arXiv subjects

Jinming Wen

Publications and source records attributed to Jinming Wen.

At least 37 records · Page 2Linked to original sources

arXiv2019

QoS-Aware Buffer-Aided Relaying Implant WBAN for Healthcare IoT: Opportunities and Challenges

Internet of Things (IoT) have motivated a paradigm shift in the development of various applications such as mobile health. Wireless body area network (WBAN) comprises many low-power devices in, on, or around the human body, which offers a desirable solution to monitor physiological signals for mobile-health applications. In the implant WBAN, an implant medical device transmits its measured biological parameters to a target hub with the help of at least one on-body device(s) to satisfy its strict requirements on size, quality of service (QoS, e.g., reliability), and power consumption. In this article, we first review the recent advances of conventional cooperative WBAN. Afterwards, to address the drawbacks of the conventional cooperative WBAN, a QoS-aware buffer-aided relaying framework is proposed for the implant WBAN. In the proposed framework, hierarchical modulations are considered to fulfill the different QoS requirements of different sensor data from an implant medical device. We further conceive some new transmission strategies for the buffer-aided signal-relay and multi-relay implant WBANs. Simulation results show that the proposed cooperative WBAN provides better performance than the conventional cooperative counterparts. Finally, some open research challenges regarding the buffer-aided multi-relay implant WBAN are pointed out to inspire more research activities.

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arXiv2018

A New Analysis for Support Recovery with Block Orthogonal Matching Pursuit

Compressed Sensing (CS) is a signal processing technique which can accurately recover sparse signals from linear measurements with far fewer number of measurements than those required by the classical Shannon-Nyquist theorem. Block sparse signals, i.e., the sparse signals whose nonzero coefficients occur in few blocks, arise from many fields. Block orthogonal matching pursuit (BOMP) is a popular greedy algorithm for recovering block sparse signals due to its high efficiency and effectiveness. By fully using the block sparsity of block sparse signals, BOMP can achieve very good recovery performance. This paper proposes a sufficient condition to ensure that BOMP can exactly recover the support of block $K$-sparse signals under the noisy case. This condition is better than existing ones.

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arXiv2018

An Efficient Optimal Algorithm for the Successive Minima Problem

In many applications including integer-forcing linear multiple-input and multiple-output (MIMO) receiver design, one needs to solve a successive minima problem (SMP) on an $n$-dimensional lattice to get an optimal integer coefficient matrix $\A^\star\in \mathbb{Z}^{n\times n}$. In this paper, we first propose an efficient optimal SMP algorithm with an $\bigO(n^2)$ memory complexity. The main idea behind the new algorithm is it first initializes with a suitable suboptimal solution, which is then updated, via a novel algorithm with only $\bigO(n^2)$ flops in each updating, until $\A^{\star}$ is obtained. Different from existing algorithms which find $\A^\star$ column by column through using a sphere decoding search strategy $n$ times, the new algorithm uses a search strategy once only. We then rigorously prove the optimality of the proposed algorithm. Furthermore, we theoretically analyze its complexity. In particular, we not only show that the new algorithm is $Ω(n)$ times faster than the most efficient existing algorithm with polynomial memory complexity, but also assert that it is even more efficient than the most efficient existing algorithm with exponential memory complexity. Finally, numerical simulations are presented to illustrate the optimality and efficiency of our novel SMP algorithm.

