This paper presents novel machine-learning-based methods for estimating the state of charge (SoC) of lithium-ion batteries, which use the Gaussian process regression (GPR) framework. The measured ...battery parameters, such as voltage, current, and temperature, are used as inputs for regular GPR, whereas the SoC estimate at the previous sample is fed back and incorporated into the input vector for recurrent GPR. The proposed methods consist of two parts. In the first part, training is performed wherein the optimal hyperparameters of a chosen kernel function are determined to model data properties. In the second part, online SoC estimation is carried out according to the trained model. One of the practical advantages of a GPR framework is to quantify estimation uncertainty and, hence, to enable reliability assessment of the battery SoC estimate. The performance of the proposed methods is evaluated by using a simulated dataset and two experimental datasets, one with constant and the other with dynamic charge and discharge currents. The simulations and experimental results show the superiority of the proposed methods in comparison to state-of-the-art techniques including a support vector machine, a relevance vector machine, and a neural network.
Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) ...measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standards - to construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous location-and-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.
We consider angular-domain channel estimation in massive MIMO systems using one-bit analog-to-digital converters (ADCs) with various thresholding schemes at the receivers. We first derive the ...performance bounds for estimating angular-domain channel parameters, including the angles-of-arrival (AoA), angles-of-departure (AoD) and the associated path gains. Specifically, we derive 1) the deterministic Cramér-Rao bound (CRB) when all of the angular-domain channel parameters are treated as deterministic unknowns; 2) the hybrid CRB when some parameters have known prior probability density functions (pdfs) while the rest are assumed to be deterministic unknowns; 3) the Bayesian CRB when all of them have known prior pdfs. We also consider using the maximum likelihood (ML) method for channel estimation and a computationally efficient relaxation based cyclic algorithm (referred to as 1bRELAX) to obtain the ML estimates. When the prior information is available, the maximum a posteriori (MAP) and joint ML-MAP (JML-MAP) estimators are derived. We also use the one-bit Bayesian information criterion (1bBIC) to determine the number of scattering paths. Numerical examples are provided to verify the derived performance bounds with different thresholding schemes and demonstrate the performance of the proposed channel estimation algorithms.
Contiguous/noncontiguous carrier aggregation (CA) is one of the key features from 4G systems, which is expected to be evolved within 5G technologies. Thus, there is a need for the development of ...flexible, agile, and reconfigurable radio transceivers with a native support for the integration of multiple bands and multiple standards. All-digital radio-frequency (RF) transmitters have demonstrated promising potential to the design of next-generation RF transceivers. However, the simultaneous multiband transmission is still one of the key limitations of current approaches. To address this problem, this paper presents a fully digital and parallel architecture that enables the real-time design of agile and concurrent triple-band transmission. The proposed architecture is suitable for both contiguous and noncontiguous CA scenarios and considerably surpasses the state of the art in terms of frequency agility, maximum spacing between bands, and aggregated bandwidth. To enhance the system performance, an extension to a multilevel architecture based on the analog combination of pulsed waveforms is also demonstrated. Both architectures (two and seven levels) were implemented in a field-programmable gate array. Measurement results in terms of signal-to-noise ratio, error-vector magnitude, and adjacent-channel power ratio are presented and discussed. In Implementation-I, the two-level architecture presents a frequency agility from 0.1 to 2.5 GHz (with a frequency resolution of 4.88 MHz) with an aggregated bandwidth of 56.26 MHz. In Implementation-II, the seven-level design presents a frequency agility from 0.1 to 2 GHz (with a frequency resolution of 3.906 MHz) with an aggregated bandwidth of 112.5 MHz.
This paper considers parameter estimation of a hybrid sinusoidal frequency modulated (FM) and polynomial phase signal (PPS) from a finite number of samples. We first show limitations of an existing ...method, the high-order ambiguity function (HAF), and then propose a new method by adopting the high-order phase function which was originally designed for the pure PPS. The proposed method estimates parameters of interest from peak locations in the time-frequency rate domain, which are less perturbed by the noise than peak values used by the HAF-based method. Numerical evaluation shows the proposed method can handle the hybrid FM-PPS signal with low sinusoidal frequency and improve estimation accuracy in terms of mean squared error for several orders of magnitude.
In this paper, a cooperative wireless system with unreliable wireless backhaul connections is investigated. To increase the throughput and maximize the receiver signal-to-noise ratio (SNR), a ...selection combining (SC) protocol is employed. Cooperative transmitters are connected to the control unit (CU) via independent but unreliable wireless backhaul connections. Simultaneously taking into account the reliability of each backhaul and different fading conditions of Nakagami-m fading channels, the statistical properties of the effective SNR (e-SNR) at the receiver are investigated. Closed-form expressions are derived for several performance metrics, including the outage probability, average spectral efficiency (ASE), and average symbol error rate (ASER). The effects of backhaul reliability on these performance metrics are also investigated. The scaling relationship between the convergence behavior of these performance metrics and the conventional diversity gain is also analytically investigated in the asymptotic high-SNR regime. Monte Carlo simulations are conducted to verify the derived impact of backhaul reliability on the performance.
In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted ...to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.
This paper investigates a distributed cyclic delay diversity (CDD) transmission scheme for cyclic-prefixed single carrier systems in non-identically and identically distributed frequency selective ...fading channels. The distinguishable feature of the proposed scheme lies in providing a transmit diversity gain while reducing the burden of estimating the channel state information, which is a challenging task in distributed and cooperative systems. To effectively use the distributed CDD scheme at the transmitters, two sufficient conditions are derived to eliminate the intersymbol interference at the receiver and leveraged to convert the multi-input single-output channel into a single-input single-output channel. These conditions allow the system to achieve the maximum diversity for frequency selective fading channels at a full rate. To achieve this maximum diversity, a fixed number of CDD transmitters are selected based on the channel conditions, symbol block size, and maximum time dispersion of the channel, and a new two-stage transmission mode is proposed. Based on the distributed CDD and the proposed selection schemes, a new expression for the signal-to-noise ratio at the receiver is obtained with the aid of order statistics, and then closed-form expressions for the outage probability and average symbol error rate (ASER) are derived. As far as the identically distributed frequency selective fading channel model is concerned, the achievable maximum diversity gain is proved, with the aid of asymptotic analysis, to be equal to the product of the total number of transmitters in the system and the number of multipath components. Link-level simulations are also conducted to validate the analytical expressions for outage probability, ASER, and maximum achievable diversity gain.
This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a ...hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received ...MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast . FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0-25 dB at a transmission symbol rate of 37.5 Msymbols/s.
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