1,007 research outputs found
Technical Efficiency of Resource Use in the Production of Irrigated Potato: A Study of Farmers Using Modern and Traditional Irrigation Schemes in Awi Zone, Ethiopia
Based on cross-sectional data collected from randomly selected 80 farmers in four districts of Awi zone in North-western Ethiopia, this study examines the technical efficiency of farmers in the production of irrigated potato. The stochastic frontier production function, which considers deviation from the frontier to be due to the effect of technical inefficiency and random noise, is used for data analysis. Technical efficiency of farmers was estimated independently for the farms under modern irrigation schemes and traditional irrigation schemes. Using likelihood ratio test, Translog production function is found to be an adequate representation of the production behavior of farms under the two types of schemes. The mean level of technical efficiency was found to be 77 percent and 97 percent respectively for modern and traditional schemes. Therefore, improving the level of efficiency could raise productivity under modern schemes, whereas improving productivity under traditional schemes needs introduction of new technology as the farmers’ level of production has approached the frontier. Irrigation experience, commodity rate of production and size of livestock are found to be the important variables that determine the level of efficiency
Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
This paper proposes novel spectrum sensing algorithms for cognitive radio
networks. By assuming known transmitter pulse shaping filter, synchronous and
asynchronous receiver scenarios have been considered. For each of these
scenarios, the proposed algorithm is explained as follows: First, by
introducing a combiner vector, an over-sampled signal of total duration equal
to the symbol period is combined linearly. Second, for this combined signal,
the Signal-to-Noise ratio (SNR) maximization and minimization problems are
formulated as Rayleigh quotient optimization problems. Third, by using the
solutions of these problems, the ratio of the signal energy corresponding to
the maximum and minimum SNRs are proposed as a test statistics. For this test
statistics, analytical probability of false alarm () and detection ()
expressions are derived for additive white Gaussian noise (AWGN) channel. The
proposed algorithms are robust against noise variance uncertainty. The
generalization of the proposed algorithms for unknown transmitter pulse shaping
filter has also been discussed. Simulation results demonstrate that the
proposed algorithms achieve better than that of the Eigenvalue
decomposition and energy detection algorithms in AWGN and Rayleigh fading
channels with noise variance uncertainty. The proposed algorithms also
guarantee the desired in the presence of adjacent channel
interference signals
Linear Transceiver design for Downlink Multiuser MIMO Systems: Downlink-Interference Duality Approach
This paper considers linear transceiver design for downlink multiuser
multiple-input multiple-output (MIMO) systems. We examine different transceiver
design problems. We focus on two groups of design problems. The first group is
the weighted sum mean-square-error (WSMSE) (i.e., symbol-wise or user-wise
WSMSE) minimization problems and the second group is the minimization of the
maximum weighted mean-squareerror (WMSE) (symbol-wise or user-wise WMSE)
problems. The problems are examined for the practically relevant scenario where
the power constraint is a combination of per base station (BS) antenna and per
symbol (user), and the noise vector of each mobile station is a zero-mean
circularly symmetric complex Gaussian random variable with arbitrary covariance
matrix. For each of these problems, we propose a novel downlink-interference
duality based iterative solution. Each of these problems is solved as follows.
First, we establish a new mean-square-error (MSE) downlink-interference
duality. Second, we formulate the power allocation part of the problem in the
downlink channel as a Geometric Program (GP). Third, using the duality result
and the solution of GP, we utilize alternating optimization technique to solve
the original downlink problem. For the first group of problems, we have
established symbol-wise and user-wise WSMSE downlink-interference duality.Comment: IEEE TSP Journa
Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems with Arbitrary Power Constraint: Generalized Duality Approach
This paper considers linear minimum meansquare- error (MMSE) transceiver
design problems for downlink multiuser multiple-input multiple-output (MIMO)
systems where imperfect channel state information is available at the base
station (BS) and mobile stations (MSs). We examine robust sum mean-square-error
(MSE) minimization problems. The problems are examined for the generalized
scenario where the power constraint is per BS, per BS antenna, per user or per
symbol, and the noise vector of each MS is a zero-mean circularly symmetric
complex Gaussian random variable with arbitrary covariance matrix. For each of
these problems, we propose a novel duality based iterative solution. Each of
these problems is solved as follows. First, we establish a novel sum average
meansquare- error (AMSE) duality. Second, we formulate the power allocation
part of the problem in the downlink channel as a Geometric Program (GP). Third,
using the duality result and the solution of GP, we utilize alternating
optimization technique to solve the original downlink problem. To solve robust
sum MSE minimization constrained with per BS antenna and per BS power problems,
we have established novel downlink-uplink duality. On the other hand, to solve
robust sum MSE minimization constrained with per user and per symbol power
problems, we have established novel downlink-interference duality. For the
total BS power constrained robust sum MSE minimization problem, the current
duality is established by modifying the constraint function of the dual uplink
channel problem. And, for the robust sum MSE minimization with per BS antenna
and per user (symbol) power constraint problems, our duality are established by
formulating the noise covariance matrices of the uplink and interference
channels as fixed point functions, respectively.Comment: IEEE TSP Journa
Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems
This paper proposes novel pilot optimization and channel estimation algorithm
for the downlink multiuser massive multiple input multiple output (MIMO) system
with decentralized single antenna mobile stations (MSs), and time division
duplex (TDD) channel estimation which is performed by utilizing pilot
symbols. The proposed algorithm is explained as follows. First, we formulate
the channel estimation problem as a weighted sum mean square error (WSMSE)
minimization problem containing pilot symbols and introduced variables. Second,
for fixed pilot symbols, the introduced variables are optimized using minimum
mean square error (MMSE) and generalized Rayleigh quotient methods. Finally,
for and settings, the pilot symbols of all MSs are optimized using
semi definite programming (SDP) convex optimization approach, and for the other
settings of and , the pilot symbols of all MSs are optimized by applying
simple iterative algorithm. When , it is shown that the latter iterative
algorithm gives the optimal pilot symbols achieved by the SDP method.
Simulation results confirm that the proposed algorithm achieves less WSMSE
compared to that of the conventional semi-orthogonal pilot symbol and MMSE
channel estimation algorithm which creates pilot contamination.Comment: Accepted in CISS 2014 Conferenc
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