Michael Reyer

Rate and Power Allocation for Multiuser OFDM: An Effective Heuristic Verified by Branch-and-Bound

The present correspondence deals with the rate and power allocation problem in multiuser orthogonal frequency division multiple (OFDM) access systems. We first derive the solution of the single user OFDM power allocation problem explicitly for a class of general rate-power functions by means of directional derivatives. This solution is employed for both designing a new heuristic and obtaining bounds in a branch-and-bound algorithm for allocating power to subcarriers. The branch-and-bound algorithm is used for performance evaluation of our new and two known power allocation heuristics by computing the exact optimum, given the number of allocated subcarriers per user.

A Cube Oriented Ray Launching Algorithm for 3D Urban Field Strength Prediction

Fast radio wave propagation prediction is of tremendous interest for planning and optimization of cellular radio networks. We propose a cube oriented 3D ray launching algorithm for both fast and accurate field strength prediction, particularly suitable for urban scenarios. Our model allows field strength prediction for a 5 km2 area with 5 meter resolution in about 8 sec with mean squared error of 7 dB. As urban environments cannot be completely described, we recommend a parameter calibration for different cities using measurement data from test runs.

Autonomous Self-Optimization of Coverage and Capacity in LTE Cellular Networks

Self-organizing networks promise significant expenditure savings for operators when rolling out modern cellular network infrastructure, such as Long-Term Evolution (LTE) and LTE-Advanced systems. Savings in capital expenditures (CAPEX) and operational expenditures (OPEX) can be achieved in both the network deployment and network operation phase. Particularly, self-organized optimization of network coverage and network capacity is a key challenge to cope with the boost in mobile data traffic that is expected in the next years and to benefit from the growing market. We present a traffic-light-related approach to autonomous self-optimization of tradeoff performance indicators in LTE multitier networks. Introducing a low-complexity interference approximation model, the related optimization problem is formulated as a mixed-integer linear program and is embedded into a self-organized network operation and optimization framework. Tuning site activity, transmission power, and antenna downtilt are parameters of eNodeBs and Home eNodeBs. The optimization procedure is carried out considering time-variant optimization parameters that are automatically adapted with respect to changes in the network. Simulation-based evaluation of representative case studies demonstrates applicability and the benefit potential of our overall concept.

Accelerating Radio Wave Propagation Predictions by Implementation on Graphics Hardware

Fast radio wave propagation predictions are of tremendous interest, e.g., for planning and optimization of cellular radio networks. We propose the use of ordinary graphics cards and specialized algorithms to achieve extremely fast predictions. Our implementation of the empirical COST-Walfisch-Ikegami model allows the computation of several hundred predictions in one second in a 7 km urban area. Further, we present a ray-optical approach exploiting the programming model of graphics cards. This algorithm combines fast computation times with high accuracy.

Part-Of-Speech Tagging for Social Media Texts

Work on Part-of-Speech (POS) tagging has mainly concentrated on standardized texts for many years. However, the interest in automatic evaluation of social media texts is growing considerably. As the nature of social media texts is clearly different from standardized texts, Natural Language Processing methods need to be adapted for reliable processing. The basis for such an adaption is a reliably tagged social media text training corpus. In this paper, we introduce a new social media text corpus and evaluate different state-of-the-art POS taggers that are retrained on that corpus. In particular, the applicability of a tagger trained on a specific social media text type to other types, such as chat messages or blog comments, is studied. We show that retraining the taggers on in-domain training data increases the tagging accuracies by more than five percentage points.

Profit-oriented combination of multiple objectives for planning and configuration of 4G multi-hop relay networks

Generally, planning and configuring cellular radio networks lead to multi-objective optimization problems with conflicting objectives, e.g., coverage and cost. In this paper, we present an approach to combine those opponents in a closed-form objective for maximization of operator profit by means of joint base station and relay station placement in 4G multi-hop relay networks. The corresponding optimization model is formulated as mixed-integer linear program and particularly considers allocation of limited bandwidth for downlink data transmission in non-cooperative relaying mode. We suggest two economically motivated options how to choose appropriate weights for combining the conflicting objectives linearly. Furthermore, we apply the proposed optimization model to an exemplary planning scenario to analyze sensitivity to weight modifications numerically.

A POS Tagger for Social Media Texts Trained on Web Comments

Using social media tools such as blogs and forums have become more and more popular in recent years. Hence, a huge collection of social media texts from different communities is available for accessing user opinions, e.g., for marketing studies or acceptance research. Typically, methods from Natural Language Processing are applied to social media texts to automatically recognize user opinions. A fundamental component of the linguistic pipeline in Natural Language Processing is Part-of-Speech tagging. Most state-of-the-art Part-of-Speech taggers are trained on newspaper corpora, which differ in many ways from non-standardized social media text. Hence, applying common taggers to such texts results in performance degradation. In this paper, we present extensions to a basic Markov model tagger for the annotation of social media texts. Considering the German standard Stuttgart/T¨ ubinger TagSet (STTS), we distinguish 54 tag classes. Applying our approach improves the tagging accuracy for social media texts considerably, when we train our model on a combination of annotated texts from newspapers and Web comments. standardized text, since they are characterized by a spoken language, a dialogic and an informal writing style. This poses some special challenges to deal with in developing methods for automatic POS tagging of Web comments. These are particularly, the treatment of unknown (out-of-vocabulary) words and the different grammatical structure of social media texts in contrast to newspaper text. Furthermore, text genre specific manually annotated corpora, i.e., Web comments are required for training and testing. To the best of our knowledge all large manually annotated corpora are exclusively newspaper texts. In this work, we propose a Markov model tagger with parameter estimation enhancements for the POS annotation of social media texts. We apply and evaluate the tagger for German social media texts exemplarily. In order to make our method usable for NLP methods requiring POS information, e.g., syntactical parsing, we use the 54 Stuttgart/T¨ ubinger

Iterated Water-Filling for OFDMA Rate and Power Allocation with Proportionality Constraints

The present correspondence deals with the rate and power allocation problem for multi-user orthogonal frequency division multiple access (OFDMA). Using directional derivatives we first derive an explicit solution of the single-user OFDM power allocation problem for a general class of rate-power functions. In a nested algorithm, this solution is used to determine the solution of the closely related rate allocation problem for both the single-user and multi-user case with proportionality constraints. The results are applied to a widely used class of rate-power function.

A semi-stochastic radio propagation model for wireless MIMO channels

Among current physical channel models, deterministic models, such as ray-launching, are accurate but site-specific. Geometry-based stochastic models are flexible, however, cannot benefit if site information is available. In this work, a semi-stochastic MIMO channel model is proposed. It integrates results from deterministic models into stochastic models, merging the advantages of both approaches.

A direction-specific land use based path loss model for suburban/rural areas

Fast and accurate path loss prediction is a prerequisite for effective planning and optimization of cellular radio networks [1]. Ray optical algorithms often achieve very high prediction accuracy, see [2], but consume much computation time. However, semi-empirical prediction models suffer from inherent low prediction quality but require reasonable computational effort.