Recently, broadcast services are introduced in cellular networks and macro diversity is an effective way to combat fading. In this paper, we propose a kind of distributed space-time block codes (STBCs) for macro diversity which is constructed from the total antennas of multiple cooperating base stations, and all the antennas form an equivalent multiple input multiple output (MIMO) system. This code is termed High-Dimension-Full-Rate-Quasi-Orthogonal STBC (HDFR-QOSTBC) which can be characterized as: (1) It can be applied with any number of transmit antennas especially when the number of transmit antennas is large; (2) The code is with full transmit rate of one; (3) The Maximum Likelihood (ML) decoding complexity of this code is controllable and limited to Nt/2-symbol-decodable for total Nt transmit antennas. Then, we completely analyze the structure of the equivalent channel for the kind of codes and reveal a property that the eigenvectors of the equivalent channel are constant and independent from the channel realization, and this characteristic can be exploited for a new transmission structure with single-symbol linear decoder. Furthermore, we analyze different macro diversity schemes and give a performance comparison. The simulation results show that the proposed scheme is practical for the broadcast systems with significant performance improvement comparing with soft-combination and cyclic delay diversity (CDD) methods.

The Viterbi algorithm is a well-established technique for channel and source decoding in high performance digital communication systems. However, excessive time consumption makes it difficult to design an efficient high-speed decoder for practical application. This paper describes the implementation of parallel Viterbi algorithm by multi-microprocessors. Internal computations are performed in a parallel fashion. The use of microprocessors allows low-cost implementation with moderate complexity. The software and hardware implementations of the Viterbi algorithm on parallel multi-microprocessors for real-time decoding are presented. The implemented method is based on a combination of forming a set of tables and calculations. For efficient operation under fully parallel Viterbi decoding by microprocessors, we considered: (1) branch metrics processing, path metrics updating, path memory updating and decoding output for microprocessor, (2) efficient decomposition of the sequential Viterbi algorithm into parallel algorithms, (3) minimization of the communication among the microprocessors. The practical solutions for the problems of synchronization among the miroprocessors, interconnection network for communication among the microprocessors and memory management are discussed. Furthermore the performance and the speed of the parallel Viterbi decoding are given. For a fixed processing speed of given hardwares, parallel Viterbi decoding allows a linear speed up in the throughput rate with a linear increase in hardware complexity.

Scheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation efficiency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce environment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling problem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks scheduling algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehensive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy.

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high-resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the reconstruction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascending dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.

This paper presents an adaptive rate error control scheme for digital communication over time-varying channels. The cyclic code with majority-logic decoding is used in a cascaded way as an inner code to create a simple and powerful hybrid-ARQ error control scheme. Inner code is used only for error correction and the outer code is used for both error correction and error detection. When an error is detected, retransmission is required. The unsuccessful packets are not discarded as with conventional schemes, but are combined with their retransmitted copies. Approximations for the throughput efficiency and the undetectable error probability are given. A high reliability coupled with a simple high-speed implementation makes it suitable for high data rate error control over both stationary and nonstationary channels. Adaptive error control scheme becomes the best solution for time-varying channels when the optimum code is selected according to the actual channel conditions to enhance the system performance. The main feature of this system is that the basic structure of the encoder and decoder need not be modified while the error-correction capability of the code increases. Results of a comparative analysis show that the proposed scheme outperforms other similar ARQ protocols.

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.

With the gradually increase of the application of new energy in microgrids, Electric Spring (ES), as a new type of distributed compensation power electronic device has been widely studied. The Generalized Electric Spring (G-ES) is an improved topology, and the space limitation problem in the traditional topology is solved. Because of the mode of G-ES use in the power grid, a reasonable solution to the voltage loss of the critical section feeder is needed. In this paper, the voltage balance equation based on the feedforward compensation coefficient is established, and a two cascade control strategy based on the equation is studied. The first stage of the two cascade control strategy is to use communication means to realize the allocation of feedforward compensation coefficients, and the second stage is to use the coefficients to realize feedforward fixed angle control. Simulation analysis shows that the proposed control strategy does not affect the control accuracy of the critical load (CL), and effectively improves the operational range of the G-ES.

