In this letter, the principle of LLR-based successive-cancellation (SC) polar decoding algorithm is explored. In order to simplify the logarithm and exponential operations in the updating rules for polar codes, we further utilize a piece-wise linear algorithm to approximate the transcendental functions, where the piece-wise linear algorithm only consists of multiplication and addition operations. It is demonstrated that with one properly allowable maximum error δ chosen for success-failure algorithm, performances approach to that of the standard SC algorithm can be achieved. Besides, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results show that our proposed piece-wise SC decoder greatly reduces the complexity of the SC-based decoders with no loss in error correcting performance.

In social networks, predicting user behavior under social hotspots can aid in understanding the development trend of a topic. In this paper, we propose a retweeting prediction method for social hotspots based on tensor decomposition, using user information, relationship and behavioral data. The method can be used to predict the behavior of users and analyze the evolvement of topics. Firstly, we propose a tensor-based mechanism for mining user interaction, and then we propose that the tensor be used to solve the problem of inaccuracy that arises when interactively calculating intensity for sparse user interaction data. At the same time, we can analyze the influence of the following relationship on the interaction between users based on characteristics of the tensor in data space conversion and projection. Secondly, time decay function is introduced for the tensor to quantify further the evolution of user behavior in current social hotspots. That function can be fit to the behavior of a user dynamically, and can also solve the problem of interaction between users with time decay. Finally, we invoke time slices and discretization of the topic life cycle and construct a user retweeting prediction model based on logistic regression. In this way, we can both explore the temporal characteristics of user behavior in social hotspots and also solve the problem of uneven interaction behavior between users. Experiments show that the proposed method can improve the accuracy of user behavior prediction effectively and aid in understanding the development trend of a topic.

This paper presents a novel test data compression scheme for SoCs based on block merging and compatibility. The technique exploits the properties of compatibility and inverse compatibility between consecutive blocks, consecutive merged blocks, and two halves of the encoding merged block itself to encode the pre-computed test data. The decompression circuit is simple to be implemented and has advantage of test-independent. In addition, the proposed scheme is applicable for IP cores in SoCs since it compresses the test data without requiring any structural information of the circuit under test. Experimental results demonstrate that the proposed technique can achieve an average compression ratio up to 68.02% with significant low test application time.

In this letter, a novel frame aggregation scheduler is proposed to solve the head-of-line blocking problem for real-time user datagram protocol (UDP) traffic in error-prone and aggregation-enabled wireless local area networks (WLANs). The key to the proposed scheduler is to break the restriction of in-order delivery over the WLAN. The simulation results show that the proposed scheduler can achieve high UDP goodput and low delay compared to the conventional scheduler.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.

In this letter, a low latency, high throughput and hardware efficient sorted MMSE QR decomposition (MMSE-SQRD) for multiple-input multiple-output (MIMO) systems is presented. In contrast to the method of extending the complex matrix to real model and thereafter applying real-valued QR decomposition (QRD), we develop a highly parallel decomposition scheme based on coordinate rotation digital computer (CORDIC) which performs the QRD in complex domain directly and then converting the complex result to its real counterpart. The proposed scheme can greatly improve the processing parallelism and curtail the nullification and sorting procedures. Besides, we also design the corresponding pipelined hardware architecture of the MMSE-SQRD based on highly parallel Givens rotation structure with CORDIC algorithm for 4×4 MIMO detectors. The proposed MMSE-SQRD is implemented in SMIC 55nm CMOS technology achieving up to 50M QRD/s throughput and a latency of 59 clock cycles with only 218 kilo-gates (KG). Compared to the previous works, the proposed design achieves the highest normalized throughput efficiency and lowest processing latency.

In this letter, a two-stage QR decomposition scheme based on Givens rotation with novel modified real-value decomposition (RVD) is presented. With the modified RVD applied to the result from complex Givens rotation at first stage, the number of non-zero terms needed to be eliminated by real Givens rotation at second stage decreases greatly and the computational complexity is thereby reduced significantly compared to the decomposition scheme with the conventional RVD. Besides, the proposed scheme is suitable for the hardware design of QR decomposition. Evaluation shows that the proposed QR decomposition scheme is superior to the related works in terms of computational complexity.

Recently, social relation analysis receives an increasing amount of attention from text to image data. However, social relation analysis from video is an important problem, which is lacking in the current literature. There are still some challenges: 1) it is hard to learn a satisfactory mapping function from low-level pixels to high-level social relation space; 2) how to efficiently select the most relevant information from noisy and unsegmented video. In this paper, we present an Attentive Sequences Recurrent Network model, called ASRN, to deal with the above challenges. First, in order to explore multiple clues, we design a Multiple Feature Attention (MFA) mechanism to fuse multiple visual features (i.e. image, motion, body, and face). Through this manner, we can generate an appropriate mapping function from low-level video pixels to high-level social relation space. Second, we design a sequence recurrent network based on Global and Local Attention (GLA) mechanism. Specially, an attention mechanism is used in GLA to integrate global feature with local sequence feature to select more relevant sequences for the recognition task. Therefore, the GLA module can better deal with noisy and unsegmented video. At last, extensive experiments on the SRIV dataset demonstrate the performance of our ASRN model.

In this paper, a hardware efficient design methodology for a configurable-point multiple-stream pipeline FFT processor is presented. We first compared the memory and arithmetic components of different pipeline FFT architectures, and obtained the conclusion that MDF architecture is more hardware efficient than MDC for the overall processor. Then, in order to reduce the computational complexity, a binary-tree representation was adopted to analyze the decomposition algorithm. Consequently, the coefficient multiplications are minimized among all the decomposition probabilities. In addition, an efficient output reorder circuit was designed for the multiple-stream architecture. An 128∼2048 point 4-stream FFT processor in LTE system was designed in SMIC 55nm technology for evaluation. It owns 1.09mm2 core area with 82.6mW power consumption at 122.88MHz clock frequency.

Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.

In this paper, we propose an improved weighted least square (IWLS) method to estimate and compensate phase variations utilizing pilots, for Orthogonal Frequency Division Multiplexing (OFDM) based very high throughput wireless local area networks (WLANs). The remaining phase is composed of the common phase error (CPE) and the sampling time offset (STO). For IWLS, the CPE maximum likelihood (ML) estimation is proposed to improve the CPE estimation accuracy, while the STO fitting is proposed to enhance the estimation of STO. With these two mechanisms, IWLS can improve phase estimation performance. Simulation results show that, compared to weighted least square (WLS) scheme, a better pocket error rate (PER) is achieved by using the proposed method, but with a comparable complexity.

This paper presents a high-throughput sliding block Viterbi decoder for IEEE 802.11ac systems. A 64-state bidirectional sliding block Viterbi method is proposed to meet the speed requirement of the system. The decoder throughput goes up to 640Mbps, which can be further increased by adding the block parallelism. Moreover, a modified add-compare-select (ACS) unit is designed to enhance the working frequency. The modified ACS unit obtains nearly 26% speed-up, compared to the conventional ACS unit. However, the area overhead and power dissipation are almost the same. The decoder is designed in a SMIC 0.13µm technology, and it occupies 1.96mm2 core area and 105mW power consumption with an energy efficiency of 0.1641nJ/bit with a 1.2V voltage supply.

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).