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arXiv2018

Closed-Form Word Error Rate Analysis for Successive Interference Cancellation Decoders

We consider the estimation of an integer vector $\hbx\in \mathbb{Z}^n$ from the linear observation $\y=\A\hbx+\v$, where $\A\in\mathbb{R}^{m\times n}$ is a random matrix with independent and identically distributed (i.i.d.) standard Gaussian $\mathcal{N}(0,1)$ entries, and $\v\in \mathbb{R}^m$ is a noise vector with i.i.d. $\mathcal{N}(0,σ^2 )$ entries with given $σ$. In digital communications, $\hbx$ is typically uniformly distributed over an $n$-dimensional box $\mathcal{B}$. For this estimation problem, successive interference cancellation (SIC) decoders are popular due to their low complexity, and a detailed analysis of their word error rates (WERs) is highly useful. In this paper, we derive closed-form WER expressions for two cases: (1) $\hbx\in \mathbb{Z}^n$ is fixed and (2) $\hbx$ is uniformly distributed over $\mathcal{B}$. We also investigate some of their properties in detail and show that they agree closely with simulated word error probabilities.

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arXiv2018

On the KZ Reduction

The Korkine-Zolotareff (KZ) reduction is one of the often used reduction strategies for lattice decoding. In this paper, we first investigate some important properties of KZ reduced matrices. Specifically, we present a linear upper bound on the Hermit constant which is around $\frac{7}{8}$ times of the existing sharpest linear upper bound, and an upper bound on the KZ constant which is {\em polynomially} smaller than the existing sharpest one. We also propose upper bounds on the lengths of the columns of KZ reduced matrices, and an upper bound on the orthogonality defect of KZ reduced matrices which are even {\em polynomially and exponentially} smaller than those of boosted KZ reduced matrices, respectively. Then, we derive upper bounds on the magnitudes of the entries of any solution of a shortest vector problem (SVP) when its basis matrix is LLL reduced. These upper bounds are useful for analyzing the complexity and understanding numerical stability of the basis expansion in a KZ reduction algorithm. Finally, we propose a new KZ reduction algorithm by modifying the commonly used Schnorr-Euchner search strategy for solving SVPs and the basis expansion method proposed by Zhang {\em et al.} Simulation results show that the new KZ reduction algorithm is much faster and more numerically reliable than the KZ reduction algorithm proposed by Zhang {\em et al.}, especially when the basis matrix is ill conditioned.

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arXiv2018

Effects of Some Lattice Reductions on the Success Probability of the Zero-Forcing Decoder

Zero-forcing (ZF) decoder is a commonly used approximation solution of the integer least squares problem which arises in communications and many other applications. Numerically simulations have shown that the LLL reduction can usually improve the success probability $P_{ZF}$ of the ZF decoder. In this paper, we first rigorously show that both SQRD and V-BLAST, two commonly used lattice reductions, have no effect on $P_{ZF}$. Then, we show that LLL reduction can improve $P_{ZF}$ when $n=2$, we also analyze how the parameter $δ$ in the LLL reduction affects the enhancement of $P_{ZF}$. Finally, an example is given which shows that the LLL reduction decrease $P_{ZF}$ when $n\geq3$.

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arXiv2018

Sharp Sufficient Conditions for Stable Recovery of Block Sparse Signals by Block Orthogonal Matching Pursuit

In this paper, we use the block orthogonal matching pursuit (BOMP) algorithm to recover block sparse signals $\x$ from measurements $\y=\A\x+\v$, where $\v$ is an $\ell_2$-bounded noise vector (i.e., $\|\v\|_2\leq ε$ for some constant $ε$). We investigate some sufficient conditions based on the block restricted isometry property (block-RIP) for exact (when $\v=\0$) and stable (when $\v\neq\0$) recovery of block sparse signals $\x$. First, on the one hand, we show that if $\A$ satisfies the block-RIP with $δ_{K+1}<1/\sqrt{K+1}$, then every block $K$-sparse signal $\x$ can be exactly or stably recovered by BOMP in $K$ iterations. On the other hand, we show that, for any $K\geq 1$ and $1/\sqrt{K+1}\leq δ<1$, there exists a matrix $\A$ satisfying the block-RIP with $δ_{K+1}=δ$ and a block $K$-sparse signal $\x$ such that BOMP may fail to recover $\x$ in $K$ iterations. Then, we study some sufficient conditions for recovering block $α$-strongly-decaying $K$-sparse signals. We show that if $\A$ satisfies the block-RIP with $δ_{K+1}<\sqrt{2}/2$, then every $α$-strongly-decaying block $K$-sparse signal can be exactly or stably recovered by BOMP in $K$ iterations under some conditions on $α$. Our newly found sufficient condition on the block-RIP of $\A$ is less restrictive than that for $\ell_1$ minimization for this special class of sparse signals. Furthermore, for any $K\geq 1$, $α>1$ and $\sqrt{2}/2\leq δ<1$, the recovery of $\x$ may fail in $K$ iterations for a sensing matrix $\A$ which satisfies the block-RIP with $δ_{K+1}=δ$. Finally, we study some sufficient conditions for partial recovery of block sparse signals. Specifically, if $\A$ satisfies the block-RIP with $δ_{K+1}<\sqrt{2}/2$, then BOMP is guaranteed to recover some blocks of $\x$ if these blocks satisfy a sufficient condition.

