arXiv subjects

Jinhong Yuan

Publications and source records attributed to Jinhong Yuan.

At least 199 records · Page 11Linked to original sources

arXiv2014

Quantum Synchronizable Codes From Quadratic Residue Codes and Their Supercodes

Quantum synchronizable codes are quantum error-correcting codes designed to correct the effects of both quantum noise and block synchronization errors. While it is known that quantum synchronizable codes can be constructed from cyclic codes that satisfy special properties, only a few classes of cyclic codes have been proved to give promising quantum synchronizable codes. In this paper, using quadratic residue codes and their supercodes, we give a simple construction for quantum synchronizable codes whose synchronization capabilities attain the upper bound. The method is applicable to cyclic codes of prime length.

cs.IT↗

arXiv2014

Design of Quantum Stabilizer Codes From Quadratic Residues Sets

We propose two types, namely Type-I and Type-II, quantum stabilizer codes using quadratic residue sets of prime modulus given by the form $p=4n\pm1$. The proposed Type-I stabilizer codes are of cyclic structure and code length $N=p$. They are constructed based on multi-weight circulant matrix generated from idempotent polynomial, which is obtained from a quadratic residue set. The proposed Type-II stabilizer codes are of quasi-cyclic (QC) structure and code length $N=pk$, where $k$ is the size of a quadratic residue set. They are constructed based on structured sparse-graphs codes derived from proto-matrix and circulant permutation matrix. With the proposed methods, we design rich classes of cyclic and quasi-cyclic quantum stabilizer codes with variable code length. We show how the commutative constraint (also referred to as the Symplectic Inner Product constraint) for quantum codes can be satisfied for each proposed construction method. We also analyze both the dimension and distance for Type-I stabilizer codes and the dimension of Type-II stabilizer codes. For the cyclic quantum stabilizer codes, we show that they meet the existing distance bounds in literature.

cs.IT↗

arXiv2013

Low Complexity List Successive Cancellation Decoding of Polar Codes

We propose a low complexity list successive cancellation (LCLSC) decoding algorithm to reduce complexity of traditional list successive cancellation (LSC) decoding of polar codes while trying to maintain the LSC decoding performance at the same time. By defining two thresholds, namely "likelihood ratio (LR) threshold" and "Bhattacharyya parameter threshold", we classify the reliability of each received information bit and the quality of each bit channel. Based on this classification, we implement successive cancellation (SC) decoding instead of LSC decoding when the information bits from "bad" subchannels are received reliably and further attempt to skip LSC decoding for the rest information bits in order to achieve a lower complexity compared to full LSC decoding. Simulation results show that the complexity of LCLSC decoding is much lower than LSC decoding and can be close to that of SC decoding, especially in low code rate regions.

cs.IT↗

arXiv2013

Physical Layer Security in Downlink Multi-Antenna Cellular Networks

In this paper, we study physical layer security for the downlink of cellular networks, where the confidential messages transmitted to each mobile user can be eavesdropped by both (i) the other users in the same cell and (ii) the users in the other cells. The locations of base stations and mobile users are modeled as two independent two-dimensional Poisson point processes. Using the proposed model, we analyze the secrecy rates achievable by regularized channel inversion (RCI) precoding by performing a large-system analysis that combines tools from stochastic geometry and random matrix theory. We obtain approximations for the probability of secrecy outage and the mean secrecy rate, and characterize regimes where RCI precoding achieves a nonzero secrecy rate. We find that unlike isolated cells, the secrecy rate in a cellular network does not grow monotonically with the transmit power, and the network tends to be in secrecy outage if the transmit power grows unbounded. Furthermore, we show that there is an optimal value for the base station deployment density that maximizes the secrecy rate, and this value is a decreasing function of the signal-to-noise ratio.

cs.IT↗

arXiv2013

Secrecy Rates in the Broadcast Channel with Confidential Messages and External Eavesdroppers

