In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.

The term "uncanny valley" refers to that unsettling feeling you get when looking at an android that has been made to appear human. Of course, the problem goes away when we can make robots that are indistinguishable from humans. A paper published last week by New Yawk University claims that "bots are more efficient than humans at certain human-machine interactions, but only if they are allowed to hide their non-human nature." In other words, once we're past that whole uncanny valley problem, we're better served letting people think they're interacting with a human when in fact it's just artificial intelligence perfected. This raises a very important question.

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A Honeywell smart thermostat is seen above. If you compare the inside of a modern home to one from about 25 years ago, you're going to notice some stark differences -- not just the phone book on the kitchen counter. Rapid advancements in tech over the past two decades have had an impact on everything from the way we communicate to the conveniences of home life. While you expect some household staples to change from generation to generation, things that were part of an average home for decades are now unnecessary. That's because just about any common household gadget can be replaced with a smarter device.

The Internet & Television Association (formerly the National Cable & Telecommunications Association, and commonly known as the NCTA) estimates that 80% of residences in the United States have access to gigabit speeds from cable companies via HFC and FTTH. Cable operators seek to solidify their position within consumer markets for broadband services as wireless carriers seek to leverage the enhanced mobile broadband (eMBB) component of 5G to gain a foothold for indoor residential and small business services. With little competition in the consumer in-home segment, certain wireless carriers see fixed wireless as a pathway to early revenue as their vendors work diligently to ensure eMBB services may be provided on a mobility basis rather than simply portable or fixed wireless solutions, which shall be predominate initially. A battleground is emerging for consumer broadband between cable companies espousing 10G (meaning symmetrical 10 Gbps speeds delivered over hybrid fiber-coaxial networks and not tenth generation) versus wireless carriers such as Verizon Wireless who will pursue the residential and small business market with fixed wireless 5G. Earlier this year, AT&T likewise stated that 5G will be a substitution for fixed-line broadband within the next three to five years. However, we see the consumer segment as a major challenge area for mobile communications service providers due to a few key factors including market inertia and deployment of WiFi6 devices.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CP A). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes. I NTRODUCTION The fifth generation (5G) wireless communication network is forecasted to provide over 1000 times higher capacity than the current system. In addition to dramatically expanding the available bandwidth, multiple-input multiple-output (MIMO) technology is playing a key role in improving the spectral efficiency (SE) and enhancing the throughput in the future wireless cellular communication systems [1]. This ambitious goal will however cause an inevitable energy consumption problem, thus limiting the number of the antennas at the base station (BS) and the user terminals in practice [2]. In the traditional design of the MIMO transceivers, each antenna is connected with one distinct radio frequency (RF) chain which includes a power amplifier (P A). This kind of structure enables the power consumption of the transmission to grow linearly with the number of the antennas. In addition, the use of Orthogonal Frequency Division Multiplexing (OFDM) signals in massive MIMO systems leads to a high peak-to-average power ratios (P APR) and exacerbates the costs of P As, thus reducing the power efficiency. On the other hand, to alleviate the effects of mutual coupling and correlated fading, the antennas should be set at least half of a wavelength apart from each other, which will inevitably cause the size problem [3].

This is an example of how digital assistants/bots have started making a difference in consumers' lives. Recently, the Singapore-based communications company UIB put out this case study on its Website of how it had tied-up with the UAE-based telco provider du Telecom to create'Blu', a – UIB UnificationEngine -powered virtual assistant on WhatsApp & Facebook Messenger. Through this UIB UnificationEngine conversational artificial intelligence (AI) platform-powered chatbot, customers can contact du 24/7 on WhatsApp with queries which receive instant responses, said Ken Herron Chief Marketing Officer, UIB, on the company Website. What's more, even Facebook sat up & took note of this partnership in an official WhatsApp case study/success story. Emirates Integrated Telecommunications Company, also known as du, is a telecom operator in the UAE, having about 9 million customers with its mobile, fixed line & broadband services.

Speaking at the third annual Digital Workforce Summit in New York City, Gonzalo Gomez Cid, Global Contact Center Director at Telefónica, discussed his company's journey from IVR-based contact centers to AI-driven operations driven by digital labor. Telefónica is a Spanish multinational telecommunications company headquartered in Madrid. It has a presence in 15 countries across Europe and Latin America. In the telco space, it ranks seventh in revenues, sixth in market capitalization and fifth in number of subscribers. Gomez Cid told DWS attendees that Telefónica describes itself as a "company of platforms," including physical assets, networks and IT, and products and services.

TCS in collaboration with Qualcomm has announced today the launch of Innovation Hub in Hyderabad, India. With 5G technology, users can experience low latency while dealing with cloud computing, highly responsive multiplayer gaming, immersive 360-degree video, and instant apps, etc. The 5G speed will make such tracking almost instantaneous. Such a tremendous amount of data can be gathered and directed at machine learning and AI (Artificial Intelligence) algorithms delivering crucial insights to make intelligent decisions. Executive Opinion Global Head, Technology Business Unit, TCS, V Rajanna, said, "The convergence of 5G, AI and edge computing will open unprecedented opportunities for value creation in industrial automation, autonomous vehicles and other industries. The new Innovation Hub brings together TCS' and Qualcomm Technologies' world-class technology expertise to unlock the potential of transformational solutions in this emerging space to help global enterprises explore the art of the possible and accelerate their Business 4.0 journeys."

Neural Architecture Search (NAS) has shown great potentials in finding a better neural network design than human design. Sample-based NAS is the most fundamental method aiming at exploring the search space and evaluating the most promising architecture. However, few works have focused on improving the sampling efficiency for a multi-objective NAS. Inspired by the nature of the graph structure of a neural network, we propose BOGCN-NAS, a NAS algorithm using Bayesian Optimization with Graph Convolutional Network (GCN) predictor. Specifically, we apply GCN as a surrogate model to adaptively discover and incorporate nodes structure to approximate the performance of the architecture. Our method further considers an efficient multi-objective search which can be flexibly injected into any sample-based NAS pipelines to efficiently find the best speed/accuracy tradeoff. Extensive experiments are conducted to verify the effectiveness of our method over many competing methods, e.g. Recently Neural Architecture Search (NAS) has aroused a surge of interest by its potentials of freeing the researchers from tedious and time-consuming architecture tuning for each new task and dataset. Specifically, NAS has already shown some competitive results comparing with handcrafted architectures in computer vision: classification (Real et al., 2019b), detection, segmentation (Ghiasi et al., 2019; Chen et al., 2019; Liu et al., 2019a) and super-resolution (Chu et al., 2019). Meanwhile, NAS has also achieved remarkable results in natural language processing tasks (Luong et al., 2018; So et al., 2019). A variety of search strategies have been proposed, which may be categorized into two groups: one-shot NAS algorithms (Liu et al., 2019b; Pham et al., 2018; Luo et al., 2018), and sample-based algorithms (Zoph & Le, 2017; Liu et al., 2018a; Real et al., 2019b).