DLP 4D-Printing of Remotely,Modularly, and Selectively Controllable Shape Memory Polymer Nanocomposites Embedding Carbon Nanotubes

An in-depth investigation on novel electro-activated shape memory polymer composites (SMPCs) for digital light processing 3D-Printing, consisting of a poly(ethylene glycol) diacrylate/poly(hydroxyethyl methacrylate) matrix embedding multi-walled carbon nanotubes (CNTs), is reported here. The composition of the photocurable (meth)acrylate system is finely tuned to tailor the thermomechanical properties of the matrix, whereas the effect of CNTs on the photoreactivity and rheological properties of the formulations is investigated to assess the printability. Electrical measurements confirmed that the incorporation of CNT into the polymeric matrix enables the electrical conductivity and thus the possibility to remotely heat the nanocomposite using the Joule effect. The feasibility to drive a shape memory cycle via Joule heating is proved, given that the high shape fixity (R_f) and shape recovery (R_r) ratios achieved (R_f ≈ 100%, R_r > 95%) confirmed the significant electrically-triggered responsiveness of such CNT/SMPCs. Finally, it is shown how to activate a modular and selective electro-activated shape recovery, which may ultimately envisage the 4D-Printing of remotely and selectively controllable smart devices.     

Structure-Based Low Complexity MMSE Channel Estimator for OFDM Wireless Systems

Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transmissions are capable of delivering high data rates over multipath frequency selective channels. This paper deals with joint estimation/interpolation of wireless channel using pilot symbols transmitted concurrently with the data. We propose a low complexity, spectrally efficient minimum mean square error channel estimator which exploits the correlation structure of channel frequency response for reducing the complexity. Specifically, it is shown that if pilots are inserted appropriately across OFDM subcarriers, the proposed algorithm requires no matrix inversion, thereby significantly relieving the computational burden without deteriorating the performance. Moreover, the knowledge of channel correlation is also not required for the proposed estimator. Simulation results validate that the proposed technique outperforms existing low-complexity variants in terms of mean square error and computational complexity.

     

Three-phase two-way relaying with imperfect channel estimation and asymmetric traffic requirements: performance analysis and optimization

We study the effect of imperfect channel estimation (ICE) and asymmetric traffic requirements (ATRs) on the performance of bidirectional relaying with a direct link by employing three-phase analog network coding under Nakagami-m fading. Under such a realistic scenario, a tight lower bound on the overall outage probability is derived in closed-form, while a useful expression is presented for the asymptotically low outage regime. We also deduce the tight closed-form expression for the ergodic sum-rate. Furthermore, we formulate and solve analytically three optimization problems viz., relay power allocation under fixed location of the relay, relay position with fixed relay power allocation, and joint optimization of relay power allocation and location. Our results reveal that for given ICE, the optimal relay location offers significant system performance enhancement under ATRs, whilst the optimal relay power allocation has a more noticeable impact under symmetric traffic. It is also shown that the joint optimization of relay power allocation and location can further enhance the system performance, regardless of ATRs and ICE. Above all, based on the direct link quality, we show that the considered scheme outperforms its two-phase counterpart, even in the low signal-to-noise ratio regime.

     

Novel semi-blind channel estimation schemes for Rician fading MIMO channel

In this paper, we propose two novel semi-blind channel estimation techniques based on QR decomposition for Rician fading Multiple Input Multiple Output (MIMO) channel. In the first technique, the MIMO channel matrix H can be decomposed as an upper triangular matrix R and unitary rotation matrix Q as H = RQ. The matrix R is estimated blindly from only received data by using the orthogonal matrix triangularization based Householder QR decomposition, while the optimum rotation matrix Q is estimated exclusively from the algorithm of Orthogonal Pilot Maximum Likelihood Estimator (OPML) based on pilot information. In the second technique, the joint semi-blind channel and data estimation is performed by using the Least Square (LS) algorithm based on QR decomposition. The simulation results obtained for 4-PSK data modulation scheme using two transmitters and six receiver antennas for different Rice factor (K) have shown that the BER performance increases with an increase in the Rice factor. Finally, we compare these two new techniques with the conventional semi-blind channel estimation technique based on Whitening Rotation (WR), and the results show that the first proposed technique outperforms and the second technique achieves a very nearby performance as compared to the technique based on whitening rotation.     