In this paper, the relationship between correlation interval (CI) and estimate is investigated. Then a special correlation interval is explored that is adaptive to all levels of signal-to-noise ratio (SNR) and velocity conditions, and the mean square error is deduced. Finally, we propose an iterative algorithm that achieves the special correlation interval and calculates the Doppler spread by increasing the resolution on time-domain iteratively. Simulation results show that compared with conventional schemes, performance of the proposed algorithm is basically independent of velocity variation and less sensitive to SNR, especially in low SNR environments. It achieves high accurate estimation directly without any post-rectification.

In this letter, we propose an FFT-based SNR estimation method for wireless OFDM systems, and analyze the impact of the proposed SNR estimation method on adaptive OFDM performance in slow Rayleigh fading channels. Numerical and simulation results show that the proposed method is effective and feasible for adaptive modulation in slow Rayleigh fading channels.

Multipath search based instantaneous root-mean-squared (RMS) delay spread (RDS) estimators mainly depend on path detection or multipath search. This paper proposes a novel method for multipath search through Minimum Descriptive Length (MDL) criterion, and hence a novel instantaneous RDS estimation method for wireless OFDM systems. compared with the conventional multipath search based instantaneous RDS estimators, the proposed estimator doesn't need any a priori information about the noise variance and the channel power delay profile (PDP) while the performance is improved. Simulation results demonstrate that the proposed estimator is also insensitive to the variance of SNR and robust against the frequency selectivity, as well as the vehicle speed.

This paper presents new go-back-N ARQ protocols for point-to-multipoint communications over broadcast channels such as satellite or broadcast radio channels. In the conventional go-back-N ARQ protocols for multidestination communications, usually only error detection codes are used for error detection and m copies of a frame are transmitted at a time. In one of our protocols, a bit-by-bit majority-voting decoder based on all of the m copies of a frame is used to recover the transmitted frame. In another protocol, a hybrid-ARQ protocol, which is an error detection code concatenated with a rate repetition convolutional code with the Viterbi decoding, is used. In these protocols, a dynamic programming technique is used to select the optimal number of copies of a frame to be transmitted at a time. The optimal number is determined by round trip propagation delay of the channel, the error probability, and the number of receivers that have not yet received the message. Analytic expressions are derived for the throughput efficiency of the proposed protocols. The proposed point-to-multipoint protocols provide satisfactory throughput efficiency and perform considerably better than the conventional protocols under high error rate conditions, especially in environments with a large number of receivers and large link round trips. In this paper we analyze the performances of the proposed protocols upon the random error channel conditions.

In this letter, an angle adjustment method is proposed to improve the accuracy of the sampling frequency offset (SFO) estimation for the very high throughput wireless local area networks (WLANs). This angle adjustment can work together with existing least square (LS) and weighted least square (WLS) to achieve better system performance. Simulation results show that, the angle adjustment can help LS and WLS to get better pocket error rate (PER).

In CDMA system, the RAKE receiver is commonly used to attain diversity gain by taking advantage of the good correlation properties of the spreading codes. However, at low spreading gains the good correlation properties of the spreading codes are lost and the RAKE receiver performance is severely degraded by intersymbol interference (ISI) due to the interpath interference (IPI). In case of multi-code CDMA system, there are exist multi-code interference (MCI). In order to suppress ISI and MCI, a novel receiver based on soft-output viterbi algorithm (SOVA) equalization is proposed in this paper. The SOVA equalization is applied to symbol sequences after RAKE combining and MCI cancellation to effectively eliminate the ISI during transmission of high rate data in wideband DS-CDMA systems. Simulation results show that the proposed RAKE-SOVA receiver significantly outperform the traditional RAKE and RAKE-VA receivers.

Video-based person re-identification (re-ID) aims at retrieving person across non-overlapping camera and has achieved promising results owing to deep convolutional neural network. Due to the dynamic properties of the video, the problems of background clutters and occlusion are more serious than image-based person Re-ID. In this letter, we present a novel triple attention network (TriANet) that simultaneously utilizes temporal, spatial, and channel context information by employing the self-attention mechanism to get robust and discriminative feature. Specifically, the network has two parts, where the first part introduces a residual attention subnetwork, which contains channel attention module to capture cross-dimension dependencies by using rotation and transformation and spatial attention module to focus on pedestrian feature. In the second part, a time attention module is designed to judge the quality score of each pedestrian, and to reduce the weight of the incomplete pedestrian image to alleviate the occlusion problem. We evaluate our proposed architecture on three datasets, iLIDS-VID, PRID2011 and MARS. Extensive comparative experimental results show that our proposed method achieves state-of-the-art results.