A virtual MIMO dual-hop architecture based on hybrid spatial modulation

International audienceIn this paper, we propose a novel Virtual Multiple-Input-Multiple-Output (VMIMO) architecture based on the concept of Spatial Modulation (SM). Using a dual-hop and Decode-and-Forward protocol, we form a distributed system, called Dual-Hop Hybrid SM (DH-HSM). DH-HSM conveys information from a Source Node (SN) to a Destination Node (DN) via multiple Relay Nodes (RNs). The spatial position of the RNs is exploited for transferring information in addition to, or even without, a conventional symbol. In order to increase the performance of our architecture, while keeping the complexity of the RNs and DN low, we employ linear precoding using Channel State Information (CSI) at the SN. In this way, we form a Receive-Spatial Modulation (R-SM) pattern from the SN to the RNs, which is able to employ a centralized coordinated or a distributed uncoordinated detection algorithm at the RNs. In addition, we focus on the SN and propose two regularized linear precoding methods that employ realistic Imperfect Channel State Information at the Transmitter. The power of each precoder is analyzed theoretically. Using the Bit Error Rate (BER) metric, we evaluate our architecture against the following benchmark systems: 1) single relay; 2) best relay selection; 3) distributed Space Time Block Coding (STBC) VMIMO scheme; and 4) the direct communication link. We show that DH-HSM is able to achieve significant Signal-to-Noise Ratio (SNR) gains, which can be as high as 10.5 dB for a very large scale system setup. In order to verify our simulation results, we provide an analytical framework for the evaluation of the Average Bit Error Probability (ABEP)

Residue Arithmetic VLSI Array Architecture for Manipulator Pseudo-Inverse Jacobian Computation

Most Cartesian-based control strategies require the computation of the manipulator inverse Jacobian in real time at every sampling period. In some cases, the Jacobian matrix is not of full column or row rank due to singularity or redundant robot configuration. This requires the computation of the manipulator pseudo-inverse Jacobian in real time. The calculation of the pseudo-inverse Jacobian may become extremely sensitive to small perturbation in the data and numerical instabilities, when the Jacobian matrix is not of full column or row rank. Even if the Jacobian matrix is of full rank, the ill-conditioned problem may still plague the computation of the pseudoinverse Jacobian. This paper presents the use of residue arithmetic for the exact computation of the manipulator pseudo-inverse Jacobian to obviate the roundoff errors normally associated with the computations. A two-level macro-pipelined residue arithmetic array architecture implementing the Decell’s pseudo-inverse algorithm has been developed to overcome the ill-conditioned problem of the pseudo-inverse computation. Furthermore, the Decell algorithm is quite suitable for VLSI array implementation to achieve the real-time computation requirement. The first-level arrays are data-driven, wavefront-like arrays and perform the matrix multiplications, matrix diagonal additions, and trace computations. A pool or sequence of the first-level arrays are then configured into a second-level macro-pipeline with outputs of one array acting as inputs to another array in the pipe. The proposed architecture can calculate the pseudoinverse Jacobian with a pipelined time in the same computational complexity order as evaluating a matrix product in a wavefront array

Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin

Randomized Local Model Order Reduction

In this paper we propose local approximation spaces for localized model order reduction procedures such as domain decomposition and multiscale methods. Those spaces are constructed from local solutions of the partial differential equation (PDE) with random boundary conditions, yield an approximation that converges provably at a nearly optimal rate, and can be generated at close to optimal computational complexity. In many localized model order reduction approaches like the generalized finite element method, static condensation procedures, and the multiscale finite element method local approximation spaces can be constructed by approximating the range of a suitably defined transfer operator that acts on the space of local solutions of the PDE. Optimal local approximation spaces that yield in general an exponentially convergent approximation are given by the left singular vectors of this transfer operator [I. Babu\v{s}ka and R. Lipton 2011, K. Smetana and A. T. Patera 2016]. However, the direct calculation of these singular vectors is computationally very expensive. In this paper, we propose an adaptive randomized algorithm based on methods from randomized linear algebra [N. Halko et al. 2011], which constructs a local reduced space approximating the range of the transfer operator and thus the optimal local approximation spaces. The adaptive algorithm relies on a probabilistic a posteriori error estimator for which we prove that it is both efficient and reliable with high probability. Several numerical experiments confirm the theoretical findings.Comment: 31 pages, 14 figures, 1 table, 1 algorith