Depeng Yang

Efficient and Secure Wireless Communications for Advanced Metering Infrastructure in Smart Grids

An experiment is carried out to measure the power consumption of households. The analysis on the real measurement data shows that the significant change of power consumption arrives in a Poisson manner. Based on this experiment, a novel wireless communication scheme is proposed for the advanced metering infrastructure (AMI) in smart grid that can significantly improve the spectrum efficiency. The main idea is to transmit only when a significant power consumption change occurs. On the other hand, the policy of transmitting only when change occurs may bring a security issue; i.e., an eavesdropper can monitor the daily life of the house owner, particularly the information of whether the owner is at home. Hence, a defense scheme is proposed to combat this vulnerability by adding artificial spoofing packets. It is shown by numerical results that the defense scheme can effectively prevent the security challenge.

Belief Propagation Based Cooperative Compressed Spectrum Sensing in Wideband Cognitive Radio Networks

Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability; the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.

An FPGA Implementation for Solving Least Square Problem

This paper proposes a high performance least square solver on FPGAs using the Cholesky decomposition method. Our design can be realized by iteratively adopting a single triangular linear equation solver for modified Cholesky decomposition and forward/backward substitutions. Good performance is achieved by optimizing the Cholesky factorization algorithms, reordering the computation and thus alleviating the data dependency. Dedicated hardware architecture for solving triangular linear equations is designed and implemented for different precision requirements. Compared to software on a Pentium 4, our design achieves a significant speedup.

Compressed sensing based UWB receiver: Hardware compressing and FPGA reconstruction

A low sampling rate approach for recovering ultra wide band (UWB) signals is proposed, using Distributed Amplifiers (DAs) and low speed Analog-to-Digital Converters (ADCs) and based on the theory of compressed sensing. A microwave circuit consisting of a bank of DAs, followed by a bank of ADCs, is designed to implement analog compressing, where the elements of measurement matrix are realized by picosecond delay tap and flexible gain coefficients in DAs. Numerical simulation shows that a bank of eight DAs and ADCs with 500MHz sampling rate can almost perfectly recover a 100ps-resolution UWB echo signal in the noiseless case. For recovering the UWB signals in a real-time way, issues in field programmable gate array (FPGA) implementation are discussed.

Compressed sensing and Cholesky decomposition on FPGAs and GPUs

Compressed sensing (CS) is a revolutionary signal acquisition theory, enabling signal acquisition at a rate that is below the Nyquist sampling rate. However, CS signal reconstruction algorithms are computationally expensive. One of the key computation steps in CS algorithms is to iteratively compute a Cholesky decomposition. Modern application acceleration devices, such as FPGAs and GPUs, can accelerate Cholesky decomposition and CS signal reconstruction computation. This paper presents high performance parallel Cholesky decomposition algorithms for GPU and FPGA implementation. For GPUs, an optimized Cholesky decomposition algorithm is developed with high parallelism, reduced data copying, and improved memory access. For FPGAs, a dedicated pipelined hardware architecture for Cholesky decomposition is designed. Only one pipelined triangular linear equation solver is needed for solving Cholesky decomposition and Cholesky decomposition-based linear equation systems. Moreover, CS signal reconstruction algorithms are accelerated on GPUs and FPGAs for fast signal recovery based on our iterative Cholesky decomposition. Results show that the proposed Cholesky decomposition on FPGAs and GPUs are much faster than LAPACK and MAGMA for small matrices. For accelerating CS signal reconstruction algorithms, our FPGA implementation can achieve around 15x speedup and our GPU implementation can achieve about a 38x speedup compared with the CPU using LAPACK and the hybrid CPU/GPU system with MAGMA.

High accuracy UWB localization in dense indoor environments

UWB communication and localization systems have many inherent advantages for robust performance in dense, indoor multipath environments. Although UWB systems have been designed for numerous industrial applications, there exists the need for short range (i.e. 5-10 m) UWB localization systems with accuracy an order of magnitude higher (i.e. mm-range) than existing commercial systems. Many system level challenges must be overcome to achieve accuracy on the order of tens of picoseconds, such as high Rx sampling rate, Tx-Rx LO phase noise, Tx-Rx pulse repetition frequency clock phase noise, Rx-induced jitter from sequential sampling, Rx-side calibration of phase offsets in I and Q channels, multipath interference due to dense indoor environments, etc. A detailed system level simulation is setup in order to quantify the contribution of these effects on overall system accuracy. Simulation results are used to highlight these system level errors and show how they can be mitigated.

