We propose a novel approach based on deep learning to address the hidden node problem which occurs in the coexistence of aeronautical communication standards. The modern aeronautical communication standard L-band Digital Aeronautical Communications System (LDACS) in Air-Air (A/A) mode needs to share spectrum with the Distance Measuring Equipment (DME), which is a legacy system. As DME is safety-critical, causing interference on it must be avoided for all newly proposed aeronautical systems spectrally coexisting with it. Recently, cognitive radio techniques have been proposed for LDACS A/A to access spectrum dynamically and to overcome the limitations of static approaches. For this, a Recurrent Neural Network (RNN) was trained to predict idle time slots on those frequency bands, where both systems operate. By exploiting patterns in the spectrum access of DME, a promising amount of idle resources could be predicted. However, previous approaches would perform poorly in a real-world deployment, as they did not take the hidden node problem into account.This paper formulates the hidden node problem for the case that an LDACS A/A user is within communication range of a DME ground station, but not within range of all airborne DME users connected to it. Through statistical analysis, we underline the problem’s significance in practical cases. We simulate the coexistence of the two systems from a channel access perspective, taking signal propagation and the behavior of the ground station into account. Further, we present an RNN that is able to predict the channel access of hidden nodes. The key idea of our algorithm is to exploit the fact that while DME request pulses from airborne users may appear as hidden, response pulses from the ground station will be visible. Our results show that by inferring DME request channel activity from the response channel, the hidden node problem can be overcome effectively. By using our approach, nearly the same performance can be achieved as in the idealized case where all nodes are visible.

In aeronautical communications, legacy systems often only use a small fraction of their historically assigned frequency spectra sparsely over time. Novel systems such as L-band Digital Aeronautical Communications System (LDACS) Air-Air (A/A) need to coexist with legacy systems, and must ensure not to cause excessive interference. The Distance Measuring Equipment (DME) is the most critical legacy system in this case. To analyze the potential number of idle communication resources in LDACS A/A and DME coexistence, we propose a statistical model, which reveals a substantial number of opportunities. Motivated by the statistical properties of the co-existence scenario, we propose a Recurrent Neural Network (RNN) to predict DME patterns reliably. Our architecture is based on a combination of Long Short-Term Memory (LSTM) and dense layers and was found with the help of hyperparameter optimization techniques. The predictor is trained and evaluated on a synthetic data set using realistic DME parameters. Furthermore, we introduce a baseline algorithm for comparison, which makes perfect predictions on a simplified periodic data set but breaks down for realistic scenarios. We argue that our Deep Learning approach can be used in realistic scenarios to detect idle resources given a strict constraint on correctly predicted busy resources.

Modern mobile networks in the era of 5G have ambitious performance goals. One envisioned goal is to have communication that is ultra-reliable and ultra-low latency (uRLLC). To ensure reliability, Automatic Repeat Request (ARQ) processes are usually deployed, sometimes combined with forward error correction. As an evolution of the Long Term Evolution (LTE) system, 5G may be foreseen to use similar methods, and so we assume LTE's Hybrid ARQ on the Medium Access Control (MAC) layer, and a Selective Repeat ARQ on the Radio Link Control (RLC) layer. Simulation of LTE systems is a time-consuming endeavor due to its complexity, and when latency and reliability are the key performance indicators, it may be preferable to isolate the ARQ processes. In this manner, this paper presents a mathematical model of the two stacked ARQ processes that are used in today's LTE system. It is based on the method of Signal Flow Graphs (SFGs) and allows the analysis of either ARQ process in isolation, and of both stacked on top of each other. Analytic results are shown for a realistic set of parameters, and in the spirit of Open Science, the MATLAB implementation files are made available in the conclusion to interested researchers, so that verification and an adaption to similar ARQ processes is easily made.

Next generation mobile systems aim to meet the strict demands of an exponentially increasing number of users. Particularly device-to-device (D2D) communication has attracted a lot of attention during the last years. D2D communication can ofïoad cellular trafïc off the base station. It may reuse radio frequency resources simultaneously used by cellular communication for D2D transmissions, thereby increasing spectral efïciency and system capacity. However, reusing frequency resources can lead to in-cell interference. The expected interference must therefore be actively mitigated as part of the scheduling process. We investigate a novel joint radio resource scheduling and allocation algorithm for D2D communication that makes use of two methods from the ïeld of game theory. It aims to meet the respective user’s Quality of Service requirements and strives for fairness between users by playing a Transferable Utility game. At the same time it actively mitigates in-cell interference from frequency resource reuse and determines an optimal transmission power through Stackelberg gameplays. It is shown through simulation that system performance increases if resource allocation and reuse is done in a coordinated manner. The proposed scheduler scales well due to its polynomial computational complexity and outperforms baseline schedulers and the single-game approach in several simulated scenarios.

MOTIVATION: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent. RESULTS: To reduce the statistical dependency between adjacent word matches, we propose to use 'spaced words', defined by patterns of 'match' and 'don't care' positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words. AVAILABILITY AND IMPLEMENTATION: Our program is freely available at http://spaced.gobics.de/.