Network support for Unmanned Aerial Vehicles (UAVs) is raising an interest among researchers due to the strong potential applications. However, current knowledge on UAV data traffic is mainly based on conceptual studies and does not provide an in-depth insight on the data traffic properties. To close this gap, we present a measurement-based study analyzing in detail the Control and Non-payload Communication (CNPC) traffic produced by three different UAVs when communicating with their remote controller over 802.11 protocol. We analyze the traffic in terms of data rate, inter-packet interval and packet length distributions, and identify their main influencing factors. The data traffic appears neither deterministic nor periodic but bursty, with a tendency towards Poisson traffic. We further create an understanding on how the traffic of the investigated UAVs are internally generated and propose a model to analytically capture their traffic processes, which provides an explanation for the observed behavior. We implemented a publicly available UAV traffic generator "AVIATOR" based on the proposed traffic model and verified the model by comparing the simulated traces with the experimental results.

In the field of networking research, increased flexibility of new system architecture proposals, protocols, or algorithms is often stated to be a competitive advantage over its existing counterparts. However, this advantage is usually claimed only on an argumentative level and neither formally supported nor thoroughly investigated due to the lack of a unified flexibility framework. As we will show in this paper, the flexibility achieved by a system implementation can be measured, which consequently can be used to make different networking solutions quantitatively comparable with each other. The idea behind our mathematical model is to relate network flexibility to the achievable subset of the set of all possible demand changes, and to use measure theory to quantify it. As increased flexibility might come with additional system complexity and cost, our framework provides a cost model which measures how expensive it is to operate a flexible system. The introduced flexibility framework contains different normalization strategies to provide intuitive meaning to the network flexibility value as well, and also provides guidelines for generating demand changes with (non-)uniform demand utilities. Finally, our network flexibility framework is applied on two different use-cases, and the benefits of a quantitative flexibility analysis compared to pure intuitive arguments are demonstrated.

Direct communication among user equipments, called Device-to-Device (D2D) communication, is envisioned in academia and industry to enhance future cellular networks for increased spectral efficiency. Mode selection considers the question which links should be offloaded to use direct communication mode and is one of the core questions in D2D research. In this work, we ask the question which information needs to be exchanged between a remote mode selection controller and Medium Access Control (MAC) layer to allow optimal operation without the burden of a full-fledged cross-layer optimization. We formulate mode selection as utility maximization with cross-layer decomposition and solve it using the Generalized Benders Decomposition technique. The result is a nested offloading structure, in which the network controller regularly updates its mode selection decision while in the meantime, the network operates in a particular fashion, gathering information to enable better future mode selection decisions. We show that the proposed structure allows achieving an optimal mode selection configuration within a finite number of optimization steps for any non-negative utility function. Due to our general network model, the solution is applicable to a variety of system set-ups, including overlay and underlay D2D, with and without power control and single or multi-antenna systems.

Network slicing is envisioned as a tool for 5G networks to provide network flexibility and isolation among different logical networks. While network slicing is well investigated in the fixed-network side, in the Radio Access Network (RAN) there remain challenging problems, which originate mainly from the stochastic nature of the wireless channels and complex resource coupling between slices. In this work, we investigate a network slicing problem for the downlink RAN of a cellular network. Our target is the reduction of resource usage while guaranteeing slice isolation and simultaneously accounting for each slice's average rate and delay requirements. We tackle the problem with a Lyapunov optimization approach, leading to a simple resource assignment procedure that we can prove to achieve isolation while satisfying all slice guarantees. The proposed procedure leads to a functional split, where resources are scheduled within each slice by a slice manager, while a Software-Defined RAN (SD-RAN) controller dynamically re-assigns resources to each slice. We verify our approach through extensive simulations and provide insight on how to fine-tune available system parameters.

For communication networks research, flexibility of network design and networking solutions is considered a competitive advantage. However, this advantage is typically only claimed on an argumentative level and neither formally supported nor thoroughly investigated. To support the claim of flexibility and to make the flexibility of different solutions comparable, its degree must be quantified and thus made measurable. In this work, we propose a mathematical basis to quantify a degree of flexibility achieved by communication networks. We motivate that flexibility can be cast to the “size” of a set of achievable demand changes. Consequently, we propose the use of mathematical measure theory to quantify achieved networking flexibility. We derive several implications on the basic structure of flexibility, extend the insights towards a utility of flexibility and develop systematic approaches for both analytical and empirical measurement of flexibility. We apply the insights to several use-cases, showing that flexibility is in fact not as straight-forward to argue as it seems at first glance.

The standard interference (SI) framework denotes a set of mathematical properties claimed capable of describing interference in numerous wireless technologies. By showing that interference behaves "standard", so the idea, known algorithms for power control and interference management can be reapplied to novel technologies. Even further, interference across physical (PHY) layers can be managed with a single technique based on SI, as long as all involved technologies behave standard. Although some examples for technologies with standard interference are well-known, e.g., single-channel communication and beamforming, a systematic search for systems with standard properties has not been done in literature. In this work, we present proofs for the standard behavior of interference in numerous wireless technologies. We show that apart from common single-input single-output (SISO) links, also links with transmit- and receive beamforming, successive interference cancellation (SIC), amplify and forward (AF) relaying and fullduplex (FD) communication exhibit standard properties. Further, the standard property can be claimed for links with probabilistic channels and/or transmissions, such as ALOHA based networks. Finally, we investigate multi-channel interference. By leveraging the newly proven result that standard properties can directly be deduced from the associated rate function, we find that interference in multi-channel and multi-input multi-output (MIMO) systems, too, exhibit the standard properties. The variety of SI systems motivates that although abstract, standard interference has the potential to create a generalized interference management framework, capable of incorporating most existing communication types.

