In this paper, we investigate the channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The recently proposed simplified information geometry (SIG) algorithm offers a promising solution for channel estimation with relatively low computational complexity. However, the damping factors used in the SIG algorithm are based on a heuristic strategy, which results in inconsistent performance. To address this issue, we propose a deep unfolding based SIG (DU-SIG) approach in this paper. Specifically, each iteration of the SIG algorithm is unfolded into a layer-wise structure resembling a neural network and the damping factors are optimized based on deep unfolding technique. Notably, the optimized damping factors can be directly integrated into the original SIG algorithm to improve the performance of channel estimation without increasing the computational complexity. Simulation results validate the effectiveness and superiority of our proposed algorithm.

With knowledge of statistical channel state information (CSI) at the base station (BS), one can reduce the pilot overhead and improve the channel estimation performance in massive multiple-input multiple-output (MIMO) systems by exploiting the non-orthogonal pilot sequences. However, due to the limited uplink (UL) training resources, it is challenging to acquire the high-accuracy statistical CSI in real-time. To tackle this problem, an emerging technique is to construct a channel knowledge map (CKM) based on the statistical CSI. In order to construct such a CKM, it is necessary to analyze the spatial and temporal correlations of statistical CSI. Specifically, this paper analyzes the spatial and temporal correlations of beam domain channel power matrices (BDCPMs), obtained from channel measurements in a massive MIMO testbed. Furthermore, to verify the effectiveness of the stored aged BDCPMs, we also simulate channel estimation performance using BDCPMs measured at different dates. Both measured and simulated results demonstrate the effectiveness of the CKM based on statistical CSI.

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

In this paper, we investigate the robust precoding for high frequency skywave massive multiple-input multiple-output communications with imperfect channel state information (CSI). Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust pre-coder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

FPGA with massive customizable parallel computation capacity, is potentially good for fast time-to-market applications. However, its complex placing and routing lead to a relatively large latency and low frequency. Besides, the clock problems might make a design hard and slow, especially the one with complex control or variant computations. All of those defects seem to be from the essence of the synchronous design methodology and there does not exist an easy way to solve by clocks. Although the FPGA vendors do not supply an asynchronous design routine, flow or tool, it is still possible to implement a clockless design with a concrete FPGA chip and then harness lots of benefits that synchronous one misses, which is shown in this paper. We adopt link-joint as the asynchronous communication mechanism that discards clock limitation, but equips high throughput due to the fast handshake among neighbor clicks. The simplest link-joint circuit is click that conforms to Bundled Bound Data (BBD) protocol for local communication. Multiple clicks can be constructed and trimmed to types of micro-pipeline structures, feasibly and flexibly. With above considerations, we propose an innovative asynchronous design method for Xilinx FPGA applications, as well as the asynchronous control framework by dedicated micro-pipeline structures. Furthermore, we introduce delay maching technologies as well as whole design flow and tool-chain. All of these supply an applicable way of accelerating an asynchronous design for a FPGA. The case-studies show that communication between neighbor clicks is less than 1.1ns and the asynchronous method accelerates FPGA latency extremely.

Summary Network coding is a new routing technique that can improve a network's throughput. The key idea is to allow network routers to code the received packets before transmission. However, network coding is vulnerable to pollution attacks where malicious node(s) can flood the network with invalid packets and prevent the receiver from the right decoding. Network coding signature offers a good solution to this problem. Nevertheless, existing network coding signature schemes cannot either be proven secure in the standard model, support multiple sources, or in‐time signing. In this paper, we propose a new network coding signature scheme to solve the aforementioned problems. Copyright © 2014 John Wiley & Sons, Ltd.

The traditional algorithm often cannot detect and extract the moving object precisely and fast, which affects result of the tracking. In order to overcome the problem, this paper proposes a method based on wavelet features for target tracking. According to the previous information obtained by Kalman filter, the possible location of the target in the frame is predicted. Multi-scale two-dimensional discrete wavelet is used to decompose the possible area in which the target exists. And then the mean and variance of the decomposed image are computed. Finally, Principal Component Analysis (PCA) is used to build an effective subspace. The target is matched to realize the tracking by measuring the similarity function. The experimental results have shown that the algorithm is robust and can improve the speed and accuracy of the target detection and tracking significantly.