157 research outputs found
A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems
Discriminatory channel estimation (DCE) is a recently developed strategy to
enlarge the performance difference between a legitimate receiver (LR) and an
unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless
system. Specifically, it makes use of properly designed training signals to
degrade channel estimation at the UR which in turn limits the UR's
eavesdropping capability during data transmission. In this paper, we propose a
new two-way training scheme for DCE through exploiting a whitening-rotation
(WR) based semiblind method. To characterize the performance of DCE, a
closed-form expression of the normalized mean squared error (NMSE) of the
channel estimation is derived for both the LR and the UR. Furthermore, the
developed analytical results on NMSE are utilized to perform optimal power
allocation between the training signal and artificial noise (AN). The
advantages of our proposed DCE scheme are two folds: 1) compared to the
existing DCE scheme based on the linear minimum mean square error (LMMSE)
channel estimator, the proposed scheme adopts a semiblind approach and achieves
better DCE performance; 2) the proposed scheme is robust against active
eavesdropping with the pilot contamination attack, whereas the existing scheme
fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication
Antenna grouping for general discriminatory channel estimation
Discriminatory channel estimation emerges as a promising method of not only increasing the secrecy rates in conventional wiretap channels, but also providing a valuable tool for solving the authentication problem. In this paper, we revisit the discriminatory channel estimation method by Chang et al. and propose a generalization to the challenging scenario in which the number of antennas at the legitimate receiver equals or exceeds those of the transmitter. The proposed method is based on the simple idea of dividing the receiver antennas into smaller groups. However, the direct application of previous approaches would result into security problems due to the multiple observations of the eavesdropper, and therefore the transmission system needs to be designed taking this fact into account. The performance of the proposed technique is illustrated by means of some numerical examples, which clearly show the feasibility of discriminatory channel estimation even in the case of systems with more antennas at the receiver side.This work has been supported by the Spanish Government, Ministerio de Ciencia e Innovación, under project RACHEL (TEC2013-47141-C4-3-R)
Capacity-Achieving Iterative LMMSE Detection for MIMO-NOMA Systems
This paper considers a iterative Linear Minimum Mean Square Error (LMMSE)
detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO)
systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE
detection greatly reduces the system computational complexity by departing the
overall processing into many low-complexity distributed calculations. However,
it is generally considered to be sub-optimal and achieves relatively poor
performance. In this paper, we firstly present the matching conditions and area
theorems for the iterative detection of the MIMO-NOMA systems. Based on the
proposed matching conditions and area theorems, the achievable rate region of
the iterative LMMSE detection is analysed. We prove that by properly design the
iterative LMMSE detection, it can achieve (i) the optimal sum capacity of
MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of
MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.Comment: 6pages, 5 figures, accepted by IEEE ICC 2016, 23-27 May 2016, Kuala
Lumpur, Malaysi
On the relaxed maximum-likelihood blind MIMO channel estimation for orthogonal space-time block codes
This paper concerns the maximum-likelihood channel estimation for MIMO
systems with orthogonal space-time block codes when the finite alphabet
constraint of the signal constellation is relaxed. We study the channel
coefficients estimation subspace generated by this method. We provide an
algebraic characterisation of this subspace which turns the optimization
problem into a purely algebraic one and more importantly, leads to several
interesting analytical proofs. We prove that with probability one, the
dimension of the estimation subspace for the channel coefficients is
deterministic and it decreases by increasing the number of receive antennas up
to a certain critical number of receive antennas, after which the dimension
remains constant. In fact, we show that beyond this critical number of receive
antennas, the estimation subspace for the channel coefficients is isometric to
a fixed deterministic invariant space which can be easily computed for every
specific OSTB code
Robust secret key capacity for the MIMO induced source model
This paper considers the problem of distilling a secret key in a Gaussian multiple-input multiple-output (MIMO) scenario with two legitimate nodes and an eavesdropper. Focusing on the realistic case without perfect knowledge of the eavesdropper channel, and following a conservative practical approach based on the maximization of the worst case secret key capacity (SKC), the problem of designing the optimal transmit covariance matrix is reformulated as a convex optimization problem. In the limiting case in which the eavesdropper channel can not be estimated, or when the estimate is highly unreliable, the optimal covariance matrix can be obtained by means of waterfilling or matched filtering like algorithms. Additionally, we illustrate the benefits of allowing time sharing between transmissions of the two legitimate nodes, and provide an efficient algorithm for obtaining the optimal transmit covariance matrices and time-sharing factor.This work was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under projects COSIMA (TEC2010-19545-C04-03) and COMONSENS (CSD2008-00010, CONSOLIDER INGENIO 2010 Program)
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