In this paper, we consider the broadcast channel with confidential messages and external eavesdroppers (BCCE), where a multi-antenna base station simultaneously communicates to multiple potentially malicious users, in the presence of randomly located external eavesdroppers. Using the proposed model, we study the secrecy rates achievable by regularized channel inversion (RCI) precoding by performing a large-system analysis that combines tools from stochastic geometry and random matrix theory. We obtain explicit expressions for the probability of secrecy outage and an upper bound on the rate loss due to the presence of external eavesdroppers. We show that both these quantities scale as $\frac{λ_e}{\sqrt{N}}$, where $N$ is the number of transmit antennas and $λ_e$ is the density of external eavesdroppers, irrespective of their collusion strategy. Furthermore, we derive a practical rule for the choice of the regularization parameter, which is agnostic of channel state information and location of eavesdroppers, and yet provides close to optimal performance.

cs.IT↗

arXiv2013

Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter

In this paper, we study the performance of regularized channel inversion precoding in MISO broadcast channels with confidential messages under imperfect channel state information at the transmitter (CSIT). We obtain an approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio. Simulations prove this anaylsis accurate even for finite-size systems. For FDD systems, we determine how the CSIT error must scale with the SNR, and we derive the number of feedback bits required to ensure a constant high-SNR rate gap to the case with perfect CSIT. For TDD systems, we study the optimum amount of channel training that maximizes the high-SNR secrecy sum-rate.

cs.IT↗

arXiv2013

Linear Precoding for Broadcast Channels with Confidential Messages under Transmit-Side Channel Correlation

In this paper, we analyze the performance of regularized channel inversion (RCI) precoding in multiple-input single-output (MISO) broadcast channels with confidential messages under transmit-side channel correlation. We derive a deterministic equivalent for the achievable per-user secrecy rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio, and we determine the optimal regularization parameter that maximizes the secrecy rate. Furthermore, we obtain deterministic equivalents for the secrecy rates achievable by: (i) zero forcing precoding and (ii) single user beamforming. The accuracy of our analysis is validated by simulations of finite-size systems.

cs.IT↗

arXiv2013

Large System Analysis of Linear Precoding in MISO Broadcast Channels with Confidential Messages

In this paper, we study the performance of regularized channel inversion (RCI) precoding in large MISO broadcast channels with confidential messages (BCC). We obtain a deterministic approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas $M$ and the number of users $K$ grow to infinity in a fixed ratio $β=K/M$. We derive the optimal regularization parameter $ξ$ and the optimal network load $β$ that maximize the per-antenna secrecy sum-rate. We then propose a linear precoder based on RCI and power reduction (RCI-PR) that significantly increases the high-SNR secrecy sum-rate for $1<β<2$. Our proposed precoder achieves a per-user secrecy rate which has the same high-SNR scaling factor as both the following upper bounds: (i) the rate of the optimum RCI precoder without secrecy requirements, and (ii) the secrecy capacity of a single-user system without interference. Furthermore, we obtain a deterministic approximation for the secrecy sum-rate achievable by RCI precoding in the presence of channel state information (CSI) error. We also analyze the performance of our proposed RCI-PR precoder with CSI error, and we determine how the error must scale with the SNR in order to maintain a given rate gap to the case with perfect CSI.

cs.IT↗

arXiv2013

Transmit Antenna Selection with Alamouti Scheme in MIMO Wiretap Channels

This paper proposes a new transmit antenna selection (TAS) scheme which provides enhanced physical layer security in multiple-input multiple-output (MIMO) wiretap channels. The practical passive eavesdropping scenario we consider is where channel state information (CSI) from the eavesdropper is not available at the transmitter. Our new scheme is carried out in two steps. First, the transmitter selects the first two strongest antennas based on the feedback from the receiver, which maximizes the instantaneous signal-to-noise ratio (SNR) of the transmitter-receiver channel. Second, the Alamouti scheme is employed at the selected antennas in order to perform data transmission. At the receiver and the eavesdropper, maximal-ratio combining is applied in order to exploit the multiple antennas.We derive a new closed-form expression for the secrecy outage probability in nonidentical Rayleigh fading, and using this result, we then present the probability of non-zero secrecy capacity in closed form and the ε-outage secrecy capacity in numerical form. We demonstrate that our proposed TAS-Alamouti scheme offers lower secrecy outage probability than a single TAS scheme when the SNR of the transmitter-receiver channel is above a specific value.

