Factor graph based detection approach for high-mobility OFDM systems with large FFT modes

In this article, a novel detector design is proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency selective and time varying channels. Namely, we focus on systems with large OFDM symbol lengths where design and complexity constraints have to be taken into account and many of the existing ICI reduction techniques can not be applied. We propose a factor graph (FG) based approach for maximum a posteriori (MAP) symbol detection which exploits the frequency diversity introduced by the ICI in the OFDM symbol. The proposed algorithm provides high diversity orders allowing to outperform the free-ICI performance in high-mobility scenarios with an inherent parallel structure suitable for large OFDM block sizes. The performance of the mentioned near-optimal detection strategy is analyzed over a general bit-interleaved coded modulation (BICM) system applying low-density parity-check (LDPC) codes. The inclusion of pilot symbols is also considered in order to analyze how they assist the detection process

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel’s sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER=10 −1.9 BER=10−1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity

Compressing measurements in quantum dynamic parameter estimation

We present methods that can provide an exponential savings in the resources required to perform dynamic parameter estimation using quantum systems. The key idea is to merge classical compressive sensing techniques with quantum control methods to significantly reduce the number of signal coefficients that are required for reconstruction of time-varying parameters with high fidelity. We show that incoherent measurement bases and, more generally, suitable random measurement matrices can be created by performing simple control sequences on the quantum system. Random measurement matrices satisfying the restricted isometry property can be used efficiently to reconstruct signals that are sparse in any basis. Because many physical processes are approximately sparse in some basis, these methods can benefit a variety of applications such as quantum sensing and magnetometry with nitrogen-vacancy centers.National Science Foundation (U.S.) (Grant NSF PHY-1125846