Joint Antenna Selection and Robust Beamforming Design in Multi-cell Distributed Antenna System

Most of studies on Distributed Antenna System （DAS） focus on maximizing the sum capacity and perfect channel state information at transmitter （CSIT）. However, CSI is inevitable imperfect in practical wireless networks. Based on the sources of error, there are two models. One assumes error lies in a bounded region, the other assumes random error. Accordingly, we propose two joint antenna selection （AS） and robust- beamforming schemes aiming to minimize the total transmit power at antenna nodes subject to quality of service （QoS） guarantee for all the mobile users （MUs） in multicell DAS. This problem is mathematically intractable. For the bounded error model, we cast it into a semidefinite program （SDP） using semidefinite relaxation （SDR） and S-procedure. For the second, we first design outage constrained robust beamforming and then formulate it as an SDP based on the Bernstein-type inequality, which we generalize it to the multi-cell DAS. Simulation results verify the effectiveness of the proposed methods.     

Exploring Semantic Association Rules Between Keyword and Visual Feature Clusters for Web Image Retrieval

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback （RF） and Latent semantic indexing （LSI） to alleviate the gap. This paper proposes an approach base on Frequent itemset mining （FIM） and Association rule （AR） techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained oftline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web im- ages. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The exper- iments are carried out in our web image retrieval system named VAST （VisuAl ＆ SemanTic image search）, and the results show the effectiveness of the proposed approach.     

Analysis and Design of Multi-aspect SAR System for Compressive Sensing-Based 3D Imaging

This paper focuses on the analysis and design of Multl-aspect SAR （MuSAR） system for Compressive sensing-based （CS-based） 3D imaging. For this purpose,the Point ambiguous function （PAF） is proposed to analyze the factors that dominate the Mutual coherence （MC） of MuSAR sensing matrix. The PAF contacts with the parameters and configuration of MuSAR system directly and is easy to manipulate. With PAF, the present study analyzes the factors that dominate the performance of CS-based MuSAR 3D imaging. First of all, the stochastic waveform is an excellent selection. Second, the angular-frequency-diversity can improve the robustness of 3D imaging. Finally, the finer sampling of received data could improve the robustness of MuSAR 3D imaging. Simulation experiments show the validity of conclusion.     

Channel Mismatches Correction in Time-Interleaved ADCs Based on Hybrid Filter Bank Reconstruction

Time-interleaved analog-to-digital con- verter （TIADC） is an efficient way to achieve higher sam- pling rate for medium-to-high resolution applications. The performance of a TIADC suffers from the mismatch errors among the sub-channels. This paper presents a method to estimate the channel mismatches using the sub-channels output data. The proposed method introduces an equiv- alent transfer function for each channel to model and es- timate the mismatch errors. A Hybrid filter bank （HFB） structure is used to both model the TIADC and recon- struct the desired uniformly sampled sequence based on the perfect reconstruction conditions of the HFB system. A four-channel 12-bit 400MHz TIADC has been imple- mented in hardware to verify the proposed calibration method. The measured results show that the Spurious- free dynamic range （SFDR） can be improved up to 74dB after being corrected with 64-tap Finite-impulse response （FIR） filters.     

Parameterized Integrated Power and Performance （PIPP） Model for Ultra High-Performance of TOPS level DSP

Amdahl＇s law is a simple and fundamen- tal tool for understanding the evolution of performance as a function of parallelism. Following a recent trend on timing and power analysis of general purpose many-core chip using this law, we develop a novel PIPP analytical model for evaluating the performance and power of hier- archical on-chip large-scale parallel architectures with the core number, super-node size, processing element number, and function unit number taken into consideration. We thereby investigate the influence of workload characteris- tics （Thread-level parallel TLP, Instruction-level parallel ILP and Data-level parallel DLP） on resource allocation with the restriction of performance and power. The re- sults provide some feasible options to design TOPS level DSP architecture as well as a theoretical basis for making the design more scalable.     

