arXiv subjects

Cong Ling

Publications and source records attributed to Cong Ling.

At least 91 records · Page 5Linked to original sources

arXiv2016

Almost universal codes for fading wiretap channels

We consider a fading wiretap channel model where the transmitter has only statistical channel state information, and the legitimate receiver and eavesdropper have perfect channel state information. We propose a sequence of non-random lattice codes which achieve strong secrecy and semantic security over ergodic fading channels. The construction is almost universal in the sense that it achieves the same constant gap to secrecy capacity over Gaussian and ergodic fading models.

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arXiv2015

Artificial-Noise-Aided Message Authentication Codes with Information-Theoretic Security

In the past, two main approaches for the purpose of authentication, including information-theoretic authentication codes and complexity-theoretic message authentication codes (MACs), were almost independently developed. In this paper, we propose a new cryptographic primitive, namely, artificial-noise-aided MACs (ANA-MACs), which can be considered as both computationally secure and information-theoretically secure. For ANA-MACs, we introduce artificial noise to interfere with the complexity-theoretic MACs and quantization is further employed to facilitate packet-based transmission. With a channel coding formulation of key recovery in the MACs, the generation of standard authentication tags can be seen as an encoding process for the ensemble of codes, where the shared key between Alice and Bob is considered as the input and the message is used to specify a code from the ensemble of codes. Then, we show that the introduction of artificial noise in ANA-MACs can be well employed to resist the key recovery attack even if the opponent has an unlimited computing power. Finally, a pragmatic approach for the analysis of ANA-MACs is provided, and we show how to balance the three performance metrics, including the completeness error, the false acceptance probability, and the conditional equivocation about the key. The analysis can be well applied to a class of ANA-MACs, where MACs with Rijndael cipher are employed.

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arXiv2014

Modulated Unit-Norm Tight Frames for Compressed Sensing

In this paper, we propose a compressed sensing (CS) framework that consists of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a column-wise orthonormal matrix. We prove that this structure satisfies the restricted isometry property (RIP) with high probability if the number of measurements $m = O(s \log^2s \log^2n)$ for $s$-sparse signals of length $n$ and if the column-wise orthonormal matrix is bounded. Some existing structured sensing models can be studied under this framework, which then gives tighter bounds on the required number of measurements to satisfy the RIP. More importantly, we propose several structured sensing models by appealing to this unified framework, such as a general sensing model with arbitrary/determinisic subsamplers, a fast and efficient block compressed sensing scheme, and structured sensing matrices with deterministic phase modulations, all of which can lead to improvements on practical applications. In particular, one of the constructions is applied to simplify the transceiver design of CS-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems.

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arXiv2014

MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer: Weighted MMSE Approaches

We consider simultaneous wireless information and power transfer (SWIPT) in MIMO Broadcast networks where one energy harvesting (EH) user and one information decoding (ID) user share the same time and frequency resource. In contrast to previous SWIPT systems based on the information rate, this paper addresses the problem in terms of the weighted minimum mean squared error (WMMSE) criterion. First, we formulate the WMMSE-SWIPT problem which minimizes the weighted sum-MSE of the message signal arrived at the ID user, while satisfying the requirement on the energy that can be harvested from the signal at the EH user. Then, we propose the optimal precoder structure of the problem and identify the best possible MSE-energy tradeoff region through the alternative update of the linear precoder at the transmitter with the linear receiver at the ID user. From the derived solution, several interesting observations are made compared to the conventional SWIPT designs.

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arXiv2014

Markov Chain Monte Carlo Algorithms for Lattice Gaussian Sampling

Sampling from a lattice Gaussian distribution is emerging as an important problem in various areas such as coding and cryptography. The default sampling algorithm --- Klein's algorithm yields a distribution close to the lattice Gaussian only if the standard deviation is sufficiently large. In this paper, we propose the Markov chain Monte Carlo (MCMC) method for lattice Gaussian sampling when this condition is not satisfied. In particular, we present a sampling algorithm based on Gibbs sampling, which converges to the target lattice Gaussian distribution for any value of the standard deviation. To improve the convergence rate, a more efficient algorithm referred to as Gibbs-Klein sampling is proposed, which samples block by block using Klein's algorithm. We show that Gibbs-Klein sampling yields a distribution close to the target lattice Gaussian, under a less stringent condition than that of the original Klein algorithm.

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arXiv2014

Diversity of Linear Transceivers in MIMO AF Half-duplex Relaying Channels

Linear transceiving schemes between the relay and the destination have recently attracted much interest in MIMO amplify-and-forward (AF) relaying systems due to low implementation complexity. In this paper, we provide comprehensive analysis on the diversity order of the linear zero-forcing (ZF) and minimum mean squared error (MMSE) transceivers. Firstly, we obtain a compact closed-form expression for the diversity-multiplexing tradeoff (DMT) through tight upper and lower bounds. While our DMT analysis accurately predicts the performance of the ZF transceivers, it is observed that the MMSE transceivers exhibit a complicated rate dependent behavior, and thus are very unpredictable via DMT for finite rate cases. Secondly, we highlight this interesting behavior of the MMSE transceivers and characterize the diversity order at all finite rates. This leads to a closed-form expression for the diversity-rate tradeoff (DRT) which reveals the relationship between the diversity, the rate, and the number of antennas at each node. Our DRT analysis compliments our previous work on DMT, thereby providing a complete understanding on the diversity order of linear transceiving schemes in MIMO AF relaying channels.

