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Register a new userMillimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
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Added by
Carlos Baquero Barneto
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospect with specific emphasis on the user equipment (UE) side. We first describe an efficient l1-regularized least-squares (LS) approach to obtain sparse range--angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. We provide extensive numerical indoor mapping results at the 28~GHz band deploying OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks.
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Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a minimum number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides high rate and reliability in all locations of the users trajectory with a minimal number of handovers. Simulation results in an outdoor environment based on geometric mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.
Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a users rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.
Millimeter wave beam alignment (BA) is a challenging problem especially for large number of antennas. Compressed sensing (CS) tools have been exploited due to the sparse nature of such channels. This paper presents a novel deterministic CS approach for BA. Our proposed sensing matrix which has a Kronecker-based structure is sparse, which means it is computationally efficient. We show that our proposed sensing matrix satisfies the restricted isometry property (RIP) condition, which guarantees the reconstruction of the sparse vector. Our approach outperforms existing random beamforming techniques in practical low signal to noise ratio (SNR) scenarios.
The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS), and it consists of digitization followed by characterization of the wideband spectrum. In this paper, we design and develop state-of-the-art UWAS prototype using USRPs and LabVIEW NXG for the validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multi-directional multi-user wideband traffic via LTE specific single carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of non-contiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore the multi-play multi-armed bandit based learning algorithm to embed intelligence. Compared to previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains and antenna array arrangements along with lower complexity validate the functional correctness, superiority and feasibility of the proposed UWAS over state-of-the-art approaches.
Mobile network is evolving from a communication-only network towards the one with joint communication and radio/radar sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers. In this paper, we provide a comprehensive survey for systems and technologies that enable JCAS in PMN, with a focus on works in the last ten years. Starting with reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context, and envisage its potential applications. We continue to provide a brief review for three types of JCAS systems, with particular attention to their differences on the design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing, and discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under eight topics: mutual information, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, pattern analysis, networked sensing under cellular topology, and sensing-assisted secure communication. This paper provides a comprehensive picture for the motivation, methodology, challenges, and research opportunities of realizing PMN. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications.
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