Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .

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Millimeter-wave communication is considered one of the key enablers for 5G systems as it contributes to achieving high data rate with very wideband transmission, high beamforming gain and massive MIMO techniques. Further, millimeter-wave technology can also be used for the accurate positioning. However, it is yet unclear how communication and positioning systems can share resources to flexibly fulfill data-rate and quality-of-position requirements, especially, in multiuser scenarios. In this regard, the objective of this paper is to investigate and quantify the trade-off between positioning quality and achievable sum-rate as a function of number of receive antennas and transmitter locations in an uplink multiuser scenario.

This paper focuses on the exploitation of fifth generation (5G) centimetre-wave (cmWave) and millimetre-wave (mmWave) transmissions for high-accuracy positioning, in order to complement the availability of Global Navigation Satellite Systems (GNSS) in harsh environments, such as urban canyons. Our goal is to present a representative methodology to simulate and assess their hybrid positioning capabilities over outdoor urban, suburban and rural scenarios. A novel scenario definition is proposed to integrate the network density of 5G deployments with the visibility masks of GNSS satellites, which helps to generate correlated scenarios of both technologies. Then, a generic and representative modeling of the 5G and GNSS observables is presented for snapshot positioning, which is suitable for standard protocols. The simulations results indicate that GNSS drives the achievable accuracy of its hybridisation with 5G cmWave, because non-line-of-sight (NLoS) conditions can limit the cmWave localization accuracy to around 20 m. The 5G performance is significantly improved with the use of mmWave positioning with dominant line-of-sight (LoS) conditions, which can even achieve sub-meter localization with one or more base stations. Therefore, these results show that NLoS conditions need to be weighted in 5G localization, in order to complement and outperform GNSS positioning over urban environments.

In this paper, over-the-air (OTA) phase measurement and calibration method for a fifth generation (5G) millimeter wave radio frequency unit is presented. The phase error measurements are done for both receiver and transmitter. The measurement results are used to calibrate the individual antenna ports of the antenna array system so that accurate radiation patterns can be formed. In order to confirm the calibration, the radiation patterns are measured with a 100 MHz wide 28 GHz 5G New Radio communication signal. The measurements show that the calibration improves the maximum power level flatness of radiation patterns and reduces side lobe levels.

A Connectivity-constrained based path planning for unmanned aerial vehicles (UAVs) is proposed within the coverage area of a 5G NR Base Station (BS) that uses mmWave technology. We consider an uplink communication between UAV and BS under multipath channel conditions for this problem. The objective is to guide a UAV, starting from a random location and reaching its destination within the BS coverage area, by learning a trajectory alongside achieving better connectivity. We propose simultaneous learning-based path planning of UAV and beam tracking at the BS side under urban macro-cellular (UMa) pathloss conditions, to reduce its sweeping time with apriori computational overhead using the deep reinforcement learning method such as Deep Q-Network (DQN). Our results show that our proposed learning-based joint path planning and beam tracking method is on par with the learning-based shortest path planning, besides beam tracking comparable to heuristic exhaustive beam searching method.

Recent results have revealed that MIMO channels at high carrier frequencies exhibit sparsity structure, i.e., a few dominant propagation paths. Also channel parameters, namely angular information and propagation delay can be modelled with the physical location of the transmitter, receiver and scatters. In this paper, we leverage these features into the development of a single base-station localization algorithm, and show that the location of an unknown device can be estimated with an accuracy below a meter based on pilot signalling with a OFDM transmission. The method relies on the utilization of the "Adaptive-LASSO" optimization method, in which an ℓ1based minimization problem is solved by adapting the sparsifying matrix (dictionary) and the sparse vector jointly. Then the location of the device is estimated from the parameters of the sparsifying matrix. Finally, the positioning method is evaluated in different channel setting utilizing a ray-tracing channel model at 28GHz.