The Price of Anarchy of Network Coding and Routing Based on an ACS Pricing Mechanism

To emphasize the decisions of all users, and the total number of users sharing the same technique, we propose a novel Average cost sharing （ACS） pricing mechanism to study the game between Network coding （NC） and routing flows sharing a single link when users are price anticipating. We characterize the worst-case efficiency bounds of the game compared with the optimal （i.e., the Price of anarchy （PoA））, which can be as low as 4/9 when the number of routing users becomes sufficiently large. NC cannot improve the PoA significantly under ACS. However, to achieve a more efficient use of limited resources, this approach indicates the sharing users have a greater tendency to choose NC. However, the users will follow the majority users＇ choice of data transmission technique.     

Unified Maneuvering Model for Target Tracking by Using Range-rate Measurement

A novel Unified maneuvering model （UMM） for tracking the target performed the complex curvilinear motion in two dimensions is presented, where the model is constructed by time-varying parameters and driven by the along-track and cross-track acceleration in- put, and the range rate plays a key role to update vari- able parameters online. In Cartesian coordinates, by incorporating the cross-track acceleration component which is estimated online by using the range-rate, and the along- track acceleration component which is determined under the assumption of zero-mean first order Markovian process, the proposed UMM exhibits highly self-adjustment capability to compensate the mismatch between the actual motion and the mathematic model adaptively, espe-cially, shows a well capability to approximate the standard Interacting multiple model （IMM） under the situation of complex curvilinear motion and a higher level of the measurement noise. Simulation results validate our theory and show that UMM-based filtering is superior to that using the Current statistic model （CSM）, and has a well approx- imation to IMM, meanwhile, has a low computational load substantially.     

An Enhanced IRC Algorithm for LTE Downlink Receiver in Multi-cell Environment

This paper proposes an enhanced Interfer- ence rejection combining （IRC） algorithm for Long term evolution （LTE） downlink receiver in multi-cell communi= cation systems. In this algorithm, a proper Multiple input multiple output （MIMO） receive method is adopted ac- cording to Generalized likelihood ratio test （GLRT） inter- cell interference detection. Iteration between channel es- timation and data detection is carried out to improve the performance of IRC algorithm. Simulation results show that this proposed algorithm can ei~ectively detect inter- cell interference and improve Block error rate （BLER） performance and channel estimation Mean squared error （MSE） compared to non-iterative IRC algorithm, making it suitable for LTE downlink receiver in multi-cell cellular systems.     

An SEU-Tolerant Approach for Space-Borne Viterbi Decoders

In the space environment, Viterbi decoder implemented on SRAM-based FPGA is sensitive to Single event upsets （SEUs）, which may lead to functional failure of the decoder. Conventional SEU mitigation techniques like modular redundancy could not exploit the characters of Viterbi decoders, therefore could not provide optimized SEU tolerance when the device resource utilization cost is a constraint. Leveraging the properties of the decoding algorithm, three effective mitigation techniques are adopted, including structure optimization, Error detection and correction （EDAC） for Block RAM （BRAM） protection, and Partial triple-modular redundancy （PTMR）, which are applied to the modules of the decoder in accordance with their characteristics. Analysis of effectiveness shows that compared with unmitigated design, the SEU induced failure rate in the proposed SEU tolerant decoder can be reduced to 1/4 at the cost of 61.1% extra resource utilization. Error detec- tion and correction （EDAC）.     

Distributed Compressive Video Sensing with Adaptive Measurements Based on Structural Similarity

This paper presents a Distributed compressive video sensing scheme with Adaptive measurements （DCVS-AM）. In this approach, the key frame in each Group of pictures （GOP） is coded by Compressive sensing （CS） with a fixed measurement rate; whereas other frames in the same GOP are compressed by an adaptive random projection in two stages, yielding the Adaptive compressive sensing （ACS） frames. The first stage uses a small and fixed measurement rate and recovers a coarse version. In the second stage, each coarse-version ACS-frame together with its proceeding and following key frames will go through a joint analysis at the decoder side and the analysis result - Structural similarity （SSIM） that is based on a motion-guided interpolation and calculated in a multilevel discrete wavelet transform domain - is sent back to the encoder side to facilitate a re-sampling of the ACS-frame with an adaptive measurement rate. Experimental results show that our proposed DCVS-AM consistently outperforms the state-of-the-art DCVS with a fixed measurement.     

