2,454 research outputs found

    Improved User Tracking in 5G Millimeter Wave Mobile Networks via Refinement Operations

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    The millimeter wave (mmWave) frequencies offer the availability of huge bandwidths to provide unprecedented data rates to next-generation cellular mobile terminals. However, directional mmWave links are highly susceptible to rapid channel variations and suffer from severe isotropic pathloss. To face these impairments, this paper addresses the issue of tracking the channel quality of a moving user, an essential procedure for rate prediction, efficient handover and periodic monitoring and adaptation of the user's transmission configuration. The performance of an innovative tracking scheme, in which periodic refinements of the optimal steering direction are alternated to sparser refresh events, are analyzed in terms of both achievable data rate and energy consumption, and compared to those of a state-of-the-art approach. We aim at understanding in which circumstances the proposed scheme is a valid option to provide a robust and efficient mobility management solution. We show that our procedure is particularly well suited to highly variant and unstable mmWave environments.Comment: Accepted for publication to the 16th IEEE Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), Jun. 201

    Blind source separation using dictionary learning over time-varying channels

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    Distributed sensors observe radio frequency (RF) sources over flat-fading channels. The activity pattern is sparse and intermittent in the sense that while the number of latent sources may be larger than the number of sensors, only a few of them may be active at any particular time instant. It is further assumed that the source activity is modeled by a Hidden Markov Model. In previous work, the Blind Source Separation (BSS) problem solved for stationary channels using Dictionary Learning (DL). This thesis studies the effect of time-varying channels on the performance of DL algorithms. The performance metric is the probability of detection, where a correct detection is the event that the estimated value of a source exceeds a threshold at a time instant when the true source is active. Using the probability of detection when the channels are stationary as a baseline, it is shown that there is significant degradation for time-varying channels and observation intervals much longer than the time coherence. Detection performance improves when the observation time is approximately equal to the time coherence. Performance is again degraded when the observation is shorter and there is not sufficient information for the DL algorithms to learn from

    Fast Convergence and Reduced Complexity Receiver Design for LDS-OFDM System

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    Low density signature for OFDM (LDS-OFDM) is able to achieve satisfactory performance in overloaded conditions, but the existing LDS-OFDM has the drawback of slow convergence rate for multiuser detection (MUD) and high receiver complexity. To tackle these problems, we propose a serial schedule for the iterative MUD. By doing so, the convergence rate of MUD is accelerated and the detection iterations can be decreased. Furthermore, in order to exploit the similar sparse structure of LDS-OFDM and LDPC code, we utilize LDPC codes for LDS-OFDM system. Simulations show that compared with existing LDS-OFDM, the LDPC code improves the system performance
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