503 research outputs found
On the SCALE Algorithm for Multiuser Multicarrier Power Spectrum Management
This paper studies the successive convex approximation for low complexity
(SCALE) algorithm, which was proposed to address the weighted sum rate (WSR)
maximized dynamic power spectrum management (DSM) problem for multiuser
multicarrier systems. To this end, we first revisit the algorithm, and then
present geometric interpretation and properties of the algorithm. A geometric
programming (GP) implementation approach is proposed and compared with the
low-complexity approach proposed previously. In particular, an analytical
method is proposed to set up the default lower-bound constraints added by a GP
solver. Finally, numerical experiments are used to illustrate the analysis and
compare the two implementation approaches.Comment: 8 pages, 5 figures; IEEE Transactions on Signal Processing, vol. 60,
no. 9, Sep. 201
Joint Estimation of the Time Delay and the Clock Drift and Offset Using UWB signals
We consider two transceivers, the first with perfect clock and the second
with imperfect clock. We investigate the joint estimation of the delay between
the transceivers and the offset and the drift of the imperfect clock. We
propose a protocol for the synchronization of the clocks. We derive some
empirical estimators for the delay, the offset and the drift, and compute the
Cramer-Rao lower bounds and the joint maximum likelihood estimator of the delay
and the drift. We study the impact of the protocol parameters and the
time-of-arrival estimation variance on the achieved performances. We validate
some theoretical results by simulation.Comment: Accepted and published in the IEEE ICC 2014 conferenc
Sum Rate Maximized Resource Allocation in Multiple DF Relays Aided OFDM Transmission
In relay-aided wireless transmission systems, one of the key issues is how to
decide assisting relays and manage the energy resource at the source and each
individual relay, to maximize a certain objective related to system
performance. This paper addresses the sum rate maximized resource allocation
(RA) problem in a point to point orthogonal frequency division modulation
(OFDM) transmission system assisted by multiple decode-and-forward (DF) relays,
subject to the individual sum power constraints of the source and the relays.
In particular, the transmission at each subcarrier can be in either the direct
mode without any relay assisting, or the relay-aided mode with one or several
relays assisting. We propose two RA algorithms which optimize the assignment of
transmission mode and source power for every subcarrier, as well as the
assisting relays and the power allocation to them for every {relay-aided}
subcarrier. First, it is shown that the considered RA problem has zero
Lagrangian duality gap when there is a big number of subcarriers. In this case,
a duality based algorithm that finds a globally optimum RA is developed.
Second, a coordinate-ascent based iterative algorithm, which finds a suboptimum
RA but is always applicable regardless of the duality gap of the RA problem, is
developed. The effectiveness of these algorithms has been illustrated by
numerical experiments.Comment: 13 pages in two-column format, 10 figures, to appear in IEEE Journal
on Selected Areas in Communication
On the Optimum Energy Efficiency for Flat-fading Channels with Rate-dependent Circuit Power
This paper investigates the optimum energy efficiency (EE) and the
corresponding spectral efficiency (SE) for a communication link operating over
a flat-fading channel. The EE is evaluated by the total energy consumption for
transmitting per message bit. Three channel cases are considered, namely static
channel with channel state information available at transmitter (CSIT),
fast-varying (FV) channel with channel distribution information available at
transmitter (CDIT), and FV channel with CSIT. A general circuit power model is
considered. For all the three channel cases, the tradeoff between the EE and SE
is studied. It is shown that the EE improves strictly as the SE increases from
0 to the optimum SE, and then strictly degrades as the SE increases beyond the
optimum SE. The impact of {\kappa}, {\rho} and other system parameters on the
optimum EE and corresponding SE is investigated to obtain insight.Some of the
important and interesting results for all the channel cases include: (1) when
{\kappa} increases the SE corresponding to the optimum EE should keep unchanged
if {\phi}(R) = R, but reduced if {\phi}(R) is strictly convex of R; (2) when
the rate-independent circuit power {\rho} increases, the SE corresponding to
the optimum EE has to be increased. A polynomial-complexity algorithm is
developed with the bisection method to find the optimum SE. The insight is
corroborated and the optimum EE for the three cases are compared by simulation
results.Comment: 12 pages, 7 figures, to appear in IEEE Transactions on Communication
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
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
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
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