Multiuser Frequency Synchronization for Interleaved-OFDMA Uplink Systems

This paper investigates the frequency synchronization in the uplink of the Orthogonal frequency- division multiple access （OFDMA） system with interleaved subcarrier assignment. In such a system, one of the key problems is the multiuser frequency synchronization, which focuses on the multiple-parameter estimation of the Carrier frequency offsets （CFOs）. In this paper, we propose a two-stage frequency offset estimation algorithm. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. In addition, a novel CFO-compensation method is presented. Based on the inner signal structure of interleaved-OFDMA uplink, the new scheme uses adaptive beamformer to compensate the CFO of each user and isolate the signals of all users at the same time. Compared to the previously known methods, the proposed algorithm can provide accurate frequency synchronization and data detection without feeding CFO estimates back to active users for frequency adjustment.     

An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model

A new algorithm, the RSEM （Recursive simultaneous equations model） algorithm, is presented for causal structure learning under the LSEM （Linear structural equations model）. The algorithm effectively applies recursive simultaneous equations model to causal structure learning. This paper makes two specific contributions. Firstly, under the assumption that knowing the causal order of the variables, we show that recursive simultaneous equations model can be used for causal structure learning under the LSEM regardless of whether the datasets follow multivariate Gaussian distribution. Secondly, the performance of the RSEM algorithm is compared with the state-of-the-art algorithms on 7 networks. Simulation results show that the RSEM algorithm outperforms existing algorithms in terms of time performance, and has a quite high accuracy for thresholds 0.005 and 0.01.     

A Non-Cyclic Prefixed SFBC-SCFDE System Based on a Recursive Algorithm of Joint Channel Estimation and Data Detection

We propose a non-cyclic prefixed Single carrier frequency-domain equalization （SCFDE） system with Space-frequency block codes （SFBC）. The transmitted signals in proposed system are composed of Singlecarrier （SC） information sequences derived from SFBC and training sequences, both of which have been placed in an alternate order without any Cyclic prefix （CP） before each SC information sequence. Furthermore, a recursire algorithm of Joint channel estimation and data detection （recursive-JCEDD） is proposed for system receiver. Simulation results show that the proposed non-cyclic prefixed SFBC-SCFDE system based on the recursive-JCEDD algorithm has better Bit error rate （BER） performance than traditional cyclic prefixed SFBC-SCFDE, Space-time block-coded SCFDE （STBC-SCFDE） and STBC-OFDM systems with some common channel estimation algorithms.     

Metric-Based and Angle-Rotated Tone Reservation Scheme for PAPR Reduction in OFDM Systems

High Peak-to-average power ratio （PAPR） has been a crucial problem in Orthogonal frequency divi- sion multiplexing （OFDM） systems. In all PAPR reduc- tion schemes, Tone reservation （TR） technology is consid- ered as one of the most promising methods because of no additional distortions no side information, and low imple- mentation cost. For conventional TR approaches, the as- signed value to reserved subcarriers just considers one peak value and this brings peak value up again easily. In this paper, a novel scheme named Metric-based angle-rotated （MBAR） for TR is presented. The scheme employs a met- ric to measure how much each subcarrier contributes to the output signal samples of large magnitude and then subcarriers with the largest positive metrics are selected to reduce PAPR. The simulation results show that when the reserved subcarriers number is 1.46 percent, the PAPR gain of the proposed method can achieve 0.4？dB at least at the probability of 10-3.     

A Novel Architecture of Special Arithmetic Function Unit for Area-Efficient Programmable Vertex Shader

A novel architecture of high precision, floating-point special Arithmetic function unit （SFU） for elementary transcendental functions is presented in this paper to provide area efficiency as well as high performance for programmable vertex shader. From the architecture point of view, the evaluation of quadratic approximation for special functions is performed by sharing the SIMD vector unit in shader architecture to minimize processing latency and to reduce area cost in SFU. An optimized minimax approach is proposed as well to obtain the finite-length and normalized quadratic coefficients for high precision. The experiment result shows that the proposed SFU can significantly reduce area cost and by adopting the proposed SFU, a vertex shader with Transport triggered architecture （TTA） can achieve 15.0% improvement on average in performance/area ratio for various shading benchmarks.     

