arXiv subjects

Jun Li

Publications and source records attributed to Jun Li.

At least 721 records · Page 40Linked to original sources

arXiv2020

Capacity Performance of Relay Beamformings for MIMO Multi-Relay Networks with Imperfect R-D CSI at Relays

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO) wireless relay network in the presence of imperfect channel state information (CSI), in which a source-destination pair both equipped with multiple antennas communicates through a large number of half-duplex amplify-and-forward (AF) relay terminals. We investigate the performance of three linear beamforming schemes when the CSI of relay-to-destination (R-D) link is not perfect at the relay nodes. The three efficient linear beamforming schemes are based on the matched-filter (MF), zero-forcing (ZF) precoding and regularized zero-forcing (RZF) precoding techniques, which utilize the CSI of both S-D channel and R-D channel at the relay nodes. By modeling the R-D CSI error at the relay nodes as independent complex Gaussian random variables, we derive the ergodic capacities of the three beamformers in terms of instantaneous SNR. Using Law of Large Number, we obtain the asymptotic capacities, upon which the optimized MF-RZF is derived. Simulation results show that the asymptotic capacities match with the respective ergodic capacities very well. Analysis and simulation results demonstrate that the optimized MF-RZF outperforms MF and MF-ZF for any power of R-D CSI error.

eess.SP↗

arXiv2020

Joint Power Allocation and Precoding for Network Coding based Cooperative Multicast Systems

In this letter, we propose two power allocation schemes based on the statistical channel state information (CSI) and instantaneous s->r CSI at transmitters respectively for a 2-N-2 cooperative multicast system with non-regenerative network coding.Then the isolated precoder and the distributed precoder are respectively applied to the schemes to further improve the system performance by achieving the full diversity gain. Finally, we demonstrate that joint instantaneous s->r CSI based power allocation and distributed precoder design achieve the best performance.

cs.IT↗

arXiv2020

Power Allocation in the High SNR Regime for A Multicast Cell with Regenerative Network Coding

This letter focuses on power allocation schemes for a basic multicast cell with wireless regenerative network coding (RNC). In RNC, mixed signals received from the two sources are jointly decoded by the relay where decoded symbols are superposed in either the complex field (RCNC) or Galois field (RGNC) before being retransmitted. We deduce the optimal statistical channels state information (CSI) based power allocation and give a comparison between the two RNCs. When instantaneous CSI is available at each transmitter, we propose a suboptimal power allocation for RCNC, which achieves better performance.

cs.IT↗

arXiv2020

Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks

In this paper, we develop an online change-point detection procedure in the covariance structure of high-dimensional data. A new stopping rule is proposed to terminate the process as early as possible when a change in covariance structure occurs. The stopping rule allows temporal dependence and can be applied to non-Gaussian data. An explicit expression for the average run length (ARL) is derived, so that the level of threshold in the stopping rule can be easily obtained with no need to run time-consuming Monte Carlo simulations. We also establish an upper bound for the expected detection delay (EDD), the expression of which demonstrates the impact of data dependence and magnitude of change in the covariance structure. Simulation studies are provided to confirm accuracy of the theoretical results. The practical usefulness of the proposed procedure is illustrated by detecting the change of brain's covariance network in a resting-state fMRI dataset.

stat.ME↗

arXiv2020

Deep-learning-based optical image hiding

A novel framework of optical image hiding based on deep learning (DL) is proposed in this paper, and hidden information can be reconstructed from an interferogram by using an end to end network with high-quality. By using the prior data between the hidden image and the object image, a generative adversarial network was trained so that it can learn the hiding model, which resulting in only an interferogram needs to be transmitted and recorded to reconstruct image. Moreover, reconstruction process can be obtained without the parameters in optical inverse diffraction and the reconstruction result will not be affected by the phase shifts deviation and noise, which is convenient for practical application. The feasibility and security of the proposed method are demonstrated by the optical experiment results.

eess.IV↗

arXiv2020

Binary Representaion for Non-binary LDPC Code with Decoder Design

The equivalent binary parity check matrices for the binary images of the cycle-free non-binary LDPC codes have numerous bit-level cycles. In this paper, we show how to transform these binary parity check matrices into their cycle-free forms. It is shown that the proposed methodology can be adopted not only for the binary images of non-binary LDPC codes but also for a large class of binary LDPC codes. Specifically, we present an extended $p$-reducible (EPR) LDPC code structure to eliminate the bit-level cycles. For the non-binary LDPC codes with short length symbol-level cycles, the EPR-LDPC codes can largely avoid the corresponding short length bit-level cycles. As to the decoding of the EPR-LDPC codes, we propose a hybrid hard-decision decoder and a hybrid parallel decoder for binary symmetric channel and binary input Gaussian channel, respectively. A simple code optimization algorithm for these binary decoders is also provided. Simulations show the comparative results and justify the advantages, i.e., better performance and lower decoding complexity, of the proposed binary constructions.

