The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.

In this paper, we propose a deep reinforcement learning (RL)-based precoding framework that can be used to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems. We model the precoding problem for a single-user MIMO system as an RL problem in which a learning agent sequentially selects the precoders to serve the environment of MIMO system based on contextual information about the environmental conditions, while simultaneously adapting the precoder selection policy based on the reward feedback from the environment to maximize a numerical reward signal. We develop the RL agent with two canonical deep RL (DRL) algorithms, namely deep Q-network (DQN) and deep deterministic policy gradient (DDPG). To demonstrate the optimality of the proposed DRL-based precoding framework, we explicitly consider a simple MIMO environment for which the optimal solution can be obtained analytically and show that DQN- and DDPG-based agents can learn the near-optimal policy to map the environment state of MIMO system to a precoder that maximizes the reward function, respectively, in the codebook-based and non-codebook based MIMO precoding systems. Furthermore, to investigate the robustness of DRL-based precoding framework, we examine the performance of the two DRL algorithms in a complex MIMO environment, for which the optimal solution is not known. The numerical results confirm the effectiveness of the DRL-based precoding framework and show that the proposed DRL-based framework can outperform the conventional approximation algorithm in the complex MIMO environment.

In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module. The likelihood module injects information about the generative (forward) process into the neural network. The encoder-predictor modules together update the state vector and symbol estimates. The encoder module updates the state vector and employs a transformer based attention network to handle the interactions among the users in a permutation equivariant manner. The predictor module refines the symbol estimates. The modular and permutation equivariant architecture allows for dealing with a varying number of users. The resulting neural detector architecture is unique and exhibits several desirable properties unseen in any of the previously proposed neural detectors. We compare its performance against existing methods and the results show the ability of our network to efficiently handle a variable number of transmitters with high accuracy.

Network embedding is a very important method for network data. However, most of the algorithms can only deal with static networks. In this paper, we propose an algorithm Recurrent Neural Network Embedding (RNNE) to deal with dynamic network, which can be typically divided into two categories: a) topologically evolving graphs whose nodes and edges will increase (decrease) over time; b) temporal graphs whose edges contain time information. In order to handle the changing size of dynamic networks, RNNE adds virtual node, which is not connected to any other nodes, to the networks and replaces it when new node arrives, so that the network size can be unified at different time. On the one hand, RNNE pays attention to the direct links between nodes and the similarity between the neighborhood structures of two nodes, trying to preserve the local and global network structure. On the other hand, RNNE reduces the influence of noise by transferring the previous embedding information. Therefore, RNNE can take into account both static and dynamic characteristics of the network.We evaluate RNNE on five networks and compare with several state-of-the-art algorithms. The results demonstrate that RNNE has advantages over other algorithms in reconstruction, classification and link predictions.

The broad deployment of 802.11 (a.k.a., WiFi) access points and significant enhancement of the energy efficiency of these wireless transceivers has resulted in increasing interest in building 802.11-based IoT systems. Unfortunately, the main energy efficiency mechanisms of 802.11, namely PSM and APSD, fall short when used in IoT applications. PSM increases latency and intensifies channel access contention after each beacon instance, and APSD does not inform stations about when they need to wake up to receive their downlink packets. In this paper, we present a new mechanism---edge-assisted predictive sleep scheduling (EAPS)---to adjust the sleep duration of stations while they expect downlink packets. We first implement a Linux-based access point that enables us to collect parameters affecting communication latency. Using this access point, we build a testbed that, in addition to offering traffic pattern customization, replicates the characteristics of real-world environments. We then use multiple machine learning algorithms to predict downlink packet delivery. Our empirical evaluations confirm that when using EAPS the energy consumption of IoT stations is as low as PSM, whereas the delay of packet delivery is close to the case where the station is always awake.

Digital transformation is the "buzzword du jour" in every industry. There have been many initiatives that should have led to a digital transformation across many industries -- supply chain integration, global ERP systems, etc. These likely should have prepared us for the digital life. This fell far short in large part to one key element -- data. Data is key to any digital transformation journey, but the foundation of all data is master data.

These kinds of mysteries used to require a lot of foresight and engineering work to deal with. But now, Comcast says it can use artificial intelligence to solve similar problems automatically. Prompted by the coronavirus pandemic, the company developed an AI system called Octave that can detect network anomalies and figure out how to address them. "It's not just automating what smart engineers can do. It's going to places where they just couldn't process that amount of information and come up with solutions quick enough to do what [Octave] does," says Tony Werner, Comcast's president of technology, product, and "Xperience."

Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a setting encountered commonly in practical applications of deep learning for graph networks. This significantly affects the performances of downstream tasks such as node classification, link prediction or community extraction. To this end, we propose SANNE -- a novel unsupervised embedding model -- whose central idea is to employ a transformer self-attention network to iteratively aggregate vector representations of nodes in random walks. Our SANNE aims to produce plausible embeddings not only for present nodes, but also for newly unseen nodes. Experimental results show that the proposed SANNE obtains state-of-the-art results for the node classification task on well-known benchmark datasets.

The Association for Unmanned Vehicle Systems International (AUVSI), the world's largest nonprofit organization dedicated to the advancement of unmanned systems and robotics, has compiled a list of "wow-worthy" examples of the vision that the fifth generation of wireless technology (5G) is inspiring for the use of connected drones. It says that 5G can: bring data-throughput speeds of up to 10 gigabytes per second, enabling real-time sharing of aerial video and other sensor data; enable devices to stay connected while traveling hundreds of miles per hour, allowing for remote deployment of AI-enabled, ultra-responsive autonomous fleets; and it could support up to a million connected devices per square kilometer -- enough capacity to absorb an explosion in the Internet of Things alongside increasingly sophisticated mobile applications, on the ground and aloft. "5G is going to be transformative," says Tom Sawanobori, chief technology officer for CTIA (Cellular Telecommunications Industry Association). He cited a 2017 study by Accenture which estimated 5G would bring 3 million new jobs, $275 billion in new investment and a $500 billion boost to the U.S. gross domestic product. Active tech companies in the markets this week include FLIR Systems, Inc. (NASDAQ: FLIR), Plymouth Rock Technologies Inc. (CSE: PRT) (OTCQB: PLRTF), Verizon Communications Inc. (NYSE: VZ), Raytheon Technologies Corporation (NYSE: RTX), QUALCOMM Incorporated (NASDAQ: QCOM).

WINNIPEG -- Canadians may soon be getting fewer scam calls. The Canadian Radio-television and Telecommunications Commission (CRTC) has approved a 90-day trial of Bell's plan to use artificial intelligence to help put an end to fraudulent phone calls. According to a news release, Bell's new system uses "defined sets of call characteristics and proprietary machine learning algorithms" to identify which calls are fraudulent. It said this system could stop about 120 million scam calls every month, in addition to the 220 million fraudulent calls it shuts down using a plan implemented in 2019. Bell is the parent company of CTV News.