Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.

Today, companies across society are applying AI to optimize internal processes to improve the quality and performance of their existing products, to design new products and/or to further optimize the workforce. AI has proven to be critical for managing and predicting operations of a telecommunication network. However, most of the time, AI is restricted to data scientists and data analysts who are specialists specifically trained in AI. At the same time, it's the subject matter expert, i.e., experienced engineers and technicians who have the expert knowledge in a specific business or technical area. They generally also own the data. One way of bringing AI closer to the subject matter expert (SME) is by democratizing AI.

From being not much more but a catchy phrase to one of the most promising and powerful technologies, AI has come a long way. While communication and digital service providers start to implement ever more solutions based on artificial intelligence and machine learning, the technology is growing stronger by the minute. The predictions, in fact, are that without AI-driven systems, telecoms won't be able to survive in a highly competitive, digital-first market. So, what is the state of AI implementation in telcos? What solutions are the most popular at the moment, and which are expected to surge in the coming months?

Accurate smartphone localization (< 1-meter error) for indoor navigation using only RSSI received from a set of BLE beacons remains a challenging problem, due to the inherent noise of RSSI measurements. To overcome the large variance in RSSI measurements, we propose a data-driven approach that uses a deep recurrent network, DeepBLE, to localize the smartphone using RSSI measured from multiple beacons in an environment. In particular, we focus on the ability of our approach to generalize across many smartphone brands (e.g., Apple, Samsung) and models (e.g., iPhone 8, S10). Towards this end, we collect a large-scale dataset of 15 hours of smartphone data, which consists of over 50,000 BLE beacon RSSI measurements collected from 47 beacons in a single building using 15 different popular smartphone models, along with precise 2D location annotations. Our experiments show that there is a very high variability of RSSI measurements across smartphone models (especially across brand), making it very difficult to apply supervised learning using only a subset of smartphone models. To address this challenge, we propose a novel statistic similarity loss (SSL) which enables our model to generalize to unseen phones using a semi-supervised learning approach. For known phones, the iPhone XR achieves the best mean distance error of 0.84 meters. For unknown phones, the Huawei Mate20 Pro shows the greatest improvement, cutting error by over 38\% from 2.62 meters to 1.63 meters error using our semi-supervised adaptation method.

Voice continue to the most widely-utilized customer service channel by consumers, with 73% of consumers calling into the call center for customer service needs, according to Forrester. Other channels are gaining ground, however, with digital channels, such as chat and email, and web-based self-service becoming increasingly utilized by consumers. New technologies are providing consumers with more options for connecting with the companies they do business with, but technology advancements are also reshaping the way companies are meeting those needs. Once a pipe dream believed to be far off in the future, artificial intelligence (AI) is one innovation that's transforming the customer service landscape. We've put together this guide to provide a comprehensive history of AI in the call center, from the advent of artificial intelligence as a whole to its first use in the call center and the potential for future disruption.

Millimeter wave vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements compared to exhaustive beam search. With fixed layouts of roadside buildings and regular vehicular moving trajectory, the dominant path directions of channels will likely be among a subset of beam directions instead of distributing randomly over the whole beamspace. In this paper, we propose a deep learning-based technique to design a structured compressed sensing (CS) matrix that is well suited to the underlying channel distribution for mmWave vehicular beam alignment. The proposed approach leverages both sparsity and the particular spatial structure that appears in vehicular channels. We model the compressive channel acquisition by a two-dimensional (2D) convolutional layer followed by dropout. We incorporate the low-resolution phase shifter constraint during neural network training by using projected gradient descent for weight updates. Furthermore, we exploit channel spectral structure to optimize the power allocated for different subcarriers. Simulations indicate that our deep learningbased approach achieves better beam alignment than standard CS techniques which use random phase shift-based design. Numerical experiments also show that one single subcarrier is sufficient to provide necessary information for beam alignment. Millimeter-wave (mmWave) vehicular communication enables massive sensor data sharing and various emerging applications related to safety, traffic efficiency and infotainment [2]-[4]. Yuyang Wang is with Apple Inc., One Apple park way, Cupertino, CA, 95014, USA, email: yuywang@utexas.edu. Nitin Jonathan Myers is with Samsung Semiconductor Inc., 5465 Morehouse Dr, San Diego, CA 92121 USA, email: nitinjmyers@utexas.edu. Nuria González-Prelcic, and Robert W. Heath Jr. are with the Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27606 USA, email: {ngprelcic, rwheathjr}@ncsu.edu. Part of this work has been presented at IEEE ICASSP 2020 [1]. This material is based upon work supported in part by the National Science Foundation under Grant No. ECCS-1711702, and by a Qualcomm Faculty Award.

In 5G Ultra-Dense Networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of KGA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.

In this paper, a proactive dynamic spectrum sharing scheme between 4G and 5G systems is proposed. In particular, a controller decides on the resource split between NR and LTE every subframe while accounting for future network states such as high interference subframes and multimedia broadcast single frequency network (MBSFN) subframes. To solve this problem, a deep reinforcement learning (RL) algorithm based on Monte Carlo Tree Search (MCTS) is proposed. The introduced deep RL architecture is trained offline whereby the controller predicts a sequence of future states of the wireless access network by simulating hypothetical bandwidth splits over time starting from the current network state. The action sequence resulting in the best reward is then assigned. This is realized by predicting the quantities most directly relevant to planning, i.e., the reward, the action probabilities, and the value for each network state. Simulation results show that the proposed scheme is able to take actions while accounting for future states instead of being greedy in each subframe. The results also show that the proposed framework improves system-level performance.

We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

In this work, we propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification easily allows us to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgements that subjects form about the relationships among actors, and perform clustering tasks at different social instances. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.