eess.SP↗

arXiv2020

Power Adaptive Network Coding for a Non-Orthogonal Multiple-Access Relay Channel

In this paper we propose a novel power adapted network coding (PANC) for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. Different from the conventional XOR-based network coding (CXNC), the relay in our PANC generates network coded bits by considering the coefficients of the source-to-relay channels, and forwards each bit with a pre-optimized power level. Specifically, by defining a symbol pair as two symbols from the two sources, we first derive the exact symbol pair error rate (SPER) of the system. Noting that the generations of the exact SPER are complicated due to the irregularity of the decision regions caused by random channel coefficients, we propose a coordinate transform (CT) method to simplify the derivations of the SPER. Next, we prove that with a power scaling factor at relay, our PANC scheme can achieve full diversity gain, i.e., two-order diversity gain, of the system, while the CXNC can only achieve one-order diversity gain due to multi-user interference. In addition, we optimize the power levels at the relay to equivalently minimize the SPER at the destination concerning the relationship between SPER and minimum Euclidean distance of the received constellation. Simulation results show that (1) the SPER derived based on our CT method can well approximate the exact SPER with a much lower complexity; (2) the PANC scheme with power level optimizations and power scaling factor design can achieve full diversity, and obtain a much higher coding gain than the PANC scheme with randomly chosen power levels.

eess.SP↗

arXiv2020

Achieving Optimality in Robust Joint Optimization of Linear Transceiver Design

This paper presents new results on linear transceiver designs in a multiple-input-multiple-output (MIMO) link. By considering the minimal total mean-square error (MSE) criterion, we prove that the robust optimal linear transceiver design has a channel-diagonalizing structure, which verifies the conjecture in the previous work \cite{JW_2011}. Based on this property, the original design problem can be transformed into a scalar problem, whose global optimal solution is first obtained in this work. Simulation results show the performance advantages of our solution over the existing schemes.

eess.SP↗

arXiv2020

Robust Joint Source-Relay-Destination Design Under Per-antenna Power Constraints

This paper deals with joint source-relay-destination beamforming (BF) design for an amplify-and-forward (AF) relay network. Considering the channel state information (CSI) from the relay to the destination is imperfect, we first aim to maximize the worst case received SNR under per-antenna power constraints. The associated optimization problem is then solved in two steps. In the first step, by revealing the rank-one property of the optimal relay BF matrix, we establish the semi-closed form solution of the joint optimal BF design that only depends on a vector variable. Based on this result, in the second step, we propose a low-complexity iterative algorithm to obtain the remaining unknown variable. We also study the problem for minimizing the maximum per-antenna power at the relay while ensuring a received signal-to-noise ratio (SNR) target, and show that it reduces to the SNR maximization problem. Thus the same methods can be applied to solve it. The differences between our result and the existing related work are also discussed in details. In particular, we show that in the perfect CSI case, our algorithm has the same performance but with much lower cost of computational complexity than the existing method. Finally, in the simulation part, we investigate the impact of imperfect CSI on the system performance to verify our analysis.

eess.SP↗

arXiv2020

Achieving Global Optimality for Joint Source and Relay Beamforming Design in Two-Hop Relay Channels

This paper deals with joint source and relay beamforming (BF) design for an amplify-and-forward (AF) multi-antenna multirelay network. Considering that the channel state information (CSI) from relays to destination is imperfect, we aim to maximize the worst case received signal-to-noise ratio (SNR). The associated optimization problem is then solved in two steps. In the first step, by fixing the source BF vector, a semi-closed form solution of the relay BF matrices is obtained, up to a power allocation factor. In the second step, the global optimal source BF vector is obtained based on the Polyblock outer Approximation (PA) algorithm. We also propose two low-complexity methods for obtaining the source BF vector, which are different in their complexities and performances. The optimal joint source-relay BF solution obtained by the proposed algorithms serves as the benchmark for evaluating the existing schemes and the proposed low-complexity methods. Simulation results show that the proposed robust design can significantly reduce the sensitivity of the channel uncertainty to the system performance.

eess.SP↗

arXiv2020

Green MU-MIMO/SIMO Switching for Heterogeneous Delay-aware Services with Constellation Optimization

In this paper, we propose adaptive techniques for multi-user multiple input and multiple output~(MU-MIMO) cellular communication systems, to solve the problem of energy efficient communications with heterogeneous delay-aware traffic. In order to minimize the total transmission power of the MU-MIMO, we investigate the relationship between the transmission power and the M-ary quadrature amplitude modulation~(MQAM) constellation size and get the energy efficient modulation for each transmission stream based on the minimum mean square error~(MMSE) receiver.Since the total power consumption is different for MU-MIMO and multi-user single input and multiple output~(MU-SIMO), by exploiting the intrinsic relationship among the total power consumption model, and heterogeneous delay-aware services, we propose an adaptive transmission strategy, which is a switching between MU-MIMO and MU-SIMO. Simulations show that in order to maximize the energy efficiency and consider different Quality of Service (QoS) of delay for the users simultaneously, the users should adaptively choose the constellation size for each stream as well as the transmission mode.