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arXiv2014

Polar Lattices for Strong Secrecy Over the Mod-$Λ$ Gaussian Wiretap Channel

Polar lattices, which are constructed from polar codes, are provably good for the additive white Gaussian noise (AWGN) channel. In this work, we propose a new polar lattice construction that achieves the secrecy capacity under the strong secrecy criterion over the mod-$Λ$ Gaussian wiretap channel. This construction leads to an AWGN-good lattice and a secrecy-good lattice simultaneously. The design methodology is mainly based on the equivalence in terms of polarization between the $Λ/Λ'$ channel in lattice coding and the equivalent channel derived from the chain rule of mutual information in multilevel coding.

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arXiv2013

Achievable Diversity-Rate Tradeoff of MIMO AF Relaying Systems with MMSE Transceivers

This paper investigates the diversity order of the minimum mean squared error (MMSE) based optimal transceivers in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems. While the diversity-multiplexing tradeoff (DMT) analysis accurately predicts the behavior of the MMSE receiver for the positive multiplexing gain, it turned out that the performance is very unpredictable via DMT for the case of fixed rates, because MMSE strategies exhibit a complicated rate dependent behavior. In this paper, we establish the diversity-rate tradeoff performance of MIMO AF relaying systems with the MMSE transceivers as a closed-form for all fixed rates, thereby providing a complete characterization of the diversity order together with the earlier work on DMT.

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arXiv2013

Deterministic Sequences for Compressive MIMO Channel Estimation

This paper considers the problem of pilot design for compressive multiple-input multiple-output (MIMO) channel estimation. In particular, we are interested in estimating the channels for multiple transmitters simultaneously when the pilot sequences are shorter than the combined channels. Existing works on this topic demonstrated that tools from compressed sensing theory can yield accurate multichannel estimation provided that each pilot sequence is randomly generated. Here, we propose constructing the pilot sequence for each transmitter from a small set of deterministic sequences. We derive a theoretical lower bound on the length of the pilot sequences that guarantees the multichannel estimation with high probability. Simulation results are provided to demonstrate the performance of the proposed method.

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arXiv2013

Semantically Secure Lattice Codes for the Gaussian Wiretap Channel

We propose a new scheme of wiretap lattice coding that achieves semantic security and strong secrecy over the Gaussian wiretap channel. The key tool in our security proof is the flatness factor which characterizes the convergence of the conditional output distributions corresponding to different messages and leads to an upper bound on the information leakage. We not only introduce the notion of secrecy-good lattices, but also propose the {flatness factor} as a design criterion of such lattices. Both the modulo-lattice Gaussian channel and the genuine Gaussian channel are considered. In the latter case, we propose a novel secrecy coding scheme based on the discrete Gaussian distribution over a lattice, which achieves the secrecy capacity to within a half nat under mild conditions. No \textit{a priori} distribution of the message is assumed, and no dither is used in our proposed schemes.

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arXiv2013

Decoding by Sampling - Part II: Derandomization and Soft-output Decoding

In this paper, a derandomized algorithm for sampling decoding is proposed to achieve near-optimal performance in lattice decoding. By setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, which brings considerable performance improvement and complexity reduction over to the randomized sampling. Moreover, the upper bound on the sample size K, which corresponds to near-maximum likelihood (ML) performance, is derived. We also find that the proposed algorithm can be used as an efficient tool to implement soft-output decoding in multiple-input multiple-output (MIMO) systems. An upper bound of the sphere radius R in list sphere decoding (LSD) is derived. Based on it, we demonstrate that the derandomized sampling algorithm is capable of achieving near-maximum a posteriori (MAP) performance. Simulation results show that near-optimum performance can be achieved by a moderate size K in both lattice decoding and soft-output decoding.

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arXiv2013

Polar Lattices: Where Arıkan Meets Forney

In this paper, we propose the explicit construction of a new class of lattices based on polar codes, which are provably good for the additive white Gaussian noise (AWGN) channel. We follow the multilevel construction of Forney \textit{et al.} (i.e., Construction D), where the code on each level is a capacity-achieving polar code for that level. The proposed polar lattices are efficiently decodable by using multistage decoding. Computable performance bounds are derived to measure the gap to the generalized capacity at given error probability. A design example is presented to demonstrate the performance of polar lattices.

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arXiv2013

Secret key generation from Gaussian sources using lattice hashing

We propose a simple yet complete lattice-based scheme for secret key generation from Gaussian sources in the presence of an eavesdropper, and show that it achieves strong secret key rates up to 1/2 nat from the optimal in the case of "degraded" source models. The novel ingredient of our scheme is a lattice-hashing technique, based on the notions of flatness factor and channel intrinsic randomness. The proposed scheme does not require dithering.