We investigate the problem of how to maximize the frequency reuse factor in a cellular network with device-to-device links and dynamic power control. Recent works in the literature mostly assume fixed powers, which we find to enable only around 70% of achievable reuse, or limit the reuse factor a priori. The problem is formulated as a mixed-integer nonlinear problem and identified as non-deterministic polynomial-time complete. Nevertheless, we present a full analysis of the involved effects and identify the main driver of complexity, that a priori, the effect of binary link activation and deactivation on the spectral radius of the relative gain matrix cannot be properly bounded. We identify a core parameter that we call relative interference pressure and that reflects the severity of interference caused by a link with dynamic power control. Based on relative interference pressure, we propose two solution algorithms, the worst pressure shutdown and the $\tilde {\varepsilon }$ Pressure Packing algorithm. Both algorithms are thoroughly evaluated with simulations and compared with state-of-the-art methods. The proposed algorithms maximize reuse nearly optimally, enabling the remaining 30% of frequency reuse.

Developing models to generate realistic graphs of communication networks often requires a deep understanding and extensive analysis of the underlying network structure. Since deployed communication networks are dynamic, the findings a generator is based on might lose validity. We alleviate the need for extensive analysis of graphs by estimating parameters of a probabilistic model. The model parameters encode the structure of the graph, which is thus learned in an unsupervised fashion. Synthetic graphs can be generated from the model and will have the structure previously inferred. For this, we use the Stochastic-Block-Model (SBM) and the Degree-Corrected-Block-Model (DCBM), a variant allowing for heavy tailed degree distributions. The models originate in the social sciences and separate a graph into groups of nodes. To show the applicability of the models to the task of synthetic graph generation in the domain of communication networks, we use one router level and one IP-to-IP communication graph. We assert the quality of the generated models by evaluating a number of graph features and comparing our results to those obtained with the network generator Orbis. We find our approach to be on par with, or even outperforming Orbis. Furthermore, the models are able to capture large-scale structure in communication networks.

It has been postulated in literature that three gain types are introduced into the radio access part of cellular networks by device-to-device communication, namely the proximity gain, hop gain and reuse gain. These gain types have further been shown to be captured by a resource efficiency metric. We investigate the influence of mode selection and scheduling on the resource efficiency gains in the D2D overlay case. We formulate mode selection as a resource efficiency maximization problem and find that for proper mode selection, the achieved resource efficiencies of schedulers must be anticipated correctly. Because this is very complex, we propose a simple, channel state based mode selection scheme and investigate how well it performs using different schedulers. We show that our proposed scheme is optimal for schedulers that do not leverage frequency diversity and optimizes a tight lower bound for those that do leverage diversity. Simulations show that in any case, our scheme produces higher D2D-gains than the state of the art.

Device-to-Device (D2D) communication is envisioned to enhance the functionality of future cellular networks. Dynamic frequency reuse in D2D enhanced networks can increase the frequency reuse factor beyond one and has achieved much attention in the last years. To leverage frequency reuse, a proper reuse link selection must be performed. We investigate a core problem of reuse link selection, namely how to find the maximum number of reuse links in a network. Efficient, optimal solutions for this problem have not been found for over a decade in arbitrary wireless network scenarios. We investigate different decision metrics for reuse link selection under dynamic power control and propose a metric that outperforms the known state of the art solutions.

Device-to-Device (D2D) communication in cellular networks allows dynamic frequency reuse even within a cell, which can increase the frequency reuse factor beyond one. To perform dynamic reuse, it must be feasible for all links to maintain a certain signal quality despite the presence of interference. This can be ensured using reuse feasibility tests. However, state of the art testing algorithms show erroneous behavior when infeasibility occurs due to limited transmission powers. In this paper we develop two testing algorithms, based on nonnegative matrix theory, that explicitly take limited transmission powers into account and show how these can be implemented in protocols. The protocols are compared with the state of the art in terms of control channel usage, speed and accuracy of decision making. We find that the proposed protocols estimate reuse feasibility more accurately than the state of the art and are comparable in terms of speed and control channel usage.

Device-to-Device (D2D) communication is the latest of a series of attempts to combine cellular and ad-hoc networks and is seen as a promising concept in the wireless research community. However, the commonly used evaluation metrics for optimization do not capture the expected advantages of D2D. In this paper, a flow-based resource efficiency is defined and applied to a D2D scenario. It is shown that the already formulated gain-expectations proximity-, hop- and reuse-gain can be captured using this efficiency metric. Applying the metric to D2D, it is found that the three gains can be traded off against each other. As a proof of concept, D2D is re-evaluated in a single cell setup. It is motivated that around each D2D transmitter there are two regions, the Tradeoff- and the Win-Win-Region, in which D2D communication is advantageous.

Device-to-device (D2D) communication as an underlay to future cellular networks has been recently considered as an efficient cell offloading and capacity increasing solution. In this paper, we propose to use the D2D underlay as a carrier for automotive safety applications with very strict quality of service and reliability requirements. We propose a location dependent resource allocation scheme (LDRAS) for mobile D2D communications that fulfills the requirements of such services, while reducing the signaling overhead and guaranteeing a certain maximum interference level within the primary network and the D2D underlay, respectively. The former is ensured by applying persistent resource allocation to the vehicular D2D network. The latter is achieved with a spatial reuse scheme with fixed resource reservation, exploiting the localized nature of vehicle-to-vehicle communications. Initial simulation results, comparing the proposed LDRAS to a state-of-the-art radio resource management algorithm, are provided as a proof-of-concept and illustrate the benefits of our solution.