Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user's needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on baseband resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This paper surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RAN.

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Cloud RAN for Mobile Networks—A Technology Overview

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore entails larger size and higher cost than would otherwise be required for the same power output. An alternative solution, which is gaining widespread popularity, is to employ digital baseband predistortion ahead of the amplifier to compensate for the nonlinearity effects, hence allowing it to run closer to its maximum output power while maintaining low spectral regrowth. Recent improvements to the technique have included memory effects in the predistortion model, which are essential as the bandwidth increases. In this paper, we relate the general Volterra representation to the classical Wiener, Hammerstein, Wiener-Hammerstein, and parallel Wiener structures, and go on to describe some state-of-the-art predistortion models based on memory polynomials. We then propose a new generalized memory polynomial that achieves the best performance to date, as demonstrated herein with experimental results obtained from a testbed using an actual 30-W, 2-GHz power amplifier

A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

The present invention relates to electroactive polymers that are pre-strained to improve conversion from electrical to mechanical energy. When a voltage is applied to electrodes contacting a pre-strained polymer, the polymer deflects. This deflection may be used to do mechanical work. The pre-strain improves the mechanical response of an electroactive polymer. The present invention also relates to actuators including an electroactive polymer and mechanical coupling to convert deflection of the polymer into mechanical work. The present invention further relates to compliant electrodes that conform to the shape of a polymer. The present invention provides methods for fabricating electromechanical devices including one or more electroactive polymers.

Electroactive polymer electrodes

A shielded medical device implanted in a biological organism. The device has a magnetic shield, and the magnetic shield contains a layer of nanomagnetic material; such layer has a morphological density of at least 98 percent; and it is bonded to the medical device by means of an interlayer with a thickness of less than about 10 microns. The nanomagnetic material in such layer has a saturation magnetization of from about 1 to about 36,000 Gauss, a coercive force of from about 0.01 to about 5,000 Oersteds, a relative magnetic permeability of from about 1 to about 500,000, and an average particle size of less than about 100 nanometers.

Magnetically shielded assembly

Most recent semi-supervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, the online fine-tuning process is usually time-consuming, limiting the practical use of such methods. We propose a directional deep embedding and appearance learning (DDEAL) method, which is free of the online fine-tuning process, for fast VOS. First, a global directional matching module, which can be efficiently implemented by parallel convolutional operations, is proposed to learn a semantic pixel-wise embedding as an internal guidance. Second, an effective directional appearance model based statistics is proposed to represent the target and background on a spherical embedding space for VOS. Equipped with the global directional matching module and the directional appearance model learning module, DDEAL learns static cues from the labeled first frame and dynamically updates cues of the subsequent frames for object segmentation. Our method exhibits state-of-the-art VOS performance without using online fine-tuning. Specifically, it achieves a & mean score of 74.8% on DAVIS 2017 dataset and an overall score of 71.3% on the large-scale YouTube-VOS dataset, while retaining a speed of 25 fps with a single NVIDIA TITAN Xp GPU. Furthermore, our faster version runs 31 fps with only a little accuracy loss.

Ieee transactions on neural networks and learning systems

In current wireless base station solutions it is becoming common to physically separate baseband units and radio subsystems. In many wireless technologies this architecture requires allocation of significant transport network resources. In this paper a low-latency baseband signal compression scheme is presented. The compression scheme significantly lowers the transport data rate while maintaining low levels of signal distortion, thus resulting in a lower-cost transport network. Considering the importance of packet-based networks, a number of additional novel compression schemes are proposed. They are optimized for transport networks that implement a quality-of-service (QoS) mechanism and/or multi-link transmission. The compression schemes are parameterized such that a smooth trade-off between the required signal quality and compression performance can be achieved through operator choice of the suitable parameter values. An attractive feature of these schemes is that they can be applied to different wireless technologies, with appropriate parameter settings, without disrupting the present architecture. The proposed solutions will lead to a cost-effective implementation of collocated and distributed network-centric baseband processing, coordinated multi-point (CoMP) and/or distributed antenna system (DAS) which are critical topics for the entire wireless telecommunications industry and infrastructure.

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Compressed Transport of Baseband Signals in Radio Access Networks

Power amplifiers (PAs) are essential in communication systems, but are inherently nonlinear. To achieve linearity with good efficiency, PA linearization is necessary. Digital baseband predistortion is a highly cost-effective way to linearize PAs, but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications, such as wideband code-division multiple access, PA memory effects can no longer be ignored. Therefore, in order to achieve good linearization performance, the predistorter needs to also have memory structure. In this paper, we propose a new model for the wideband predistorter and a least-squares(LS)/Newton algorithm to estimate the model parameters. Performance of the LS/Newton algorithm is studied through computer simulations. Good linearization performance is achieved by using the new model in an experimental testbed.

A least-squares/Newton method for digital predistortion of wideband signals

In one embodiment, the method of compressing a digital signal includes reducing redundancies in the digital signal, scaling a block of samples output from the reducing step by a scaling factor, and quantizing the scaled samples to produce compressed samples The digital signal being compressed may be a digital radio frequency signal

Method And Apparatus For Signal Compression And Decompression

A method for achieving improved piezoelectric films for use in a resonator device is disclosed. The method is based on applicant's recognition that the texture of a piezoelectric film (e.g., as used in a piezoelectric resonator) is directly affected by the surface morphology of the underlying electrode, and additionally, the surface morphology of the electrode is affected by the surface morphology of the underlying oxide layer or Bragg stack. Accordingly, the invention comprises a method of making a device having a piezoelectric film and electrode comprising controlling the deposition and surface roughness of the electrode and optionally, the Bragg stack.

John Z. Pastalan

Papers

A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Compressed Transport of Baseband Signals in Radio Access Networks

A least-squares/Newton method for digital predistortion of wideband signals

Method And Apparatus For Signal Compression And Decompression

Method for producing devices having piezoelectric films