NSVM Decision Tree for Multi-class Classification

A presented classification algorithm of non- linear Support vector machine （SVM） decision tree extends SVM to non-linear SVM by using kernel functions and calculates the relativity separability measure between classes after non-linear mapping. This method is introduced on the basis of the SVM decision tree. As a result, the iterative error is effectively restrained and the efficiency improved accordingly. Experimental results have shown that, compared with the original SVM decision tree algorithm, the classification rate has increased greatly and the classification time decreased apparently.     

Layered Dynamic Schedulings for BP Decoding of LDPC Codes over GF（q）

A Layered dynamic scheduling （LDS） for Belief-propagation （BP） decoding of LDPC codes over GF（q） is presented, which is derived from the dynamic scheduling for the BP decoding of binary LDPC codes. In order to restrain the LDS from cycling in certain checknodes, a life-index for each check-node is adopted and the optimal value of the life-index is analyzed. Furthermore, in consideration of hardware implementation and decoding latency, a strategy, which allows many more checknodes to be updated in parallel, is introduced. Simulations show that the LDS with life-index speeds up the convergence rate and greatly improves the performance of the BP decoding at medium to high signal-to-noise ratio value, and the algorithm employing the LDS with life-index and the new strategy offers good trade-off between the performance and the decoding latency.     

The Modulated Spectra Analysis of Backscattered Field Caused by Rotating Metal Airscrew

High-frequency asymptotic technique is currently the most effective method to work out the electromagnetic scattering of rotating objects. In this letter, the backscattered field of the rotating metal airscrew is investigated based on Physical optics （PO） and Physical theory of diffraction （PTD）, in conjunction with the quasistationary method. Using the Fourier transforms, theory predicted model of the Doppler spectra of the radar return from rotating objects can be founded, so as to predict variety rule of the modulated spectra of radar return signal. Taking the φ -φ polarization as an example, the time and frequency domain responses of radar echo from rotating objects are predicted. By comparing the radar modulated spectra of a rotating target with that of a relatively fixed or nonrotational moving target, some important information can be obtained. These will help to identify targets.     

Amended Truncated Union Bounds on the ML Decoding Performance of Binary Linear Codes over AWGN Channels

In this paper＇s simple upper bounds are derived on the frame and bit error probabilities of binary linear codes over Additive white Gaussian noise （AWGN） channels. The conventional union bound is first truncated and then amended, which can be justified by invoking Gallager＇s first bounding technique （GFBT）. Different from most other works, the ＂good region＂ is specified by a suboptimal list decoding algorithm. The error probability caused by the good region can be upper-bounded by the union bounds using pair-wlse error probability and tripletwise error probability, which can be further tightened by exploiting the independence between the error events and certain components of the receiving signal vector. The proposed bounds are simple since they involve only the Q-function. The proposed bounds improve our recently proposed bounds. Numerical results are also presented to compare the proposed bounds with the Divsalar bound, which is a simple tight upper bound with closed-form, and the Tangential-sphere bound （TSB）, which is considered as one of the tightest upper bounds.     

A Secure Adaptive Routing Strategy Using Immune Mechanism in MANET

Many protocols have been proposed to in- crease efficiency and security of traditional protocols in Mobile ad-hoc networks （MANET）, but they are all facing the problem of computation overhead which leads to im- practicability. Adaptive routing strategy （ARS） is a novel routing strategy that switches routing protocol according to the network condition. Though this method alleviates the problem of efficiency, it doesn＇t cover the secure issues. We integrate our security mechanism based on Artificial immune system （AIS）, with adaptive routing strategy to enhance efficiency and security. First we presented a more appropriate definition to the measurement of network con- dition, then we designed a secure adaptive routing strategy based on the definition. At last, we gave a performance analysis to validate the correctness and reliability of our scheme.