Resource allocation for heterogeneous wireless networks

Demand for high volumes of mobile data traffic with better quality-of-service (QoS) support and seamless network coverage is ever increasing, due to growth of the number of smart mobile devices and the applications that run on these devices. Also, most of these high volumes of data traffic demanding areas are covered by heterogeneous wireless networks, such as cellular networks and wireless local area networks (WLANs). Therefore, interworking mechanisms can be used in these areas to enhance the network capacity, QoS support and coverage. Interworking enhances network capacity and QoS support by jointly allocating resources of multiple networks and enabling user multi-homing, where multi-homing allows users to simultaneously communicate over multiple networks. It widens network coverage by merging coverage of individual networks. However, there are areas where interworking cannot improve network capacity or QoS support, such as the areas with coverage of only one networks. Therefore, to achieve network-wide uniform capacity and QoS support enhancements, interworking can be integrated with device-to-device (D2D) communication and small cell deployment techniques. One of the challenging issues that need to be solved before these techniques can be applied in practical networks is the efficient resource allocation, as it has a direct impact on the network capacity and QoS support. Therefore, this thesis focuses on studying and developing efficient resource allocation schemes for interworking heterogeneous wireless networks which apply D2D communication and small cell deployment techniques. First, uplink resource allocation for cellular network and WLAN interworking to provide multi-homing voice and data services is investigated. The main technical challenge, which makes the resource allocation for this system complicated, is that resource allocation decisions need to be made capturing multiple physical layer (PHY) and medium access control layer (MAC) technologies of the two networks. This is essential to ensure that the decisions are feasible and can be executed at the lower layers. Thus, the resource allocation problem is formulated based on PHY and MAC technologies of the two networks. The optimal resource allocation problem is a multiple time-scale Markov decision process (MMDP) as the two networks operate at different time-scales, and due to voice and data service requirements. A resource allocation scheme consisting of decision policies for the upper and the lower levels of the MMDP is derived. To reduce the time complexity, a heuristic resource allocation algorithm is also proposed. Second, resource allocation for D2D communication underlaying cellular network and WLAN interworking is investigated. Enabling D2D communication within the interworking system further enhances the spectrum efficiency, especially at areas where only one network is available. In addition to the technical challenges encountered in the first interworking system, interference management and selection of users' communication modes for multiple networks to maximize hop and reuse gains complicate resource allocation for this system. To address these challenges, a semi-distributed resource allocation scheme that performs mode selection, allocation of WLAN resources, and allocation of cellular network resources in three different time-scales is proposed. Third, resource allocation for interworking macrocell and hyper-dense small cell networks is studied. Such system is particularly useful for interference prone and high capacity demanding areas, such as busy streets and city centers, as it uses license frequency bands and provides a high spectrum efficiency through frequency reuse and bringing network closer to the users. The key challenge for allocating resources for this system is high complexity of the resource allocation scheme due to requirement to jointly allocate resources for a large number of small cells to manage co-channel interference (CCI) in the system. Further, the resource allocation scheme should minimize the computational burden for low-cost small cell base stations (BSs), be able to adapt to time-varying network load conditions, and reduce signaling overhead in the small cell backhauls with limited capacity. To this end, a resource allocation scheme which operates on two time-scales and utilizes cloud computing to determine resource allocation decisions is proposed. Resource allocation decisions are made at the cloud in a slow time-scale, and are further optimized at the BSs in a fast time-scale in order to adapt the decisions to fast varying wireless channel conditions. Achievable throughput and QoS improvements using the proposed resource allocation schemes for all three systems are demonstrated via simulation results. In summary, designing of the proposed resource allocation schemes provides valuable insights on how to efficiently allocate resources considering PHY and MAC technologies of the heterogeneous wireless networks, and how to utilize cloud computing to assist executing a complex resource allocation scheme. Furthermore, it also demonstrates how to operate a resource allocation scheme over multiple time-scales. This is particularly important if the scheme is complex and requires a long time to execute, yet the resource allocation decisions are needed to be made within a short interval

Validation platform implementation description – D5.2

Deliverable D5.2 del projecte OneFITPostprint (published version

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Validation platform implementation description - D5.2

This deliverable describes different test-beds for the validation of the architecture, algorithms and protocols for the operator governed opportunistic networking as defined in the OneFIT Project. Further on, this deliverable provides a description of the implementation of the OneFIT cognitive management systems CSCI and CMON as well as the C4MS protocol. Also, implementation of the blocks supporting the OneFIT system (JRRM, CCM, DSONPM, and DSM) is described. This document also describes the implementation of the OneFIT scenarios for opportunistic coverage extension, opportunistic capacity extension, infrastructure supported ad-hoc networking and device-to-device communication as well as opportunistic resource aggregation in the backhaul network