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arXiv2018

Some Properties of Successive Minima and Their Applications

A lattice is a set of all the integer linear combinations of certain linearly independent vectors. One of the most important concepts on lattice is the successive minima which is of vital importance from both theoretical and practical applications points of view. In this paper, we first study some properties of successive minima and then employ some of them to improve the suboptimal algorithm for solving an optimization problem about maximizing the achievable rate of the integer-forcing strategy for cloud radio access networks in [1].

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arXiv2018

SMDP-based Downlink Packet Scheduling Scheme for Solar Energy Assisted Heterogeneous Networks

Renewable energy assisted heterogeneous networks can improve system capacity and reduce conventional energy consumption. In this paper, we propose a semi-Markov decision process (SMDP)-based downlink packet scheduling scheme for solar energy assisted heterogeneous networks (HetNets), where solar radiation is modeled as a continuous-time Markov chain (CTMC) and the arrivals of multi-class downlink packets are modeled as Poisson processes. The proposed downlink packet scheduling scheme can be compatible with the mainstream wireless packet networks such as long-term evolution (LTE) networks and the fifth-generation (5G) networks because the SMDP is a real-time admission control model. To obtain an asymptotically optimal downlink packet scheduling policy, we solve the semi-Markov decision problem using the relative value iteration algorithm under average criterion and the value iteration algorithm under discounted criterion, respectively. The simulation results show that the average cost of the SMDP-based packet scheduling scheme is less than that of the greedy packet scheduling scheme.

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arXiv2017

A Sharp Condition for Exact Support Recovery of with Orthogonal Matching Pursuit

Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied. In this paper, we show that for any $K$-sparse signal $\x$, if a sensing matrix $\A$ satisfies the restricted isometry property (RIP) with restricted isometry constant (RIC) $δ_{K+1} < 1/\sqrt {K+1}$, then under some constraints on the minimum magnitude of nonzero elements of $\x$, OMP exactly recovers the support of $\x$ from its measurements $\y=\A\x+\v$ in $K$ iterations, where $\v$ is a noise vector that is $\ell_2$ or $\ell_{\infty}$ bounded. This sufficient condition is sharp in terms of $δ_{K+1}$ since for any given positive integer $K$ and any $1/\sqrt{K+1}\leq δ<1$, there always exists a matrix $\A$ satisfying the RIP with $δ_{K+1}=δ$ for which OMP fails to recover a $K$-sparse signal $\x$ in $K$ iterations. Also, our constraints on the minimum magnitude of nonzero elements of $\x$ are weaker than existing ones. Moreover, we propose worst-case necessary conditions for the exact support recovery of $\x$, characterized by the minimum magnitude of the nonzero elements of $\x$.

