Tutorial on LTE/LTE-A Cellular Network Dimensioning Using Iterative Statistical Analysis

LTE is the fastest growing cellular technology and is expected to increase its footprint in the coming years, as well as progress toward LTE-A. The race among operators to deliver the expected quality of experience to their users is tight and demands sophisticated skills in network planning. Radio network dimensioning (RND) is an essential step in the process of network planning and has been used as a fast, but indicative, approximation of radio site count. RND is a prerequisite to the lengthy process of thorough planning. Moreover, results from RND are used by players in the industry to estimate preplanning costs of deploying and running a network; thus, RND is, as well, a key tool in cellular business modelling. In this work, we present a tutorial on radio network dimensioning, focused on LTE/LTE-A, using an iterative approach to find a balanced design that mediates among the three design requirements: coverage, capacity, and quality. This approach uses a statistical link budget analysis methodology, which jointly accounts for small and large scale fading in the channel, as well as loading due to traffic demand, in the interference calculation. A complete RND manual is thus presented, which is of key importance to operators deploying or upgrading LTE/LTE-A networks for two reasons. It is purely analytical, hence it enables fast results, a prime factor in the race undertaken. Moreover, it captures essential variables affecting network dimensions and manages conflicting targets to ensure user quality of experience, another major criterion in the competition. The described approach is compared to the traditional RND using a commercial LTE network planning tool. The outcome further dismisses the traditional RND for LTE due to unjustified increase in number of radio sites and related cost, and motivates further research in developing more effective and novel RND procedures

Simplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. In this work, the impact of implementing Deep Reinforcement Learning (DRL) in predicting the channel parameters for user devices in a Power Domain Non-Orthogonal Multiple Access system (PD-NOMA) is investigated. In the channel prediction process, DRL based on deep Q networks (DQN) algorithm will be developed and incorporated into the NOMA system so that this developed DQN model can be employed to estimate the channel coefficients for each user device in NOMA system. The developed DQN scheme will be structured as a simplified approach to efficiently predict the channel parameters for each user in order to maximize the downlink sum rates for all users in the system. In order to approximate the channel parameters for each user device, this proposed DQN approach is first initialized using random channel statistics, and then the proposed DQN model will be dynamically updated based on the interaction with the environment. The predicted channel parameters will be utilized at the receiver side to recover the desired data. Furthermore, this work inspects how the channel estimation process based on the simplified DQN algorithm and the power allocation policy, can both be integrated for the purpose of multiuser detection in the examined NOMA system. Simulation results, based on several performance metrics, have demonstrated that the proposed simplified DQN algorithm can be a competitive algorithm for channel parameters estimation when compared to different benchmark schemes for channel estimation processes such as deep neural network (DNN) based long-short term memory (LSTM), RL based Q algorithm, and channel estimation scheme based on minimum mean square error (MMSE) procedure.This research received no external funding

Contention techniques for opportunistic communication in wireless mesh networks

Auf dem Gebiet der drahtlosen Kommunikation und insbesondere auf den tieferen Netzwerkschichten sind gewaltige Fortschritte zu verzeichnen. Innovative Konzepte und Technologien auf der physikalischen Schicht (PHY) gehen dabei zeitnah in zelluläre Netze ein. Drahtlose Maschennetzwerke (WMNs) können mit diesem Innovationstempo nicht mithalten. Die Mehrnutzer-Kommunikation ist ein Grundpfeiler vieler angewandter PHY Technologien, die sich in WMNs nur ungenügend auf die etablierte Schichtenarchitektur abbilden lässt. Insbesondere ist das Problem des Scheduling in WMNs inhärent komplex. Erstaunlicherweise ist der Mehrfachzugriff mit Trägerprüfung (CSMA) in WMNs asymptotisch optimal obwohl das Verfahren eine geringe Durchführungskomplexität aufweist. Daher stellt sich die Frage, in welcher Weise das dem CSMA zugrunde liegende Konzept des konkurrierenden Wettbewerbs (engl. Contention) für die Integration innovativer PHY Technologien verwendet werden kann. Opportunistische Kommunikation ist eine Technik, die die inhärenten Besonderheiten des drahtlosen Kanals ausnutzt. In der vorliegenden Dissertation werden CSMA-basierte Protokolle für die opportunistische Kommunikation in WMNs entwickelt und evaluiert. Es werden dabei opportunistisches Routing (OR) im zustandslosen Kanal und opportunistisches Scheduling (OS) im zustandsbehafteten Kanal betrachtet. Ziel ist es, den Durchsatz von elastischen Paketflüssen gerecht zu maximieren. Es werden Modelle für Überlastkontrolle, Routing und konkurrenzbasierte opportunistische Kommunikation vorgestellt. Am Beispiel von IEEE 802.11 wird illustriert, wie der schichtübergreifende Entwurf in einem Netzwerksimulator prototypisch implementiert werden kann. Auf Grundlage der Evaluationsresultate kann der Schluss gezogen werden, dass die opportunistische Kommunikation konkurrenzbasiert realisierbar ist. Darüber hinaus steigern die vorgestellten Protokolle den Durchsatz im Vergleich zu etablierten Lösungen wie etwa DCF, DSR, ExOR, RBAR und ETT.In the field of wireless communication, a tremendous progress can be observed especially at the lower layers. Innovative physical layer (PHY) concepts and technologies can be rapidly assimilated in cellular networks. Wireless mesh networks (WMNs), on the other hand, cannot keep up with the speed of innovation at the PHY due to their flat and decentralized architecture. Many innovative PHY technologies rely on multi-user communication, so that the established abstraction of the network stack does not work well for WMNs. The scheduling problem in WMNs is inherent complex. Surprisingly, carrier sense multiple access (CSMA) in WMNs is asymptotically utility-optimal even though it has a low computational complexity and does not involve message exchange. Hence, the question arises whether CSMA and the underlying concept of contention allows for the assimilation of advanced PHY technologies into WMNs. In this thesis, we design and evaluate contention protocols based on CSMA for opportunistic communication in WMNs. Opportunistic communication is a technique that relies on multi-user diversity in order to exploit the inherent characteristics of the wireless channel. In particular, we consider opportunistic routing (OR) and opportunistic scheduling (OS) in memoryless and slow fading channels, respectively. We present models for congestion control, routing and contention-based opportunistic communication in WMNs in order to maximize both throughput and fairness of elastic unicast traffic flows. At the instance of IEEE 802.11, we illustrate how the cross-layer algorithms can be implemented within a network simulator prototype. Our evaluation results lead to the conclusion that contention-based opportunistic communication is feasible. Furthermore, the proposed protocols increase both throughput and fairness in comparison to state-of-the-art approaches like DCF, DSR, ExOR, RBAR and ETT