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Register a new userModel-based Learning Network for 3-D Localization in mmWave Communications
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This study considers the joint location and velocity estimation of UE and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results with neural networks (NNs) for localization. However, the black box NN localization method has limited performance and relies on a prohibitive amount of training data. Thus, we propose a model-based learning network for localization by combining NNs with geometric models. Specifically, we first develop an unbiased WLS estimator by utilizing hybrid delay/angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the CRLB and outperforms state-of-the-art methods. Second, we establish a NN-assisted localization method (NN-WLS) by replacing the linear approximations in the proposed WLS localization model with NNs to learn higher-order error components, thereby enhancing the performance of the estimator. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.
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Beamforming structures with fixed beam codebooks provide economical solutions for millimeter wave (mmWave) communications due to the low hardware cost. However, the training overhead to search for the optimal beamforming configuration is proportional to the codebook size. To improve the efficiency of beam tracking, we propose a beam tracking scheme based on the channel fingerprint database, which comprises mappings between statistical beamforming gains and user locations. The scheme tracks user movement by utilizing the trained beam configurations and estimating the gains of beam configurations that are not trained. Simulations show that the proposed scheme achieves significant beamforming performance gains over existing beam tracking schemes.
In millimeter-wave (mmWave) channels, to overcome the high path loss, beamforming is required. Hence, the spatial representation of the channel is essential. Further, for accurate beam alignment and minimizing the outages, inter-beam interferences, etc., cluster-level spatial modeling is also necessary. Since, statistical channel models fail to reproduce the intra-cluster parameters due to the site-specific nature of the mmWave channel, in this paper, we propose a ray tracing intra-cluster model (RT-ICM) for mmWave channels. The model considers only the first-order reflection; thereby reducing the computation load while capturing most of the energy in a large number of important cases. The model accounts for diffuse scattering as it contributes significantly to the received power. Finally, since the clusters are spatially well-separated due to the sparsity of first-order reflectors, we generalize the intra-cluster model to the mmWave channel model via replication. Since narrow beamwidth increases the number of single-order clusters, we show that the proposed model suits well to MIMO and massive MIMO applications. We illustrate that the model gives matching results with published measurements made in a classroom at 60 GHz. For this specific implementation, while the maximum cluster angle of arrival (AoA) error is 1 degree, mean angle spread error is 9 degrees. The RMS error for the cluster peak power is found to be 2.2 dB.
While molecular communication via diffusion experiences significant inter-symbol interference (ISI), recent work suggests that ISI can be mitigated via time differentiation pre-processing which achieves pulse narrowing. Herein, the approach is generalized to higher order differentiation. The fundamental trade-off between ISI mitigation and noise amplification is characterized, showing the existence of an optimal derivative order that minimizes the bit error rate (BER). Theoretical analyses of the BER and a signal-to-interference-plus-noise ratio are provided, the derivative order optimization problem is posed and solved for threshold-based detectors. For more complex detectors which exploit a window memory, it is shown that derivative pre-processing can strongly reduce the size of the needed window. Extensive numerical results confirm the accuracy of theoretical derivations, the gains in performance via derivative pre-processing over other methods and the impact of the optimal derivative order. Derivative pre-processing offers a low complexity/high-performance method for reducing ISI at the expense of increased transmission power to reduce noise amplification.
In this paper, we propose a physical layer security scheme that exploits a novel index modulation (IM) technique for coordinate interleaved orthogonal designs (CIOD). Utilizing the diversity gain of CIOD transmission, the proposed scheme, named CIOD-IM, provides an improved spectral efficiency by means of IM. In order to provide a satisfactory secrecy rate, we design a particular artificial noise matrix, which does not affect the performance of the legitimate receiver, while deteriorating the performance of the eavesdropper. We derive expressions of the ergodic secrecy rate and the theoretical bit error rate upper bound. In addition, we analyze the case of imperfect channel estimation by taking practical concerns into consideration. It is shown via computer simulations that the proposed scheme outperforms the existing IM-based schemes and might be a candidate for future secure communication systems.
In this work, we propose a novel approach for high accuracy user localization by merging tools from both millimeter wave (mmWave) imaging and communications. The key idea of the proposed solution is to leverage mmWave imaging to construct a high-resolution 3D image of the line-of-sight (LOS) and non-line-of-sight (NLOS) objects in the environment at one antenna array. Then, uplink pilot signaling with the user is used to estimate the angle-of-arrival and time-of-arrival of the dominant channel paths. By projecting the AoA and ToA information on the 3D mmWave images of the environment, the proposed solution can locate the user with a sub-centimeter accuracy. This approach has several gains. First, it allows accurate simultaneous localization and mapping (SLAM) from a single standpoint, i.e., using only one antenna array. Second, it does not require any prior knowledge of the surrounding environment. Third, it can locate NLOS users, even if their signals experience more than one reflection and without requiring an antenna array at the user. The approach is evaluated using a hardware setup and its ability to provide sub-centimeter localization accuracy is shown.
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