Gurhan Bulu

Green communications by demand shaping and user-in-the-loop tariff-based control

The new field of green communications can be divided into a) energy-efficient communications equipment or b) using information and communication technology to improve the world to become more energy aware. Mobile data traffic and utility energy consumption have a lot in common. There is a limited supply due to limited resources, and only growth (of quantities, technology) can increase this, at the cost of a higher carbon footprint. The green index is defined here for cellular wireless. On the other hand there is a demand which is user-generated, variable over time and space, and ever increasing at a fast pace. Flat rates or almost flat utility tariffs have spoiled users and established high user expectations. Instead of engineering for the growth of supply, this paper investigates how to engineer for controlling the demand side. Dealing with congestion is a consequence of the supply=demand regime and the end of overprovisioning. New tariffs are required that are tailored to the major QoS classes and help to shape demand at the user and application level. This paper investigates QoS-aware demand shaping and control by user-in-the-loop and tariff-based control.

First Survey Results of Quantified User Behavior in User-in-the-Loop Scenarios for Sustainable Wireless Networks

Traffic in mobile radio networks is expected to continue to increase by 100% per year. This imposes a big challenge for future generations (4G and 5G) of access technologies which were previously dimensioned for over-provisioning especially in the busy hours. Recent forecasts hint that this assumption and approach will not be tractable anymore. Instead the demand will exceed the supply more and more frequently causing congestion not only in short term but over longer periods of time. Any approach to engineer networks greener by better spectral efficiency only will fail to meet the global objective, if demand keeps on increasing faster than supply and efficiency. Instead, the User-in-the-loop approach allows to shape the demand where it originates either in space or time. This is achieved by incentives for users to change location or by dynamic tariffs to shift the use of congested resources out of the busy hours. This works as the smart grid of communications. While some work has already been done in this new field, the user behavior to the controlled demand shaping was based on assumptions. In this paper, the assumptions were confirmed by recent survey results which indicate that shaping user behavior works sufficiently well. Models of the user response are given and analysis and simulation results show the advantages and gains of this approach.

Generalized coordinated port selection in a multi-cell distributed antenna system using semidefinite relaxation

The downlink of a coordinated multi-cell distributed antenna system is considered. In [1], coordinated port selection was shown to achieve significant performance gains. However, in the system considered therein, the ports in each cell were constrained to transmit only to user terminals (UTs) in that cell. In this work, we consider a generalization of the problem considered in [1] by alleviating this constraint. We formulate the problem of determining the ports that maximize the minimum signal to interference plus noise ratio observed by the UTs as a binary-constrained optimization problem. Observing that it is NP-hard, we propose a semidefinite relaxation and Gaussian randomization based technique to obtain close-to-optimal solutions. Our simulation results show that the performance achieved by the proposed technique approaches that of the optimal solution. It is also shown that the proposed technique outperforms the one in [1], particularly for cell-edge UTs, albeit with an increased computational complexity.

Space weather studies of IONOLAB group

IONOLAB is an interdisciplinary research group dedicated for handling the challenges of near earth environment on communication, positioning and remote sensing systems. IONOLAB group contributes to the space weather studies by developing state-of-the-art analysis and imaging techniques. On the website of IONOLAB group, www.ionolab.org, four unique space weather services, namely, IONOLAB-TEC, IRI-PLAS-2015, IRI-PLAS-MAP and IRI-PLAS-STEC, are provided in a user friendly graphical interface unit. Newly developed algorithm for ionospheric tomography, IONOLAB-CIT, provides not only 3-D electron density but also tracking of ionospheric state with high reliability and fidelity. The algorithm for ray tracing through ionosphere, IONOLAB-RAY, provides a simulation environment in all communication bands. The background ionosphere is generated in voxels where IRI-Plas electron density is used to obtain refractive index. One unique feature is the possible update of ionospheric state by insertion of Total Electron Content (TEC) values into IRI-Plas. Both ordinary and extraordinary paths can be traced with high ray and low ray scenarios for any desired date, time and transmitter location. 2-D regional interpolation and mapping algorithm, IONOLAB-MAP, is another tool of IONOLAB group where automatic TEC maps with Kriging algorithm are generated from GPS network with high spatio-temporal resolution. IONOLAB group continues its studies in all aspects of ionospheric and plasmaspheric signal propagation, imaging and mapping.

Radio Resource Management in a Coordinated Cellular Distributed Antenna System by Using Particle Swarm Optimization

In this paper, we consider a coordinated multi-point transmission (CoMP) scheme used in a cellular system where antenna ports are distributed throughout the cell, instead of using a single base station. Two schemes are considered; either ports can be switched on and off (Binary Power Management, BPM) or their transmission power can be adjusted (Continuous Power Management, CPM). The goal is to maximize the minimum signal to interference plus noise ratio (SINR) in the network for both schemes. The first problem is NP-hard and the second one is multi- modal. We propose to use particle swarm optimization (PSO) as a solver for both problems. It is demonstrated that the proposed PSO based algorithms can efficiently solve both problems. Furthermore, through simulations, it is shown that for the same total transmit power per port, CPM outperforms BPM.

Antenna Port Selection in a Coordinated Cloud Radio Access Network

We investigate the optimization of antenna port selection in the downlink of a cloud radio access network in which a user terminal can be served by multiple ports. The goal is to maximize the minimum weighted signal-to-interference-plus-noise ratios observed by the users while satisfying their quality-of-service requirements. This optimization is formulated as a mixed integer programming problem and solved using semidefinite relaxation and Gaussian randomization. It is shown that our technique outperforms baseline schemes by 6–8 dB.

Energy efficient power control and radio resource management in multi-connectivity framework

In this paper, CoMP (Coordinated Multi-Point Transmission and Reception) technique is studied to mitigate interference effects in cellular networks in which distributed antenna systems (DAS) are utilized. Power control and resource allocation problem is jointly optimized in a multi-user, multi-carrier downlink scenario where backhaul between base station and remote radio heads (antenna) are assumed lossless. Energy efficient radio resource management (RRM) algorithms are produced offering (sub)optimum solutions in a multi-connectivity framework providing user equipments connecting multiple access points simultaneously.

Energy efficient radio resource management in a coordinated multi-cell distributed antenna system

As well as the demand for higher data rates goes on, the rising energy costs and environmental concerns have also led researchers to increase energy efficiency in terms of bits-per-Joule. Cooperative communications with low power nodes, switching off under-utilized cells due to low traffic load and energy efficient radio resource management are some of the energy saving methods. It is observed that the conventional energy efficiency expression, which is defined as the ratio of the data rate to the total transmission power, can limit the system throughput significantly. In this paper, we introduce a novel energy efficiency expression which provides a trade-off between sum-rate maximization and total transmission power minimization in the network. A coordinated multi-cell distributed antenna system is considered and two energy efficient radio resource management schemes are investigated; either ports can be switched on and off or their transmission power can be adjusted in order to maximize energy efficiency. It is demonstrated that the proposed expression offers significant increase in data rate while keeping total transmission power low. The proposed and the traditional energy efficiency methods asymptotically converge when tradeoff effect diminishes, i.e., transmission power minimization takes the full priority.

Spectral envelope extension of narrowband speech using neural networks

This paper describes a Neural Network (NN) based method that extends the bandwidth of narrowband speech to obtain the spectral envelope of the wideband speech. Line Spectral Frequencies (LSF) which are obtained from Linear Prediction Coefficients (LPC) are used to represent the spectral envelope. Spectral distortion measure is used to test the performance of the method.