17,553 research outputs found
Large System Analysis of Base Station Cooperation for Power Minimization
This work focuses on a large-scale multi-cell multi-user MIMO system in which
base stations (BSs) of antennas each communicate with
single-antenna user equipments. We consider the design of the linear precoder
that minimizes the total power consumption while ensuring target user rates.
Three configurations with different degrees of cooperation among BSs are
considered: the coordinated beamforming scheme (only channel state information
is shared among BSs), the coordinated multipoint MIMO processing technology or
network MIMO (channel state and data cooperation), and a single cell
beamforming scheme (only local channel state information is used for
beamforming while channel state cooperation is needed for power allocation).
The analysis is conducted assuming that and grow large with a non
trivial ratio and imperfect channel state information (modeled by the
generic Gauss-Markov formulation form) is available at the BSs. Tools of random
matrix theory are used to compute, in explicit form, deterministic
approximations for: (i) the parameters of the optimal precoder; (ii) the powers
needed to ensure target rates; and (iii) the total transmit power. These
results are instrumental to get further insight into the structure of the
optimal precoders and also to reduce the implementation complexity in
large-scale networks. Numerical results are used to validate the asymptotic
analysis in the finite system regime and to make comparisons among the
different configurations.Comment: 32 pages, 6 figures, to appear IEEE Trans. Wireless Commun. A
preliminary version of this paper was presented at the IEEE Global
Communication Conference, San Diego, USA, Dec. 201
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
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
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