Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless networks. However, when the users move randomly in the network, the transmitting distance between the transceivers changed randomly, causing the time-varying channel conditions. To fit this situation, in this paper, we considered the heterogenous wireless channels of the users caused by user movement and analyzed the energy consumption of overall network. Then, we introduce the Kalman filter to predict the locations of the users and proposed a new power control metric that both considered the energy consumption and the network throughput. Based on the new metric and the Kalman prediction, we optimized the transmitting power of the BS and proposed an energy-efficient ARNC scheduling to improve quality of service. The simulation results also showed the effectiveness of the optimization and proposed methods compared with the traditional methods.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

This paper addresses two main issues present in compressive imaging: namely, the issue of long acquisition time by using a single-pixel sensor and that of high computational complexity for high-resolution image recovery. We show that these issues are resolved in a new architecture, which is referred to as block-based compressive imaging. The new architecture consists of a lens, a programmable aperture assembly, and multiple single-pixel sensors. The aperture assembly equally applies to all single-pixel sensors, which make the compressive measurements of a scene with seamless block views. The combined measurements from the sensors can be used to reconstruct a unified image with a large number of pixels.

The existing lensless compressive camera (L2C2) [1] suffers from low capture rates, resulting in low resolution images when acquired over a short time. In this work, we propose a new regime to mitigate these drawbacks. We replace the global-based compressive sensing used in the existing L2C2by the local block (patch) based compressive sensing. We use a single sensor for each block, rather than for the entire image, thus forming a multiple but spatially parallel sensor L2C2. This new camera retains the advantages of existing L2C2while leading to the additional benefits of fast acquisition, real time reconstruction, and the flexibility to image size. We develop multiple geometries of this block-wise L2C2in this paper. We have built prototypes of the proposed blockwise L2C2and demonstrated excellent results of real data.

The iterative decoding threshold (IDT) estimation for LDPC convolutional codes (LDPC-CCs) may become difficult over additive white Gaussian noise channels, especially when the termination length L gets very large or even approaches infinity. In this paper, we devise a novel fast IDT estimation scheme using the protograph-based extrinsic information transfer (PEXIT) analysis for protograph-based LDPC-CCs. Based on our new analysis, we propose a PEXIT-fast algorithm in which only the mutual information (MI) of a posteriori probability (APP) of the first variable node will be monitored to determine whether the current E_b/N_0 (signal-energy-per-information-bit to noise-power- spectral-density ratio) in evaluation is an upper bound or a lower bound of the IDT. Hence, it is no longer necessary to get through the whole MI evolution and the computational complexity can be greatly reduced thereby. We also design an efficient approach to determine the IDT for an LDPC-CC with an arbitrary large termination length L which can also be allowed to be infinity. The closeness between the known IDTs and the estimated IDTs using our proposed new method for several LDPC-CCs in simulation confirms the effectiveness of our scheme.

Orthogonal frequency-division multiplexing (OFDM) has been widely used for many communication technologies nowadays due to its high spectral efficiency, robustness against inter-symbol-interference and multipath fading, simplicity for implementation, and so on. In this paper, we propose the frequency-domain pilot multiplexing techniques (FDPMTs) for the channel estimation and equalization in OFDM systems. A robust and effective pilot insertion and detection scheme is devised thereby. The information signal sequence resulting from the constellation mapper is spread over all subcarriers by a precoder and certain subcarriers can be nulled for the insertion of training pilots. These pilot positions are optimally selected to minimize the distortion of the transmitted time-domain signal (OFDM modulated signal) caused by the aforementioned subcarrier-removal at the corresponding pilot positions. The associated new receiver structure is also presented, where three different blind pilot-detection techniques are designed without any a priori knowledge of the pilot positions (based on sample variance, subspace decomposition, and Jarqur-Bera (JB) statistics, respectively), and the distorted data symbols can thus be iteratively reconstructed. Besides, rigorous theoretical analysis and Monte Carlo simulation results both demonstrate that our proposed new OFDM system using dynamical pilot positions is more robust than the conventional OFDM system using the fixed pilot positions over multipath fading channels.

Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames such that the spectral efficiency can be adapted to various channel conditions. The spectrum and energy efficiency would decrease if the adopted MCS option at the transmitter needs to be dynamically transmitted to the receiver through a secure control channel. To combat this problem, in this paper, we would like to propose a novel blind channel-encoder identification scheme with low computational complexity for Reed-Solomon (RS) codes over Galois field GF(q), which could also be applied to other similar non-binary channel codes as well. Our proposed new scheme involves the estimation of the channel parameters using the expectation-maximization (EM) algorithm, the calculation of the log-likelihood ratio vectors (LLRVs) of the syndrome a posteriori probabilities over GF(q), and the identification of the non-binary RS encoder in use subject to the maximum average log-likelihood ratio (LLR) over the pre-selected candidate encoder set. Simulation results justify the effectiveness of this new mechanism.

In this paper, we propose a lensless compressive imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The proposed architecture is simple and reliable because no lens is used. The architecture can be used for capturing images of visible and other spectra such as infrared, or millimeter waves, in surveillance applications for detecting anomalies or extracting features such as speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multiview images simultaneously. A prototype was built by using a LCD panel and a photoelectric sensor for capturing images of visible spectrum.

We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.

Multi-view images are acquired by a lensless compressive imaging architecture, which consists of an aperture assembly and multiple sensors. The aperture assembly consists of a two dimensional array of aperture elements whose transmittance can be individually controlled to implement a compressive sensing matrix. For each transmittance pattern of the aperture assembly, each of the sensors takes a measurement. The measurement vectors from the multiple sensors represent multi-view images of the same scene. We present theoretical framework for multi-view reconstruction and experimental results for enhancing quality of image using multi-view.

A novel bind estimation method for encoder parameters operating over the noisy received signal is proposed in this paper. This scheme can blindly identify the turbo encoder adopted at the transmitter so as to correctly decode the received signal sequence. An iterative expectation-maximization algorithm is designed to estimate the coding parameters, which are the weighting coefficients in a recursive convolutional encoder. These coefficients are associated with the feedback and forward connections in the encoder. To tackle this blind encoder-parameter estimation, we separate the feedback portion from the forward structure and then convert the recursive systematic convolutional encoder into a non-systematic convolutional encoder preceded by a feedback encoder. Our new encoder structure will be investigated. The effect of the separate feedback encoder on the state sequence resulting from the forward convolutional encoder will be studied. Monte Carlo simulation results will be demonstrated to evaluate the effectiveness of our proposed new scheme.

A new video coding method based on compressive sampling is proposed, in which a video is coded using compressive measurements on video cubes. Video reconstruction is based on minimizing the total variation (TV) of the pixelwise DCT coefficients along temporal direction. The reconstruction algorithm is an efficient TV minimization algorithm based on the augmented Lagrangian method and an alternating minimization technique.