Space-Time Bayesian Compressed Spectrum Sensing for Wideband Cognitive Radio Networks

Wideband spectrum sensing in cognitive radio networks remains an open challenge due to wideband spectrum acquisition implementation. Compressed spectrum sensing provides a powerful approach to acquire wideband signals. We purpose a probabilistic Space-time Bayesian Compressed Spectrum Sensing (ST-BCSS) to combat the noise in wideband compressed spectrum sensing. We present an informative hierarchical prior probabilistic model to recover the compressed spectrum by exploiting the temporal and spatial prior information. These priori information endows the robustness of spectrum sensing subject to noise and low sampling rate. We present a probabilistic framework to address how to represent, convey and fuse multi-prior information to improve the local compressed spectrum reconstruction. Numerical simulation results demonstrate that the ST-BCSS algorithm improves the performance of compressed spectrum sensing under low sampling rate and low Signal Noise Ratio (SNR), compared with the traditional Basis Pursuit and Orthogonal Matching Pursuit algorithms. A correlation based algorithm for the detection of reconstruction failure due to non-sparse spectrum is also proposed and demonstrated using numerical simulations.

Millimeter accuracy UWB positioning system using sequential sub-sampler and time difference estimation algorithm

A compact sequential sampling scheme using a high sampling rate analog digital converter (ADC) and direct digital synthesis (DDS) technology has been proposed for the millimeter accuracy UWB positioning system, which can achieve the equivalent of a 100GHz sampling rate. The analog bandwidth, frequency resolution, and time jitter of the sampler for UWB signal acquisitions are detailed. Based on the sampled UWB signal, a modified correlation algorithm for time difference estimation is proposed to further improve positioning accuracy and reduce the computational burden. Simulation results based on IEEE802.15.4a channel models and experiments utilizing the proposed sub-sampler show that our UWB positioning system can achieve up to millimeter accuracy.

Efficient and reliable multiple access for advanced metering in future smart grid

Smart metering, which reports the power activities to the control center, is a key task in smart grid and faces the challenges of realtime requirement and the large population of users. In this paper, the power consumptions of houses and offices are measured using a power clamp. It is found that the power consumption stays almost constant for most of the time, thus justifying the strategy of transmitting only when the power consumption is significantly changed. By using Kolmogorov- Smirnov test, it is also demonstrated that the arrival of changes of the power consumption satisfies Poisson distribution with proper time intervals. For such a random arrival of events, both the multiple access schemes using dedicated channels like TDMA and OFDMA and using contentions like Aloha and CSMA are considered and compared. Numerical simulations are carried out for evaluating the performance, measured by the delay and packet loss rate, using the measurement data and simple communication models. The results show that the contention based multiple access outperforms the dedicated channels, given the configuration of the simulation.

Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach

A key challenge to achieve very high positioning accuracy (such as sub-mm accuracy) in Ultra-Wideband (UWB) positioning systems is how to obtain ultra-high resolution UWB echo pulses, which requires ADCs with a prohibitively high sampling rate. The theory of Compressed Sensing (CS) has been applied to UWB systems to acquire UWB pulses below the Nyquist sampling rate. This paper proposes a front-end optimized scheme for the CS-based UWB positioning system. A Space-Time Bayesian Compressed Sensing (STBCS) algorithm is developed for joint signal reconstruction by transferring mutual a priori information, which can dramatically decrease ADC sampling rate and improve noise tolerance. Moreover, the STBCS and time difference of arrival (TDOA) algorithms are integrated in a pipelined mode for fast tracking of the target through an incremental optimization method. Simulation results show the proposed STBCS algorithm can significantly reduce the number of measurements and has better noise tolerance than the traditional BCS, OMP, and multi-task BCS (MBCS) algorithms. The sub-mm accurate CS-based UWB positioning system using the proposed STBCS-TDOA algorithm requires only 15% of the original sampling rate compared with the UWB positioning system using a sequential sampling method.