4,116 research outputs found
Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs
Optimal power allocation for orthogonal frequency division multiplexing
(OFDM) wiretap channels with Gaussian channel inputs has already been studied
in some previous works from an information theoretical viewpoint. However,
these results are not sufficient for practical system design. One reason is
that discrete channel inputs, such as quadrature amplitude modulation (QAM)
signals, instead of Gaussian channel inputs, are deployed in current practical
wireless systems to maintain moderate peak transmission power and receiver
complexity. In this paper, we investigate the power allocation and artificial
noise design for OFDM wiretap channels with discrete channel inputs. We first
prove that the secrecy rate function for discrete channel inputs is nonconcave
with respect to the transmission power. To resolve the corresponding nonconvex
secrecy rate maximization problem, we develop a low-complexity power allocation
algorithm, which yields a duality gap diminishing in the order of
O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that
independent frequency-domain artificial noise cannot improve the secrecy rate
of single-antenna wiretap channels. Towards this end, we propose a novel
time-domain artificial noise design which exploits temporal degrees of freedom
provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and
boost the secrecy rate even with a single antenna at the transmitter}.
Numerical results are provided to illustrate the performance of the proposed
design schemes.Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Wireless
Communications, Jan. 201
Precoding by Pairing Subchannels to Increase MIMO Capacity with Discrete Input Alphabets
We consider Gaussian multiple-input multiple-output (MIMO) channels with
discrete input alphabets. We propose a non-diagonal precoder based on the
X-Codes in \cite{Xcodes_paper} to increase the mutual information. The MIMO
channel is transformed into a set of parallel subchannels using Singular Value
Decomposition (SVD) and X-Codes are then used to pair the subchannels. X-Codes
are fully characterized by the pairings and a real rotation matrix
for each pair (parameterized with a single angle). This precoding structure
enables us to express the total mutual information as a sum of the mutual
information of all the pairs. The problem of finding the optimal precoder with
the above structure, which maximizes the total mutual information, is solved by
{\em i}) optimizing the rotation angle and the power allocation within each
pair and {\em ii}) finding the optimal pairing and power allocation among the
pairs. It is shown that the mutual information achieved with the proposed
pairing scheme is very close to that achieved with the optimal precoder by Cruz
{\em et al.}, and is significantly better than Mercury/waterfilling strategy by
Lozano {\em et al.}. Our approach greatly simplifies both the precoder
optimization and the detection complexity, making it suitable for practical
applications.Comment: submitted to IEEE Transactions on Information Theor
Linear MIMO Precoding in Jointly-Correlated Fading Multiple Access Channels with Finite Alphabet Signaling
In this paper, we investigate the design of linear precoders for
multiple-input multiple-output (MIMO) multiple access channels (MAC). We assume
that statistical channel state information (CSI) is available at the
transmitters and consider the problem under the practical finite alphabet input
assumption. First, we derive an asymptotic (in the large-system limit) weighted
sum rate (WSR) expression for the MIMO MAC with finite alphabet inputs and
general jointly-correlated fading. Subsequently, we obtain necessary conditions
for linear precoders maximizing the asymptotic WSR and propose an iterative
algorithm for determining the precoders of all users. In the proposed
algorithm, the search space of each user for designing the precoding matrices
is its own modulation set. This significantly reduces the dimension of the
search space for finding the precoding matrices of all users compared to the
conventional precoding design for the MIMO MAC with finite alphabet inputs,
where the search space is the combination of the modulation sets of all users.
As a result, the proposed algorithm decreases the computational complexity for
MIMO MAC precoding design with finite alphabet inputs by several orders of
magnitude. Simulation results for finite alphabet signalling indicate that the
proposed iterative algorithm achieves significant performance gains over
existing precoder designs, including the precoder design based on the Gaussian
input assumption, in terms of both the sum rate and the coded bit error rate.Comment: 7 pages, 2 figures, accepted for ICC1
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