Analysis and Design of Channel Estimation in Multicell Multiuser MIMO OFDM Systems

This paper investigates the uplink transmission in multicell multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The system model considers imperfect channel estimation, pilot contamination (PC), and multicarrier and multipath channels. Analytical expressions are first presented on the mean square error (MSE) of two classical channel estimation algorithms [i.e., least squares (LS) and minimum mean square error (MMSE)] in the presence of PC. Then, a simple H-infinity (H-inf) channel estimation approach is proposed to have good suppression to PC. This approach exploits the space-alternating generalized expectation–maximization (SAGE) iterative process to decompose the multicell multiuser MIMO (MU-MIMO) problem into a series of single-cell single-user single-input single-output (SISO) problems, which reduces the complexity significantly. According to the analytic results given herein, increasing the number of pilot subcarriers cannot mitigate PC, and a clue for suppressing PC is obtained. It is shown from the results that the H-inf has better suppression capability to PC than classical estimation algorithms. Its performance is close to that of the optimal MMSE as the length of channel impulse response (CIR) is increased. By using the SAGE process, the performance of the H-inf does not degrade when the number of antennas is large at the base station (BS)

Adaptive Waveform Design for Multiple Radar Tasks Based on Constant Modulus Constraint

Cognitive radar is an intelligent system, and it can adaptively transmit waveforms to the complex environment. The intelligent radar system should be able to provide different trade-offs among a variety of performance objectives. In this paper, we investigate the mutual information (MI) in signal-dependent interference and channel noise. We propose a waveform design method which can efficiently synthesize waveforms and provide a trade-off between estimation performance and detection performance. After obtaining a local optimal waveform, we apply the technique of generating a constant modulus signal with the given Fourier transform magnitude to the waveform. Finally we obtain a waveform that has constant modulus property

An Optimal Operating Strategy for Battery Life Cycle Costs in Electric Vehicles

Impact on petroleum based vehicles on the environment, cost, and availability of fuel has led to an increased interest in electric vehicle as a means of transportation. Battery is a major component in an electric vehicle. Economic viability of these vehicles depends on the availability of cost-effective batteries. This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization for battery. Assume that the deterioration of the battery is stochastic. Under the assumptions, the proposed operating strategy for battery is formulated as a nonlinear optimization problem considering reliability and failure number. And an explicit expression of the average cost rate is derived for battery lifetime. Results show that the proposed operating strategy enhances the availability and reliability at a low cost

Improved QR Decomposition-Based SIC Detection Algorithm for MIMO System

Abstract: Multiple-Input Multiple-Output (MIMO) systems can increase wireless communication system capacity enormously. Maximum Likelihood (ML) detection algorithm is the optimum detection algorithm which computational complexity growing exponentially with the number of transmit-antennas, which makes it difficult to use it in practice system. Ordered Successive Interference Cancellation (SIC) algorithm with lower computing complexity will suffer from error propagation when an incorrect symbol is selected in the early layers. An MIMO signal detection algorithm based on Improved Sorted-QR decomposition (ISQR) is presented in this study. According to the rule of SNR, ISQR can obtain the optimum detection order with less calculation. Based on ISQR an improved detection algorithm is proposed which providing 2 adjustable parameters. Trade-off between performance and complexity can be selected properly by setting the 2 parameters at different values. Simulation experiments are given under the multiple scattering wireless communication environments and the simulation experiment results show the validity of proposed algorithm

A Game-Theoretic Based Resource Allocation Strategy for Cloud Computing Services

We propose an economics-oriented cloud computing resources allocation strategy with the use of game theory. Then we develop a resource allocation algorithm named NCGRAA (noncooperative game resource allocation algorithm) to search the Nash equilibrium solution that makes the utility of various resource providers achieve optimum. We also propose an algorithm named BGRAA (bargaining game resource allocation algorithm) to further increase the overall revenue with the constraints of efficiency and fairness. Based on numerical results, we discuss the influence of NCGRAA and BGRAA for the utility of resource on the system performance. It shows that the choice of parameters of the two algorithms is significant in improving the system performance and converging to the Nash equilibrium and Nash bargaining

