Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios

Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication

Millimeter wave picocellular networks: capacity analysis and system design

The explosive growth in demand for wireless mobile data, driven by the proliferationof ever more sophisticated handhelds creating and consuming rich multimedia, calls fororders of magnitude increase in the capacity of cellular data networks. Millimeter wavecommunication from picocellular base stations to mobile devices is a particularly promisingapproach for meeting this challenge because of two reasons. First, there is a largeamount of available spectrum, enabling channel bandwidths of the order of Gigahertz(GHz) which are 1-2 orders of magnitude higher than those in existing WiFi and cellularsystems at lower carrier frequencies. Second, the small carrier wavelength enables therealization of highly directive steerable arrays with a large number of antenna elements,in compact form factors, thus significantly enhancing spatial reuse. Hence, we propose toemploy the 60 GHz unlicensed band for basestation to mobile communication in outdoorpicocells.We first investigate the basic feasibility of such networks, showing that 60GHz linksare indeed viable for outdoor applications. For this purpose, we provided link budgetcalculations along with preliminary simulations which show that despite the commonconcerns about higher oxygen absorption and sensitivity to movement and blockage,picocloud architecture provides availability rate of more than 99%.Next, we explore the idea of increasing spatial reuse by shrinking picocells hopingthat interference is no longer the bottleneck given the highly directive antenna arrays atthis band. Our goal is to estimate the achievable capacity for small picocells along an urban canyon. We consider basestations with multiple faces or sectors, each with one or more antenna arrays. Each such array, termed subarray can employ Radio Frequency(RF) beamforming to communicate with one mobile user at a time. We first focus oncharacterization and modeling the inter-cell interference for one subarray on each face.Our analysis provides a strong indication of very large capacity (in the order of Tbps/km)with a few GHz of bandwidth.Following this, we explore the impact of adding multiple subarrays per face. This leadsto intra-cell interference as well as additional inter-cell interference. While the effect ofadditional inter-cell interference can be quantified within our previous framework, intracellinterference has inherently different features that call for new approaches for analysisand design. We propose a cross-layer approach to suppress the intra-cell interference intwo stages: (a) Physical layer (PHY-layer) method which mitigates interference by jointprecoding and power adaptation and (b) Medium Access Control layer (MAC-layer)method which manages the residual interference by optimizing resource allocation. Wethen estimate the capacity gain over conventional LTE cellular networks and establishthat 1000-fold capacity increase is indeed feasible via mm-wave picocellular networks.Lastly, we examine fundamental signal processing challenges associated with channelestimation and tracking for large arrays, placed within the context of system designfor a mm-wave picocellular network. Maintainance of highly directive links in the faceof blockage and mobility requires accurate estimation of the spatial channels betweenbasestation and mobile users. Here we develop the analytical framework for compressivechannel estimation and tracking. We also address the system level design discussinglink budget, overhead, and inter-cell beacon interference. Simulation results demonstratethat our compressive scheme is able to resolve mm-wave spatial channels with a relativelysmall number of compressive measurements

Channel Simulators for MmWave and 5G Applications

Along with the tremendous growth of extremely high traffic demand, 5G radio access technology, is becoming the core component to support massive and multifarious connected devices and real-time, and to offer high reliability wireless communications with high data rate. And millimeter-wave (mmWave) range with a huge frequency spectrum from 3 GHz to 300GHz will perfectly meet the multi-gigabit communicative demand. However, mmWave usage also generally brings new challenges, such as coping with high attenuation or path losses. As an effective method to evaluate the performance of the new concept in communication networks, nowadays, several channel models and simulators have been proposed and developped, such as, WINNER, COST-2100, IMT-Advanced, METIS, NYU Wire-less and QuaDRiGa etc. The thesis goals have been to offer an overview of the advantages and disadvantages of various mmWave channel models existing in the literature, based on the published literature, and to compare based on simulations some of the main features of two selected open-source models, namely the WINNER 2 and QuaDRiGa channel models. In the future, more mmWave channel models are planned to be tested and simulated for a better understanding of their suitability for various mmWave applications