eess.SP↗

arXiv2020

Towrad 5G Air Interface Technology: Sparse Code Muliple Access

The fifth generation wireless networks focus on the design of low latency, high data rate, high reliability, and massive connectivity communications. Non-orthogonal multiple access (NOMA) is an essential enabling technology to accommodate the wide range of communication requirements. By coordinating the massive devices within the same resource block on power domain, frequency domain or code domain, NOMA is superior to conventional orthogonal multiple access in terms of the network connectivity, the throughputs of system and etc. Sparse code multiple access (SCMA) is a kind of multi-carrier code domain NOMA and has been studied extensively. The challenges for designing a high quality SCMA system is to seek the feasible encoding and decoding schemes to meet the desired requirements. In this article, we present some recent progresses towards the design of multi-dimensional codebooks, the practical low complexity decoder, as well as the Grant-Free multiple access for SCMA system. In particular, we show how the SCMA codebooks construction are motived by the combined design of multi-dimensional constellation and factor graphs. In addition, various low complexity SCMA decoders are also reviewed with a special focus on sphere decoding. Moreover, based on the framework of belief propagation, the SCMA Grant-Free transmission is introduced and the problem of collision resolution is also discussed.

eess.SP↗

arXiv2020

Naive Gabor Networks for Hyperspectral Image Classification

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set.

eess.IV↗

arXiv2020

On Safeguarding Privacy and Security in the Framework of Federated Learning

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.

cs.NI↗

arXiv2020

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL aided design.

eess.SY↗

arXiv2020

Concurrent probing of electron-lattice dephasing induced by photoexcitation in 1T-TaSeTe using ultrafast electron diffraction

It has been technically challenging to concurrently probe the electrons and the lattices in materials during non-equilibrium processes, allowing their correlations to be determined. Here, in a single set of ultrafast electron diffraction patterns taken on the charge-density-wave (CDW) material 1T-TaSeTe, we discover a temporal shift in the diffraction intensity measurements as a function of scattering angle. With the help of dynamic models and theoretical calculations, we show that the ultrafast electrons probe both the valence-electron and lattice dynamic processes, resulting in the temporal shift measurements. Our results demonstrate unambiguously that the CDW is not merely a result of the periodic lattice deformation ever-present in 1T-TaSeTe but has significant electronic origin. This method demonstrates a novel approach for studying many quantum effects that arise from electron-lattice dephasing in molecules and crystals for next-generation devices.

cond-mat.mtrl-sci↗

arXiv2020

A Systematic Study of the dust of Galactic Supernova Remnants I. The Distance and the Extinction

By combining the photometric, spectroscopic, and astrometric information of the stars in the sightline of SNRs, the distances to and the extinctions of 32 Galactic supernova remnants (SNRs) are investigated. The stellar atmospheric parameters are from the SDSS$-$DR14$/$APOGEE and LAMOST$-$DR5$/$LEGUE spectroscopic surveys. The multi-band photometry, from optical to infrared, are collected from the {\it Gaia}, APASS, Pan--STARRS1, 2MASS, and {\it WISE} surveys. With the calibrated {\it Gaia} distances of individual stars, the distances to 15 of 32 SNRs are well determined from their produced extinction and association with molecular clouds. The upper limits of distance are derived for 3 SNRs. The color excess ratios $E(g_{\rm P1}-λ) / E(g_{\rm P1}-r_{\rm P1})$ of 32 SNRs are calculated, and their variation with wavebands is fitted by a simple dust model. The inferred dust grain size distribution bifurcates: while the graphite grains have comparable size to the average ISM dust, the silicate grains are generally larger. Along the way, the average extinction law from optical to near-infrared of the Milky Way is derived from the 1.3 million star sample and found to agree with the CCM89 law with $R_{\rm V}=3.15$.

astro-ph.SR↗

arXiv2020

A Robust Stochastic Method of Estimating the Transmission Potential of 2019-nCoV

The recent outbreak of a novel coronavirus (2019-nCoV) has quickly evolved into a global health crisis. The transmission potential of 2019-nCoV has been modelled and studied in several recent research works. The key factors such as the basic reproductive number, $R_{0}$, of the virus have been identified by fitting contagious disease spreading models to aggregated data. The data include the reported cases both within China and in closely connected cities over the world. In this paper, we study the transmission potential of 2019-nCoV from the perspective of the robustness of the statistical estimation, in light of varying data quality and timeliness in the initial stage of the outbreak. Sample consensus algorithm has been adopted to improve model fitting when outliers are present. The robust estimation enables us to identify two clusters of transmission models, both are of substantial concern, one with $R_0:8\sim14$, comparable to that of measles and the other dictates a large initial infected group.

q-bio.PE↗