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

LTE Optimization and Resource Management in Wireless Heterogeneous Networks

Mobile communication technology is evolving with a great pace. The development of the Long Term Evolution (LTE) mobile system by 3GPP is one of the milestones in this direction. This work highlights a few areas in the LTE radio access network where the proposed innovative mechanisms can substantially improve overall LTE system performance. In order to further extend the capacity of LTE networks, an integration with the non-3GPP networks (e.g., WLAN, WiMAX etc.) is also proposed in this work. Moreover, it is discussed how bandwidth resources should be managed in such heterogeneous networks. The work has purposed a comprehensive system architecture as an overlay of the 3GPP defined SAE architecture, effective resource management mechanisms as well as a Linear Programming based analytical solution for the optimal network resource allocation problem. In addition, alternative computationally efficient heuristic based algorithms have also been designed to achieve near-optimal performance

one6G white paper, 6G technology overview:Second Edition, November 2022

6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond. These are characterized by a variety of diverse, often conflicting requirements, from technical ones such as extremely high data rates, unprecedented scale of communicating devices, high coverage, low communicating latency, flexibility of extension, etc., to non-technical ones such as enabling sustainable growth of the society as a whole, e.g., through energy efficiency of deployed networks. On the one hand, 6G is expected to fulfil all these individual requirements, extending thus the limits set by the previous generations of mobile networks (e.g., ten times lower latencies, or hundred times higher data rates than in 5G). On the other hand, 6G should also enable use cases characterized by combinations of these requirements never seen before, e.g., both extremely high data rates and extremely low communication latency). In this white paper, we give an overview of the key enabling technologies that constitute the pillars for the evolution towards 6G. They include: terahertz frequencies (Section 1), 6G radio access (Section 2), next generation MIMO (Section 3), integrated sensing and communication (Section 4), distributed and federated artificial intelligence (Section 5), intelligent user plane (Section 6) and flexible programmable infrastructures (Section 7). For each enabling technology, we first give the background on how and why the technology is relevant to 6G, backed up by a number of relevant use cases. After that, we describe the technology in detail, outline the key problems and difficulties, and give a comprehensive overview of the state of the art in that technology. 6G is, however, not limited to these seven technologies. They merely present our current understanding of the technological environment in which 6G is being born. Future versions of this white paper may include other relevant technologies too, as well as discuss how these technologies can be glued together in a coherent system

Adaptive reinforcement learning for heterogeneous network selection

Next generation 5G mobile wireless networks will consist of multiple technologies for devices to access the network at the edge. One of the keys to 5G is therefore the ability for device to intelligently select its Radio Access Technology (RAT). Current fully distributed algorithms for RAT selection although guaranteeing convergence to equilibrium states, are often slow, require high exploration times and may converge to undesirable equilibria. In this dissertation, we propose three novel reinforcement learning (RL) frameworks to improve the efficiency of existing distributed RAT selection algorithms in a heterogeneous environment, where users may potentially apply a number of different RAT selection procedures. Although our research focuses on solutions for RAT selection in the current and future mobile wireless networks, the proposed solutions in this dissertation are general and suitable to apply for any large scale distributed multi-agent systems. In the first framework, called RL with Non-positive Regret, we propose a novel adaptive RL for multi-agent non-cooperative repeated games. The main contribution is to use both positive and negative regrets in RL to improve the convergence speed and fairness of the well-known regret-based RL procedure. Significant improvements in performance compared to other related algorithms in the literature are demonstrated. In the second framework, called RL with Network-Assisted Feedback (RLNF), our core contribution is to develop a network feedback model that uses network-assisted information to improve the performance of the distributed RL for RAT selection. RLNF guarantees no-regret payoff in the long-run for any user adopting it, regardless of what other users might do and so can work in an environment where not all users use the same learning strategy. This is an important implementation advantage as RLNF can be implemented within current mobile network standards. In the third framework, we propose a novel adaptive RL-based mechanism for RAT selection that can effectively handle user mobility. The key contribution is to leverage forgetting methods to rapidly react to the changes in the radio conditions when users move. We show that our solution improves the performance of wireless networks and converges much faster when users move compared to the non-adaptive solutions. Another objective of the research is to study the impact of various network models on the performance of different RAT selection approaches. We propose a unified benchmark to compare the performances of different algorithms under the same computational environment. The comparative studies reveal that among all the important network parameters that influence the performance of RAT selection algorithms, the number of base stations that a user can connect to has the most significant impact. This finding provides some guidelines for the proper design of RAT selection algorithms for future 5G. Our evaluation benchmark can serve as a reference for researchers, network developers, and engineers. Overall, the thesis provides different reinforcement learning frameworks to improve the efficiency of current fully distributed algorithms for heterogeneous RAT selection. We prove the convergence of the proposed reinforcement learning procedures using the differential inclusion (DI) technique. The theoretical analyses demonstrate that the use of DI not only provides an effective method to study the convergence properties of adaptive procedures in game-theoretic learning, but also yields a much more concise and extensible proof as compared to the classical approaches.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201