Terahertz image super-resolution based on a complex convolutional neural network

Terahertz (THz) imaging has been applied successfully in numerous applications, from medical imaging to industrial non-destructive detection. However, low resolution has always been a problem due to its long wavelength. A convolution neural network (CNN) is quite effective at improving the resolution of images in optics, in which real numbers are manipulated corresponding to measured intensity. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging. In this Letter, we have extended the CNN from a real number domain to a complex number domain based on the wave nature of THz light. To the best of our knowledge, this is the first time that such a complex convolution neural network (CCNN) has been shown to be successful in THz imaging. We have proved that resolution can be 0.4 times of the beam size via this approach, and half a wavelength resolution can be obtained easily. Compared to the CNN, the CCNN generates an extra 27.8% increase in terms of contrast, implying a better image. Phase information can be recovered well, which is impossible for the CNN. Although the network is trained by the MNIST dataset, it is quite powerful for image reconstruction. Again, the CCNN outperforms the CNN in terms of generalization capability. We believe such an approach can help to overcome the lower-resolution bottleneck in THz imaging, and it can release the requirement of critical optical components and extensive fine-tuning in systems. THz biomedical imaging, non-destructive testing (NDT), and a lot of imaging applications can benefit from this approach. (C) 2021 Optical Society of Americ

Effect of pilot contamination on channel estimation in massive MIMO systems

In this paper, we consider the uplink transmission in Massive MIMO systems with OFDM over frequency selective channels. Channel state information (CSI) is essential for exploiting the potential benefits of such systems. So far, few researches have addressed the effect of pilot contamination (PC) on channel estimation. In this paper, analytical expressions on the mean square error (MSE) of two classical channel estimation algorithms in the presence of PC are presented. It is shown that minimum mean square error (MMSE) is more resistant to PC compared to least square (LS). Increasing the number of pilot subcarriers, for both algorithms, does not contribute to better suppression to PC. However, from the results given herein, a clue for mitigating PC can be obtained. The performance of LS and MMSE algorithms in the presence of PC could be improved as the increase in the length of channel impulse response (CIR) and OFDM subcarriers, respectively

Decoupled Nominal 2-D Direction-of-Arrival Estimation Algorithm for Coherently Distributed Source

Abstract: A computationally efficient method for nominal 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation of coherently distributed source impinging on the far field is presented. Since the coherently distributed source is characterized by four parameters, the nominal azimuth DOA, angular spread of the nominal azimuth DOA, the nominal elevation DOA, and angular spread of the nominal elevation DOA, the computational complexity of the parameter estimation is normally high demanding. So a low complexity estimation algorithm is proposed in this paper, the key idea of which is to apply a subspace-based method without eigendecomposition in beamspace and a proposed second-order statistics for estimating the nominal elevation and azimuth DOAs. The proposed decoupled estimation algorithm does not involve any searching. It has a lower computational complexity particularly when the radio of array size to the number of source is large, at the expense of negligible performance loss. Simulation results are included to demonstrate the performance of the proposed technique

Optimal Waveform Design with Constant Modulus Constraint for Rank-One Target Detection

In radar systems, many researchers focused on the waveform design to improve the performance. And many algorithms just only consider the energy of the transmitted waveform. However, in practice, only the energy constraint is insufficient to guarantee that the signal will satisfy common envelope requirements, such as constant modulus that is important to radar transmitter. In this paper, we propose a constant modulus waveform design method for rank-one target detection. Firstly the optimal waveform under the energy constraint is obtained. Then the optimal waveform owns the constant modulus property in time domain from its Fourier transform magnitude is obtained finally. Finally, simulation results show the effectiveness of the proposed algorithm