30,603 research outputs found
Cooperative Wideband Spectrum Sensing Based on Joint Sparsity
COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY
By Ghazaleh Jowkar, Master of Science
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University
Virginia Commonwealth University 2017
Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering
In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically
Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks
In this paper, we examine the fundamental trade-off between radiated power
and achieved throughput in wireless multi-carrier, multiple-input and
multiple-output (MIMO) systems that vary with time in an unpredictable fashion
(e.g. due to changes in the wireless medium or the users' QoS requirements).
Contrary to the static/stationary channel regime, there is no optimal power
allocation profile to target (either static or in the mean), so the system's
users must adapt to changes in the environment "on the fly", without being able
to predict the system's evolution ahead of time. In this dynamic context, we
formulate the users' power/throughput trade-off as an online optimization
problem and we provide a matrix exponential learning algorithm that leads to no
regret - i.e. the proposed transmit policy is asymptotically optimal in
hindsight, irrespective of how the system evolves over time. Furthermore, we
also examine the robustness of the proposed algorithm under imperfect channel
state information (CSI) and we show that it retains its regret minimization
properties under very mild conditions on the measurement noise statistics. As a
result, users are able to track the evolution of their individually optimum
transmit profiles remarkably well, even under rapidly changing network
conditions and high uncertainty. Our theoretical analysis is validated by
extensive numerical simulations corresponding to a realistic network deployment
and providing further insights in the practical implementation aspects of the
proposed algorithm.Comment: 25 pages, 4 figure
Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks
This paper investigates the cross-layer optimization of fast video delivery
and caching for minimization of the overall video delivery time in a two-hop
relaying network. The half-duplex relay nodes are equipped with both a cache
and a buffer which facilitate joint scheduling of fetching and delivery to
exploit the channel diversity for improving the overall delivery performance.
The fast delivery control is formulated as a two-stage functional non-convex
optimization problem. By exploiting the underlying convex and quasi-convex
structures, the problem can be solved exactly and efficiently by the developed
algorithm. Simulation results show that significant caching and buffering gains
can be achieved with the proposed framework, which translates into a reduction
of the overall video delivery time. Besides, a trade-off between caching and
buffering gains is unveiled.Comment: 7 pages, 4 figures; accepted for presentation at IEEE Globecom, San
Diego, CA, Dec. 201
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
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