nodes in a small area. Thus, multi-hop transmissions in CRN are considered in this dissertation to enable the connections between any unlicensed users in the network. CRN typically consist of intermittent links of varying bandwidth so that the decision of routing is closely related with the spectrum sensing and sharing operations in the lower layers. An efficient opportunistic cognitive routing (OCR) scheme is proposed in which the forwarding decision at each hop is made by jointly considering physical characteristics of spectrum bands and diverse activities of PUs in each single band. Such discussion on spectrum aware routing continues coupled with the sensing selection and contention among multiple relay candidates in a multi-channel multi-hop scenario. An SU selects the next hop relay and the working channel based upon location information and channel usage statistics with instant link quality feedbacks. By evaluating the performance of the routing protocol and the joint channel and route selection algorithm with extensive simulations, we determine the optimal channel and relay combination with reduced searching complexity and improved spectrum utilization. Besides, we investigate the medium access control (MAC) protocol design in support of multimedia applications in CRN. To satisfy the quality of service (QoS) requirements of heterogeneous applications for SUs, such as voice, video, and data, channels are selected to probe for appropriate spectrum opportunities based on the characteristics and QoS demands of the traffic along with the statistics of channel usage patterns. We propose a QoS-aware MAC protocol for multi-channel single hop scenario where each single SU distributedly determines a set of channels for sensing and data transmission to satisfy QoS requirements. By analytical model and simulations, we determine the service differentiation parameters to provision multiple levels of QoS. We further extend our discussion of dynamic resource management to a more practical deployment case. We apply the experiences and skills learnt from cognitive radio study to cellular communications. In heterogeneous cellular networks, small cells are deployed in macrocells to enhance link quality, extend network coverage and offload traffic. As different cells focus on their own operation utilities, the optimization of the total system performance can be analogue to the game between PUs and SUs in CRN. However, there are unique challenges and operation features in such case. We first present challenging issues including interference management, network coordination, and interworking between cells in a tiered cellular infrastructure. We then propose an adaptive resource management framework to improve spectrum utilization and mitigate the co-channel interference between macrocells and small cells. A game-theory-based approach is introduced to handle power control issues under constrained control bandwidth and limited end user capability. The inter-cell interference is mitigated based upon orthogonal transmissions and strict protection for macrocell users. The research results in the dissertation can provide insightful lights on flexible network deployment and dynamic spectrum access for prioritized spectrum reuse in modern wireless systems. The protocols and algorithms developed in each topic, respectively, have shown practical and efficient solutions to build and optimize CRN

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..

MAC Aspects of Millimeter-Wave Cellular Networks

The current demands for extremely high data rate wireless services and the spectrum scarcity at the sub-6 GHz bands are forcefully motivating the use of the millimeter-wave (mmWave) frequencies. MmWave communications are characterized by severe attenuation, sparse-scattering environment, large bandwidth, high penetration loss, beamforming with massive antenna arrays, and possible noise-limited operation. These characteristics imply a major difference with respect to legacy communication technologies, primarily designed for the sub-6 GHz bands, and are posing major design challenges on medium access control (MAC) layer. This book chapter discusses key MAC layer issues at the initial access and mobility management (e.g., synchronization, random access, and handover) as well as resource allocation (interference management, scheduling, and association). The chapter provides an integrated view on MAC layer issues for cellular networks and reviews the main challenges and trade-offs and the state-of-the-art proposals to address them