HTRCI and Channel Ranking Based Joint Symbols Detection for MQRD-PCM/MIMO-OFDM

MIMO-OFDM is considered a key technology in emerging high-data rate systems. In MIMO-OFDM systems, channel estimation and signal detection are important to distinguish transmit signals from multiple transmit antennas. Previously, we have proposed a parallel detection algorithm using multiple QR decompositions (Q is an orthogonal square matrix, R is upper triangular matrix) with permuted channel matrix (MQRD-PCM) to reduce the system complexity of MIMO-OFDM. This method achieves a good BER performance with a low system complexity. However, since MQRD-PCM is a kind of parallel detection method, the wrong detection probability is increased due to the bad channel signal-to-interference plus noise power ratio (SINR) of the transmitted signals. As a result, the average BER performance is influenced by the wrong detection probability of the bad channel SINR. To overcome the above-mentioned problems, in this paper, we propose the high time resolution carrier interferometry and channel ranking based joint signal detection for MQRD-PCM/MIMO-OFDM.     

Joint Estimation of Doubly Selective Channels and Carrier Frequency Offsets in High Mobility MIMO-OFDMA Uplink

In this paper, we address the joint estimation of doubly selective channels (DSCs) and carrier frequency offsets (CFOs) in multiple input multiple output orthogonal frequency division multiple access uplink with highly mobile users. Since the channel coefficients are rapidly varying over time and the base station has to perform the estimation task from the received composite signal, the exact solution to this joint estimation problem requires multidimensional search which is computationally intensive. We propose an iterative technique for the joint estimation of DSCs and CFOs based on space alternating generalized expectation maximization algorithm which will decompose the multidimensional optimization to many one dimensional searches. The proposed method works even in the presence of residual timing offsets and it does not require the knowledge of channel statistics at the receiver. Convergence properties of the proposed algorithm in terms of rate matrix is studied and analytically proved that the proposed joint estimation algorithm converges. Simulation studies illustrate that the proposed technique offers good performance even at very high mobile speeds.

     

A smart spectrum utilization approach using multiantenna‐based cognitive relays in cognitive radio network

Efficient spectrum utilization is a promising technique for a prolonged unused radio frequency (RF) spectrum in a wireless network. In this paper, an adaptive spectrum sharing cognitive radio (CR) network has been proposed consisting of a primary user (PU) and secondary user transmitter (SU ? Tx) that communicates with secondary user receiver (SU ? Rx) via multiantenna‐based proactive decode‐and‐forward (DF) relay selection scheme. In our model, strategically an adaptable joint venture on underlay/overlay protocol is defined based on channel occupancy using spectrum sensing technique. Here, secondary transmitters (i.e., source transmitter) continuously sense the PU activities by energy detector and can simultaneously transmit to secondary receivers. Depending on sensing result secondary transmitters automatically switches in underlay mode if PU is active otherwise operates in overlay mode. The advantage of this scheme is that the joint mode of transmission allows the SUs to maximize their transmission rate. The outage performance at SU ? Rx and closed‐form expressions of joint underlay/overlay protocol has been evaluated. The power control policies at different transmitter nodes are taken care of. With the same diversity order, a trade‐off between multiantenna and multirelay is shown. This comparison shows improvement in outage behavior when the count in relays surpasses the number of antennas. Finally, the analytical model of smart efficient spectrum utilization without harming license users in CR is validated by MATLAB simulation.     

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Several approximation approaches including the Gauss–Seidel (GS) method have been proposed to reduce the complexity of matrix inversion for zero-forcing pre-coding in massive multiple-input–multiple-output systems. However, extra computation is required to obtain the matrix inversion from the iteration result of the GS method. In this paper, we propose a new GS-based matrix inversion approximation (GSBMIA) approach. Unlike the traditional GS method, the GSBMIA approach approximates the matrix inversion, which will simplify further calculations. Furthermore, in order to speed up convergence, we propose a joint algorithm based on the GSBMIA and Newton iteration method where the GSBMIA approach is employed to provide an efficient searching direction for the following Newton iterations. Compared with other approximation methods, the joint algorithm can accommodate more single antenna users for the same base station antenna number. Simulation results demonstrate that the joint algorithm and the GSBMIA approach converge faster than the Neumann series and Newton iteration method.

     

Performance of a MANET directional MAC protocol with angle‐of‐arrival estimation

The use of directional antennas in mobile ad hoc networks (MANETs) has shown to offer large throughput gains relative to omnidirectional antennas. When used in ad hoc networks, directional medium‐access‐control (DMAC) protocols usually require all nodes, or part of nodes, to be aware of their exact locations. This location information is typically provided using a global positioning system (GPS). Although GPS systems are designed to be as nearly accurate as possible, there are still estimation errors that can cause a relatively large deviation from the actual GPS receiver position. In this paper, we investigate the effect of inaccurate node position estimation on the throughput of these protocols. Our results clearly indicate that the advantages of DMAC protocols diminish if the available position information is not accurate enough. As an alternative, we propose an efficient DMAC protocol that utilizes signal parameter estimation via the rotational invariance technique (ESPRIT) for direction‐of‐arrival (DOA) estimation; alleviating the need for GPS and, hence, avoiding the degrading associated with typical GPS position estimation errors. Moreover, unlike GPS‐based protocols, our protocol is suitable for both outdoor and indoor applications. Under different operating conditions and channel models, our simulation results show the throughput improvement achieved using the proposed protocol relative to the IEEE 802.11. Copyright © 2007 John Wiley & Sons, Ltd.     