cs.IT↗

arXiv2013

Design of Binary Network Codes for Multi-user Multi-way Relay Networks

We study multi-user multi-way relay networks where $N$ user nodes exchange their information through a single relay node. We use network coding in the relay to increase the throughput. Due to the limitation of complexity, we only consider the binary multi-user network coding (BMNC) in the relay. We study BMNC matrix (in GF(2)) and propose several design criteria on the BMNC matrix to improve the symbol error probability (SEP) performance. Closed-form expressions of the SEP of the system are provided. Moreover, an upper bound of the SEP is also proposed to provide further insights on system performance. Then BMNC matrices are designed to minimize the error probabilities.

cs.IT↗

arXiv2012

Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding

In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter $α$ and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter $α_{\mathrm{LS}}$ of the precoder using large-system analysis. We show that the RCI precoder with $α_{\mathrm{LS}}$ outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed $α$. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing $α$ and the power allocation vector. The jointly optimized precoder outperforms RCI with $α_{\mathrm{LS}}$ and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas.

cs.IT↗

arXiv2012

Eigen-Direction Alignment Based Physical-Layer Network Coding for MIMO Two-Way Relay Channels

In this paper, we propose a novel communication strategy which incorporates physical-layer network coding (PNC) into multiple-input multiple output (MIMO) two-way relay channels (TWRCs). At the heart of the proposed scheme lies a new key technique referred to as eigen-direction alignment (EDA) precoding. The EDA precoding efficiently aligns the two-user's eigen-modes into the same directions. Based on that, we carry out multi-stream PNC over the aligned eigen-modes. We derive an achievable rate of the proposed EDA-PNC scheme, based on nested lattice codes, over a MIMO TWRC. Asymptotic analysis shows that the proposed EDA-PNC scheme approaches the capacity upper bound as the number of user antennas increases towards infinity. For a finite number of user antennas, we formulate the design criterion of the optimal EDA precoder and present solutions. Numerical results show that there is only a marginal gap between the achievable rate of the proposed EDA-PNC scheme and the capacity upper bound of the MIMO TWRC, in the median-to-large SNR region. We also show that the proposed EDA-PNC scheme significantly outperforms existing amplify-and-forward and decode-and-forward based schemes for MIMO TWRCs.

cs.IT↗

arXiv2012

Channel Identification and its Impact on Quantum LDPC Code Performance

In this work we probe the impact of channel estimation on the performance of quantum LDPC codes. Our channel estimation is based on an optimal estimate of the relevant decoherence parameter via its quantum Fisher information. Using state-of-the art quantum LDPC codes designed for the quantum depolarization channel, and utilizing various quantum probes with different entanglement properties, we show how the performance of such codes can deteriorate by an order of magnitude when optimal channel identification is fed into a belief propagation decoding algorithm. Our work highlights the importance in quantum communications of a viable channel identification campaign prior to decoding, and highlights the trade-off between entanglement consumption and quantum LDPC code performance.