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arXiv2017

Nearly Optimal Bounds for Orthogonal Least Squares

In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On the one hand, we show that if the sampling matrix $\mathbf{A}$ satisfies the restricted isometry property (RIP) of order $K + 1$ with isometry constant $$ δ_{K + 1} < \frac{1}{\sqrt{K+1}}, $$ then OLS exactly recovers the support of any $K$-sparse vector $\mathbf{x}$ from its samples $\mathbf{y} = \mathbf{A} \mathbf{x}$ in $K$ iterations. On the other hand, we show that OLS may not be able to recover the support of a $K$-sparse vector $\mathbf{x}$ in $K$ iterations for some $K$ if $$ δ_{K + 1} \geq \frac{1}{\sqrt{K+\frac{1}{4}}}. $$

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arXiv2017

GfcLLL: A Greedy Selection Based Approach for Fixed-Complexity LLL Reduction

The LLL lattice reduction has been widely used to decrease the bit error rate (BER) of the Babai point, but its running time varies much from matrix to matrix. To address this problem, some fixed-complexity LLL reductions (FCLLL) have been proposed. In this paper, we propose two greedy selection based FCLLL algorithms: GfcLLL(1) and GfcLLL(2). Simulations show that both of them give Babai points with lower BER in similar or much shorter CPU time than existing ones.

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arXiv2017

On the Success Probability of the Box-Constrained Rounding and Babai Detectors

In communications, one frequently needs to detect a parameter vector $\hbx$ in a box from a linear model. The box-constrained rounding detector $\x^\sBR$ and Babai detector $\x^\sBB$ are often used to detect $\hbx$ due to their high probability of correct detection, which is referred to as success probability, and their high efficiency of implimentation. It is generally believed that the success probability $P^\sBR$ of $\x^\sBR$ is not larger than the success probability $P^\sBB$ of $\x^\sBB$. In this paper, we first present formulas for $P^\sBR$ and $P^\sBB$ for two different situations: $\hbx$ is deterministic and $\hbx$ is uniformly distributed over the constraint box. Then, we give a simple example to show that $P^\sBR$ may be strictly larger than $P^\sBB$ if $\hbx$ is deterministic, while we rigorously show that $P^\sBR\leq P^\sBB$ always holds if $\hbx$ is uniformly distributed over the constraint box.

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arXiv2016

A Novel Sufficient Condition for Generalized Orthogonal Matching Pursuit

Generalized orthogonal matching pursuit (gOMP), also called orthogonal multi-matching pursuit, is an extension of OMP in the sense that $N\geq1$ indices are identified per iteration. In this paper, we show that if the restricted isometry constant (RIC) $δ_{NK+1}$ of a sensing matrix $\A$ satisfies $δ_{NK+1} < 1/\sqrt {K/N+1}$, then under a condition on the signal-to-noise ratio, gOMP identifies at least one index in the support of any $K$-sparse signal $\x$ from $\y=\A\x+\v$ at each iteration, where $\v$ is a noise vector. Surprisingly, this condition does not require $N\leq K$ which is needed in Wang, \textit{et al} 2012 and Liu, \textit{et al} 2012. Thus, $N$ can have more choices. When $N=1$, it reduces to be a sufficient condition for OMP, which is less restrictive than that proposed in Wang 2015. Moreover, in the noise-free case, it is a sufficient condition for accurately recovering $\x$ in $K$ iterations which is less restrictive than the best known one. In particular, it reduces to the sharp condition proposed in Mo 2015 when $N=1$.

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arXiv2016

Joint Antenna Selection and Spatial Switching for Energy Efficient MIMO SWIPT System

In this paper, we investigate joint antenna selection and spatial switching (SS) for quality-of-service (QoS)-constrained energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigen-channel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigen-channel assignment and power allocation, and low-complexity multi-objective optimization (MOO)-based approach. On the other hand, the number of active receive antennas induces a trade-off in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

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arXiv2016

Success probability of the Babai estimators for box-constrained integer linear models