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arXiv2013

Proximity Factors of Lattice Reduction-Aided Precoding for Multiantenna Broadcast

Lattice precoding is an effective strategy for multiantenna broadcast. In this paper, we show that approximate lattice precoding in multiantenna broadcast is a variant of the closest vector problem (CVP) known as $η$-CVP. The proximity factors of lattice reduction-aided precoding are defined, and their bounds are derived, which measure the worst-case loss in power efficiency compared to sphere precoding. Unlike decoding applications, this analysis does not suffer from the boundary effect of a finite constellation, since the underlying lattice in multiantenna broadcast is indeed infinite.

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arXiv2012

Noncoherent Interference Alignment: Trade Signal Power for Diversity Towards Multiplexing

This paper proposes the first known universal interference alignment scheme for general $(1\times{}1)^K$ interference networks, either Gaussian or deterministic, with only 2 symbol extension. While interference alignment is theoretically powerful to increase the total network throughput tremendously, no existing scheme can achieve the degree of freedom upper bound exactly with finite complexity. This paper starts with detailed analysis of the diagonality problem of naive symbol extension in small $(1\times1)^3$ networks, a technique widely regarded as necessary to achieve interference alignment with insufficient diversity. Then, a joint bandpass noncoherent demodulation and interference alignment scheme is proposed to solve the diagonality problem by trading signal power for increased system diversity, which is further traded for multiplexing improvement. Finally, the proposed noncoherent interference alignment scheme is extended to general $(1\times{}1)^K$ cases and is proven to achieve the degree of freedom upper bound exactly. Simulation results verify the correctness and powerfulness of the proposed scheme and show significant degree of freedom improvement compared to the conventional orthogonal transmission scheme.

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arXiv2012

Decoding by Sampling: A Randomized Lattice Algorithm for Bounded Distance Decoding

Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-likelihood (ML) performance as the dimension increases. To improve its performance, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., successive interference cancelation (SIC)). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is two-fold: we analyze and optimize the decoding radius of sampling decoding resulting in better error performance than Klein's original algorithm, and propose a very efficient implementation of random rounding. Of particular interest is that a fixed gain in the decoding radius compared to Babai's decoding can be achieved at polynomial complexity. The proposed decoder is useful for moderate dimensions where sphere decoding becomes computationally intensive, while lattice reduction-aided decoding starts to suffer considerable loss. Simulation results demonstrate near-ML performance is achieved by a moderate number of samples, even if the dimension is as high as 32.

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arXiv2012

Decoding by Embedding: Correct Decoding Radius and DMT Optimality

The closest vector problem (CVP) and shortest (nonzero) vector problem (SVP) are the core algorithmic problems on Euclidean lattices. They are central to the applications of lattices in many problems of communications and cryptography. Kannan's \emph{embedding technique} is a powerful technique for solving the approximate CVP, yet its remarkable practical performance is not well understood. In this paper, the embedding technique is analyzed from a \emph{bounded distance decoding} (BDD) viewpoint. We present two complementary analyses of the embedding technique: We establish a reduction from BDD to Hermite SVP (via unique SVP), which can be used along with any Hermite SVP solver (including, among others, the Lenstra, Lenstra and Lovász (LLL) algorithm), and show that, in the special case of LLL, it performs at least as well as Babai's nearest plane algorithm (LLL-aided SIC). The former analysis helps to explain the folklore practical observation that unique SVP is easier than standard approximate SVP. It is proven that when the LLL algorithm is employed, the embedding technique can solve the CVP provided that the noise norm is smaller than a decoding radius $λ_1/(2γ)$, where $λ_1$ is the minimum distance of the lattice, and $γ\approx O(2^{n/4})$. This substantially improves the previously best known correct decoding bound $γ\approx {O}(2^{n})$. Focusing on the applications of BDD to decoding of multiple-input multiple-output (MIMO) systems, we also prove that BDD of the regularized lattice is optimal in terms of the diversity-multiplexing gain tradeoff (DMT), and propose practical variants of embedding decoding which require no knowledge of the minimum distance of the lattice and/or further improve the error performance.

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arXiv2012

Convolutional Compressed Sensing Using Deterministic Sequences

In this paper, a new class of circulant matrices built from deterministic sequences is proposed for convolution-based compressed sensing (CS). In contrast to random convolution, the coefficients of the underlying filter are given by the discrete Fourier transform of a deterministic sequence with good autocorrelation. Both uniform recovery and non-uniform recovery of sparse signals are investigated, based on the coherence parameter of the proposed sensing matrices. Many examples of the sequences are investigated, particularly the Frank-Zadoff-Chu (FZC) sequence, the \textit{m}-sequence and the Golay sequence. A salient feature of the proposed sensing matrices is that they can not only handle sparse signals in the time domain, but also those in the frequency and/or or discrete-cosine transform (DCT) domain.

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