A joint TDOA and FDOA estimation algorithm based on second-order cone programming

ABSTRACT

Considering the low estimation accuracy of the traditional interpolation method, this paper, on the basis of second-order cone programming (SOCP), proposes a novel joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation interpolation method, which can attain the sub-sample precision. The proposed method uses several discrete samples produced by cross ambiguity function (CAF) to structure the convex optimization models with regard to the interpolation surface. Then, the SOCP is utilized to obtain the interpolation surface which matches the discrete surface of CAF well. Finally, the method achieves the precision superior to the traditional TDOA and FDOA estimation directly through the search for the maximum of the continuous approximate surface. This method decreases the computational load without loss of precision and can efficiently reduce the limitation of finite sampling interval and sampling time in estimation precision. Numerical simulations show that the method in this paper is efficient and outperforms existing interpolation algorithms about estimation precision.     

基于 GSVD 的分布式 MIMO 雷达测向算法

针对分布式多输入多输出（multi-input multi-output, MIMO）雷达测向中存在的数据信息提取不充分、运算量偏大等问题，开展了基于广义奇异值分解（generalized singular value decomposition, GSVD）的测向算法研究，以提高低信噪比条件下的角度估计性能。首先，建立了分布式阵列MIMO雷达回波信号的统一化表征模型；其次，将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题，运用交替最小二乘（alternating least squares, ALS）技术实现阵列信号流行矩阵的拟合，并引入子空间类算法实现目标角度联合估计；最后，对优化问题增加l1范数约束，避免了每次迭代中进行的奇异值分解运算，降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。     

一种新的MIMO系统空间相关性的估计技术

该文提出了在MIMO系统中一种估计发射及接收天线相关矩阵RT,RR的新算法。算法揭示了对于发射端(接收端)相关矩阵的估计精度和接收端(发射端)的天线的相关度密切有关,而与发射端(接收端)自身天线的相关度无关。此外,该文对估计性能与天线数量的关系也进行了分析。模拟结果表明,在使用约200个训练符号时,相关矩阵中每个元素的平均估计误差在0.05以下,而使用更多的训练符号时,估计精度将会进一步提高。     

Environmental-adaptive indoor radio path loss model for wireless sensor networks localization

In the received signal strength indicator (RSSI) based indoor wireless sensor networks localization system, RSSI measurements are very susceptible to multipath fading, anisotropy of antenna, low supply voltage of node and so on, which will cause the system failure to achieve a high location accuracy. This paper presents an environmental-adaptive path loss model. In the process of localization, the calibrated coefficient LSV of low supply voltage, which can be determined by monitoring the supply voltage of the sender, is used to calibrate ranging errors caused by its low supply voltage. The blind node utilizes the absolute value of RSSI to generate the phase of the corresponding receiver's location so as to determine the correction coefficient of indoor multipath fading R_i. Furthermore, in order to improve the accuracy of RSSI measurements, we also take full consideration of the effect of antenna to accurately determine the corresponding path loss model of the two communication nodes. The proposed path loss model is suitable for the majority of wireless location systems that are on the basis of RSSI-based ranging techniques. Experiment results show that the estimation accuracy and adaptability of the proposed path loss model are significantly higher than that of the traditional one.     

Direct position determination of cyclostationary signals

Direct position determination (DPD) was recently proposed as a new method for passive localization of narrowband radio transmitters. Its superior performance, especially in the low signal-to-noise ratio (SNR) regime, is ascribed to both the joint optimization it performs over all base stations that participate in the estimation process and the direct determination of the emitter position as opposed to conventional methods which typically process the information from the individual base stations separately and determine the location using two estimation steps.In this paper, we introduce the cyclic direct position determination (CDPD) algorithm, a novel method which is the cyclostationary counterpart of the DPD. Despite its name, the CDPD is not a simple extension of the DPD but a unique algorithm which deviates substantially from its ancestor. Yet, the CDPD enjoys both the benefits of the DPD algorithm and the great performance robustness in the presence of narrowband interferences thanks to its cyclostationarity exploitation.     

