Coded Orthogonal Frequency Division Modulation

Abstract: Future broadband picocellular networks demand powerful modulation and coding methods under the tight constraints of affordable modem complexity and low power consumption for the mobile terminal (MT). In order to increase battery life and allow low-cost mobile equipment to be effectively integrated in such a broadband system we propose the use of a separate low-rate signaling channel (SSC) on the physical layer. This additional channel has the primary function of power saving. For this purpose it takes on connection and mobility control functions as well as further tasks on broadcasting and synchronization. A new concept of a broadband TDMA system is presented, incorporating OFDM for the physical high rate data channel (D-channel) and CPFSK modulation with non-coherent detection for the low-rate SSC, whereby both channels are separated in frequency. Modem and system design issues are discussed and evaluated by simulation

Investigations on Capacity in the Integrated Broadband Mobile System (IBMS) Using a Wireless Network Simulator

In this paper, the capacity in the Integrated Broadband Mobile System (IBMS) is studied. This new mobile system approach utilizes smart antennas. Capacity increase due to smart antennas has already been investigated, but typically only concerning the number of users per cell. But IBMS follows the approach to support higher data rates with the use of smart antennas. It turns out that in the proposed system the number of high rate users is almost the same as the number of low rate users. If the net data rate is incorporated into the definition of capacity, it can be shown that at high data rate most of the network capacity is offered. So IBMS promises not only an increase in the number of users, but also a data rate increase per user. I. INTRODUCTION In a multi-service/multirate mobile network as IBMS capacity analysis is faced with new challenges. A wide range of data rates is provided utilizing smart antennas. Moreover, there is a mixture of several multiple access methods. All these..

Automated and robust organ segmentation for 3D-based internal dose calculation

Abstract Purpose In this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts. Methods We adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the activity values from quantitatively reconstructed SPECT images for “volumetric”/3D dosimetry. The resulting activities were used to perform dosimetry calculations with the kidneys as source organs. Results The computational expense of the algorithm was sufficient for clinical daily routine, required minimum pre-processing and performed with acceptable accuracy a Dice coefficient of $$93\%$$ 93 % for liver segmentation and of $$94\%$$ 94 % for kidney segmentation, respectively. In addition, kidney self-absorbed doses calculated using automated segmentation differed by $$7\%$$ 7 % from dosimetry performed by two medical physicists in 8 patients. Conclusion The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radiopharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmentation methodology based on CT images accelerates organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images. Trial registration EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13