SINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs)

To guarantee the phenomenon of "Always Best Connection" in heterogeneous wireless networks, a vertical handover optimization is necessary to realize seamless mobility. Received signal strength (RSS) from the user equipment (UE) contains interference from surrounding base stations, which happens to be a function of the network load of the nearby cells. An expression is derived for the received SINR (signal to interference and noise ratio) as a function of traffic load in interfering cells of data networks. A better estimate of the UE SINR is achieved by taking into account the contribution of inter-cell interference. The proposed scheme affords UE to receive high throughput with less data rate, and hence benefits users who are located far from the base station. The proposed scheme demonstrates an improved throughput between the serving base station and the cell boundary

Heterogeneous integration of optical wireless communications within next generation networks

Unprecedented traffic growth is expected in future wireless networks and new technologies will be needed to satisfy demand. Optical wireless (OW) communication offers vast unused spectrum and high area spectral efficiency. In this work, optical cells are envisioned as supplementary access points within heterogeneous RF/OW networks. These networks opportunistically offload traffic to optical cells while utilizing the RF cell for highly mobile devices and devices that lack a reliable OW connection. Visible light communication (VLC) is considered as a potential OW technology due to the increasing adoption of solid state lighting for indoor illumination. Results of this work focus on a full system view of RF/OW HetNets with three primary areas of analysis. First, the need for network densication beyond current RF small cell implementations is evaluated. A media independent model is developed and results are presented that provide motivation for the adoption of hyper dense small cells as complementary components within multi-tier networks. Next, the relationships between RF and OW constraints and link characterization parameters are evaluated in order to define methods for fair comparison when user-centric channel selection criteria are used. RF and OW noise and interference characterization techniques are compared and common OW characterization models are demonstrated to show errors in excess of 100x when dominant interferers are present. Finally, dynamic characteristics of hyper dense OW networks are investigated in order to optimize traffic distribution from a network-centric perspective. A Kalman Filter model is presented to predict device motion for improved channel selection and a novel OW range expansion technique is presented that dynamically alters coverage regions of OW cells by 50%. In addition to analytical results, the dissertation describes two tools that have been created for evaluation of RF/OW HetNets. A communication and lighting simulation toolkit has been developed for modeling and evaluation of environments with VLC-enabled luminaires. The toolkit enhances an iterative site based impulse response simulator model to utilize GPU acceleration and achieves 10x speedup over the previous model. A software defined testbed for OW has also been proposed and applied. The testbed implements a VLC link and a heterogeneous RF/VLC connection that demonstrates the RF/OW HetNet concept as proof of concept

Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks

Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In this paper, we decompose the mobility-aware user association task into (i) forecasting of user rate and then (ii) convex utility maximization for user association accounting for the effects of BS load and handover overheads. Using a linear combination of normalized mean-squared error and normalized discounted cumulative gain as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. Based on the forecast, the controller optimizes the association decisions to maximize the service rate-based network utility using our computationally efficient (speed up of 100x versus generic convex solver) algorithm based on the Frank-Wolfe method. Using an industry-grade network simulator developed by Meta, we show that the proposed model predictive control (MPC) approach improves the 5th percentile service rate by 3.5x compared to the traditional signal strength-based association, reduces the median number of handovers by 7x compared to a handover agnostic strategy, and achieves service rates close to a genie-aided scheme. Furthermore, our model-based approach is significantly more sample-efficient (needs 100x less training data) compared to model-free reinforcement learning (RL), and generalizes well across different user drop scenarios

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Towards a GNU/Linux IEEE 802.21 Implementation

Abstract-Multiaccess mobile devices and overlapping wireless network deployments have emerged as a next generation network fixture. To make the most of all available networks, mobile devices should be capable of handing over between heterogeneous networks seamlessly and automatically. At the same time, operators should be able to steer network attachment based on their criteria. Although several cross layer mechanisms have been proposed in recent years, only the Media Independent Handover (MIH) Services framework has advanced in any of the established standardization bodies. This paper presents a blueprint for a GNU/Linux implementation of IEEE 802.21. We review the salient points of the standard, introduce our software implementation architecture, detail information gathering in GNU/Linux, and show how our prototype implementation can be used in practice. In contrast with prior published work, this paper presents a real IEEE 802.21 implementation, not an abstracted or reduced MIH-like framework, tested and empirically evaluated over real heterogeneous networks

Mobility-Aware Video Streaming in MIMO-Capable Heterogeneous Wireless Networks

Multiple input and multiple output (MIMO) is a well-known technique for the exploitation of the spatial multiplexing (MUX) and spatial diversity (DIV) gains that improve transmission quality and reliability. In this paper, we propose a quality-adaptive scheme for handover and forwarding that supports mobile-video-streaming services in MIMO-capable, heterogeneous wireless-access networks such as those for Wi-Fi and LTE. Unlike previous handover schemes, we propose an appropriate metric for the selection of the wireless technology and the MIMO mode, whereby a new address availability and the wireless-channel quality, both of which are in a new wireless-access network so that the handover and video-playing delays are reduced, are considered. While an MN maintains its original care-of address (oCoA), the video packets destined for the MN are forwarded with the MIMO technique (MUX mode or DIV mode) on top of a specific wireless technology from the previous Access Router (pAR) to the new Access Router (nAR) until they finally reach the MN; however, to guarantee a high video-streaming quality and to limit the video-packet-forwarding hops between the pAR and the nAR, the MN creates a new CoA (nCOA) within the delay threshold of the QoS/quality of experience (QoE) satisfaction result, and then, as much as possible, the video packet is forwarded with the MUX. Through extensive simulations, we show that the proposed scheme is a significant improvement upon the other schemes

Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds

Improving next-generation wireless network performance and reliability with deep learning

A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for “network intelligence” to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirement—a period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards todayElectrical and Computer Engineerin