cs.IT↗

arXiv2012

System Identification in Wireless Relay Networks via Gaussian Process

We present a flexible stochastic model for a class of cooperative wireless relay networks, in which the relay processing functionality is not known at the destination. In addressing this problem we develop efficient algorithms to perform relay identification in a wireless relay network. We first construct a statistical model based on a representation of the system using Gaussian Processes in a non-standard manner due to the way we treat the imperfect channel state information. We then formulate the estimation problem to perform system identification, taking into account complexity and computational efficiency. Next we develop a set of three algorithms to solve the identification problem each of decreasing complexity, trading-off the estimation bias for computational efficiency. The joint optimisation problem is tackled via a Bayesian framework using the Iterated Conditioning on the Modes methodology. We develop a lower bound and several sub-optimal computationally efficient solutions to the identification problem, for comparison. We illustrate the estimation performance of our methodology for a range of widely used relay functionalities. The relative total error attained by our algorithm when compared to the lower bound is found to be at worst 9% for low SNR values under all functions considered. The effect of the relay functional estimation error is also studied via BER simulations and is shown to be less than 2dB worse than the lower bound.

cs.IT↗

arXiv2011

Cooperative Spectrum Sensing for Amplify-and-Forward Cognitive Networks

We develop a framework for spectrum sensing in cooperative amplify-and-forward cognitive radio networks. We consider a stochastic model where relays are assigned in cognitive radio networks to transmit the primary user's signal to a cognitive Secondary Base Station (SBS). We develop the Bayesian optimal decision rule under various scenarios of Channel State Information (CSI) varying from perfect to imperfect CSI. In order to obtain the optimal decision rule based on a Likelihood Ratio Test (LRT), the marginal likelihood under each hypothesis relating to presence or absence of transmission needs to be evaluated pointwise. However, in some cases the evaluation of the LRT can not be performed analytically due to the intractability of the multi-dimensional integrals involved. In other cases, the distribution of the test statistic can not be obtained exactly. To circumvent these difficulties we design two algorithms to approximate the marginal likelihood, and obtain the decision rule. The first is based on Gaussian Approximation where we quantify the accuracy of the approximation via a multivariate version of the Berry-Esseen bound. The second algorithm is based on Laplace approximation for the marginal likelihood, which results in a non-convex optimisation problem which is solved efficiently via Bayesian Expectation-Maximisation method. We also utilise a Laguerre series expansion to approximate the distribution of the test statistic in cases where its distribution can not be derived exactly. Performance is evaluated via analytic bounds and compared to numerical simulations.

cs.IT↗

arXiv2011

Spectrum Sensing in Cooperative Cognitive Radio Networks with Partial CSI

We develop an efficient algorithm for cooperative spectrum sensing in a relay based cognitive radio network. We consider a stochastic model where data is sent from the Base Station (BS) of the Primary User (PU). The data is relayed by the Secondary Users (SU) to the SU BS. The SU BS has only partial CSI knowledge of the wireless channels. In order to obtain the optimal decision rule based on Likelihood Ratio Test (LRT), the marginal likelihood under each hypothesis needs to be evaluated pointwise. These, however, cannot be obtained analytically due to the intractability of the integrals. Instead, we approximate these quantities by utilising the Laplace method. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve superior results to the energy detection scheme.

cs.IT↗

arXiv2011

Blind Spectrum Sensing in Cognitive Radio over Fading Channels and Frequency Offsets

This paper deals with the challenging problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets due to oscillator mismatch, and Doppler offset is studied. We show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated poitnwise. We present a novel approach to approximate the marginilisation of the frequency offset using a single point estimate. This is obtained via a low complexity Constrained Adaptive Notch Filter (CANF) to estimate the frequency offset. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve the same performance as the near-optimal scheme, that is based on a bank of matched filters, using only a fraction of the complexity required.

cs.IT↗

arXiv2010

Channel Tracking for Relay Networks via Adaptive Particle MCMC

This paper presents a new approach for channel tracking and parameter estimation in cooperative wireless relay networks. We consider a system with multiple relay nodes operating under an amplify and forward relay function. We develop a novel algorithm to efficiently solve the challenging problem of joint channel tracking and parameters estimation of the Jakes' system model within a mobile wireless relay network. This is based on \textit{particle Markov chain Monte Carlo} (PMCMC) method. In particular, it first involves developing a Bayesian state space model, then estimating the associated high dimensional posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter.

cs.IT↗