The commercial availability of low-cost millimeter-wave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used. We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standard-compliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Device-free (i.e., radar-based) sensing systems still have to be improved in terms of higher accuracy in the detection of vital signs (respiration and heart rate), and enhanced robustness/generalization capabilities across different environments. Moreover, improved support is needed for the tracking of multiple users, and for the automatic creation of radar networks to enable large-scale sensing applications. Finally, integrated systems performing joint communications and sensing are still in their infancy: theoretical and practical advancements are required to add sensing functionalities to mmWave-based channel access protocols based on orthogonal frequency-division multiplexing (OFDM) and multi-antenna technologies.

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

A recent market prediction is that 5G Fixed Wireless Access (FWA) will more than double over the next five years and trials at the same period in London suggest promising results. However, the shift to 5G FWA has raised a new set of research challenges in relation to speed of deployment and re-deployment, coverage, power consumption, end user mobility and last mile connectivity, to name just a few, because of the much higher expectations. A recent review reveals that key 5G Physical Layer technologies that will enable wide mobile and FWA have not kept up pace. In response to some of those research challenges, this paper presents the design of a 5G Multiple Input Multiple Output (MIMO) Antenna that is mounted on a tethered aerostat, and the combination of which serves as a 5G FWA aerial station. The antenna design features several novelties and the aerial station can provide last mile connectivity to a wide coverage footprint, with moderate power consumption and operating at high speeds. Both the evaluation of the antenna performance using several key performance indicators and the validation of the aerial station as a 5G FWA in a wireless sensor network (WSN) proof-of-concept application reveal efficiency gains.

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An enhanced design of a 5G MIMO antenna for fixed wireless aerial access

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Broadband Communications Networks - Recent Advances and Lessons from Practice

The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used. We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standardcompliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Device-free (i.e., radar-based) sensing systems still have to be improved in terms of: improved accuracy in the detection of vital signs (respiration and heart rate) and enhanced robustness/generalization capabilities across different environments; moreover, improved support is needed for the tracking of multiple users, and for the automatic creation of radar networks to enable largescale sensing applications. Finally, integrated systems performing joint communications and sensing are still in their infancy: theoretical and practical advancements are required to add sensing functionalities to mmWave-based channel access protocols based on orthogonal frequency-division multiplexing (OFDM) and multi-antenna technologies. 

/pdf/a-review-of-millimeter-wave-device-based-localization-and-2h82n6td.pdf

There is strong impetus by the Telecom Infra Project to exploit the 60 GHz unlicensed band for public Wi-Fi in urban environments, by installing access points on light poles. Although many 60 GHz urban channel measurements have been recorded to date, they have resulted only in path loss models or root-mean-square (RMS) delay spreads. What is needed at millimeter-wave is a spatially consistent channel model for beamtracking that embodies the characteristics of these short wavelengths—sparsity and rough surface scattering—such as the Quasi-Deterministic model. In this letter, we fit the model to channel measurements we recorded in an urban environment. The measurements were recorded at 4, 6, and 9 m antenna heights to investigate the tradeoffs between light pole heights. The large-scale channel metrics between the model and the measurements were shown to match very well.

Quasi-Deterministic Channel Propagation Model for 60 GHz Urban Wi-Fi Access From Light Poles

The use of Massive MIMO and Millimeter-wave (mmWave) networks can be beneficial for a number of Fixed Wireless Access use cases, such as Smart City (SC) Services, providing connectivity for SC infrastructure, and Fixed Wireless Access using street-level distribution and access via so-called "street furniture" equipment. In this paper, we present the findings from a mmWave channel measurement campaign performed in different indoor and outdoor scenarios as part of our contribution to the mmWave Network Project Group of the Telecom Infra Project see (https://telecominfraproject.com/mmwave/). Indoor scenarios included workspace environments such as office rooms, corridors, etc. whereas, outdoor scenarios included urban and suburban environments. A set of two 802.11ad TerragraphTM Channel Sounder nodes (provided by Facebook under TIP) equipped with massive MIMO antenna arrays were used as the transmitter and the receiver for the characterization of the 60GHz mmWave channel. The measurement results include path loss, received power, input and output SNR and delay spread values for each specified beam combination. Urban, suburban and indoor environments were tested in both Line of Sight (LoS) and Non-Line of Sight (N-LoS) configurations.

/pdf/mmwave-massive-mimo-channel-measurements-for-fixed-wireless-mhi8xsvr63.pdf

mmWave Massive MIMO Channel Measurements for Fixed Wireless and Smart City Applications

Innovative 5G orchestration architectures so far, have been mainly designed and optimized for Quality of Service (QoS), but are not aware of Quality of Experience (QoE). This makes intent recognition and End-to-End interpretability an inherited problem for orchestration systems, leading to possible creation of ineffective control policies. In this paper, an AI-driven intent-based networking for autonomous robots is proposed and demonstrated through the 5G-ERA project. In particular, to map an intent from individual vertical action to a global OSM control policy, a workflow of four tools is proposed: i) Action Sequence Generation, ii) Network Intent Estimation, iii) Resource Usage Forecasting, and iv) OSM Control Policy Generation. All of these tools are described in the paper with specific function descriptions, inputs, outputs and the semantic models/Machine Learning tools that have been used. Finally, the paper presents the developed intent-based dashboard for the visualization of the tools’ outputs, whilst taking QoE into consideration. 

AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy

This work introduces the advanced orchestration and diagnostic components for 5G and beyond infrastructures enabling intelligent automated management of deployed vertical services. The paper presents the progress and results produced and the actions for creating, deploying and monitoring a network service. The developed components can support both currently developing innovative 5G and 6G network services and solutions for vertical industries. These include features that enable the design phase of vertical users, intelligent orchestration mechanisms with optimal placement of services and finally offer insights and performance analysis. In the context of the performance diagnosis mechanisms we propose a fault detection and localization model based on a combination of two respective modules, one that utilizes Self-Organizing Maps (SOM) as a Machine Learning technique and one that implements an algorithm based on adjacency lists to perform Root Cause Analysis (RCA). Finally, the aforementioned aspects are supported by some preliminary results in order to show the effectiveness of such approach.

Ioannis Chondroulis

Papers

mmWave Massive MIMO Channel Measurements for Fixed Wireless and Smart City Applications

AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy

Performance-Aware Orchestration and Management over 5G and Beyond Infrastructures Based on Diagnostic Mechanisms