Optimization Between Estimation Error and Transmit Energy in Cross-Correlation Based Underwater Network Cardinality Estimation

Network cardinality is a very crucial factor to ensure proper functionality of a communications network. Due to the harsh underwater environment, cardinality estimation of underwater wireless communications network using conventional protocol based methods of terrestrial networks is very challenging and inefficient. Hence, a cross-correlation based estimation approach using Gaussian signals is applied for this purpose. To obtain better performance using this estimation approach, it is assumed that the length of the signals is infinity, which is practically impossible because it implies infinite transmit energy. For this reason, it is required to investigate the effect of various signal length (N _s) on estimation performance. In process of achieving this goal, a relation between the N _s and estimation error is derived in this paper. Using this relationship, optimization between estimation error and transmit energy is obtained as N _s is an energy related term in the estimation process.

     

A new method for video data compression by quadratic Bézier curve fitting

This paper presents a new and efficient method for video data compression using quadratic Bézier curve fitting. The method treats the luminance or color variations of a spatial location in a sequence of frames as input points in Euclidean space R ¹ or R ³. The input points are approximated using quadratic Bézier least square fitting. The output data consists of quadratic Bézier control points and difference between original and fitted data. Video data compression is the main application of proposed method. It is shown that entropy of output data is significantly less than the entropy of input data. The method can be applied to 1-D space like luminance and chrominance components separately or 3-D color spaces such as RGB and YC _b C _r .     

基于分簇结构无线传感器网络的高效无源定位方法

针对基于节点通信能力和能量受限的无线传感器网络实现高精度无源定位的问题,首先,在分簇结构网络中,通过折中单个簇的TDOA定位精度和运算复杂度,确定了簇规模;其次,基于直达波环境中TDOA定位误差是按布站GDOP对测量误差放大的原理,提出第一轮定位先使用网内所有节点以RSSI定位方法粗估计目标辐射源位置,并根据各簇在该粗估计位置处的布站GDOP和测量误差估算TDOA定位标准差,第二轮定位选择具有较低TDOA定位估计标准差的部分簇参与TDOA定位,最后将这些簇的TDOA定位结果按估算的定位估计标准差加权平均,求得最终定位解。仿真结果证明该方法有效的去除了冗余节点,实现以半数节点接近使用全部节点的定位精度。      

Linear Chirp Signal DOA Estimation Using Sparse Time–Frequency Dictionary

This paper is concerned with the estimation of the directions-of-arrival (DOA) of multiple linear chirp signals. We construct a novel time-frequency dictionary based on the properties of chirp signals in the fractional Fourier domain, and a sparse reconstruction algorithm is proposed to achieve high performance. Then, the errors resulting from the off-grid model mismatch is considered, and the dictionary matrix is reformulated into a multiplication of a fixed matrix and a sparse matrix. Further, an iterative alternating approach is proposed to improve the accuracy of the DOA estimates. The proposed algorithm provides better estimation, anti-correlation performances and increased resolution than Multiple Signal Classification (MUSIC) and the time–frequency MUSIC (TF-MUSIC) based on the spatial time–frequency distributions. Simulation results demonstrate the effectiveness of the proposed approach.

     

Integrated Swarming Computing Paradigm for Efficient Estimation of Channel Parameters in MIMO System

In this study, optimum channel estimation in MIMO network is investigated by an integrated computing paradigm using Particle Swarm Optimization (PSO) and Nelder Mead Method (NMM). The efficacy of global optimization is exploited through PSO while for fine-tuning of channel coefficients, the strength of NMM as an efficient local optimization technique is utilized. Hybrid swarm intelligence framework is applied for square channel matrix having identical array elements at both transmitter and receiver end. Different signal–noise ratios are applied for analyzing the response of complex received signal from flat fading Additive White Gaussian Noise channel. Numerical simulations are done for evaluating Mean Square Error among true and estimated channel coefficients. Performance analysis is conducted not only for a single run of proposed hybrid swarm intelligence but also on extensive simulations on multiple independent trials to prove the worth of the scheme.

     

Reduced-rank technique for joint channel estimation in TD-SCDMA systems

In time division-synchronous code division multiple access systems, increasing the system capacity by exploiting the inserting of the largest number of users in one time slot (TS) requires adding more estimation processes to estimate the joint channel matrix for the whole system. The increase in the number of channel parameters due the increase in the number of users in one TS directly affects the precision of the estimator's performance. This article presents a novel channel estimation with low complexity, which relies on reducing the rank order of the total channel matrix H. The proposed method exploits the rank deficiency of H to reduce the number of parameters that characterise this matrix. The adopted reduced-rank technique is based on truncated singular value decomposition algorithm. The algorithms for reduced-rank joint channel estimation (JCE) are derived and compared against traditional full-rank JCEs: least squares (LS) or Steiner and enhanced (LS or MMSE) algorithms. Simulation results of the normalised mean square error showed the superiority of reduced-rank estimators. In addition, the channel impulse responses founded by reduced-rank estimator for all active users offers considerable performance improvement over the conventional estimator along the channel window length.