Vladimir Poulkov

Improving cell edge throughput for LTE using combined uplink power control

Uplink power control is used in 3GPP Long Term Evolution (LTE) systems to maximize the power of the desired received signals while limiting the interference. This paper analyzes two power control mechanisms, Fractional Power Control (FPC) and Interference Based Power Control (IBPC). A way of combining them is proposed in order to find an efficient algorithm to control the transmitted Power Spectral Density (PSD) in order to compensate poor channel conditions and thus to obtain better performance in terms of cell edge throughput.

Self-Resource Allocation and Scheduling Challenges for Heterogeneous Networks Deployment

A reliable solution for meeting the high demand of throughput in areas called hotspots is the heterogeneous network. Heterogeneous networks are different depending on their coverage, their type of radio access technique and the way there are connected to the core network. This paper proposes a novel algorithm for semi-coordinated resource allocation and scheduling based on mobile positioning information, game theory and reinforcement learning technique. The capabilities of such an approach to support the practical deployment of heterogeneous networks is analyzed. Further, a reasoning strategy is proposed to justify the choice of Wi-Fi versus other small cell technologies from a practical deployment viewpoint.

Recognition of Human daily activities

Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we propose an algorithm for Human Activity Recognition based on the application of a geometrically motivated feature selection method. We test the algorithm on a standard data set and validate its performance by comparing it with the existing results of other known algorithms.

On Modeling the Psychology of Wireless Node Interactions in the Context of Internet of Things

The expectations for the Internet of Things (IoT) and its capabilities are central to a lot of research efforts at the moment. The main concern is the realization of autonomous decision-making and successful communication establishment between the resource-constrained nodes, present in such a dynamic network architecture. The focus is turned to the implementation of fast, reliable and energy-efficient methodology for the transmission of information with high data rates. This paper proposes and evaluates a model for the psychology of the wireless node interactions in the context of IoT and the selection of a partner for the most reliable communication, depending on the needs of the service required. The key functions of the proposed model are the Influence and the Behavior functions. Additional parameters are defined, which enable the incorporation of different kinds of expectations, reliability levels and service types. A flexible hybrid architecture, including a method for neighbor node and path discovery and evaluation, is defined to assess the function models for a two-node and a multi-node communication paths.

Noise Cancellation in OFDM Systems Using Adaptive Complex Narrowband IIR Filtering

In this paper a very low sensitivity fourth-order complex band-pass filter section with independent tuning of the central frequency and the bandwidth is developed. A narrowband adaptive filter structure is formed around this section, using LMS algorithm to adapt the central frequency. The developed filter structure is providing a low computational complexity and a very fast convergence. We use this adaptive complex filter for noise cancellation in an OFDM transmission scheme and show that under certain conditions SNR gain and better bit error rate (BER) performance can be achieved.

Challenges in designing and implementation of an effective Ambient Assisted Living system

In this paper some challenges in the design and realization of an effective Ambient Assisted Living (AAL) system are discussed. Solutions to meet those challenges are proposed. Example of the practical implementation of the architecture of an AAL system - “eWall for Active Long Living” (eWALL) and the related context-aware services are presented.

Node discovery and interpretation in unstructured resource-constrained environments

A main characteristic of the Internet of Things networks is the large number of resource-constrained nodes, which, however, are required to perform reliable and fast data exchange; often of critical nature; over highly unpredictable and dynamic connections and network topologies. Reducing the number of message exchanges and retransmission of data, while guaranteeing the lifetime of the data session duration as per service requirements are vital for enabling scenarios such as smart home, intelligent transportation systems, eHealth, etc. This paper proposes a novel theoretical model for the discovery, linking and interpretation of nodes in unstructured and resource-constrained network environments and their interrelated and collective use for the delivery of smart services. The model is based on a basic mathematical approach, which describes and predicts the success of human interactions in the context of long-term relationships and identifies several key variables in the context of communications in resource-constrained environments. The general theoretical model is described and several algorithms are proposed as part of the node discovery, identification, and linking processes in relation to the key variables. The algorithms are each evaluated by simulations to determine which parameters are key for optimal node grouping.

Heuristic approach to dynamic Uplink Power Control in LTE

In this paper an approach for Uplink Power Control for Long Term Evolution is proposed. The developed method is based on a heuristic algorithm for the division of the users in mobile cell into sets with different uplink power settings. By applying such an approach, a dynamic power control mechanism could be implemented depending on the distribution, location and the required throughput of the User Equipments in the cell with minimum Inter-Cell Interference. The results from the simulation experiments show that such approach could be an effective solution for dynamic Uplink Power Control.

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

AbstractnSpectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency–time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the “accuracy versus latency” trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.

Increasing throughput and fairness for users in heterogeneous semi coordinated deployments

Incorporation of the geographical positions of mobile users into the resource assignment process in uncoordinated heterogeneous cell deployments, can lead to significant improvements of cell and user throughputs. This paper proposes a novel algorithm that combines the knowledge of the users positions with a Q-learning and game-theoretic approaches to enhance the dynamic physical resource allocation during carrier aggregation (CA) in a semi-and uncoordinated deployment of Heterogeneous Networks (HetNet). The algorithm is evaluated through MATLAB simulation setup and in terms of macro-and pico- cell and user throughputs. It has been shown that regardless of the approach chosen for physical resource assignment, positioning information increases the system and user performances. Use of Q-learning and positioning information leads to increased cell throughput without degrading the user experience. Application of a game theoretic approach for resource assignment leads to increased fairness for both macro-and pico- users.