In many applications including communications, one may encounter a linear model where the parameter vector $\hbx$ is an integer vector in a box. To estimate $\hbx$, a typical method is to solve a box-constrained integer least squares (BILS) problem. However, due to its high complexity, the box-constrained Babai integer point $\x^\sBB$ is commonly used as a suboptimal solution. In this paper, we first derive formulas for the success probability $P^\sBB$ of $\x^\sBB$ and the success probability $P^\sOB$ of the ordinary Babai integer point $\x^\sOB$ when $\hbx$ is uniformly distributed over the constraint box. Some properties of $P^\sBB$ and $P^\sOB$ and the relationship between them are studied. Then, we investigate the effects of some column permutation strategies on $¶^\sBB$. In addition to V-BLAST and SQRD, we also consider the permutation strategy involved in the LLL lattice reduction, to be referred to as LLL-P. On the one hand, we show that when the noise is relatively small, LLL-P always increases $P^\sBB$ and argue why both V-BLAST and SQRD often increase $P^\sBB$; and on the other hand, we show that when the noise is relatively large, LLL-P always decreases $P^\sBB$ and argue why both V-BLAST and SQRD often decrease $P^\sBB$. We also derive a column permutation invariant bound on $P^\sBB$, which is an upper bound and a lower bound under these two opposite conditions, respectively. Numerical results demonstrate our findings. Finally, we consider a conjecture concerning $\x^\sOB$ proposed by Ma et al. We first construct an example to show that the conjecture does not hold in general, and then show that it does hold under some conditions.

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arXiv2016

A Quadratic Programming Relaxation Approach to Compute-and-Forward Network Coding Design

Using physical layer network coding, compute-and-forward is a promising relaying scheme that effectively exploits the interference between users and thus achieves high rates. In this paper, we consider the problem of finding the optimal integer-valued coefficient vector for a relay in the compute-and-forward scheme to maximize the computation rate at that relay. Although this problem turns out to be a shortest vector problem, which is suspected to be NP-hard, we show that it can be relaxed to a series of equality-constrained quadratic programmings. The solutions of the relaxed problems serve as real-valued approximations of the optimal coefficient vector, and are quantized to a set of integer-valued vectors, from which a coefficient vector is selected. The key to the efficiency of our method is that the closed-form expressions of the real-valued approximations can be derived with the Lagrange multiplier method. Numerical results demonstrate that compared with the existing methods, our method offers comparable rates at an impressively low complexity.

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arXiv2016

An Efficient Algorithm for Optimally Solving a Shortest Vector Problem in Compute-and-Forward Protocol Design

We consider the problem of finding the optimal coefficient vector that maximizes the computation rate at a relay in the compute-and-forward scheme. Based on the idea of sphere decoding, we propose a highly efficient algorithm that finds the optimal coefficient vector. First, we derive a novel algorithm to transform the original quadratic form optimization problem into a shortest vector problem (SVP) using the Cholesky factorization. Instead of computing the Cholesky factor explicitly, the proposed algorithm realizes the Cholesky factorization with only $\bigO(n)$ flops by taking advantage of the structure of the Gram matrix in the quadratic form. Then, we propose some conditions that can be checked with $\bigO(n)$ flops, under which a unit vector is the optimal coefficient vector. Finally, by taking into account some useful properties of the optimal coefficient vector, we modify the Schnorr-Euchner search algorithm to solve the SVP. We show that the estimated average complexity of our new algorithm is $\bigO(n^{1.5}P^{0.5})$ flops for i.i.d. Gaussian channel entries with SNR $P$ based on the Gaussian heuristic. Simulations show that our algorithm is not only much more efficient than the existing ones that give the optimal solution, but also faster than some best known suboptimal methods. Besides, we show that our algorithm can be readily adapted to output a list of $L$ best candidate vectors for use in the compute-and-forward design. The estimated average complexity of the resultant list-output algorithm is $\bigO\left(n^{1.5}P^{0.5}\log L + nL\right)$ flops for i.i.d. Gaussian channel entries.

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